ES2374110T3 - RAIN SENSOR WITH A CAPACITIVE INCLUSIVE CIRCUIT. - Google Patents

Abstract

A rain sensor, comprising: a detection circuit comprising at least first and second detection capacitors (C1, C2) that are sensitive to moisture on an external surface of a window (1; 10); characterized in that the detection circuit further comprises at least one imitating capacitor (Cint) that mimics the loading and unloading of at least the first detection capacitor; in which a write pulse causes each of the first detection capacitor (C1) and the mimic capacitor (Cint) to be charged, and a clear pulse causes each of the first detection capacitor (C1) and the mimic capacitor (Cint) are substantially discharged; in which the presence of rain on the external surface of the window in a detection field of the first detection capacitor (C1) causes a voltage at an output electrode of the imitation capacitor (Cint) to fluctuate in a manner proportional to the fluctuation of a voltage at an output electrode of the first detection capacitor (C1) although no rain is present in a field of the imitator capacitor; and in which rain is detected as a function of an output signal of the output electrode of the imitator capacitor, in which the output signal is read at least between the end of the write pulse and the start of the erase pulse.

Description

Rain sensor with an capacitive inclusive circuit

Field of the Invention

This invention relates to a system and / or a method for detecting the presence of rain and / or disturbances or the presence of other materials on a glass sheet (s) such as the windshield, the rear window or the sunroof of a vehicle. Detection capacitors are supported by a window such as the windshield of a vehicle and have a field that is affected by moisture (for example, rain) on a window surface. A detection circuit provides an analog signal based on and / or related to the capacitances of the detection capacitors. The analog output of the circuit can be converted into a digital signal and subjected to a processing (for example, correlation) to determine if there is humidity (for example, rain, dew, fog, etc.) or the like on the window surface.

Background and summary of exemplary embodiments of the invention

The presence of moisture (e.g. rain or condensation) and / or other materials or debris on the windshield and / or the rear window of a vehicle can create dangerous driving situations for drivers, passengers and pedestrians if they are not removed from immediate way. Windshield wiper blades are a widely known and common way to remove such materials and reduce driving hazards during dangerous situations. Rain sensors have been developed to detect the presence of moisture (for example, rain or other condensation) on the windshield of a vehicle and to turn the windshield wipers on and off, as necessary, when such moisture is detected. Automatically detect rain, sleet, fog and the like, and take appropriate action, for example turning on / off the windshield wiper blades at an appropriate speed, potentially reduces driver distractions, allowing the driver to concentrate better on the road. However, turning on / off improperly the windshield wipers or not being able to activate the windshield wipers when there is humidity can also create dangerous situations. In addition, such systems are also sensitive to distractions caused by "dirt", which can cause false readings / sweeps when there is dirt on the windshield.

Certain conventional rain sensors are based on a capacitive concept, such as those described in DE 3736049.

U.S. Patent No. 6,373,263 on behalf of Netzer teaches the use of capacitive rain sensors and the differential current reading between two capacitors on the windshield. Unfortunately, the Netzer system also has significant disadvantages. For example, the Netzer system is only sensitive to changes. Therefore, for example, if there is already moisture (e.g. rain or condensation) on a windshield because a vehicle has been parked outdoors in the presence of rain or fog, the Netzer system cannot detect them when it starts the vehicle. In addition, the Netzer system may be subject to certain harmful effects of electromagnetic interference (EMI), temperature changes, as well as interference from other sources. For example, when external bodies (for example, a person's hand, radio waves, etc.) interfere with the function of the capacitors, the charges of the excitation and reception electrodes may vary uncontrollably in the system. Netzer, thus leading to alarms or false detections. Therefore, for example, with the Netzer system, CB radio waves, microwaves, manual devices, human contact with the windshield, objects that can be grounded and / or the like, may undesirably interfere with the system and, thus, possibly produce sweeps and / or false detections. The Netzer system is also subject to possible false readings caused by sudden temperature changes in view of the reference condenser system used by the Netzer system, where the reference condenser of the Netzer system has a different geometry / shape / size to those of the detection capacitor.

Therefore, it is evident that there is a need in the art for a humidity sensor (eg rain) that is effective in terms of operation and / or detection, and that overcomes one or more of the above-mentioned disadvantages . According to this invention, a rain sensor and a method as defined in claims 1 and 10 are provided.

Brief description of the drawings

These and other features and advantages will be better and more fully understood by reference to the following detailed description of illustrative embodiments by way of example together with the drawings, in which:

FIGURE 1 (a) is a block diagram of the components of an example rain sensor.

FIGURE 1 (b) is a cross-sectional view of a rain sensor that can use the characteristics of

Fig. 1 (a) and / or one or more of Fig. 2-12.

FIGURE 1 (c) is a cross-sectional view of a rain sensor that may utilize the features of Fig. 1 (a) and / or one or more of Fig. 2-12.

FIGURE 1 (d) is a cross-sectional view of a rain sensor that can utilize the features of Fig. 1 (a) and / or one or more of Fig. 2-12.

FIGURE 1 (e) is a cross-sectional view of a rain sensor that can utilize the features of Fig. 1 (a) and / or one or more of Fig. 2-12.

FIGURE 1 (f) is a cross-sectional view of a rain sensor that can utilize the features of Fig. 1 (a) and / or one or more of Fig. 2-12.

FIGURE 2A is an example optimized pattern for a capacitive matrix in quadrants based on Hilbert fractals, where such capacitors may be provided in the window as a sensor matrix in one or more of Fig. 1 (a) - 1 (f) and 4-12, for example.

FIGURE 2B is another example optimized pattern for a capacitive quadrant matrix, where such capacitors may be provided in the window as a sensor matrix in one or more of Fig. 1 (a) - 1 (f) and 4-12, for example.

FIGURE 3 is an enlarged image of another capacitive matrix in quadrants by way of example, where such capacitors may be provided in the window as a sensor array in one or more of Fig. 1 (a) - 1 (f) and 4-12, for example.

FIGURE 4 is an example circuit diagram that includes a circuit system used for a writing clock pulse in electronic output reading devices, for use in one or more of Fig. 1 (a) - 1 ( f) and 5-12, according to the invention.

FIGURE 5 is an example circuit diagram that includes a circuit system used for an erase clock pulse in electronic output reading devices, for use in one or more of Fig. 1 (a) - 1 ( f), 4 and 6-12, according to the invention.

FIGURE 6 is an exemplary timing diagram obtained from the output reading circuit system of Figs. 4-5.

FIGURE 7 is an exemplary flow or state diagram showing how autocorrelation and cross correlation data can be used to control windshield wipers, which can be used in conjunction with a

or more of Figs. 1-6 and 8-12.

FIGURE 8 is an exemplary flow chart showing how autocorrelation and cross correlation data can be used to control the windshield wipers, which can be used in conjunction with one or more of Figs. 1-7 and 9-12.

FIGURE 9 is an exemplary stylized view of how a raindrop can travel through a windshield wiper.

FIGURE 10 is a graph plotting maximum sample values obtained experimentally from non-standardized autocorrelations for different disturbances.

FIGURE 11A is an example autocorrelation snapshot obtained experimentally indicative of heavy rain.

FIGURE 11B is an example autocorrelation snapshot obtained experimentally indicative of a slight haze.

FIGURE 11C is an example autocorrelation snapshot obtained experimentally indicative of CB radio interference.

FIGURE 11D is an example autocorrelation snapshot obtained experimentally indicative of a grounded body with a voltage.

FIGURE 12A is an exemplary correlation matrix indicative of light rain.

FIGURE 12B is an exemplary correlation matrix indicative of heavy rain.

FIGURE 13 is an example of autocorrelation.

FIGURE 14 is a table showing example cross-correlation data of capacitors C1, C2.

FIGURE 15 is a cross-correlation graph that plots cross-correlation values against time periods (the time periods are in microseconds in the time domain), using certain signals from Fig. 14.

FIGURE 16 is a cross-correlation graph that plots cross-correlation values against time periods (the time periods are in microseconds in the time domain), using certain signals from Fig. 14.

FIGURE 17 is a cross-correlation graph that plots cross-correlation values against time periods (the time periods are in microseconds in the time domain), using certain signals from Fig. 14.

FIGURE 18 is a cross-correlation graph that plots cross-correlation values against time periods (the time periods are in microseconds in the time domain), using certain signals from Fig. 14.

FIGURE 19 is a cross-correlation graph that plots cross-correlation values against time periods (the time periods are in microseconds in the time domain), using certain signals from Fig. 14.

FIGURE 20 is a cross-correlation graph that plots cross-correlation values against time periods (the time periods are in microseconds in the time domain), using certain signals from Fig. 14.

FIGURE 21 is a cross-correlation graph that plots cross-correlation values against time periods (the time periods are in microseconds in the time domain), using certain signals from Fig. 14.

FIGURE 22 is a cross-correlation graph that plots cross-correlation values against time periods (the time periods are in microseconds in the time domain), using certain signals from Fig. 14.

FIGURE 23 is a cross-correlation graph that plots cross-correlation values against time periods (the time periods are in microseconds in the time domain), using certain signals from Fig. 14.

FIGURE 24 is a cross-correlation graph that plots cross-correlation values against time periods (the time periods are in microseconds in the time domain), using certain signals from Fig. 14.

FIGURE 25 is a block diagram illustrating a circuit system and / or signal processing when a detection capacitor (eg, C1) is present, including sigma-delta modulation.

FIGURE 26 is a block diagram illustrating a circuit system and / or signal processing when a plurality of capacitors (eg, C1-C4) is present, including sigma-delta modulation.

FIGURE 27 is a block diagram illustrating sigma-delta modulation; This processing is carried out in a circuit system, in firmware and / or in software.

FIGURES 28 (a) and (28b) are schematic diagrams illustrating the advantages of using floating electrodes for detection capacitors (eg, C1-C4).

Detailed description of exemplary embodiments of the invention

Reference is now made in particular to the accompanying drawings in which similar reference numbers indicate similar parts throughout the various views.

A rain detection system and / or procedure is provided that includes a capacitance based detection that converts a physical input signal (eg, the presence of a drop of water on a windshield, or the like) into a digital signal of electrical voltage that is received and interpreted by a software program (s) or a circuit (s) that decides whether the windshield wipers should be activated and, if so, optionally their proper speed. Therefore, a capacitive coupling is used to detect water and / or other

material on the exterior surface of a window such as the windshield, sunroof and / or the rear window of a vehicle. It can be appreciated that computational procedures can be carried out by hardware or a combination of hardware and software. No capacitance or reference capacitor may be necessary, (that is, no compensation capacitor is necessary).

A permittivity equation provides a physical quantity that describes how an electric field affects and is affected by a medium. An example basic permittivity equation is:

where D is the electric flux, ε0 is the dielectric constant of a vacuum, E is an electric field (for example, the voltage established between plates or electrodes divided by distance, or V / m), and P is polarization. Polarization P can also be described mathematically as:

where εr is the relative permittivity (for example, the dielectric constant of water, ice, dirt or anything else that could be found on an exterior surface of a window such as a windshield). In general, a high value of εi will correspond to a high polarizability. The permittivity of the glass is approximately 8, and the permittivity of the water is approximately 85. Then, by substitution and factorization, the permittivity equation can be rewritten as:

In this way, it can be seen that D is the response to excitation E.

The capacitance C is given by C = Q / V, where Q is the load and V is the potential, in volts. In addition, C = Φ / V, where Φ is the electrical flow associated with the charge Q. By Gauss's Law:

where dA is the area of a differential square on the closed surface S. Then, by substitution, it is evident that the capacitance is related to the potential difference:

These equations form the basis of an example technique to measure the interaction of water on glass using a sensor with a capacitive matrix to probe above the window (for example, glass). In particular, data from a sensor that includes at least one, two or more capacitors (for example, C1, C2, C3, etc.) can be used to detect if there is rain on an exterior surface of a window such as the windshield or The rear moon of a vehicle. The above equations illustrate that the presence of water on the surface of a window can affect the capacitance of a properly placed detection capacitor.

Fig. 1 (a) is a block diagram of example components of a rain sensor. A power supply 10 is connected to an electronic output reading device 12 that may include one or more of hardware, firmware and / or software. As will be described in greater detail below, the sensor includes one or more capacitors to form a capacitive sensor 5. Although different types of capacitors can be used, capacitors can be used in the sensor each having an approximately coplanar pair of electrodes arranged in A fractal pattern A fractal pattern can be divided into a capacitive matrix. The data of and / or related to the capacitor sensing capacitor (s) of the capacitive sensor 5 are received and read by means of the electronic output reading device 12 which may be constituted by one or more of hardware, firmware and / or software. The electronic output reading device 12 captures the electrical noise and converts it into a digital signal (s). This digital signal (s) is passed to a calculation module 14 (which may be constituted by one or more of hardware, firmware and / or software) that determines what action they should take. the windscreen cleaners. For example, windshield wipers can initiate a single sweep, low speed sweeps, high speed sweeps, etc., depending on the analyzed data of and / or related to the capacitive sensor. Windshield wipers can also be deactivated, reduce / increase the

scanning speed, etc., depending on the analyzed data of and / or related to the capacitive sensor. A windshield wiper control system motor 16 receives instructions from the calculation module 14 and instructs the windshield wipers 18 to carry out the appropriate action.

Capacitive sensor 5 interacts with a local interconnect bus (LIN bus) of a vehicle. A LIN bus (not shown) is normally a serial bus to which slave devices of a car are connected. A LIN bus normally performs the establishment with the slave devices to ensure that they are, for example, connected and in operational state. In addition, a LIN bus can provide other information to slave devices such as, for example, the current time.

Capacitive sensor 5 includes a plurality of capacitors in the form of any suitable arrangement.

Fig. 1 (b) is a cross-sectional view of a vehicle window that includes a rain sensor according to an exemplary embodiment of this invention. The windshield of a vehicle includes an inner glass substrate 1 and an exterior glass substrate 2 that are laminated together by means of a polymer-based interlayer 3 of a material such as polyvinyl butyral (PVB) or the like. An optional low e (low emissivity) 4 coating may be provided on the inner surface of the outer glass substrate 2 (or even on the surface of the substrate 1). A low E 4 coating normally includes at least one thin IR reflective layer of a material such as silver, gold or the like, sandwiched between at least a first and a second dielectric layer of a material such as silicon nitride, tin oxide , zinc oxide or the like. Example low E 4 coatings are described in U.S. Pat. 6,686,050, 6,723,211, 6,782,718, 6,749,941, 6,730,352, 6,802,943, 4,782,216, 3,682,528 and 6,936,347.

Fig. 1 (b) illustrates an example capacitor of the capacitive sensor. While the capacitive sensor of Fig. 1 (a) normally includes a plurality of capacitors in a matrix, in Fig. 1 (b) only one capacitor of the sensor is shown for simplicity. The other capacitors are similar in cross section to that shown in Fig. 1 (b) in certain exemplary embodiments of this invention. The example capacitor (C1, C2, C3 or C4) of the capacitive sensor shown in Fig. 1 (b) includes a pair of coplanar or substantially coplanar spaced capacitor electrodes 7 and 8. Electrodes 7 and 8 are made of a conductive material that can be printed or otherwise formed in the window. For example, the capacitor electrodes 7 and 8 of the detection capacitor may be made of or include silver, ITO (indium tin oxide), or other suitable conductive material. In certain exemplary embodiments, the capacitor shown in Fig. 1 (b) is affected by a raindrop on the outer surface of the window because the electric field Es of the capacitor extends to or beyond the surface outside of the window as shown in Fig. 1 (b) and, therefore, can interact with the raindrop or other material on the outside surface of the window. This document describes the signals received from and / or related to the detection capacitor (s) and their analysis.

In Fig. 1 (b) an opaque insulating layer 9 (for example, black frit or enamel, or the like) is provided in the window on the electrodes 7 and 8 in order to hide the electrodes 7, 8 from the view of the (of) the passenger (s) who are sitting inside the vehicle. Therefore, it can be seen that the opaque layer 9 is provided only in a small part of the window, including the area in which the capacitive matrix of the capacitor matrix of the rain sensor is located. The capacitive matrix of the rain sensor and, therefore, the opaque layer 9, can be placed on the windshield of a vehicle in an area close to the mounting bracket of the rearview mirror. The opaque layer 9 (for example, black frit or enamel) can establish direct contact with the fractal pattern of the condenser electrodes 7, 8 since the layer 9 is not conductive. However, even if a layer of black frit 9 was conductive (which is possible), its dielectric constant is similar to that of water, so that it will not adversely interfere with the capture of data from and / or related to the C1-C4 capacitors and in the associated analysis.

Fig. 2A is a top or plan view illustrating an example capacitive sensor matrix that includes four capacitors C1, C2, C3 and C4. Each of these capacitors C1, C2, C3 and C4 includes first and second coplanar capacitor electrodes spaced 7 and 8 as shown in Fig. 1 (b) (or in any of Fig. 1 (c) - 1 (f)). The capacitor electrodes 7 and 8 of each capacitor C1-C4 may be made of conductive silver frit or the like, as shown in Fig. 2A. In addition, there may be a gap 22 of about 0.2 to 1.5 mm, more preferably about 0.3 to 1.0 mm (for example, 0.6 mm), between the coplanar condenser electrodes 7 and 8 of a capacitor (C1, C2, C3 and / or C4), as shown in Fig. 2A. In Fig. 2A, capacitors C1-C4 are covered by an insulating black frit layer 9 which is the same as the opaque layer 9 mentioned above with respect to Fig. 1 (b). In Fig. 2A, an array of contact plates is provided in the center of the sensor array, and includes four contact plates electrically connected to the respective electrodes 7 of capacitors C1-C4, and four contact plates electrically connected to the respective electrodes 8 of capacitors C1-C4. An example contact plate is indicated by reference number 28 in Fig. 2A. The four contact plates of

white color 28 of Fig. 2A are electrically connected to the respective capacitor electrodes 7 of capacitors C1-C4, while the dark gray contact plates 28 of Fig. 2A are electrically connected to the respective capacitor electrodes 8 of the C1-C4 capacitors. All C1-C4 sensing capacitors are sensitive to moisture, such as rain on the external surface of the window.

In Fig. 2A, each capacitor C1-C4 of the capacitive sensor is formed using a fractal geometry. In particular, each of the coplanar electrodes 7 and 8 of each capacitor C1-C4 is formed with a fractal geometry. Fractal design patterns make it possible, for example, to obtain a large capacitance in a small area and, therefore, are desirable over other geometries in certain example rain sensor applications. Fractal geometry can be grouped into (a) random fractals, which can be referred to as chaotic or Brownian fractals and that include a random noise component, and into (b) deterministic fractals

or exact. In deterministic fractal geometry, a self-similar structure is obtained from the repetition of a design or motif (or "generator") (that is, self-similarity and structure on all scales). In deterministic or exact self-similarity, fractal capacitors can be manufactured through recursive or iterative means. In other words, fractals are usually formed by or include many copies of themselves on different scales.

In Fig. 2A it can be seen that the coplanar electrodes 7 and 8 of each capacitor (where electrodes 7 and 8 are shown but are not labeled in Fig. 2A due to the dark color of the frit 9, but are spaced by gaps 22) they have fractal geometries and are arranged substantially parallel to each other over the entire sinuous length of each capacitor. In other words, each electrode 7, 8 of a given capacitor (for example, C1, C2, C3 or C4) has a sinuous shape in fractal geometry, but remains substantially parallel to the other electrode (the other of 7, 8) of the condenser over the entire sinuous length of the condenser. The overall length of each capacitor (for example, C1), along the sinuous length of the fractal, is approximately 25 to 200 mm in certain exemplary embodiments of this invention, more preferably approximately 30 to 90 mm, an example being approximately 50 mm.

The fractal pattern of Fig. 2A is a Hilbert fractal pattern. Electrodes 7, 8 of capacitors C1-C4 in Fig. 2A form a Hilbert fractal pattern, by way of example only and not limitation. In particular, the capacitors shown in Fig. 2A have a third-order Hilbert fractal shape. Hilbert's fractals are continuous fractals that fill the space, with fractal dimensions of two. This means that higher order fractals will have a more square shape. A Hilbert fractal can be formed using the following L system:

Hilbert {Angle 90

Axiom X

X = -YF + XFX + FY-

Y = + XF-YFY-FX +

}

where " Angle 90 " set the following rotations to 90 degrees, X and Y are defined functions, " F " means " carry forward ", " + " means "turn counterclockwise", and "-" means "turn clockwise". Although Hilbert's fractal geometries can be used to form capacitors C1-C4, other types of fractals can also be used to create the shape of the capacitors. For example, capacitor electrodes 7, 8 of capacitors C1-C4 can be formed using any of the fractal designs disclosed in any of U.S. Pat. Nos. 6,552,690, 6,104,349, 6,140,975, 6,127,977, 6,084,285, 6,975,277. As shown in Fig. 2A, 2B and 3, all detection capacitors of the detection matrix may be identical or substantially identical with respect to the shape.

Each of the capacitors C1-C4 of the sensor array can be electrically floating (this can be referred to as a virtual ground in certain example cases) in order not to have a fixed common ground such as zero fixed volts, and / or be spatially separated or similar, which may be useful with respect to correlation functions. In addition, the lack of a common ground means that the capacitive matrix will not be subject to adverse effects of interference such as, for example, EMI interference, thereby reducing the possibility of false sweeps, false detections and the like.

The fractal design for capacitors C1-C4 can be used in any of the embodiments of Fig. 1 (a) - 1 (f).

Fig. 1 (c) is a cross-sectional view of another example, which can use the system of Fig. 1 (a) and one or more of Fig. 2-12. In Fig. 1 (c ), the vehicle window (for example, the rear window) is formed

only by a glass sheet 10, and the condenser electrodes 7, 8 are provided, directly or indirectly, on the main inner surface of the glass sheet 10. The capacitor (eg, C1) shown in Fig. 1 ( c) is designed in such a way that it is affected by a raindrop on the exterior surface of the window because the electric field Es of the capacitor extends to or beyond the exterior surface of the window as shown in the Fig. 1 (c) and, therefore, can interact with the raindrop or other material on the exterior surface of the window. Each of the capacitors C1-C4 is similarly formed. It should be noted that the use of the word " about " This document covers both directly and indirectly, and is not limited to physical contact unless expressly stated. An opaque layer 9, similar to that shown in Fig. 1 (b), can also be provided in Fig. 1 (c) if desired.

Fig. 1 (d) is a cross-sectional view of another example, which may use the system of Fig. 1 (a) and one or more of the embodiments of Fig. 2-12. Fig. 1 (d), the vehicle window (for example, a laminated windshield) includes glass sheets 1 and 2 laminated together by means of a polymer-based interlayer 3, and optionally includes a low E 4 overlay either the substrate 1 or the substrate 2. Fig. 1 (d) differs from Fig. 1 (b) in that the electrodes 7, 8 of the condenser are provided on the main surface of the glass substrate 1 which is more away from inside the vehicle. The capacitor electrodes 7, 8 can make contact with the polymer interlayer 3 in this embodiment, in certain example cases. The condenser (for example, C1, C2, C3 or C4) shown in Fig. 1 (d) is designed in such a way that it is affected by a raindrop (or other material) on the exterior surface of the window because The electric field Es of the capacitor extends to or beyond the outer surface of the window as shown in Fig. 1 (d) and, therefore, can interact with the raindrop or other material on the surface outside of the window. Each of the capacitors C1-C4 of the sensor array is formed in a manner similar to that shown for the capacitor of Fig. 1 (d). The opaque layer 9 can also be provided in Fig. 1 (d) if desired, on a part of the window so that the condenser electrodes are hidden from the view of the passengers of the vehicle. In Fig. 1 (d), electrodes 7 and 8 may be formed by a conductive silver frit or ITO printed or stamped directly on and in contact with the substrate surface 1. However, electrodes 7 and 8 of one or more of the sensor capacitors may be formed and stamped from a metallic conductive IR reflective layer (for example, a silver-based layer) of a low E 4 coating that is supported by the window.

Fig. 1 (e) is a cross-sectional view of another example, which may use the system of Fig. 1 (a) and one or more of Fig. 2-12. In Fig. 1 (e ), the vehicle window (for example, a laminated windshield) includes glass sheets 1 and 2 laminated together by means of a polymer-based interlayer 3, and optionally includes a low E 4 coating on or the substrate 1 or the substrate 2. Fig. 1 (e) differs from Fig. 1 (b) in that the electrodes 7, 8 of the capacitor (for example, C1, C2, C3 or C4) are provided on the main surface of the exterior glass substrate 2 that is closer to the interior of the vehicle. The capacitor electrodes 7, 8 can make contact with the polymer interlayer 3. The capacitor (for example, C1, C2, C3 or C4) shown in Fig. 1 (e) is designed such that it is affected by a raindrop (or other material) on the outer surface of the window because the electric field of the capacitor extends to or beyond the outer surface of the window as shown in Fig. 1 (e) and Therefore, it can interact with the raindrop or other material on the exterior surface of the window. Each of the capacitors C1-C4 of the sensor array is formed in a manner similar to that shown for the capacitor of Fig. 1 (e). The opaque layer 9 can also be provided in the embodiment of Fig. 1 (e) if desired, on a part of the window so that the condenser electrodes are hidden from the view of a passenger (s) ) vehicle.

Fig. 1 (f) is a cross-sectional view of another example, which may use the system of Fig. 1 (a) and one or more of Fig. 2-12. In Fig. 1 (f ), the vehicle window (for example, a laminated windshield) includes glass sheets 1 and 2 laminated together by means of a polymer-based interlayer 3, and optionally includes a low E 4 coating on or the substrate 1 or 2. Fig. 1 (f) differs from Fig. 1 (b) in that the electrodes 7, 8 of the capacitor (for example, C1, C2, C3 or C4) are provided on the main surface of the glass substrate interior 1 that is closer to the interior of the vehicle, through a support element 12. The support element 12, located between the glass substrate 1 and the electrodes 7, 8, can be made of glass, silicon or the like. The condenser (for example, C1, C2, C3 or C4) shown in Fig. 1 (e) is designed in such a way that it is affected by a raindrop (or other material) on the exterior surface of the window because The electric field Es of the capacitor extends to or beyond the outer surface of the window as shown in Fig. 1 (f) and, therefore, can interact with the raindrop or other material on the surface outside of the window. Each of the capacitors C1-C4 of the sensor array is formed in a manner similar to that shown for the capacitor of Fig. 1 (f). The opaque layer 9 can also be provided in the embodiment of Fig. 1 (f) if desired, on a part of the window so that the condenser electrodes 7, 8 are hidden from the view of one (s) passenger (s) of the vehicle.

Fig. 2B is a plan view of an example pattern for a capacitive matrix in quadrants of the

C1 - C4 fractal shaped capacitors for the capacitive sensor. The four capacitors shown in Fig. 2B are similar to those in Fig. 2A, except for the precise shapes thereof. The capacitors of Fig. 2B can be used in any of Fig. 1 (a) - (f). The overlapping dotted lines show the divisions in four different C1-C4 capacitors. The width of the outer line can be about 2 mm, and the width of the inner line can be about 1 mm.

Fig. 3 is an enlarged drawing of another capacitive matrix in quadrants as an example of capacitors with a C1-C4 fractal shape for the capacitive sensor. The four capacitors shown in Fig. 3 are similar to those in Fig. 2A and 2B, except for the precise shapes thereof. The fractal capacitors of Fig. 3 can be used in any of Fig. 1 (a) - (f). The superimposed lines show an example division between capacitors C1-C4 of Fig. 3. It can be seen that some embodiments may have capacitive matrices with only two capacitors. However, it is preferable to have at least four capacitors to capture and obtain nuances in the disturbances.

The use of fractal geometry for detection capacitors C1 to C4 can be advantageous for reducing false readings due to EMI interference. In particular, fractals at high iterations help reduce incidents due to EMI interference, because the Faraday cage or the Faraday quasi-cage of the fractal at high iterations reduce EMI coupling, thereby reducing the adverse effects of EMI interference. Fractals at high iterations form quasi-cages of Faraday.

Electronic output reading devices examine the interaction of rain and / or other disturbances on the window. This process is achieved by sequentially charging the capacitors, reading their data, quantifying that data and eliminating the charges, as defined in claims 1 and 10.

Fig. 4 is a circuit diagram of a detection or output reading circuit. The detection circuit of Fig. 4 may be formed by the electronic unit 12 and by the capacitive sensor matrix 5 of Fig. 1. Any of the capacitors of Fig. 1 (b) - 1 (f), 2A , 2B and / or 3 can be used as the capacitors C1-C4 of the circuit of Fig. 4. The circuit system of Fig. 4 is used for a write clock pulse in electronic output reading devices. Transistors Q1, Q2 and Q7 are p-channel MOSFET, with transistors Q1 and Q2 being primarily responsible for a writing phase. Transistors Q5 and Q6 are channel MOSFET n.

Still in relation to Fig. 4, during a writing phase a ClkWr writing pulse is introduced into the gate of transistor Q7, which functions as a resistor or a switch, charging one or more capacitors C1-C4 capacitors of sensor Cs. Fig. 6 includes certain signals used in the circuit of Fig. 4 in the writing cycle. In the write cycle, transistor Q1 is in a saturated mode, since its door and drain are connected, so that Q1 is activated. Q4, Q5 and Q6 are disabled, and Q2 is activated during write mode. Transistors Q3 and Q4 are optional. When Q7 is activated by the write pulse, a write cycle occurs, and Vcc appears in Cs through A and charges one or more of the capacitors C1-C4 of the sensor capacitance Cs. Vcc can be a constant voltage, such as 5V. One or more of the C1-C4 capacitors can be charged at the same time during a write cycle, or the circuit charges and reads the capacitors C1, C2, C3 and C4, one by one (for example, see Fig. 6). During a write cycle, only one of the capacitors C1, C2, C3 or C4 is charged.

The above process described for the left side of the detection circuit of Fig. 4 is basically duplicated on the opposite side or right side of the circuit of Fig. 4. As the current flows through the branch on the left side, the current also flows in B through the branch of the right side, and changes with respect to Cs are imitated, or substantially imitated in the internal imitation capacitance Cint. When Q7 is activated, the current also flows through Q2 (which is activated) and loads Cint using Vcc. Therefore, the charge of the Cint capacitor mimics the charge of one of the capacitors C1-C4. In other words, Cint is charged at the same level, or substantially at the same level, as the capacitor (for example, C1) that is charging on the other side of the circuit of Fig. 4. The output voltage of the circuit of Fig. 4, Vout (or Vo), is based on Cint and is taken at or near an electrode of the Cint capacitor as shown in Fig. 4. An example formula that reflects Vout (or Vo) is shown in the lower part of Fig. 4. Accordingly, it can be seen that the Vout (or Vo) output of the circuit of Fig. 4-5 refers to and is based on capacitors C1-C4 of the sensor Cs. More specifically, the output Vout of the circuit of Figs. 4-5 refers to and is indicative of the state of the capacitors C1-C4 and of the effects of moisture in said capacitors on the outer surface of the window, although Vout is not taken directly from capacitors C1 - C4. In particular, Vout (or Vo) is read during a write cycle, due to the write pulse shown in Fig. 4 (see also Fig. 6). In the formula at the bottom of Fig. 4 for Vout, W1 is for Q1, W2 is for Q2, L1 is for Q1, L2 is for Q2, where W is the channel width of transistors and L is the length of transistor channel; and VT is a threshold voltage of each MOSFET.

Transistors Q3 and Q4 are optional. These transistors can be found at low voltages (for example, deactivated) during the writing phase, and activated during the erase phase.

The output signal Vout (or Vo) of the detection circuit of Fig. 4 (and of Fig. 5) is modulated in sigma-delta. The sigma-delta modulators, which can be used in a digital-to-analog converter (DAC) sigma-delta, can provide a level of conformation or filtering of quantization noise that may be present. Sample sigma-delta modulators that can be used are described in U.S. Pat. Nos. 6,975,257, 6,972,704, 6,967,608 and 6,980,144. In certain examples of sigma-delta conversion, oversampling, noise shaping and / or decimation filtering can be carried out. Exemplary advantages of sigma-delta modulation include one or more of the following: (i) the analog anti-lapping filter requirements are less complex and, therefore, may be more economical than certain nyquist-based example systems; (ii) a sampling and retention circuit system can be used due to the high input sampling frequency and the low precision A / D conversion; (iii) Since there may be digital filtering phase (s) behind the A / D conversion, noise introduced during the conversion process such as power supply oscillation, voltage reference noise and noise can be controlled of the A / D converter itself; (iv) Since the sigma-delta converter can be essentially linear, it may not suffer from appreciable differential nonlinearities and / or the level (s) of background noise may be independent of the Input signal level. Improved S / N ratios can be obtained.

Fig. 25 is a simplified version of a sigma-delta modulator system for modulating and / or converting the Vout (or Vo) output signal of the circuit of Fig. 4 (and of Fig. 5). In Fig. 25, a write pulse (see the pulse at the bottom of Fig. 25) is used to charge the sensing capacitor (C1, C2, C3 or C4) as explained above with respect to to Fig. 5. Square wave excitation (for example, for write and / or erase cycles) is used in the detection capacitor to charge and discharge it This process is duplicated or mimicked for Cint, as explained in this document. The Vout (or Vo) output signal of the circuit of Fig. 4 is modulated in sigma-delta by means of a sigma-delta 60 modulator. The modulator 60 can take the form of a hardware, firmware and / or software circuit. The clock pulses 62 of a clock are introduced into the modulator 60, which activate the internal blocking of a quantizer of the modulator 60. After the output signal Vout (or Vo) has been modulated in sigma-delta by the modulator 60 , the modulated signals 64 are sent to an optional digital filter 66 (for example, a low pass filter or the like). The digital filter 66 processes the digital output 64 of the sigma-delta modulator, which is a train of several 0 and 1. Then, the data is properly scaled using a calibration coefficient (s). Then, the filtered data 68 is read through a serial interface 69 or similar, and sent to a computer that performs correlation calculations for groups of data packets. Therefore, the data of the interface 69 is correlated (for example, an autocorrelation and / or a cross correlation is applied to them) as explained in this document. Fig. 26 is similar to Fig. 25, except that Fig. 26 illustrates an array of C1-C4 sensing capacitors that are multiplexed through a multiplexer.

Fig. 27 is a block diagram illustrating an example of sigma-delta modulation that can be carried out in the modulator 60 of Fig. 25-26. Again, this modulation can be carried out by a circuit system, firmware and / or software. The analog output signal Vout (or Vo) of the circuit of Fig. 4 (and Fig. 5) is received by an adder 70 of the sigma-delta 60 modulator. Adder 70 receives the analog signal Vout (or Vo) as well as a feedback signal from a feedback loop 71 of modulator 60. The output of adder 70 is received by an integrator 72 whose output is received by a quantizer 74, such as a one-bit quantizer. Then, digital output 64 is filtered 66 as explained above, etc. Sigma-delta modulation is advantageous since it provides an oversampling and allows to treat noises such as EMI and reduce its adverse effects. In particular, the noise is distributed by the sigma-delta modulator in the frequency band so that the signal to noise ratio (S / N) can be improved.

Referring again to Fig. 4, each capacitor (C1, C2, C3, C4) is discharged before the next one is charged. The process of discharging each capacitor is described in relation to the erase pulse, with respect to Figs. 5-6.

Fig. 5 is a circuit diagram of the detection circuit of Fig. 4, with respect to an erase cycle. During a erase cycle, a capacitor (C1, C2, C3 or C4) previously charged is discharged before the next write cycle. Fig. 6 includes sample signals used during the erase cycle (s). In certain example cases, no reading is carried out during the erase phase. During an erase cycle or phase, Q7 is deactivated (the ClkWr write pulse is not present), and transistors Q5 and Q6 are activated by a ClkEr erase pulse (see also Fig. 6). Therefore, the capacitor (C1, C2, C3 and / or C4) is discharged to ground (for example, V = 0) or to a virtual ground (VG), as does Cint. Again, Cint mimics the capacitance of the Cs sensor. Once the Cs and Cint capacitances have been grounded and discharged, the pulse and erase cycle ends. Then the next capacitor (C1, C2, C3 or C4) of the sequence can be prepared, charged and read.

Therefore, in relation to Figs. 4-6, it can be seen that, according to this invention, a rain sensor comprises: a detection circuit comprising at least first and second detection capacitors (for example, C1 and C2) that are sensitive to moisture on an external surface of a window, and at least one

imitator capacitor (Cint) that mimics at least the charge and / or discharge of at least one of the first and second detection capacitors; wherein a write pulse (ClkWr) causes at least the first detection capacitor to be charged (for example, C1), and an erase pulse (ClkEr) causes each of the first detection capacitor to discharge substantially (by example, C1) and the mimic capacitor (Cint); in which the presence of rain on the external surface of the window in a detection field of the first detection capacitor (for example, C1) causes a voltage (see Vo or Vout) at an output electrode of the mimic capacitor (Cint ) fluctuate proportionally to the fluctuation of the voltage at an output electrode (8) of the first detection capacitor (for example, C1), even if there is no rain present in a field of the mimic capacitor (Cint); and in which the rain is detected as a function of an output signal (see Vo or Vout) of the output electrode of the imitator capacitor (Cint), in which the output signal is read at least between an end of the writing pulse (ClkWr) and a start of the erase pulse (ClkEr) (see the "read" area in Fig. 6).

Still in relation to Fig. 5, during the erase cycle, the ClkEr erase pulse causes the capacitor (C1, C2, C3 and / or C4) and, therefore, also the imitation capacitance Cint to discharge to ground ( for example, a fixed potential such as V = 0) (see conventional ground symbol in Fig. 5). However, in other exemplary embodiments of this invention, it has been discovered that a fixed land can give rise to certain problems. Therefore, during the erase cycle, the ClkEr erase pulse causes the capacitor (C1, C2, C3 and / or C4) and, therefore, also the imitation capacitor Cint, to be discharged to a virtual ground VG which is floating (see VG and the earth symbol in Fig. 5). In other words, an electrode of each of the capacitors C1-C4 is floating. It can be at a floating or reference potential / voltage. It has been discovered that a floating or virtual earth can be highly advantageous (for example, a floating earth and / or a condenser electrode (s) can lead to a considerable reduction in EMI interference problems). For example, such floating or virtual ground can help reduce the chances of the detection system being distorted by EMI interference. In this regard, reference is made to Fig. 28 (a) and 28 (b) (together with Fig. 5).

In Fig. 28 (a) and (b), reference numbers 7 and 8 refer to the electrodes of a capacitor (for example, C1, C2, C3 or C4). In these figures, " q " refers to the load and Φ refers to the potential (Φ1 is different from Φ2). In Fig. 28 (a), the capacitor (for example, C1) is grounded to a fixed potential such as 0 volts (the charge on the grounded electrode 7 is fixed at + q). In this regard, when the charge on the grounded electrode 7 is fixed at + q, when an external body EB (for example, the finger of a person with a higher dielectric constant) is brought to a capacitor detection zone (for example, by touching the front surface of the windshield above the condenser) this external body induces a change in the charge of -Δq and the other electrode 8 that is not fixed changes from a charge of -q to a charge of -q + Δq in An attempt to balance the load. Therefore, if the capacitor was grounded to a fixed potential such as 0 volts and a capacitor output voltage was read, charge changes caused by a Δq that is not necessary would be read, and this may result in false readings . Comparing Fig. 28 (a) and 28 (b), Fig. 28 (b) illustrates an advantage of making a sensing electrode 7 (for example, any of C1-C4) floating (for example, to a floating or virtual land). In Fig. 28 (b), the charge q on electrode 7 is not fixed. For example, the charge of electrode 7 changes from + q 'to + q' 'when the external body makes contact with the windshield in a condenser sensing zone, thereby indicating the floating nature of the electrode. In Fig. 28 (b), when the external body (for example, a person's finger) is applied to the windshield above the condenser detection zone, the free charges on both electrodes 7 and 8 of the condenser change. Therefore, the adverse effect of Δq is eliminated or reduced using the floating or virtual ground VG (electrode 7 is floating). In particular, when electrode 7 is floating as in Fig. 28 (b), the external body (EB) does not adversely affect the sum of the charge since adding the charges (+ q '' and -q ' ') of electrodes 7 and 8 when the external body is present gives a result of zero or substantially zero. False readings due to EMI interference can also be reduced using this floating feature. Therefore, the floating nature may allow the absolute values of the charges q on the capacitor electrodes 7 and 8 to be equal or substantially equal even when the external body is present since the electrode 7 is floating and not grounded. . This is an example reason why it may be advantageous to make electrodes 7 of capacitors C1-C4 floating, or placed on a virtual ground VG as shown in Fig. 5. Therefore, referring to Fig. 5 and 28, the C1-C4 detection capacitors are floating and their two electrodes are insulated with respect to ground. Accordingly, the rain sensor comprises at least one detection capacitor (C1, C3, C3 and / or C4) that is sensitive to moisture on an external surface of a window, the detection condenser including a first condenser electrode ( 8) receiving a charge signal and a second capacitor electrode (7) spaced from the first capacitor electrode (8); and wherein the second capacitor electrode (7) is floating so that the sensing capacitor is insulated with respect to ground.

Fig. 6 is an example timing diagram of the signals applied to or read from the circuit of Figs. 4-5 during write / erase modes / cycles. As indicated above, the capacitors (C1-C4) are charged, read, quantified and deleted sequentially. Fig. 6 shows a

Write clock (ClkWr) and erase (ClkEr) pulse for each C1 - C4 capacitor, in sequence. Then, the voltages are quantified and provided at the output. Variable output voltages Vo1 - Vo4 correspond to capacitors C1 - C4, respectively, and therefore Cint. It should be noted that the Vo1-Vo4 output signals of Fig. 6 are taken in Vout (or Vo) in Fig. 4-5. In addition, in Fig. 6, the Vo output signals are read or analyzed ( for example, for autocorrelation and / or cross correlation) at the peak of the reading zones (see "Reading" in Fig. 6) of the output signals where the output signals are substantially stabilized and / or the capacitor It saturates. In particular, the output signal Vout (or Vo) of Fig. 6 for a particular capacitor (C1) is read in the "reading zone" upon completion of the write pulse (ClkWr) for that capacitor, and before and / or until the start of the erase pulse (ClkEr) for that capacitor.

Still in reference to Fig. 6, for example, a drop of water on the exterior surface of a windshield will affect the magnitude of the output signal (s) Vout (or Vo). For example, a drop of water above the area of a given capacitor (for example, C1) will cause the level of the output signal (s) Vout (or Vo) for that capacitor in the "read" zone. ; of the signal is higher compared to a situation in which such a drop is not present. The exact magnitude or level depends on the size of the water drop. With increasing amounts of water, the magnitude of the signal in the "read" zone it becomes superior because the dielectric constant of water is superior to that of glass and / or that of air and this increases capacitance. Similarly, if there is no drop of water present on the windshield above the area of a given condenser (for example, C1) then this will cause the level of the output signal (s) Vout (or Vo) for that capacitor in the "read" zone of the output signal is lower compared to a situation in which there is a drop.

The signals (for example, from the capacitor (s)) can be converted from analog to digital through a sigma-delta modulation scheme or the like, which can be implemented at the software level or in any other suitable way, such as through hardware. The underlying principle of a sigma-delta architecture is to make approximate evaluations of the signal, measure the error, integrate it and then compensate for that error. The data can be oversampled at a given frequency of at least 32 kHz, for example, more preferably 64 kHz, although it can be appreciated that other sampling frequencies can also be used. The quantization in progress can be recovered by the sigma-delta modulation scheme to produce a simple binary output of 0 or 1, corresponding to activated and deactivated, respectively. Therefore, the sigma-delta modulation scheme can be used to reduce noise ( for example, in the tail of a signal) and produce a digital output train (for example, several 1 and 0).

Before analyzing the detailed operation and the example mathematics underlying an algorithm of an example sensor, an overview of the states that the sensor and / or the windshield wipers may adopt in relation to Fig. 7, which is an exemplary state diagram showing how autocorrelation and cross correlation data can be used to control windshield wipers of a vehicle. The system starts in a start / initialization state S702. In this state, all buffers are emptied in certain example cases. Depending on the inputs of capacitors C1, C2, ..., Cn, an analog to digital conversion of the signals of the respective inputs is carried out through sigma-delta modulation. The data for the plurality of channels are read over a period of time T. The operating mode selector state S704 functions as a switch that selects between manual mode or automatic windshield wiper mode. If the operating mode selector status S704 indicates that the manual mode is selected, then in the manual mode status S706 an automatic mode can be disabled and a pre-existing manual mode can be enabled. Then, the system returns to the start / initialization state S702. However, if the operating mode selector state S704 indicates that the automatic mode is selected, then the automatic windshield wiper mode is enabled in the automatic mode state S708.

In the state of S710 autocorrelator motor, at least three calculations are carried out. First, a normalized autocorrelation is calculated for each signal input of the capacitive matrix. Second, the autocorrelation gradient is calculated. Thirdly, the difference between the signal input and an undisturbed reference signal (Δ1) can be calculated. This information is passed to the “is it raining?” State S712, in which at least three conditions are checked to determine if it is likely to be raining, there is moisture on the windshield, etc. Probable indications of rainfall are that the autocorrelation gradient is greater than 1, that all autocorrelation values are positive, and / or that Δ1 is greater than a predefined threshold value t1. If these conditions are not fulfilled, the system goes to the “stop windshield / stop engine” S714 state, in which the windshield wipers are deactivated (if they are in motion) or do not operate, and the engine stops (if it is running) , and the system returns to the start / initialization state S702.

On the other hand, if all conditions are met (for example, it is likely that there is an interaction of water, humidity or some other disturbance on the glass, etc.), the system goes to the lowest speed state S716, in which The windshield wiper motor is activated at the lowest available speed. In the state of cross correlation motor S718, the cross correlation between the input signals of the capacitors is calculated. Be

determine the shape of the cross correlation curve and verify the symmetry of the two sides of the cross correlation curve. As will be described later, these checks help, for example, to determine the type of disturbance (for example, light rain, heavy rain, fog, snow, etc.) that affects the window (for example, the windshield). In the valuation state of the S720 rain level, the "rain level" is determined (for example, strong, light, etc.). Depending on this determination, the windshield wiper motor is activated at the appropriate speed in the speed selector state S722. Finally, the system returns to the start / initialization state S702 to determine if there has been any change in the conditions outside the car.

The steps carried out by the rain sensor will be described in greater detail in relation to Fig. 8, which is an example flow chart showing how autocorrelation and cross correlation data can be used to control the windshield wipers. . In Fig. 8, in step S800 the buffers are emptied and sigma-delta modulation is applied to the data obtained from the circuit of Figs. 4-5 (for example, from the Cint or capacitors C1-C4), and they are read in S802.

The algorithm for determining whether to start the windshield wiper, and if so, the speed at which to start the windshield wiper, begins by autocorrelating the modulated data in sigma-delta in step S804. Autocorrelation can be used to analyze value series functions, such as time domain signals. An autocorrelation is the cross correlation of a signal with itself. Autocorrelation is used to find repetitive or substantially repetitive patterns in a signal such as, for example, determining the presence of a periodic signal hidden under noise, identifying the fundamental frequency of a signal that does not properly contain that frequency component but that implies within of it many harmonic frequencies, etc. Cross correlation is a measure of the similarity of two signals and is used to find characteristics in an unknown signal by comparing it with a known one; In other words, it can be used to carry out the fingerprint identification of a signal in certain cases. Cross correlation is a function of the relative time between the signals. Cross correlation is applied to the digital signals of any two capacitors (for example, C1 and C2), in close spatial proximity, and the system searches for any degree of correlation in periods of time other than a time period of zero. This space-time transverse correlation allows the system to extract patterns of how the falling rain is projected electrically onto the sensor arrangement. As an example, the system can take the case of raindrops that move on a condenser C1 in an instant t0 and the same drop that "touches" another capacitor C4 (spatially separated by a distance L from C1). If the drop moves at an average velocity of Vi, the time (t0 + T), where T = L / Vi, the cross-correlation function will have another end or fold. The standardized magnitude of this extreme value may allow the system to determine the degree of rainfall that falls on the sensor.

Each capacitor C1-C4 has an autocorrelation function associated with the digitized Vout resulting from its reading (or the corresponding Cint reading). The autocorrelation function depends on the time difference, rather than the real time. Calculating autocorrelations is beneficial because it allows, for example, the deduction of the fundamental frequency regardless of the phase. Autocorrelations are advantageous over other procedures, such as Fourier transforms (which can also be used) that provide information only on the underlying harmonics. Therefore, the use of autocorrelation of the readings of capacitors C1 to C4 (which as explained above, includes the corresponding readings of the imitator belt) can be used to detect and distinguish between drops of water, dirt, dust, droplets , a shower, etc.

It should be noted that in this document the Cint data are considered the data of the C1-C4 capacitors since the Cint capacitance mimics or substantially mimics the C1-C4 capacitances, as explained above. Therefore, when describing how to receive data from the capacitors (for example, C1-C4), this encompasses and includes receiving data from the Cint capacitance. In other words, the circuit output of Fig. 4-5 is considered to come from capacitors C1-C4, although it is not taken directly from them.

Rain, as a function of time, can be represented by the following formula:

Essentially, b takes a binary value that indicates whether it is raining (1), or not (0). It should be noted that b is at least two bits, and that 24 bits can be used for sigma-delta modulation in certain exemplary embodiments. It should also be noted that a scale can be introduced, potentially to capture more data related to voltages in capacitors C1-C4 (or Cint).

At the end of a sampling cycle L, for example, the circuit output of Fig. 4-5, for example, of the array of

Four capacitors C1 - C4 (or through Cint), ranges between 0000 and 1111, using binary digital data. A single activated bit can initiate a single scan in certain example cases. In the case where all the bits are off (0000) or all the bits are on (1111), no sweep can be started in certain example cases, because it is likely that there is nothing on the windshield, the car is 5 completely submerged, etc., since all the capacitors in the matrix will be reading the same, which is not consistent with the rain that falls on a window. Therefore, the most likely events in which windshield wipers will be needed are those in the range of 0001 to 1110 (that is, when the output of all the capacitors in the array is not the same). When the data is in this range, or even if it is not within this range, the correlation functions (autocorrelation and / or correlation functions

10 cross) can be carried out using the following integral. It should be appreciated that the integral shown below can be rewritten in other ways, such as a sum. The correlations between two drops over a long period of time can be calculated according to the following formula:

where Rb is the correlation of a binary event, provided as a function of the ri resistors at given times; and L is a long sampling period during which a burst of data is captured. The sampling period L may be approximately 10 to 100 ms and, more preferably, approximately 20-30 ms,

 r

which corresponds roughly to the frequency at which an average human eye can discern. Rb is also equal to a function of the correlation of the changes in the resistances across the capacitors and the = 0, the autocorrelation value is determined since the data is being analyzed

 r

 r

Δ

time change. When

Δ

of the same capacitor, and when

Δ

f 0, cross correlations are calculated since the correlation is taken to

out on data from different capacitors. These functions are subject to several example restrictions and underlying assumptions. First,

This restriction basically means that a drop of water or the like is advancing on a given time scale. Secondly,

This restriction mimics or substantially mimics what happens when water droplets or the like move from one condenser to another. Therefore, correlation functions can be considered as discrete stages p in space and T in time. This characteristic can be represented mathematically as the following equation:

Essentially, the left side of the equation establishes a theoretical grid in space and time by which a drop of water or the like moves. For example, Fig. 9 is an exemplary stylized view of how a raindrop can travel through a windshield. Fig. 9 shows a raindrop that moves through a windshield in the X-Z plane for an initial period of time (t = 0) and a quantum of time later

35 (t = T). The assumption that the distribution of the drops is uniform in space and time allows the creation of a binary field caused by rain that is, in a broad sense, stationary. The system also assumes that the temporal correlation between preferred pixels in the same vicinity is high in the direction of rain. Finally, the degree of autocorrelation and cross correlation over time quantifies the fall of rain and other disturbances.

40 It should be appreciated that computational time can be saved due to the nature of the correlation matrices and the nature of the rain. For example, correlation matrices can be symmetric in certain example cases. In addition, as another example, because the rain tends to fall from the sky and up a windshield, it may be sufficient to compare only the capacitors that are arranged vertically with each other in cross correlation, ignoring the horizontally adjacent condensers.

After the autocorrelation has been carried out in step S804 (for example, using the equation (s) described above, or some other suitable equation or correlation equations), it can be carried out

one or more checks to improve system accuracy. Examples of such checks (for example, if the autocorrelated Rxx data have negative values, if a gradient is greater than one, and / or if the shape of an Rxx curve is different or substantially different from standardized and undisturbed autocorrelation data stored in memory) are listed in the lower part of the table in step S804 in Fig. 8. One, two or all three checks can be carried out.

For example, a check of the autocorrelation data in step S806 may be to determine whether the autocorrelated data of one or more of the capacitors (C1, C2, C3 and / or C4; or through the imitator belt) comprise negative values. For example, when the autocorrelated data has a negative value (s), then the system or procedure may indicate that it is not raining, it may stop the windshield wipers, and / or it may not activate the windshield wiper blades (see step S808). This check is to determine, for example, if a disturbance detected is really rain. In this regard, Fig. 10 is a graph plotting maximum sample values obtained experimentally from non-standardized autocorrelations for different disturbances. Fig. 10 illustrates that the water signals are more intense than the undisturbed and positive signals, and that external interference such as electromagnetic waves of CB radii and the hand of a person touching a window tend to be below Non-disturbance levels and can be negative. Therefore, to eliminate or reduce false detections due to external disturbances such as, for example, the hand of a person who touches the window, radio signal interference, etc., any signal with negative autocorrelation values is considered a event of "no rain". It should be appreciated that some embodiments may consider negative autocorrelation values. Other measures to eliminate or reduce false detections due to external interference are, for example, comparing gradients (for example, any curve lower or smaller than the non-perturbation curve / graph of Fig. 10 can be considered as an event of " no rain "), protect the capacitors, etc.

A second example check of the autocorrelation data is to check if a gradient of an autocorrelation curve associated with the autocorrelated data is greater than one; otherwise, the system or procedure may indicate that it is not raining, stop the windshield wipers and / or do not operate the windshield wipers of the vehicle (see step S808). In this check, the gradient of the normalized autocorrelation of the disturbance is checked. The gradient of the normalized autocorrelation of an undisturbed signal is almost

1. Measuring the gradient is beneficial since it is not affected by temperature changes. In this way, the rain sensor can be substantially immune to false readings due to temperature changes. In certain example cases, gradients below 1 (or some other predetermined value) can be considered as non-rain events.

A third example check of the autocorrelation data is to determine if there is a correspondence

or a substantial correspondence between an autocorrelation curve associated with the autocorrelated data and one or more predetermined autocorrelation curves stored in a database and / or in a memory. When the shape of the autocorrelation curve associated with the autocorrelated data of the circuit of Fig. 4

-

5 is different or substantially different from an autocorrelation curve concerning normalized undisturbed autocorrelation data, this can be considered as a non-rain event and it can be indicated that it is not raining, the windshield wipers may stop, and / or the windshield wipers may not be operated (see step S808). However, when there is a correspondence or substantial correspondence between the autocorrelation curve associated with the autocorrelated data of the circuit of Fig. 4-5 and a predetermined autocorrelation curve associated with moisture such as rain, then it can be indicated that it is raining, The windshield wipers can be operated, or continue moving.

In this regard, the shape of the autocorrelation curve can be used to reduce false scans and / or false detections. In particular, the normalized autocorrelation of an undisturbed signal is used as a reference. Then, the normalized autocorrelation of each signal captured from the circuit of Figs. 4-5 is compared with the reference to identify the closest footprint in certain example cases. Generally, the more water that is present in the detection area, the greater the difference between the reference signal and the observed signal. In this way, correlation snapshots can be compared with reference snapshots of well-known events such as the presence of rain, dirt, non-disturbance, ice, etc. In general, correlation snapshots can be normalized, although the invention is not limited to this. Preferably, the correlation snapshots plot r values against amounts of time in a discrete time interval.

When there is a correspondence or substantial correspondence between the autocorrelation curve associated with the autocorrelated data of the circuit of Fig. 4-5 and a predetermined autocorrelation curve associated with a non-moisture substance such as dirt, then this can be considered as a event of no rain and it can be indicated that it is not raining, the windshield wipers can stop and / or not be operated (see step S808).

Therefore, it should be appreciated that the shape of the autocorrelation curve resulting from the output data of the

Circuit of Figs. 4-5 (of capacitors C1-C4, or through Cint) can be used to reduce false scans as a third condition. For example, a normalized autocorrelation curve of an undisturbed signal can be used as a reference. Then, the normalized autocorrelation of each signal captured from the circuit of Figs. 4-5 is compared with the reference to identify the closest fingerprint. Generally, the more water that is present in the detection area, the greater the difference between the reference signal and the observed / detected signal. In this way, correlation snapshots can be compared with reference snapshots of well-known events. In general, correlation snapshots are preferably normalized, although the invention is not limited to this. Preferably, the correlation snapshots plot r values against amounts of time in a discrete time interval.

A possible problem with capacitive rain sensors is that rapid temperature changes (for example, due to the black radiation absorption frit used to aesthetically hide the sensor pattern) modify the "constant" dielectric (permittivity) of the glass. This is then recorded as a change in capacitance and can be misinterpreted as a rain signal. However, a normalized autocorrelation function is not modified, or substantially not modified, for different temperatures although there may be differences for the non-standardized autocorrelation functions for the different temperatures. Therefore, the detection system is unaffected or substantially unaffected by temperature changes.

In addition, an extremely slow accumulation of water such as an ultrafine mist can slowly accumulate to a level that activates the sensors based on Nyquist frequency converters. At the time of observation that refers to human vision (for example, from 30-60 Hz), the autocorrelation function can discriminate between ultra-slow accumulation of dew or condensation and normal rain and fog.

Figs. 11A-11D provide experimentally obtained sample correlation snapshots. These correlation snapshots, or traces of an event, can be stored as reference traces or correlation curves. The observed / detected correlation snapshots (for example, autocorrelation curves) can be compared with these reference traces to determine the type of event that is occurring. For example, Fig. 11A is an autocorrelation snapshot obtained experimentally indicative of heavy rain. Fig. 11B is an experimentally obtained autocorrelation snapshot indicative of a light haze. Fig. 11C is an experimentally obtained autocorrelation snapshot indicative of CB radio interference. Fig. 11D is an experimentally obtained autocorrelation snapshot indicative of a grounded body with a voltage. It should be appreciated that these traces are provided as non-limiting examples and reflect experimentally obtained data. Actual events may differ in various characteristics. Therefore, in certain exemplary embodiments of this invention, when it is determined that there is a correspondence or substantial correspondence between the autocorrelation curve associated with the autocorrelated data of the circuit of Figs. 4-5 and an autocorrelation curve predetermined non-humidity like that of Fig. 11C or Fig. 11D, then this can be considered as a non-rain event and it can be indicated that it is not raining, the windshield wipers can stop and / or not operate (see step S808 ). However, when it is determined that there is a substantial correspondence or correspondence between the autocorrelation curve associated with the autocorrelated data of the circuit of Figs. 4 and 5 and a predetermined moisture-related autocorrelation curve such as that of Fig. 11A or Fig. 11B, then this can be considered as a rain event and it can be indicated that it is raining, the windshield wipers can be operated and / or kept moving. In addition to the predetermined autocorrelation curves of Figs. 11A to 11D, other reference traces can be stored and / or compared with the observed correlation snapshots.

Returning to Fig. 8, in step S806 it is determined whether each of the three conditions presented in the lower part of box S804 is met. In particular, in S806 it is determined whether each of the following is met: (a) the autocorrelated data does not have negative values; (b) a gradient of an autocorrelation curve associated with said autocorrelated data is greater than a predetermined value such as one; and (c) the shape of the autocorrelation curve associated with the autocorrelated data of the circuit of Figs. 4 and 5 is different from a predetermined autocorrelation curve associated with undisturbed autocorrelation data. If all are not met, this indicates a non-rain event and the process proceeds to step S808, where the windshield wiper (s) stops (if it was moving) or remains deactivated (s), and the S800 initialization begins again. However, if all these requirements are met in S806, then the process proceeds to S810 and the windshield wipers of the vehicle (for example, windshield wipers) are activated at their lowest speed.

For the purposes of example and understanding only, Fig. 13 illustrates an example of autocorrelation. In Fig. 13, the values of (or referring to) detection capacitor C1, in the sequential times -t2, -t1, t0, t1, t2 and t3, are 0, 0, 1, 1, 0 and 0, respectively. The autocorrelation for the time 0 (aco) is determined by multiplying the values referring to C1 in an outdated manner and then adding or adding the results. In Fig. 13

it can be seen that aco is equal to 2 in this case. Therefore, in the autocorrelation graph at the bottom of Fig. 13, an entry is created in the graph at time 0 for an autocorrelation value of 2. It should be noted that the autocorrelation graph at the bottom of Fig. 13 is similar, but simpler, than the autocorrelation graph of Fig. 10 and the autocorrelation values can be obtained for Fig. 10 in a similar manner. Then, still in relation to Fig. 13, autocorrelation is carried out using the capacitance values referring to C1 for the next instant of time to obtain the autocorrelation value ac1. This next autocorrelation value (ac1) is obtained by moving the sequence of the lower row of the values for C1 with respect to the upper row, as shown in Fig. 13, and then multiplying the values of the rows that align with each other and adding the results. Fig. 13 illustrates that ac1 is equal to 1 for time 1. Therefore, this autocorrelation value of 1 for time t1 can be entered in the graph at the bottom of Fig. 13, drawing a line between the two data points entered for the purpose of example and understanding. Then, for the next time (or period) value, the lower row moves again another segment with respect to the upper row and the process is repeated, and so on. It can be seen that the autocorrelation plots of Fig. 10 can be obtained in a similar manner. In Fig. 13, it can be seen that this cross correlation can be carried out by replacing the values related to C1 of the lower row with the values of or related to another capacitor, such as C2 (or C3 or C4).

Examining autocorrelation and / or cross correlation can also help distinguish between, for example, light rain and heavy rain. For example, only if the autocorrelation in time is high (and the cross correlation is low), then it is likely that there is only light rain. Fig. 12A is an exemplary correlation matrix showing light rain. In Fig. 12A it is indicated that the correlations between C1 and C1, C2 and C2, C3 and C3, and C4 and C4 (the autocorrelations) during a given period of time are high, while the rest of the correlations (the correlations crossed) be low. By hypothesis and by confirmed experimental data, such a matrix indicates a light rain.

On the other hand, if autocorrelation and cross-correlation over time between capacitor signals are high, there is probably rapid rain. Fig. 12B is an exemplary correlation matrix that shows heavy rain. In Fig. 12B, not only are the autocorrelations of individual capacitors high (i.e., the autocorrelations are the correlations between C1 and C1, C2 and C2, C3 and C3, and C4 and C4), but they are also generally high. cross correlations between different capacitors (the correlations in Fig. 12B diagonally from the top left to the bottom right are the autocorrelations, and the rest are the cross correlations). By hypothesis and by confirmed experimental data, such a matrix indicates a rapid rainfall. The degree of cross correlation can be quantified to determine the relative rate of rainfall. This data can in turn be used to activate various windshield wiper speeds, as appropriate for the speed of the rain. For example, the more high cross correlations there are, the higher the speed of the windshield wiper used.

More systematically, in step S812 the cross correlations are calculated (the correlations between the data referring to different capacitors), and the two sides of the cross correlation curve are used to determine the level of symmetry L. If the level of symmetry is less than a predefined threshold tmin, step S814 directs the system to step S816 where the windshield wipers are activated at the lowest speed, and the system returns to the initialization stage S800. If the level of symmetry is greater than tmin but less than an arbitrary value t, step S818 directs the system to step S820 where the windshield wipers are activated at a faster or medium speed, and the system returns to the initialization stage S800 . It should be noted that a plurality of arbitrary values ti can be specified, and a level of symmetry between you and ti + 1 will activate an appropriate corresponding windshield wiper speed and then the system will return to the initialization step S800. Finally, in step S822, if the level of symmetry is above a predefined level tmax, step S822 directs the system to step S824 where the windshield wipers are activated at the highest speed, and the system returns to the stage of S800 initialization. Therefore, correlations from the data output of the circuit of Figs. 4-5 can be used to adjust the windshield wiper speed. The more high cross correlations there are, the higher the speed of the windshield wiper used due to the likelihood of stronger rain.

For the purposes of example and understanding, Figs. 14-24 illustrate examples of cross correlation. Fig. 14 shows the cross correlation data in certain example cases, while Figs. 15-24 illustrate cross-correlation graphs of some of the data in Fig. 14, where rain is detected. In Figs. 15-24, each period in the horizontal axis is a microsecond (1 µs) for example purposes, and sampling was carried out every microsecond. As explained above with respect to Fig. 13, in Figs. 15-24 in time = 0 (period 0), there is no shift in time of the values of the different capacitors that are being correlated. Fig. 14 illustrates that when there was rain (see signals S1 - S5 and W1 - W5), delta signals related to autocorrelation were high. Figs. 15-24 are cross-correlation graphs referring to these signals. It is useful to look for symmetry between the graphs on the left and right sides of each of Figs. 15-24 (one side of zero is compared to the other side of zero). In general terms, if there is symmetry around the zero period axis, there is not much cross correlation, which indicates that the rainfall detected is not very strong. However, if there is asymmetry around the zero period axis, then this means more cross correlation and indicates that the rain is strong or stronger. For example, note the asymmetry in Figs. 18, 19 and 23 around the zero period axis due to depressions or valleys on one or both sides. More cross correlation indicates that raindrops are moving from a detection area of a condenser to another area of

5 detection of another capacitor. In this regard, each interaction of a raindrop and the surface of a windshield has its own correlation signature in the time domain. A high cross correlation indicates that the same drop is being detected in different capacitors, at different time points (for example, see also Fig. 9). It should be noted that the "t" lower case in Fig. 9 is the same as the axis of periods in Figs. 15-24.

10 Therefore, the rain sensor can detect rain or other material in the vehicle window or in another type of window or sheet / surface, without the need for a reference condenser. Spacetime correlation can be used. All capacitors, or a plurality of capacitors, of the sensor array may have an identical or substantially identical shape in certain exemplary embodiments. For example, at a given time (for example, t1), the system can compare the values related to

15 C1 with the values related to C2 and / or with the values related to another capacitor. For this time t1, the system can also compare the values related to C1 with itself (autocorrelation), and it can also compare the autocorrelation of C1 with the autocorrelation of C2 and / or other capacitor (s) detection (s).

Claims (12)

1. A rain sensor, comprising: a detection circuit comprising at least first and second detection capacitors (C1, C2) that are sensitive to moisture on an external surface of a window (1; 10); characterized in that the detection circuit further comprises at least one imitator capacitor (Cint) that mimics the loading and discharge of at least the first detection capacitor; in which a write pulse causes each of the first detection capacitor (C1) and the mimic capacitor (Cint) to be charged, and a clear pulse causes each of the first detection capacitor (C1) and the mimic capacitor (Cint) are substantially discharged; in which the presence of rain on the external surface of the window in a detection field of the first detection capacitor (C1) causes a voltage at an output electrode of the imitator capacitor (Cint) to fluctuate in a manner proportional to the fluctuation of a voltage at an output electrode of the first detection capacitor (C1) even if no rain is present in a field of the mimic capacitor; and in which rain is detected as a function of an output signal of the output electrode of the imitator capacitor, in which the output signal is read at least between the end of the pulse 15 write and the start of the erase pulse. 2. The rain sensor according to claim 1, wherein the output signal of the output electrode of the imitator capacitor (Cint) is converted from analog to digital and then subjected to a processing to determine if there is rain on the external surface of the window, and wherein said processing preferably comprises autocorrelation and / or cross correlation. The rain sensor according to claim 1, wherein the detection circuit comprises a matrix of detection capacitors (C1, C2, C3, C4), and wherein the imitating capacitor (Cint) mimics each of matrix detection capacitors that are loaded and discharged sequentially. 4. The rain sensor according to claim 1, wherein electrodes of the detection capacitors (C1, C2) first and second are floating, so that the detection capacitors are electrically insulated from ground.

5.

The rain sensor according to claim 1, wherein the first and second detection capacitors (C1, C2) have a fractal geometry.

6.

The rain sensor according to claim 1, wherein the imitation capacitor (Cint) is physically separated from the detection capacitors (C1, C2), and wherein the writing pulse causes it to

30 charges the first detection capacitor (C1), but not the second detection capacitor (C2), and also causes the imitator capacitor (Cint) to be charged. 7. The rain sensor according to claim 1, further comprising: means for correlating imitator capacitor (Cint) data referring to at least one of the detection capacitors to obtain correlated data; 35 means to determine, based on at least the correlated data, if there is rain on an exterior surface of the window; Y wherein said correlation is autocorrelation and / or cross correlation. 8. The rain sensor according to claim 1, wherein each of the detection capacitors (C1, C2) first and second comprises first and second spaced electrodes (7, 8) that are substantially coplanar with each other. 9. The rain sensor according to claim 1, wherein the write pulse is supplied to a gate of a first transistor (Q1) that is electrically connected to the first sensing capacitor (C1), and the erase pulse is applied to respective doors of second and third transistors (Q2, Q3) that are electrically connected to the first detection capacitor and the imitator capacitor (Cint), 45 respectively. 10. A method for detecting rain on a surface of a window (1; 10), the procedure comprising: supplying first and second spaced write pulses that respectively cause first and second detection capacitors (C1, C2) of a detection circuit to be charged, in which the first detection capacitor is charged when the second detection capacitor is substantially discharged, and the second detection capacitor is charged when the first detection capacitor is substantially discharged, so that the first and second detection capacitors are charged at different times; that each of the first and second detection capacitors be sensitive to moisture on the window surface; 5 supply a first erase pulse, between times of the first and second write pulses, causing the first erase pulse that the first detection capacitor is substantially discharged, and supply a second erase pulse after the second write pulse, in that the second erase pulse causes the second detection capacitor to discharge substantially; 10 in which a magnitude of an output of the detection circuit is affected by the presence of rain on the window surface; convert an analog output signal from the detection circuit to a digital signal and, depending on the digital signal, determine if there is rain on the window surface; wherein the detection circuit further comprises at least one imitator capacitor (Cint) that mimics the 15 charge and discharge of at least one of the first and second detection capacitors, and the presence of rain on the window surface in a detection field of the first detection capacitor causes a voltage at an output electrode of the capacitor imitator fluctuates in a manner proportional to the fluctuation of a voltage at an output electrode of the first sensing capacitor, even if there is no rain present in a field of the mimic capacitor; Y 20 detect rain as a function of an output signal of the imitation capacitor output electrode.

eleven.

The method according to claim 10, wherein each of the first and second detection capacitors comprises separate first and second electrodes (7, 8) that are substantially coplanar to each other and that are supported by the window.

12.

The method according to claim 10, wherein the writing pulses are supplied to a door of

A first transistor (Q1) that is electrically connected to at least one of the detection capacitors (C1, C2, C3, C4), and the erase pulses are applied to respective second and third transistor doors (Q2, Q3) . 13. The method according to claim 10, further comprising: correlate digital signal data to obtain correlated data; 30 determine, based on at least the correlated data, if there is rain on the window surface; Y wherein said correlation is autocorrelation and / or cross correlation. Field Line of Electrical Detection Field Line of Electrical Detection Electric Detection Field Line or Hollow air gap = 0.6 mm  Matrix Matrix Note: Delta = calculated difference between the standard autocorrelation data point of each signal and the normalized autocorrelation of a reference (no disturbance)