Abstract: A system, method, and apparatus for vehicle brake monitoring are disclosed. An example method includes receiving three dimensional imagery of a vehicle brake and recording, via a digital interface card, the imagery with and without the brake applied. The method also includes processing, via a processor, the digital imagery samples into a real-world distance measurement of brake components with and without the brake applied. The method further includes applying, via the processor, feature matching to compare the brake component travel distance with at least one measurement recorded previously and stored in a database. The method moreover includes, conditioned on significant changes from that previously recorded measurements, transmitting via the processor, an alert that problems may exist with the sampled vehicle brake.

Abstract: A system, method, and apparatus for reducing or isolating electric noise in personal grounding systems. An example method includes placing a diode between the personal grounding device and the grounding circuitry of a structure. The method also includes modifying or tuning the electrical components to specifically reduce bands of noise present in different circuits (60 Hz in North America versus 50 Hz in Europe, for example). The method further includes enclosing the components within the cord of the personal grounding system or a separate enclosure that would be plugged in serial between the personal grounding device and the structure's grounding circuitry. The method moreover includes a variety of different physical plug types including round pins or flat blades to adapt to any existing electrical outlet around the world.

Abstract: A system, method, and apparatus for vehicle brake monitoring are disclosed. An example method includes receiving a vehicle brake sound signal in a microphone and recording, via a sound card, a digital sound sample of the sound signal, the digital sound sample having a predetermined duration. The method also includes processing, via a processor, the digital sound sample into a feature frequency spectrum. The method further includes applying, via the processor, broad spectrum matching to compare the feature frequency spectrum at least one spectrum recorded previously and stored in a database. The method moreover includes, conditioned on significant changes from that previously recorded spectrum, transmitting via the processor, an alert that problems may exist with the sampled vehicle brake.

Abstract: A system, method, and apparatus for vehicle brake monitoring are disclosed. An example method includes receiving a vehicle brake sound signal in a microphone and recording, via a sound card, a digital sound sample of the sound signal, the digital sound sample having a predetermined duration. The method also includes processing, via a processor, the digital sound sample into a feature frequency spectrum. The method further includes applying, via the processor, broad spectrum matching to compare the feature frequency spectrum at least one spectrum recorded previously and stored in a database. The method moreover includes, conditioned on significant changes from that previously recorded spectrum, transmitting via the processor, an alert that problems may exist with the sampled vehicle brake.

Abstract: A system, method, and apparatus for detecting drones are disclosed. An example method includes receiving a digital sound sample and partitioning the digital sound sample into segments. The method also includes applying a frequency and power spectral density transformation to each of the segments to produce respective sample vectors. For each of the sample vectors, the example method determines a combination of drone sound signatures and background sound signatures that most closely match the sample vector. The method further includes determining, for the sample vectors, if the drone sound signatures in relation to the background sound signatures that are included within the respective combinations are indicative of a drone. Conditioned on determining that the drone sound signatures are indicative of a drone, an alert message indicative of the drone is transmitted.

Abstract: A system, method, and apparatus for drone detection and classification are disclosed. An example method includes receiving a sound signal in a microphone and recording, via a sound card, a digital sound sample of the sound signal, the digital sound sample having a predetermined duration. The method also includes processing, via a processor, the digital sound sample into a feature frequency spectrum. The method further includes applying, via the processor, broad spectrum matching to compare the feature frequency spectrum to at least one drone sound signature stored in a database, the at least one drone sound signature corresponding to a flight characteristic of a drone model. The method moreover includes, conditioned on matching the feature frequency spectrum to one of the drone sound signatures, transmitting, via the processor, an alert.

Abstract: A system, method, and apparatus for drone detection and classification are disclosed. An example method includes receiving a sound signal in a microphone and recording, via a sound card, a digital sound sample of the sound signal, the digital sound sample having a predetermined duration. The method also includes processing, via a processor, the digital sound sample into a feature frequency spectrum. The method further includes applying, via the processor, broad spectrum matching to compare the feature frequency spectrum to at least one drone sound signature stored in a database, the at least one drone sound signature corresponding to a flight characteristic of a drone model. The method moreover includes, conditioned on matching the feature frequency spectrum to one of the drone sound signatures, transmitting, via the processor, an alert.

Abstract: A system, method, and apparatus for drone detection and classification are disclosed. An example method includes receiving a sound signal in a microphone and recording, via a sound card, a digital sound sample of the sound signal, the digital sound sample having a predetermined duration. The method also includes processing, via a processor, the digital sound sample into a feature frequency spectrum. The method further includes applying, via the processor, broad spectrum matching to compare the feature frequency spectrum to at least one drone sound signature stored in a database, the at least one drone sound signature corresponding to a flight characteristic of a drone model. The method moreover includes, conditioned on matching the feature frequency spectrum to one of the drone sound signatures, transmitting, via the processor, an alert.

Abstract: A system, method, and apparatus for detecting drones are disclosed. An example method includes receiving a digital sound sample and partitioning the digital sound sample into segments. The method also includes applying a frequency and power spectral density transformation to each of the segments to produce respective sample vectors. For each of the sample vectors, the example method determines a combination of drone sound signatures and background sound signatures that most closely match the sample vector. The method further includes determining, for the sample vectors, if the drone sound signatures in relation to the background sound signatures that are included within the respective combinations are indicative of a drone. Conditioned on determining that the drone sound signatures are indicative of a drone, an alert message indicative of the drone is transmitted.

Abstract: A system, method, and apparatus for drone detection and classification are disclosed. An example method includes receiving a sound signal in a microphone and recording, via a sound card, a digital sound sample of the sound signal, the digital sound sample having a predetermined duration. The method also includes processing, via a processor, the digital sound sample into a feature frequency spectrum. The method further includes applying, via the processor, broad spectrum matching to compare the feature frequency spectrum to at least one drone sound signature stored in a database, the at least one drone sound signature corresponding to a flight characteristic of a drone model. The method moreover includes, conditioned on matching the feature frequency spectrum to one of the drone sound signatures, transmitting, via the processor, an alert.

Abstract: A system, method, and apparatus for drone detection and classification are disclosed. An example method includes receiving a sound signal in a microphone and recording, via a sound card, a digital sound sample of the sound signal, the digital sound sample having a predetermined duration. The method also includes processing, via a processor, the digital sound sample into a feature frequency spectrum. The method further includes applying, via the processor, broad spectrum matching to compare the feature frequency spectrum to at least one drone sound signature stored in a database, the at least one drone sound signature corresponding to a flight characteristic of a drone model. The method moreover includes, conditioned on matching the feature frequency spectrum to one of the drone sound signatures, transmitting, via the processor, an alert.

Abstract: A system, method, and apparatus for drone detection and classification are disclosed. An example method includes receiving a sound signal in a microphone and recording, via a sound card, a digital sound sample of the sound signal, the digital sound sample having a predetermined duration. The method also includes processing, via a processor, the digital sound sample into a feature frequency spectrum. The method further includes applying, via the processor, broad spectrum matching to compare the feature frequency spectrum to at least one drone sound signature stored in a database, the at least one drone sound signature corresponding to a flight characteristic of a drone model. The method moreover includes, conditioned on matching the feature frequency spectrum to one of the drone sound signatures, transmitting, via the processor, an alert.

Abstract: A system, method, and apparatus for drone detection and classification are disclosed. An example method includes receiving a sound signal in a microphone and recording, via a sound card, a digital sound sample of the sound signal, the digital sound sample having a predetermined duration. The method also includes processing, via a processor, the digital sound sample into a feature frequency spectrum. The method further includes applying, via the processor, broad spectrum matching to compare the feature frequency spectrum to at least one drone sound signature stored in a database, the at least one drone sound signature corresponding to a flight characteristic of a drone model. The method moreover includes, conditioned on matching the feature frequency spectrum to one of the drone sound signatures, transmitting, via the processor, an alert.

Abstract: A system, method, and apparatus for drone detection and classification are disclosed. An example method includes receiving a sound signal in a microphone and recording, via a sound card, a digital sound sample of the sound signal, the digital sound sample having a predetermined duration. The method also includes processing, via a processor, the digital sound sample into a feature frequency spectrum. The method further includes applying, via the processor, broad spectrum matching to compare the feature frequency spectrum to at least one drone sound signature stored in a database, the at least one drone sound signature corresponding to a flight characteristic of a drone model. The method moreover includes, conditioned on matching the feature frequency spectrum to one of the drone sound signatures, transmitting, via the processor, an alert.