Abstract: A robot having anatomical robot joints with convex surfaces significantly less in average overall curvature than their corresponding concave surfaces; having single lobe, double lobe, or saddle shapes; and held together by flexible bands that allow rocking, slipping, and twisting types of motion.

Abstract: A robot having anatomical robot joints with convex surfaces significantly less in average overall curvature than their corresponding concave surfaces; having single lobe, double lobe, or saddle shapes; and held together by flexible bands that allow rocking, slipping, and twisting types of motion.

Abstract: A timestamp neural network comprised of sensor elements, internal elements, and motor elements is responsive to timestamps. Sensor elements transform a wide variety of signals into events that trigger the updating of timestamps. Internal elements are responsive to timestamps. Motor elements convert timestamps into useful output signals. A real time video pattern recognition system is implemented.

Abstract: A timestamp neural network comprised of sensor elements, internal elements, and motor elements is responsive to timestamps. Sensor elements transform a wide variety of signals into events that trigger the updating of timestamps. Internal elements are responsive to timestamps. Motor elements convert timestamps into useful output signals. A real time video pattern recognition system is implemented.

Abstract: An object recognition apparatus and method for real-time training and recognition/inspection of test objects. To train the system, digital features of an object are captured as sub-frames extracted from a data stream. The data is thresholded and digitized and used to produce an address representing the digital feature. The address is used to write a value into a memory. During recognition or inspection, extracting digital features from a test object, converting the digital features extracted from the test object into addresses, and using the addresses developed from the test object to address the memory to correlate whether the same memory locations are addressed determines whether the test object matches the reference object.

Abstract: An object recognition apparatus and method for real-time training and recognition/inspection of test objects. To train the system, digital features of an object are captured as sub-frames extracted from a data stream. The data is thresholded and digitized and used to produce an address representing the digital feature. The address is used to write a value into a memory. During recognition or inspection, extracting digital features from a test object, converting the digital features extracted from the test object into addresses, and using the addresses developed from the test object to address the memory to correlate whether the same memory locations are addressed determines whether the test object matches the reference object.