Abstract: Method and system for lossless and stateless compression scheme is used with a fixed-length data such as frames. Frames having a payload of M bits length are mapped into a payload of N bits length, where N<M. The N bits payload of each received frame is extracted, and mapped using a memory, PLD, or a processor, to reconstruct the uncompressed M bits payload, and to form the original frame. The reconstruction may use a set of N coefficients that are each multiplied by the corresponding received payload bit, and summarized modulo-2 to obtain the original pre-compressed M payload bits. The method and system may be used with a vehicle bus, such as Controller Area Network (CAN). The compressed frames may use the same or different protocol than the uncompressed ones, and may further carry an additional code such as metadata, error detection or correction code, or authentication related code.

Abstract: Method and system for lossless and stateless compression scheme is used with a fixed-length data such as frames. Frames having a payload of M bits length are mapped into a payload of N bits length, where N<M. The N bits payload of each received frame is extracted, and mapped using a memory, PLD, or a processor, to reconstruct the uncompressed M bits payload, and to form the original frame. The reconstruction may use a set of N coefficients that are each multiplied by the corresponding received payload bit, and summarized modulo-2 to obtain the original pre-compressed M payload bits. The method and system may be used with a vehicle bus, such as Controller Area Network (CAN). The compressed frames may use the same or different protocol than the uncompressed ones, and may further carry an additional code such as metadata, error detection or correction code, or authentication related code.

Abstract: Method and system for lossless and stateless compression scheme is used with a fixed-length data such as frames. Frames having a payload of M bits length are mapped into a payload of N bits length, where N<M. The N bits payload of each received frame is extracted, and mapped using a memory, PLD, or a processor, to reconstruct the uncompressed M bits payload, and to form the original frame. The reconstruction may use a set of N coefficients that are each multiplied by the corresponding received payload bit, and summarized modulo-2 to obtain the original pre-compressed M payload bits. The method and system may be used with a vehicle bus, such as Controller Area Network (CAN). The compressed frames may use the same or different protocol than the uncompressed ones, and may further carry an additional code such as metadata, error detection or correction code, or authentication related code.

Abstract: Method and system for lossless and stateless compression scheme is used with a fixed-length data such as frames. Frames having a payload of M bits length are mapped into a payload of N bits length, where N<M. The N bits payload of each received frame is extracted, and mapped using a memory, PLD, or a processor, to reconstruct the uncompressed M bits payload, and to form the original frame. The reconstruction may use a set of N coefficients that are each multiplied by the corresponding received payload bit, and summarized modulo-2 to obtain the original pre-compressed M payload bits. The method and system may be used with a vehicle bus, such as Controller Area Network (CAN). The compressed frames may use the same or different protocol than the uncompressed ones, and may further carry an additional code such as metadata, error detection or correction code, or authentication related code.

Abstract: A sub-phoneme model given acoustic data which corresponds to a phoneme. The acoustic data is generated by sampling an analog speech signal producing a sampled speech signal. The sampled speech signal is windowed and transformed into the frequency domain producing Mel frequency cepstral coefficients of the phoneme. The sub-phoneme model is used in a speech recognition system. The acoustic data of the phoneme is divided into either two or three sub-phonemes. A parameterized model of the sub-phonemes is built, where the model includes Gaussian parameters based on Gaussian mixtures and a length dependency according to a Poisson distribution. A probability score is calculated while adjusting the length dependency of the Poisson distribution. The probability score is a likelihood that the parameterized model represents the phoneme. The phoneme is subsequently recognized using the parameterized model.