Certain aspects of the present disclosure relate to a method for compressed sensing (CS). The CS is a signal processing concept wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. In this disclosure, the CS framework is applied for sensor signal processing in order to support low power robust sensors and reliable communication in Body Area Networks (BANs) for healthcare and fitness applications.

The invention relates to the representation of digital signals. In order to improve the perception by a user of the quality of a digital signal, a first sample of first digital signal is approximated to a second sample of a second digital signal having a second number of significant bits lower than the first number of significant bits of the first sample. The second number of significant bits is also lower than a number of significant bits allowing the second digital signal, or a signal derived therefrom to match an expected bit depth of a processing unit said second digital signal, or a signal derived therefrom is to be sent to.

Compressed domain processors configured to perform operations on data compressed in a format that preserves order. The Compressed domain processors may include operations such as addition, subtraction, multiplication, division, sorting, and searching. In some cases, compression engines for compressing the data into the desired formats are provided.

The invention relates to improved convolutions of digital signals. When a first digital signal is convoluted with a second digital signal to obtain an output digital signal, to be converted afterwards using a limited number of bits. In order to prevent a loss of information, and therefore a degradation of the output digital signal upon the future conversion, at least one of the first and the second digital signal is formed of suitable values that store the information from the first digital signal within the most significant bits of the output digital signal.

A data compression method comprises encoding groups of data items by generating, for each group, header data comprising h-bits and a plurality of body portions each comprising b-bits and each body portion corresponding to a data item in the group. The value of h may be fixed for all groups and the value of b is fixed within a group, wherein the header data for a group comprises an indication of b for the body portions of that group. In various examples, b=0 and so there are no body portions. In examples where b is not equal to zero, a body data field is generated for each group by interleaving bits from the body portions corresponding to data items in the group. The resultant encoded data block, comprising the header data and, where present, the body data field can be written to memory.

Various energy efficient data encoding schemes and computing devices are disclosed. In one aspect, a method of transmitting data from a transmitter to a receiver connected by plural wires is provided. The method includes sending from the transmitter on at least one but not all of the wires a first wave form that has first and second signal transitions. The receiver receives the first waveform and measures a first duration between the first and second signal transitions using a locally generated clock signal not received from the transmitter. The first duration is indicative of a first particular data value.

An encoding apparatus for encoding a vibrotactile signal includes a first transforming unit configured to perform a discrete wavelet transform of the signal, a second transforming unit configured to generate a frequency domain representation of the signal, a psychohaptic model unit configured to generate at least one quantization control signal based on the generated frequency domain representation of the sampled signal and on a predetermined perceptual model based on human haptic perception, a quantization unit configured to quantize wavelet coefficients resulting from the performed discrete wavelet transform and adapted by the quantization control signal, a compression unit configured to compress the quantized wavelet coefficients, and a bitstream generating unit configured to generate a bitstream corresponding to the encoded signal based on the compressed quantized wavelet coefficients. The subject matter described herein also includes a corresponding decoding unit, an encoding method and a decoding method.

A decompression system includes a first memory including a first write port configured to receive decompressed data from a decompressor, and a first read port configured to receive a back-reference read request, the first memory being configured to output the decompressed data to the decompressor in response to receiving the back-reference read request at the first read port, and a second memory including a second write port electrically coupled to the first write port and configured to receive the decompressed data, the second memory being configured to buffer the decompressed data for retrieval by a receiver.

An apparatus to facilitate deep learning numeric data and sparse matrix compression is disclosed. The apparatus includes a processor comprising a compression engine to: receive a data packet comprising a plurality of cycles of data samples, and for each cycle of the data samples: pass the data samples of the cycle to a compressor dictionary; identify, from the compressor dictionary, tags for each of the data samples, wherein the compressor dictionary comprises at least a first tag for data having a value of zero and a second tag for data having a value of one; and compress the data samples into compressed cycle data by storing the tags as compressed data, wherein the data samples identified with the first tag are compressed using the first tag and the data samples identified with the second tag are compressed using the second tag at the same time as values of the data samples identified with the first tag or the second tag are excluded from the compressed cycle data.

A data compression method comprises encoding groups of data items by generating, for each group, header data comprising h-bits and a plurality of body portions each comprising b-bits and each body portion corresponding to a data item in the group. The value of h may be fixed for all groups and the value of b is fixed within a group, wherein the header data for a group comprises an indication of b for the body portions of that group. In various examples, b=0 and so there are no body portions. In examples where b is not equal to zero, a body data field is generated for each group by interleaving bits from the body portions corresponding to data items in the group. The resultant encoded data block, comprising the header data and, where present, the body data field can be written to memory.