A device and computer-executable method is provided for adaptively determining a sampling scheme to be applied at a first sensor from among a plurality of sensors for sampling sensor data values corresponding to a signal. A sparsifying transform for a subsequent sampling time window of the first sensor is predicted, wherein the sparsifying transform is determined based on a predictive model of the sparsity of the signal. Moreover, a subsampling parameter for the subsequent sampling time window is determined. The subsampling parameter corresponds to a number of sensor data values to be acquired within the sampling time window. This subsampling parameter is determined based on the predicted sparsifying transform. Further determined is a compressive sampling scheme for the subsequent sampling time window of the first sensor. The compressive sampling scheme is determined based on the predicted sparsifying transform.

A method of compressive read mapping. A high-resolution homology table is created for the reference genomic sequence, preferably by mapping the reference to itself. Once the homology table is created, the reads are compressed to eliminate full or partial redundancies across reads in the dataset. Preferably, compression is achieved through self-mapping of the read dataset. Next, a coarse mapping from the compressed read data to the reference is performed. Each read link generated represents a cluster of substrings from one or more reads in the dataset and stores their differences from a locus in the reference. Preferably, read links are further expanded to obtain final mapping results through traversal of the homology table, and final mapping results are reported. As compared to prior techniques, substantial speed-up gains are achieved through the compressive read mapping technique due to efficient utilization of redundancy within read sequences as well as the reference.

An imaging system includes a Read-Out Integrated Circuit (ROIC) configured to receive high spatial resolution imagery having a detected amount of energy from a detection device. The ROIC includes a mask generator and a high-resolution image decode. The mask generator applies a pixel mask to the high spatial resolution imagery so as to generate compressed high spatial resolution imagery that preserves the detected amount of energy. The high-resolution image decoder receives the compressed high spatial resolution imagery and decompresses the compressed high spatial resolution imagery and obtain the high spatial resolution imagery having a detected amount of energy.

A first value of a first data element in a first set of data elements is obtained, the first set of data elements being based on a first time sample of a signal. A second value of a second data element in a second set of data elements is obtained, the second set of data elements being based on a second, later time sample of the signal. A measure of similarity is derived between the first value and the second value. Based on the derived measure, a quantisation parameter useable in performing quantisation on data based on the first time sample of the signal is determined. Output data is generated using the quantisation parameter.

An electromagnetic wave phase/amplitude generation device includes a radiation unit configured to radiate electromagnetic waves of a random radiation pattern on a spatial frequency in which a state of the electromagnetic waves to be radiated for each divided region is determined to an imaging object, an imaging unit configured to generate a captured image by imaging scattered electromagnetic waves that are electromagnetic waves generated when the imaging object scatters the electromagnetic waves of the radiation pattern radiated by the radiation unit, and a generation unit configured to generate information indicating at least a phase and amplitude of the electromagnetic waves from the imaging object by performing an arithmetic sparsity constraint operation according to sparsity of the imaging object on the basis of the captured image generated by the imaging unit, information indicating the radiation pattern, and information indicating a signal of the imaging object.

[Object] To achieve both continuity of the system running and reduction of the running cost under a situation in which a storage region on a network is used as a saving destination of various kinds of data. [Solution] Provided is a an information processing device including: a signal processing unit that encodes a first signal including one or more non-zero components based on first data and one or more zero components into a second signal having a shorter signal length than a signal length of the first signal on the basis of a matrix generated in accordance with a predetermined condition; a data generation unit that generates one or more pieces of second data by associating information indicating positions of signal elements in the second signal with the signal elements in the second signal; and a transmission unit that transmits each of the one or more generated pieces of second data to one or more devices connected via a network.

Real-world data may not be sparse in a fixed basis, and current high-performance recovery algorithms are slow to converge, which limits compressive sensing (CS) to either non-real-time applications or scenarios where massive back-end computing is available. Presented herein are embodiments for improving CS by developing a new signal recovery framework that uses a deep convolutional neural network (CNN) to learn the inverse transformation from measurement signals. When trained on a set of representative images, the network learns both a representation for the signals and an inverse map approximating a greedy or convex recovery algorithm. Implementations on real data indicate that some embodiments closely approximate the solution produced by state-of-the-art CS recovery algorithms, yet are hundreds of times faster in run time.

A terminal device obtains coding information that includes a first information bit and a second information bit. The terminal device selects, based on the first information bit, K groups from a codebook having L groups as activation groups, where both L and K are positive integers, and K is less than L. The terminal device then selects, based on the second information bit, a sequence from each of the activation groups as an activation sequence, and obtains a first sequence by superimposing the K activation sequences.

Certain aspects of the present disclosure relate to a wearable system including one or more wearable acquisition devices. Each acquisition device includes a sensor to capture samples of a biomedical signal and circuitry to process the samples for transmission to a mobile device. The samples are encoded for transmission and decoded at the mobile device to reconstruct the biomedical signal and, based on the reconstructed biomedical signal, provide output through a user interface of the mobile device. The wearable system includes at least an acquisition device for capturing an electro-cardiogram signal (ECG). Other biomedical signals, such as a photoplethysmograph (PPG) signal, may also be captured. The wearable system may comprise a Body Area Network (BAN).

An apparatus comprising a processor and memory including computer program code, the memory and computer program code configured to, with the processor, enable the apparatus at least to: based on a predetermined dark current component for each photodetector in an array of photodetectors, identify a plurality of subsets of photodetectors from the array for signal readout and amplification by a readout circuit, each photodetector of the array configured to provide a photodetector output signal comprising the dark current component and an image component on exposure to incident electromagnetic radiation from a target scene, wherein each subset of photodetectors is identified such that the combined dark current component of the constituent photodetector output signals for each subset is substantially the same; and provide the identified plurality of subsets for use in signal readout and amplification by the readout circuit.