Optimization of Design of Space Experiments from the Standpoint of Data Processing Semiannual Report, May 1 - Oct. 31, 1966

Encoding algorithm for processing data from space experiment

A robust compression system for low bit rate telemetry: Test results with lunar data

A robust noiseless encoding scheme is presented for encoding the gamma ray spectroscopy data. The encoding algorithm is simple to implement and has minimal buffering requirements. The decoder contains error correcting capability in the form of a MAP receiver. While the MAP receiver adds some complexity, this is limited to the decoder. Nothing additional is needed at the encoder side for its functioning

Flexible and Low-Complexity Encoding and Decoding of Systematic Polar Codes

In this work, we present hardware and software implementations of flexible polar systematic encoders and decoders. The proposed implementations operate on polar codes of any length less than a maximum and of any rate. We describe the low-complexity, highly parallel, and flexible systematic-encoding algorithm that we use and prove its correctness. Our hardware implementation results show that the overhead of adding code rate and length flexibility is little, and the impact on operation latency minor compared to code-specific versions. Finally, the flexible software encoder and decoder implementations are also shown to be able to maintain high throughput and low latency.Comment: Submitted to IEEE Transactions on Communications, 201

Fast systematic encoding of multiplicity codes

We present quasi-linear time systematic encoding algorithms for multiplicity codes. The algorithms have their origins in the fast multivariate interpolation and evaluation algorithms of van der Hoeven and Schost (2013), which we generalise to address certain Hermite-type interpolation and evaluation problems. By providing fast encoding algorithms for multiplicity codes, we remove an obstruction on the road to the practical application of the private information retrieval protocol of Augot, Levy-dit-Vehel and Shikfa (2014)

A Novel High Frequency Encoding Algorithm for Image Compression

In this paper a new method for image compression is proposed whose quality is demonstrated through accurate 3D reconstruction from 2D images. The method is based on the Discrete Cosine Transform (DCT) together with a high frequency minimization encoding algorithm at compression stage and a new concurrent binary search algorithm at decompression stage. The proposed compression method consists of five main steps: (1) Divide the image into blocks and apply DCT to each block; (2) Apply a high frequency minimization method to the AC-coefficients reducing each block by 2/3 resulting in a Minimized Array; (3) Build a look up table of probability data to enable the recovery of the original high frequencies at decompression stage; (4) Apply a delta or differential operator to the list of DC-components; and (5) Apply arithmetic encoding to the outputs of steps (2) and (4). At decompression stage, the look up table and the concurrent binary search algorithm are used to reconstruct all high frequency AC-coefficients while the DC-components are decoded by reversing the arithmetic coding. Finally, the inverse DCT recovers the original image. We tested the technique by compressing and decompressing 2D images including images with structured light patterns for 3D reconstruction. The technique is compared with JPEG and JPEG2000 through 2D and 3D RMSE. Results demonstrate that the proposed compression method is perceptually superior to JPEG with equivalent quality to JPEG2000. Concerning 3D surface reconstruction from images, it is demonstrated that the proposed method is superior to both JPEG and JPEG2000