An experimental study of the concatenated Reed-Solomon/Viterbi channel coding system performance and its impact on space communications

The need for efficient space communication at very low bit error probabilities to the specification and implementation of a concatenated coding system using an interleaved Reed-Solomon code as the outer code and a Viterbi-decoded convolutional code as the inner code. Experimental results of this channel coding system are presented under an emulated S-band uplink and X-band downlink two-way space communication channel, where both uplink and downlink have strong carrier power. This work was performed under the NASA End-to-End Data Systems program at JPL. Test results verify that at a bit error probability of 10 to the -6 power or less, this concatenated coding system does provide a coding gain of 2.5 dB or more over the Viterbi-decoded convolutional-only coding system. These tests also show that a desirable interleaving depth for the Reed-Solomon outer code is 8 or more. The impact of this "virtually" error-free space communication link on the transmission of images is discussed and examples of simulation results are given

Automatic generation of hardware Tree Classifiers

Machine Learning is growing in popularity and spreading across different fields for various applications. Due to this trend, machine learning algorithms use different hardware platforms and are being experimented to obtain high test accuracy and throughput. FPGAs are well-suited hardware platform for machine learning because of its re-programmability and lower power consumption. Programming using FPGAs for machine learning algorithms requires substantial engineering time and effort compared to software implementation. We propose a software assisted design flow to program FPGA for machine learning algorithms using our hardware library. The hardware library is highly parameterized and it accommodates Tree Classifiers. As of now, our library consists of the components required to implement decision trees and random forests. The whole automation is wrapped around using a python script which takes you from the first step of having a dataset and design choices to the last step of having a hardware descriptive code for the trained machine learning model

A Turbo Detection and Sphere-Packing-Modulation-Aided Space-Time Coding Scheme

Arecently proposed space-time block-coding (STBC) signal-construction method that combines orthogonal design with sphere packing (SP), referred to here as STBC-SP, has shown useful performance improvements over Alamouti’s conventional orthogonal design. In this contribution, we demonstrate that the performance of STBC-SP systems can be further improved by concatenating SP-aided modulation with channel coding and performing demapping as well as channel decoding iteratively. We also investigate the convergence behavior of this concatenated scheme with the aid of extrinsic-information-transfer charts. The proposed turbo-detected STBC-SP scheme exhibits a “turbo-cliff” at Eb/N0 = 2.5 dB and provides Eb/N0 gains of approximately 20.2 and 2.0 dB at a bit error rate of 10?5 over an equivalent throughput uncoded STBC-SP scheme and a turbo-detected quadrature phase shift keying (QPSK) modulated STBC scheme, respectively, when communicating over a correlated Rayleigh fading channel. Index Terms—EXIT charts, iterative demapping, multidimensional mapping, space-time coding, sphere packing, turbo detection

Convolutional Codes in Rank Metric with Application to Random Network Coding

Random network coding recently attracts attention as a technique to disseminate information in a network. This paper considers a non-coherent multi-shot network, where the unknown and time-variant network is used several times. In order to create dependencies between the different shots, particular convolutional codes in rank metric are used. These codes are so-called (partial) unit memory ((P)UM) codes, i.e., convolutional codes with memory one. First, distance measures for convolutional codes in rank metric are shown and two constructions of (P)UM codes in rank metric based on the generator matrices of maximum rank distance codes are presented. Second, an efficient error-erasure decoding algorithm for these codes is presented. Its guaranteed decoding radius is derived and its complexity is bounded. Finally, it is shown how to apply these codes for error correction in random linear and affine network coding.Comment: presented in part at Netcod 2012, submitted to IEEE Transactions on Information Theor

Searching for Exoplanets Using Artificial Intelligence

In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects which, unlike current methods uses a neural network. Neural networks, also called "deep learning" or "deep nets" are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time-series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.Comment: Accepted, 16 Pages, 14 Figures, https://github.com/pearsonkyle/Exoplanet-Artificial-Intelligenc