Order Statistics Based List Decoding Techniques for Linear Binary Block Codes

The order statistics based list decoding techniques for linear binary block codes of small to medium block length are investigated. The construction of the list of the test error patterns is considered. The original order statistics decoding is generalized by assuming segmentation of the most reliable independent positions of the received bits. The segmentation is shown to overcome several drawbacks of the original order statistics decoding. The complexity of the order statistics based decoding is further reduced by assuming a partial ordering of the received bits in order to avoid the complex Gauss elimination. The probability of the test error patterns in the decoding list is derived. The bit error rate performance and the decoding complexity trade-off of the proposed decoding algorithms is studied by computer simulations. Numerical examples show that, in some cases, the proposed decoding schemes are superior to the original order statistics decoding in terms of both the bit error rate performance as well as the decoding complexity.Comment: 17 pages, 2 tables, 6 figures, submitted to IEEE Transactions on Information Theor

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.Comment: 10 page

Critical Point for Maximum Likelihood Decoding of Linear Block Codes

In this letter, the SNR value at which the error performance curve of a soft decision maximum likelihood decoder reaches the slope corresponding to the code minimum distance is determined for a random code. Based on this value, referred to as the critical point, new insight about soft bounded distance decoding of random-like codes (and particularly Reed-Solomon codes) is provided.Comment: to appear IEEE Communications Letter

Improving Short-Length LDPC Codes with a CRC and Iterative Ordered Statistic Decoding

We present a CRC-aided decodingscheme of LDPC codes that can outperform the underlying LDPC code underordered statistic decoding (OSD). In this scheme, the CRC is usedjointly with the LDPC code to construct a candidate list, insteadof conventionally being regarded as a detection code to prunethe list generated by the LDPC code alone. As an example weconsider a (128,64) 5G LDPC code with BP decoding, which we canoutperform by 2dB using a (128,72) LDPC code in combinationwith a 8-bit CRC under OSD order of 3. The CRC-aided decoding scheme also achieves a better performance than the conventional one where CRC is used to prune the list. A manageable complexity can be achievedwith iterative reliability based OSD, which is demonstrated toperform well with a small OSD order

Talking the Talk: The Effect of Vocalics in an Interview

Our voices carry more than just content. People continuously make assumptions of one’s intelligence, credibility, personality, and other characteristics merely based on the way we talk. As the diversity of individuals in the workplace increases, so too do the differences in how those individuals talk. It is important that we understand how these different ways of speaking are being perceived in the workplace. More specifically, how are individuals being perceived prior to being hired via the interview process? This Honors Capstone project aims to understand the impact that vocal characteristics in an individual have on the interviewer’s perception of the interviewee, and how that impacts the hiring process. This project will offer professionals of all ages tangible advice on ways to increase one’s chances of receiving a job just by altering aspects of one’s voice