An upper bound is given on the cutoff rate of discrete memoryless channels. This upper bound, which coincides with a known lower bound, determines the cutoff rate, and settles a long-standing open problem.

We demonstrate a fully functional handheld brain scanner consisting of a low-cost 14-channel EEG headset with a wireless connection to a smartphone, enabling minimally invasive EEG monitoring in naturalistic settings. The smartphone provides a touch-based interface with real-time brain state decoding and 3D reconstruction.

In a sensor network, exploiting the correlation among different sources allows a significant reduction of the transmitted energy at the cost of a complex decoder scheme. This paper introduces the first hardware implementation for joint source-channel decoding of correlated sources. Specifically, a dual-clustered VLIW processor with a highly optimized datapath is presented.

This paper shows how performance gain in a mobile radio environment can be achieved by using joint space-time equalization and decoding. We apply different joint procedures with two types of space-time equalization techniques. As a matter of fact, the joint techniques are able to provide a considerable gain with none to small additional computational cost.

In [1] we derived one shot achievable and converse bounds with possible mismatched decoding. In this paper, we extend the results to the list decoding case.

Reconstructing stimuli from the spike trains of neurons is an important approach for understanding the neural code. One of the difficulties associated with this task is that signals which are varying continuously in time are encoded into sequences of discrete events or spikes. An important problem is to determine how much information about the continuously varying stimulus can be extracted from...

The rapidly improving performance of modern hardware renders convolutional codes obsolete, and allows for the practical implementation of more sophisticated correction codes such as low density parity check (LDPC) and turbo codes (TC). Both are decoded by iterative algorithms, which require a disproportional computational effort for low channel noise. They are also unable to correct higher nois...

Convolution encoding with Viterbi decoding is a powerful method for forward error correction. It has been widely deployed in many wireless communication systems to improve the limited capacity of the communication channels. Viterbi algorithm, is the most extensively employed decoding algorithm for convolutional codes. In this paper, we present ARM based implementation of Viterbi decoder that us...

Malicious social media bots are disseminators of malicious information on networks and seriously affect security the network environment. Efficient reliable classification is crucial for detecting manipulation in networks. Aiming to correct defects high-cost labeling unbalanced positive negative samples existing methods bot detection, reduce training abnormal model, we propose an anomaly detect...

It is shown how the decoding algorithms of Pellikaan and Rosenthal can be coupled to produce a algorithm for convolutional codes. Bounds computational cost per decoded codeword are also computed. As case study, our results applied family codes constructed by Rosenthal–Schumacher–York and, in this situation, previous bounds turn out polynomial on degree code.