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Abstract-We investigate the computation of Csiszár's bounds for the joint source-channel coding (JSCC) error exponent of a communication system consisting of a discrete memoryless source and a discrete memoryless channel. We provide equivalent expressions for these bounds and derive explicit formulas for the rates where the bounds are attained. These equivalent representations can be readily computed for arbitrary source-channel pairs via Arimoto's algorithm. When the channel's distribution satisfies a symmetry property, the bounds admit closed-form parametric expressions. We then use our results to provide a systematic comparison between the JSCC error exponent and the tandem coding error exponent , which applies if the source and channel are separately coded. It is shown that Index Terms-Discrete memoryless sources and channels, error exponent, Fenchel's duality, Hamming distortion measure, joint source-channel coding, random-coding exponent, reliability function, sphere-packing exponent, symmetric channels, tandem source and channel coding.

Abstract-Consider transmitting two discrete memoryless correlated sources, consisting of a common and a private source, over a discrete memoryless multi-terminal channel with two transmitters and two receivers. At the transmitter side, the common source is observed by both encoders but the private source can only be accessed by one encoder. At the receiver side, both decoders need to reconstruct the common source, but only one decoder needs to reconstruct the private source. We hence refer to this system by the asymmetric 2-user source-channel system. In this work, we derive a universally achievable joint source-channel coding (JSCC) error exponent pair for the 2-user system by using a technique which generalizes Csiszár's method [3] for the pointto-point (single-user) discrete memoryless source-channel system. We next investigate the largest convergence rate of asymptotic exponential decay of the system (overall) probability of erroneous transmission, i.e., the system JSCC error exponent. We obtain lower and upper bounds for the exponent. As a consequence, we establish the JSCC theorem with single letter characterization.

Purpose A positive circumferential resection margin (CRM) for oesophageal and gastric carcinoma is associated with local recurrence and poorer long-term survival. Diffuse reflectance spectroscopy (DRS) is a non-invasive technology able to distinguish tissue type based on spectral data. The aim of this study was to develop a deep learning-based method for DRS probe detection and tracking to aid classification of tumour and non-tumour gastrointestinal (GI) tissue in real time. Methods Data collected from both ex vivo human tissue specimen and sold tissue phantoms were used for the training and retrospective validation of the developed neural network framework. Specifically, a neural network based on the You Only Look Once (YOLO) v5 network was developed to accurately detect and track the tip of the DRS probe on video data acquired during an ex vivo clinical study. Results Different metrics were used to analyse the performance of the proposed probe detection and tracking framework, such as precision, recall, mAP 0.5, and Euclidean distance. Overall, the developed framework achieved a 93% precision at 23 FPS for probe detection, while the average Euclidean distance error was 4.90 pixels. Conclusion The use of a deep learning approach for markerless DRS probe detection and tracking system could pave the way for real-time classification of GI tissue to aid margin assessment in cancer resection surgery and has potential to be applied in routine surgical practice.

We investigate the joint source-channel coding (JSCC) excess distortion exponent E J (the exponent of the probability of exceeding a prescribed distortion level) for memoryless communication systems with continuous alphabets. We first establish upper and lower bounds for E J for systems consisting of a memoryless Gaussian source under the squared-error distortion fidelity criterion and a memoryless additive Gaussian noise channel with a quadratic power constraint at the channel input. A necessary and sufficient condition for which the two bounds coincide is provided, thus exactly determining the exponent. This condition is observed to hold for a wide range of source-channel parameters. The problem of transmitting memoryless Laplacian sources over the Gaussian channel under the magnitudeerror distortion is also carried out. We also establish a lower bound for E J for a certain class of continuous source-channel pairs when the distortion measure is a metric. The advantage in terms of the excess distortion exponent of JSCC over traditional tandem coding for Gaussian systems is next studied. A formula for the tandem exponent is derived in terms of the Gaussian source and Gaussian channel exponents. By numerically comparing the lower bound of the joint exponent and the upper bound of the tandem exponent, it is observed that, as for the discrete systems, JSCC often substantially outperforms tandem coding.

Abstract-We study the joint source-channel coding (JSCC) error exponent for discrete memoryless source-channel systems with side information which is correlated to the transmitted source. Two cases are considered: (1) the side information is available only at the decoder; (2) the side information is available at both the encoder and decoder. We employ the method of types to establish a lower bound for the JSCC error exponent for each case. As a consequence, a JSCC theorem on the reliable transmissibility of the source over the channel is obtained. It is noted that the same JSCC theorem applies for both cases. For binary sources and symmetric channels, we derive a sufficient condition for which the side information at the decoder can strictly improve the JSCC error exponent. Numerical results show that side information can enlarge the region for reliable transmissibility and increase the JSCC error exponent for a wide class of source-channel parameters.