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In 4G turbo and 5G LDPC, in order to realize a flexible, low-power, low-cost shared general-purpose block interleaving hardware module, it faces the challenges of interleaving structure integration, fewer gate circuits, parallel multistream operation, and switching between standards. Facing these challenges, after studying 3GPP TS 36.212 V15.4.0 and 3GPP TS 38.212 V15.4.0 protocols, common part of two major interleaving module standards is found. For the block interleave module in the rate matching of the 4G downlink turbo code and the bit interleave module after the rate matching of the 5G NR downlink LDPC code, this paper first designs a memory and implements the two codes interleaving on it. Then, based on the Altera Quartus prime platform and ModelSim for functional verification. Experimental results show that under SMIC 28 nm, operating frequency 50MHz, after synopsys synthesis, the memory module area is 0.17 μ m 2 , and the power consumption is 6.45 mW. Through the shared design, 32 bits parallel access, and switching between standards, the proposed scheme reduces the hardware cost, power consumption, and clock overhead, and improves the flexibility of 4G LTE and 5G NR communication downlink hardware implementation. [ABSTRACT FROM AUTHOR]

In an ideal case, the time-lapse differences in 4-D seismic data should only reflect the changes of the subsurface geology. Practically, however, undesirable discrepancies are generated because of various reasons. Therefore, proper time-lapse processing techniques are required to improve the repeatability of time-lapse seismic data and to capture accurate seismic information to analyse target changes. In this study, we propose a machine learning-based time-lapse seismic data processing method improving repeatability. A training data construction method, training strategy and machine learning network architecture based on a convolutional autoencoder are proposed. Uniform manifold approximation and projection are applied to the training and target data to analyse the features corresponding to each data point. When the feature distribution of the training data is different from the target data, we implement data augmentation to enhance the diversity of the training data. The method is verified through numerical experiments using both synthetic and field time-lapse seismic data, and the results are analysed with several methods, including a comparison of repeatability metrics. From the results of the numerical experiments, we can conclude that the proposed convolutional autoencoder can enhance the repeatability of the time-lapse seismic data and increase the accuracy of observed variations in seismic signals generated from target changes. [ABSTRACT FROM AUTHOR]

Serial decoding algorithms of low-density parity-check (LDPC) code converge efficiently with low errors. Previously, a serial decoding algorithm, named a shuffled belief-propagation (SBP), was applied in turbo equalization of bit-patterned magnetic recording (BPMR) systems. With the SBP algorithm, an LDPC decoder converged twice as fast as one using conventional BP algorithms. We further improved the convergence speed of SBP by updating the messages in an adaptive order, which played a flexible role throughout decoding. We proposed two adaptive-serial algorithms for LDPC codes in turbo equalization. One updated the messages using the extrinsic loglikelihood ratio (LLR) and the result of the parity-check equation checking. The second contained an additional rule that tracked the LLR sign changes in each iteration. Both algorithms converged faster and with lower bit error rates (BERs) than the SBP and previous adaptive-serial algorithms in a BPMR system with media noise. [ABSTRACT FROM AUTHOR]

Nowadays, activity prediction is key to understanding the mechanism-of-action of active structures discovered from phenotypic screening or found in natural products. Machine learning is currently one of the most important and rapidly evolving topics in computer-aided drug discovery to identify and design new drugs with superior biological activities. The performance of a predictive machine learning model can be enhanced through the optimal selection of learning data, algorithm, algorithm parameters, and ensemble methods. In this article, we focus on how to enhance the prediction model using the learning data. However, get an option to add more and accurate data is not easy and available in many cases. This motivated us to propose the turbo prediction model, in which nearest neighbour structures are used to increase prediction accuracy. Five datasets, well known in the literature, were used in this article and experimental results show that turbo prediction can improve the quality prediction of the conventional prediction models, particularly for heterogeneous datasets, without any additional effort on the part of the user carrying out the prediction process, and at a minimal computational cost. [ABSTRACT FROM AUTHOR]

The Japan Meteorological Agency operates gridded temperature guidance to predict two-dimensional snowfall amounts and precipitation types, e.g., rain and snow, because surface temperature is one of the key elements to predict them. Operational temperature guidance is based on the Kalman filter, which uses temperature observation and numerical weather prediction (NWP) outputs only around observation sites. Correcting a temperature field when NWP models incorrectly predict a front's location or when observed temperatures are extremely cold or hot has been challenging. In this study, an encoder-decoder-based convolutional neural network has been proposed to predict gridded temperatures at the surface around the Kanto region in Japan. Verification results showed that the proposed model greatly improves the operational guidance and can correct NWP model biases, such as a positional error of fronts and extreme temperatures. [ABSTRACT FROM AUTHOR]

Performance of visible light communication systems which is encoded using turbo product codes is studied in this article. It is the first time to show the effect of turbo product codes in these systems. We chose ceiling bounce model since its accuracy with the practical measurements is proved. Our simulations are carried out in two different link distance and two different data rates. Encoder parameters are selected according to the code rate. This way, meaningful comparison can be made among the results considering distance, data rate, and code rate. Our investigations infer that using turbo product codes, higher data rate is achieved where link distance is the same. Similarly, it can be reached higher link distance when data rate is set to a constant value. [ABSTRACT FROM AUTHOR]

Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is much more strenuous than in 2D images. For 4D time-series tomograms, this is usually handled by segmenting the constituent tomograms independently through time with 3D convolutional neural networks. Inter-volume information is therefore not utilized, potentially leading to temporal incoherence. In this paper, we attempt to resolve this by proposing two hidden Markov model variants that refine 4D segmentation labels made by 3D convolutional neural networks working on each time point. Our models utilize not only inter-volume information, but also the prediction confidence generated by the 3D segmentation convolutional neural networks themselves. To the best of our knowledge, this is the first attempt to refine 4D segmentations made by 3D convolutional neural networks using hidden Markov models. During experiments we test our models, qualitatively, quantitatively and behaviourally, using prespecified segmentations. We demonstrate in the domain of time series tomograms which are typically undersampled to allow more frequent capture; a particularly challenging problem. Finally, our dataset and code is publicly available. [ABSTRACT FROM AUTHOR]

Aiming at the problem of feature extraction and recognition in high resolution range profile (IIRRP) target recognition, a recognition method based on one-dimensional stacked pooling fusion convolutional autoencoder (ID SPF-CAE) is proposed in this paper. Firstly, a one-dimensional pooling fusion convolutional autoencoder (ID PF-CAE) is constructed. In the encoding stage, the maximum pooling and average pooling are used to extract different encoding features and fuse them to extract the structural features of IIRRP. Then, multiple ID PF-CAEs are stacked to form ID SPF-CAE. Finally, the network is fine-tuned using label data to realize IIRRP target recognition. And the AdaBound algorithm is used to optimize network training for improving the recognition performance. The experimental results based on the simulated data of the target in the middle part of the trajectory show that the method has strong feature extraction capability, and has high accuracy and robustness for IIRRP target recognition. [ABSTRACT FROM AUTHOR]

Extra short packets are common in the 5G Internet which provides low latency, ultra-reliable services for applications such as autonomous driving, remote surgery, industry automation, to name just a few. The multitude number of low cost devices cannot afford to run high power forward error control coding algorithms, such as Turbo codes, LDPC codes, Polar codes, and so on, to meet the stringent requirements of ultra-reliability and extremely low latency. In this paper, we propose using patching as the basis for forward error control coding of extra short packets, where replicas of a packet along with its checksum are transmitted adaptively. At the receiver, we propose using dynamic formation of a full packet based on the theory of Finite Projective Plane. The newly formed packet is then checked for being non-faulty. The approach of patching with dynamic formation of a full packet is systematic and well suitable for extra short packets. Despite the fact that the proposed coding scheme provides lower coding rate for a fixed packet error rate in comparison to the Polar codes and LDPC codes, the main advantages lie in its flexibility and simplicity of processing ideal for asymmetric configuration with low power receiving devices providing ultra-reliable low latency communications. [ABSTRACT FROM AUTHOR]

Channel coding is a fundamental procedure in wireless telecommunication systems and has a strong impact on the data transmission quality. This effect becomes more important when the transmission must be characterised by low latency and low bit error rate, as in the case of vehicle-to-everything (V2X) services. Thus, V2X services must use powerful and efficient coding schemes. In this paper, we thoroughly examine the performance of the most important channel coding schemes in V2X services. More specifically, the impact of use of 4th-Generation Long-Term Evolution (4G-LTE) turbo codes, 5th-Generation New Radio (5G-NR) polar codes and low-density parity-check codes (LDPC) in V2X communication systems is researched. For this purpose, we employ stochastic propagation models that simulate the cases of line of sight (LOS), non-line of sight (NLOS) and line of sight with vehicle blockage (NLOSv) communication. Different communication scenarios are investigated in urban and highway environments using the 3rd-Generation Partnership Project (3GPP) parameters for the stochastic models. Based on these propagation models, we investigate the performance of the communication channels in terms of bit error rate (BER) and frame error rate (FER) performance for different levels of signal to noise ratio (SNR) for all the aforementioned coding schemes and three small V2X-compatible data frames. Our analysis shows that turbo-based coding schemes have superior BER and FER performance than 5G coding schemes for the vast majority of the considered simulation scenarios. This fact, combined with the low-complexity requirements of turbo schemes for small data frames, makes them more suitable for small-frame 5G V2X services.

In this paper, we propose a frequency domain data hiding method for the JPEG compressed images. The proposed method embeds data in the DCT coefficients of the selected 8 × 8 blocks. According to the theories of Human Visual Systems (HVS), human vision is less sensitive to perturbation of pixel values in the uneven areas of the image. In this paper we propose a Singular Value Decomposition based image roughness measure (SVD-IRM) using which we select the coarse 8 × 8 blocks as data embedding destinations. Moreover, to make the embedded data more robust against re-compression attack and error due to transmission over noisy channels, we employ Turbo error correcting codes. The actual data embedding is done using a proposed variant of matrix encoding that is capable of embedding three bits by modifying only one bit in block of seven carrier features. We have carried out experiments to validate the performance and it is found that the proposed method achieves better payload capacity and visual quality and is more robust than some of the recent state-of-the-art methods proposed in the literature. [ABSTRACT FROM AUTHOR]

The detailed monitoring of jointed plain concrete pavement (JPCP) slab condition is essential for cost‐effective JPCP maintenance and rehabilitation. However, existing visual inspection practices for detailed slab condition classification are time‐consuming and labor‐intensive. In this paper, we proposed an automated JPCP slab condition classification model based on convolutional neural networks (ConvNets), which is the first to perform multi‐label classification on the JPCP slab condition based on both crack types and severity levels. To handle the different scales between JPCP slab condition states, the model includes a novel global context block with atrous spatial pyramid pooling, denoted as a GC‐ASPP block. The block can be flexibly applied to any ConvNets to effectively model the global context of images with the extraction of multiscale image features. The proposed model was evaluated using real‐world 3D JPCP surface data. With the GC‐ASPP block, our best model achieved an average precision of 85.42% on multi‐label slab condition classification. [ABSTRACT FROM AUTHOR]

Summary: A precise forecast of wind speed is a fundamental requirement of wind power integration. The nonlinear and intermittent nature of the wind makes wind speed forecasting (WSF) complicated for linear approaches. Addressing the complications faced by the linear approaches, this paper proposed a novel and robust approach using long short‐term memory (LSTM) autoencoder, convolutional neural network (CNN), and LSTM model for enhanced WSF. The proposed hybrid approach is divided into two main components: feature encoding, dimensionality reduction using LSTM autoencoder and forecasting using convolutional LSTM. In the first stage, the LSTM autoencoder eliminates the uncertainties present in raw wind speed data and also reduces the computational load on the forecasting convolutional LSTM approach. Then, in the second stage, CNN is used to extract the optimum features, and the LSTM network is used to forecast the wind speed. Five different benchmark forecasting models are used to evaluate and study the proposed hybrid approach's performance. The experiment is performed with real‐time wind speed data from the Garden city wind farm, USA. The proposed hybrid approach performance is verified using various performance metrics. The experimental results demonstrate that the proposed approach improved by 40% over the second best benchmark forecasting approach. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Subsurface flow model calibration is commonly performed by assuming that a known conceptual model of geologic continuity is available and can be used to constrain the solution search space. In real applications, however, the knowledge about geologic continuity is far from certain and subjective interpretations can lead to multiple distinct plausible geologic scenarios. Conventional parameterization methods that are widely used in model calibration, such as the principal component analysis, encounter difficulty in capturing diverse spatial patterns from distinct geologic scenarios. We propose a deep learning architecture, known as variational auto‐encoder, for robust dimension‐reduced parameterization of spatially distributed aquifer properties, such as hydraulic conductivity, in solving model calibration problems under uncertain geostatistical models. We show that convolutional autoencoders offer the versatility and robustness required for nonlinear parameterization of complex subsurface flow property distributions when multiple distinct geologic scenarios are present. The robustness of these models results, in part, from the use of many convolutional filters that afford the redundancy needed to extract, classify and encode very diverse spatial patterns at different abstraction levels/scales and enable their mapping onto low‐dimensional variables in a learned latent space. The resulting low‐dimensional latent variables control the salient spatial patterns in different geologic continuity models and are effective for parameterization of model calibration problems under uncertainty in geologic continuity, a task that is not trivial to accomplish using traditional parameterization methods. Several numerical experiments are used to demonstrate the robustness of convolutional deep learning models for reduced‐order parameterization of flow model calibration problems when alternative plausible geologic continuity models are present. Key Points: Deep learning enables robust subsurface flow model calibration when multiple conceptual prior models of geologic continuity are presentDeep convolutional networks offer flexibility to adapt to diversity in training data (geologic uncertainty) by using many trainable filtersA comprehensive set of experiments demonstrate the robustness of variational autoencoder for model calibration under uncertain geostatistical models [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Analysis of the current state and development of wireless communication systems, such as first generation 1G, second generation 2G, third generation 3G, fourth generation 4G LTEAdvanced, fifth generation 5G, Wi-Max and Wi-Fi, shows the need application of information technologies that allow efficient data transmission in real time. In such systems, ensuring a given level of reliability of information transmission is carried out through the use of information technology adaptation and coding. With increasing noise levels, existing methods of ensuring the reliability of information based on correction codes do not provide the specified indicators of the efficiency of information transmission. With restrictions on code parameters, in systems with adaptation, in the case of increasing noise levels to a certain value, ensuring the specified characteristics of the reliability of information becomes impossible. Therefore, the question arises of the use of turbo codes and their multilevel parametric adaptation. In the case of reverse data decoding, there is a need for a priori information on the plausibility functions of the received data, taking into account the procedure of decoding algorithms based on probabilistic decoding, namely for turbo codes. It is possible to form estimates of uncertainty of information during decoding, to minimize these estimates by choosing the rules of decision using the methods of parametric adaptation and to ensure the reliability of information transfer. [ABSTRACT FROM AUTHOR]

The study sought to determine the extent fine and applied arts instructors acquires the needed entrepreneurial skills that would change the wrong motivate of dependency syndromes among Nigerian secondary school leavers. The scholars reviewed fine and applied art skills that would enable fine and applied arts instructors to properly educate and acquaint their learners with appropriate thoughts about entrepreneurial skill development. The study was guided by two main objectives and two research questions. The study was carried out in South-East, Nigeria. The study population was 115 fine and applied arts instructors while 55 were sampled using a simple sampling procedure. A structured questionnaire was used as the instrument for data collection. The instrument was face-validated by three experts, two in the field of Fine and Applied Arts and one in Measurement and Evaluation all in the University of Nigeria Nsukka. Mean and standard deviation was used to answer the research questions. The finding showed that fine and applied arts instructors balance knowledge on entrepreneurial skills would enable secondary school leavers an opportunity to create a job for themselves instead of depending wholly on the state and family after graduation. [ABSTRACT FROM AUTHOR]

Summary: Among communication systems, multiple input multiple output (MIMO) system has the capability of providing spectral efficiency, high data rates, diversity gain, and improved reliability. Suitably designed orthogonal frequency division multiplexing (OFDM) transmits the signal without inter symbol interference and multipath fading. Hence, MIMO‐OFDM is a strong candidate for 4G and 5G wireless systems. However, variations in signal‐to‐noise ratio (SNR) due to the random nature of the channel and poor capacity at low SNR are critical issues in conventional MIMO‐OFDM system. This paper explains how optimal power allocation (OPA), channel estimation, and coding improve the capacity and bit error rate (BER) performance of MIMO‐OFDM system. In OPA, the transmitter power is distributed adaptively to the channels based on the SNR statistics using singular value decomposition and water filling algorithm. In case of low SNR, less number of channels are selected for power allocation. Least squares (LS), minimum mean square error (MMSE), and least mean square (LMS) estimators are considered in comb type pilot channel estimation while turbo code is used with code rates of 1/2 and 1/3. From the simulation results, capacity improvement of 5 b/s/Hz and 11 b/s/Hz is observed for MIMO‐OFDM systems with four antennas and eight antennas, respectively. It is also found that performance of LMS estimator improved by 3.5‐dB SNR over LS and 1 dB SNR over MMSE. In this paper is shown how LMS estimator and 1/3 turbo code have been integrated with MIMO‐OFDM, and this has resulted in around 4‐dB SNR improvement over conventional MIMO‐OFDM. [ABSTRACT FROM AUTHOR]

High-Efficiency Video Coding (HEVC) is the latest video encoding standard that achieves much better compression efficiency compared to the earlier encoding standards. Satellite channels have a long round trip time (RTT) making it difficult to use packet acknowledgments. Real-time video streaming applications preclude such packet acknowledgments in satellite networks due to strict delay constraints. We propose a combined use of Turbo Coding (TC) and Network Coding (NC) techniques to achieve better video quality over the noisy satellite links using UDP at the transport layer. We evaluate the performance improvement of turbo network coding (TNC-UDP) over the traditional turbo-coded (TC-UDP) protocol for HEVC video streaming in satellite networks. The simulation results show that compared to TC-UDP, the proposed scheme achieves PSNR improvements ranging from 14-20 dB for poor channel conditions (1-2 dB) for the two selected video sequences. [ABSTRACT FROM AUTHOR]

We propose a novel method for medical image watermarking in the DCT domain using the AES encryption algorithm. First, we decompose the original medical image into subblocks of 8 × 8. Besides, we apply the DCT and the quantization, respectively, to each subblock. However, in the DCT domain, an adequate choice of the DCT coefficients according to the quantization table in the middle frequencies band is performed. After that, we embed the patient's data into the corresponding medical image. The insertion step is carried out just after the quantization phase. To increase the robustness, we encrypt the watermarked medical images by using the AES algorithm based on chaotic technique. Arnold's cat map is used to shuffle the pixel values, and a chaotic Henon map is utilized to generate an aleatory sequence for the AES algorithm. The shuffled watermarked image is encrypted using the modified AES algorithm. The constant of Weber is used to choose the suitable visibility factor for embedding a watermark with high robustness. To control identification, after application of attacks, we use the serial turbo code for correction of the watermark to recover the data inserted. The average peak signal-to-noise ratio (PSNR) of the medical images obtained is 61,7769 dB. Experimental results demonstrate the robustness of the proposed schema against various types of attacks. [ABSTRACT FROM AUTHOR]

Early detection, early diagnosis and classification of the cancer type facilitates faster disease management of patients. Cervical cancer is fourth most pervasive cancer type which affects life of many people worldwide. The intent of this study is to automate cancer diagnosis and classification through deep learning techniques to ensure patients health condition progress timely. For this research, Herlev dataset was utilized which contains 917 benchmarked pap smear cells of cervical with 26 attributes and two target variables for training and testing phase. We have adopted combination of convolutional network with variational autoencoder for data classification. The usage of variational autoencoder reduces the dimensionality of data for further processing with involvement of softmax layer for training. The results have been obtained over 917 cancerous image type pap smear cells, where 70% (642) allocated for training and remaining 30% (275) considered for test data set. The proposed architecture achieved variational accuracy of 99.2% with 2*2 filter size and 99.4% with 3*3 filter size using different epochs. The proposed hybrid variational convolutional autoencoder approach applied first time for cervical cancer diagnosis and performed better than traditional machine learning methods. [ABSTRACT FROM AUTHOR]

Data encoded in molecules offers opportunities for secret messaging and extreme information density. Here, we explore how the same chemical and physical dimensions used to encode molecular information can expose molecular messages to detection and manipulation. To address these vulnerabilities, we write data using an object's pre-existing surface chemistry in ways that are indistinguishable from the original substrate. While it is simple to embed chemical information onto common objects (covers) using routine steganographic permutation, chemically embedded covers are found to be resistant to detection by sophisticated analytical tools. Using Turbo codes for efficient digital error correction, we demonstrate recovery of secret keys hidden in the pre-existing chemistry of American one dollar bills. These demonstrations highlight ways to improve security in other molecular domains, and show how the chemical fingerprints of common objects can be harnessed for data storage and communication. [ABSTRACT FROM AUTHOR]

Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is widely used to provide high speed data transmission and spectrum efficiency in modern wireless communication systems. Specifically, the transmission of Electrocardiogram (ECG) signal plays a main role in health monitoring systems. The privacy and security of the patient identification and medical records are considered as a main concern in health monitoring systems. In this paper, the Lightweight cryptography (LWC) is proposed to secure the ECG signal transmission from unauthorized users through the MIMO-OFDM system. The LWC is mainly used to minimize the amount of logical elements using the gate level architecture and simple key schedule in the MIMO-OFDM. The turbo code is used in MIMO-OFDM is due to its error correcting capacity that minimizes the amount of error caused during communication under the constraints of burst error and Inter Symbol Interference (ISI). Here, the ECG signals from the MIT arrhythmia database is used to analyse the secure ECG signal transmission of LWC-MIMO-OFDM method. The performance of the proposed LWC-MIMO-OFDM is taken by means of area, delay, power, number of slices, flipflops and LUTs. The LWC-MIMO-OFDM method is compared with Advanced Encryption Standard (AES) to evaluate the efficiency of LWC-MIMO-OFDM. The delay of the LWC-MIMO-OFDM for Virtex 5 device is 13.3ns, it is less when compared to the delay caused by the AES. [ABSTRACT FROM AUTHOR]

[Display omitted] • A Prior-guided Gated Convolutional Network for rainstorm forecasting. • Modeling substantial derivatives of atmospheric dynamics as physical priors. • A novel gated convolutional encoder-decoder network to represent rainstorms. • Priors help make physically consistent & spatially-temporally coherent forecasts. Accurate rainstorm forecasting is crucial for the sustainable development of human society. Recently, machine learning-based rainstorm prediction methods have shown promising results. However, these methods often fail to adequately consider the prior knowledge of rainstorms and do not explicitly account for the dynamic spatio-temporal patterns of rainstorm events. This study introduces a novel end-to-end prior-informed rainstorm forecasting model that incorporates both fundamental physical priors and the spatio-temporal development patterns of rainstorms. The model utilizes a gated convolutional encoder-decoder network to effectively represent the spatio-temporal patterns of rainstorm events. A key component of the representation network is the Substantial Derivative-GuIded gated convolutional Unit (SDGiU), which updates latent states under the constraints of physical priors. Additionally, an integrated loss function is designed to minimize reconstruction errors on multiple scales and facilitate the generation of forecasts that reproduce the actual spatio-temporal patterns of rainstorm formation, development and dissipation. Experimental results on two reanalysis datasets show that the proposed forecasting model outperforms competing state-of-the-art baselines by at least 19.7% (15.0%) in overall Critical Success Index (Heidke Skill Score). Qualitative analysis indicates that the proposed model can generate predictions that are both physically consistent and spatially-temporally coherent. [ABSTRACT FROM AUTHOR]

Abstract In recent years, many efforts have been made to find the optimal labeling maps for bit-interleaved coded modulation with iterative decoding (BICM-ID) with the aim to exploit the benefits of iterative decoding to the maximal extent. The current paper reveals new opportunities that BICM-ID signal labeling brings for system designers: it enables recipient addressing without any explicitly sent identifier. Instead, the recipient ID is represented by a labeling map, selected from a set of equally optimal labelings. A simple method of frame filtering, necessary to retrieve the desired data frame at the receiver side, is proposed and evaluated. It is also shown that the proposed Labeling-Based Recipient Identification (LABRID) approach does not cause any inferior performance outcome in terms of bit error rate.

In this paper we propose a model for the generation of error patterns at the output of a turbo decoder. One of the advantages of this model is that it can be used to generate the error sequence with little effort. Thus, it provides a basis for designing hybrid concatenated codes (HCCs) employing the turbo code as inner code. These coding schemes combine the features of parallel and serially concatenated codes and thus offer more freedom in code design. It has been demonstrated, in fact, that HCCs can perform closer to capacity than serially concatenated codes while still maintaining a minimum distance that grows linearly with block length. In particular, small memory-one component encoders are sufficient to yield asymptotically good code ensembles for such schemes. The resulting codes provide low complexity encoding and decoding and, in many cases, can be decoded using relatively few iterations.

A complete impulse radio-ultra wide band (IR-UWB) system is proposed to mitigate the impairments concerning on-body to off-body Wireless Body Area Networks (WBAN). The proposed system maintains the practical communication link between on-body sensors to the fusion center or the monitoring device held by the medical representative. Also, these sensors are assigned a recently proposed pulse shaping to mitigate the noise that existed in the IR-UWB WBAN channel. Furthermore, virtual multiple-input multiple-output (VMIMO) is proposed to perform spatial multiplexing between various sensors transmitted data. Moreover, low complex low density parity check (LDPC) encoding/decoding algorithms, including a new low complex hybrid LDPC decoding algorithm, are proposed to reduce the complexity and to enhance the bit error rate of the on-body sensors. Interestingly, low energy consumption at every sensor participating in the on-body to off-body WBAN was achieved by adopting the combination between LDPC decoders and VMIMO technique. According to simulation results, the new system enhanced the BER performance and reduced the complexity of the on-body to off-body IR-UWB communication system effectively.

The effect of parallelism on Bit Error Rate (BER) performance of Turbo Code (TC) and Self Concatenated Convolutional Code (SECCC) with different levels of parallelism and frame sizes is investigated. Next Iteration Initialization (NII) method is employed for mitigating the BER degradation resulting from increased parallelism. In order to analyze and compare the architectural performance of both schemes, this paper presents the Very High Speed Integrated Circuit Hardware Description Language (VHDL) design of Maximum Aposteriori Probability (MAP) decoder for TC and SECCC, both employing the same constituent code. The simulation results show that for BER of 10-4, without parallelism, TC is 0.4 dB superior to SECCC, whereas, with parallelism of 64, the difference in performance between both schemes reduces to 0.25 dB. It is found that SECCC outperforms TC for frame sizes less than or equal to 2048 bits, when invoking a parallelism of 16, 32 and 64. The BER performance of both schemes shows that SECCC outperforms TC at parallelism of 256 by 0.3 dB at BER of 10-4. Hence, for high throughput architectures employing higher parallelism (beyond 64 and 128) without significant degradation in BER performance, SECCC performs better than TC. The synthesis results of VHDL design of the MAP decoder obtained using Xilinx ISE verify that both schemes have equal clock frequency and resource consumption. It is demonstrated that the MAP decoder achieves the clock frequency of 86.3 MHz which is capable of producing a throughput of 691 Mbps using parallelism of 64.

Evaluating the computational complexity of decoders is a very important aspect in the area of Error Control Coding. However, most evaluations have been performed based on hardware implementations. In this paper, different decoding algorithms for binary Turbo codes which are used in LTE standards are investigated. Based on the different mathematical operations in the diverse equations, the computational complexity is derived in terms of the number of binary logical operations. This work is important since it demonstrates the computational complexity breakdown at the binary logic level as it is not always evident to have access to hardware implementations for research purposes. Also, in contrast to comparing different Mathematical operations, comparing binary logic operations provides a standard pedestal in view to achieve a fair comparative analysis for computational complexity. The usage of the decoding method with fewer number of binary logical operations significantly reduces the computational complexity which in turn leads to a more energy efficient/power saving implementation. Results demonstrate the variation in computational complexities when using different algorithms for Turbo decoding as well as with the incorporation of Sign Difference Ratio (SDR) and Regression-based extrinsic information scaling and stopping mechanisms. When considering the conventional decoding mechanisms and streams of 16 bits in length, Method 3 uses 0.0065% more operations in total as compared to Method 1. Furthermore, Method 2 uses only 0.0035% of the total logical complexity required with Method 1. These computational complexity analysis at the binary logical level can be further used with other error correcting codes adopted in different communication standards. [ABSTRACT FROM AUTHOR]

As the abnormalities of long non-coding RNAs (lncRNAs) are closely related to various human diseases, identifying disease-related lncRNAs is important for understanding the pathogenesis of complex diseases. Most of current data-driven methods for disease-related lncRNA candidate prediction are based on diseases and lncRNAs. Those methods, however, fail to consider the deeply embedded node attributes of lncRNA–disease pairs, which contain multiple relations and representations across lncRNAs, diseases and miRNAs. Moreover, the low-dimensional feature distribution at the pairwise level has not been taken into account. We propose a prediction model, VADLP, to extract, encode and adaptively integrate multi-level representations. Firstly, a triple-layer heterogeneous graph is constructed with weighted inter-layer and intra-layer edges to integrate the similarities and correlations among lncRNAs, diseases and miRNAs. We then define three representations including node attributes, pairwise topology and feature distribution. Node attributes are derived from the graph by an embedding strategy to represent the lncRNA–disease associations, which are inferred via their common lncRNAs, diseases and miRNAs. Pairwise topology is formulated by random walk algorithm and encoded by a convolutional autoencoder to represent the hidden topological structural relations between a pair of lncRNA and disease. The new feature distribution is modeled by a variance autoencoder to reveal the underlying lncRNA–disease relationship. Finally, an attentional representation-level integration module is constructed to adaptively fuse the three representations for lncRNA–disease association prediction. The proposed model is tested over a public dataset with a comprehensive list of evaluations. Our model outperforms six state-of-the-art lncRNA–disease prediction models with statistical significance. The ablation study showed the important contributions of three representations. In particular, the improved recall rates under different top |$k$| values demonstrate that our model is powerful in discovering true disease-related lncRNAs in the top-ranked candidates. Case studies of three cancers further proved the capacity of our model to discover potential disease-related lncRNAs. [ABSTRACT FROM AUTHOR]

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In this paper, we study a class of spatially coupled turbo codes, namely partially information- and partially parity-coupled turbo codes. This class of codes enjoy several advantages such as flexible code rate adjustment by varying the coupling ratio and the encoding and decoding architectures of the underlying component codes can remain unchanged. For this work, we first provide the construction methods for partially coupled turbo codes with coupling memory m and study the corresponding graph models. We then derive the density evolution equations for the corresponding ensembles on the binary erasure channel to precisely compute their iterative decoding thresholds. Rate-compatible designs and their decoding thresholds are also provided, where the coupling and puncturing ratios are jointly optimized to achieve the largest decoding threshold for a given target code rate. Our results show that for a wide range of code rates, the proposed codes attain close-to-capacity performance and the decoding performance improves with increasing the coupling memory. In particular, the proposed partially parity-coupled turbo codes have thresholds within 0.0002 of the BEC capacity for rates ranging from 1/3 to 9/10, yielding an attractive way for constructing rate-compatible capacity-approaching channel codes. [ABSTRACT FROM AUTHOR]

The research domain of underwater communication has garnered much interest among researchers exploring underwater activities. The underwater environment differs from the terrestrial setting. Some of the main challenges in underwater communication are limited bandwidth, low data rate, propagation delay, and high bit error rate (BER). As such, this study assessed the underwater acoustic (UWA) aspect and explored the expression of error performance based on t‐distribution noise. Filter orthogonal frequency‐division multiplexing refers to a new waveform candidate that has been adopted in UWA, along with turbo and polar codes. The empirical outcomes demonstrated that the noise did not adhere to Gaussian distribution, whereas the simulation results revealed that the filter applied in orthogonal frequency‐division multiplexing could significantly suppress out‐of‐band emission. Additionally, the performance of the turbo code was superior to that of the polar code by 2 dB at BER 10−3. [ABSTRACT FROM AUTHOR]

In order to guarantee a robust transmission of JPWL (JPEG Wireless: Joint Photographic Experts Group Wireless) images through time and frequency selective wireless channels, an efficient adaptive communication strategy is proposed. It is based on an optimization of a closed-loop adaptive multiple-input multiple-output, orthogonal frequency division multiplexing (MIMO-OFDM) scheme associated with a shaping BICM (bit-interleaved coded modulation) technique composed of a duo binary turbo code (DBTC), high-order modulations such as 64–256 QAM (Quadrature Amplitude Modulation) and a shaping code. According to the CSI (channel state information) knowledge at the transmitter side, an algorithm based on unequal error protection (UEP) and unequal power allocation (UPA) is used to select the transmitter key parameters (source/channel encoder rate, modulation order, power, number of quality layers and number of iterations of the Turbo decoder) to achieve the target Quality of Service (QoS). The proposed DBTC-shaping BICM scheme reaches a shaping gain of 1.2 dB for a 256 QAM modulation over a SISO Gaussian channel, whereas only 0.7 dB of shaping gain can be achieved in a scheme that uses the LDPC shaping BICM scheme for the same modulation order. Based on a DBTC shaping BICM scheme and an adaptive algorithm, the proposed MIMO-OFDM strategy achieves better performance compared to a strategy using an iterative process between an RS (Reed-Solomon) and arithmetic decoders. As a result, and on the one hand, a gain of 5.38 dB can be achieved in terms of PSNR (peak signal-to-noise ratio). On the other hand, a gain of 78% in terms of power consumption is obtained for the same QoS level. Moreover, the adaptive number of iterations in the proposed strategy can minimize the computational complexity of the turbo decoding compared to a scheme using four iterations whatever the channel conditions. [ABSTRACT FROM AUTHOR]

In intelligent transportation systems (ITS), communications between vehicles, i.e. vehicle-to-vehicle (V2V) communications are of greatest importance to facilitate autonomous driving. The current state-of-the-art for secure data exchange in V2V communications relies on public-key cryptography (PKC) consuming significant computational and energy resources for the encryption/decryption process and large bandwidth for the key distribution. To overcome these limitations, physical-layer security (PLS) has emerged as a lightweight solution by exploiting the physical characteristics of the V2V communication channel to generate symmetric cryptographic keys. Currently, key-generation algorithms are designed via empirical parameter settings, without resulting in optimum key-generation performance. In this paper, we devise a key-generation algorithm for PLS in V2V communications by introducing a novel channel response quantisation method that results in optimum performance via analytical parameter settings. Contrary to the current state-of-the-art, the channel responses incorporate all V2V channel attributes that contribute to temporal variability, such as three dimensional (3D) scattering and scatterers’ mobility. An extra functionality, namely, Perturbe-Observe (PO), is further incorporated that enables the algorithm to adapt to the inherent non-reciprocity of the V2V channel responses at the legitimate entities. Optimum performance is evidenced via maximisation of the key bit generation rate (BGR) and key entropy (H) and minimisation of the key bit mismatch rate (BMR). A new metric is further introduced, the so-called secret-bit generation rate (SBGR), as the ratio of the number of bits which are successfully used to compose keys to the total amount of channel samples. SBGR unifies BGR and BMR and is thus maximised by the proposed algorithmic process. [ABSTRACT FROM AUTHOR]

Analysis of error correction performance for error correcting codes is very important when using such codes in digital communication systems. At medium-to-high signal-to-noise ratios, the distance spectrum of the error correcting code represents a good indicator for the error correction performance of the code. It is desired that the minimum distance of the code is as large as possible and that the corresponding multiplicity (i.e. the number of codewords having the weight equal to the minimum distance) is as small as possible. If we know an upper bound of the minimum distance of the code, then we have a good indication about the capabilities and the limitations of the code. One of the classes of the error correcting codes with the best performance is that of turbo codes. For such codes, establishing upper bounds on the minimum distance is challenging because it depends on the interleaver component of the turbo code. In this paper we consider turbo codes with component convolutional codes as in the Long Term Evolution standard. The interleaver lengths are of the form 16 Ψ or 48 Ψ , with Ψ a product of different prime numbers greater than three. The first achievement in the paper is that for these interleaver lengths, we show that cubic permutation polynomials (CPP), with some constraints on the coefficients, when 3 ∤ (p i - 1) for a prime p i > 3 , always have a true inverse CPP. The most accurate upper bounds on the minimum distance for turbo codes are achieved by identifying bit information sequences leading to a certain weight of the corresponding turbo-codeword. In this paper we have indentified such bit information sequences by means of the full range dual impulse method to estimate the weight of the turbo-codewords. For the previously mentioned turbo codes and CPP interleavers, we show that the minimum distance is upper bounded by the values of 38, 36, and 28, for three different classes of coefficients. Previously, it was shown that for the same interleaver lengths and for quadratic PP (QPP) interleavers, the upper bound of the minimum distance is equal to 38. Several examples show that d min -optimal CPP interleavers are better than d min -optimal QPP interleavers because the multiplicities corresponding to the minimum distances for CPPs are about a half of those for QPPs. A theoretical explanation in terms of nonlinearity degrees for this result is given for all considered interleaver lengths and for the class of CPPs for which the upper bound is equal to 38. [ABSTRACT FROM AUTHOR]

The probability of the received bit values has been directly used in the performance enhanced reliability based direct decoding algorithm for turbo codes proposed recently and has achieved a significant coding gain improvement. This is in contrast to the maximum a posteriori (MAP)—type decoders and their variants in vogue which use log-likelihood ratio (LLR) in place of reliability and go through an iterative process. In this paper reliability has been identified in terms of probability densities of the bit values and the same used with the direct decoding algorithm. Extensive simulations with commonly used encoders show a clear performance improvement especially for short block lengths. Neither does the approach constrain noise distribution in any way as brought out by considering different distributions. [ABSTRACT FROM AUTHOR]

Turbo codes are widely used in satellite communications. When a turbo decoder is implemented on a field-programmable gate array (FPGA) in a space platform, it will suffer single-event upsets (SEUs) that can cause failures and disrupt communications. Therefore, the protection of turbo decoders implemented on FPGAs is important. In this article, first the reliability of an SRAM-FPGA-implemented turbo decoder to SEUs on user memory and configuration memory is evaluated based on fault injection experiments. Then, based on the features of the turbo decoder and the characteristics of the failures revealed by the reliability study, a duplication with comparison (DWC) scheme is proposed for the protection of the turbo decoder. Experimental results show that the reliability of the protected turbo decoder to SEUs on user memory and configuration memory is improved by 99.4% and 95.6%, respectively. The resource usage is about $2.2\times $ that of an unprotected turbo decoder, which is significantly lower than the more than $3\times $ required by the traditional triple modular redundancy (TMR) protection. Finally, the proposed scheme is compared with another two protection schemes. [ABSTRACT FROM AUTHOR]

Satellite communication systems are able to provide diverse services for ground terminals in ubiquitous global coverage, which play a vital role in high-mobility communication environments. Existing technologies developed primarily for satellite communications cannot be readily applied to satellite high-mobility communication scenarios, since high Doppler frequency offset caused by the fast movement of wireless terminals, and low signal-to-noise ratio (SNR) circumstances caused by limited link budgets in satellites incur more difficulty of the synchronization, especially for short burst transmission. To solve such a problem in satellite high-mobility communications, we propose a novel method named GP-MASO-MLE, which consists of a coarse estimation algorithm based on the Gaussian process (GP) model and Newton-Raphson method, and a fine correction algorithm based on the improved maximum likelihood estimation (MLE) jointly with turbo decoding iterations. Simulation results show that the proposed algorithm can approach to the bit error rate (BER) performance bound of ideal Doppler frequency offset correction within 0.1 dB, which can be well applied in code-aided (CA) satellite high-mobility communication systems for its good performance. In addition, the computational complexity of the proposed algorithm is lower than other traditional turbo synchronization algorithms. [ABSTRACT FROM AUTHOR]