In today's industrial Internet of Things systems, functional safety communication protocols are widely adopted to transmit safety protocol data unit (SPDU). While the guessing random additive noise decoding (GRAND) algorithm can improve the reliability of cyclic redundancy check (CRC)-coded SPDU, the decoding complexity of long SPDU remains too high for practical deployment. To address this, we extend the GRAND-based joint error detection and correction (JEDeC) strategy to long SPDU and propose a JEDeC-based packet fragmentation/assembling mechanism that fragments a long SPDU into multiple short SPDUs for parallel correction of erroneous bits. We clarify the decoder input settings and channel model used in this work. We show that the proposed approach achieves tractable decoding complexity and latency: for an assembled SPDU length of 1024 bits, fragment length of 64 bits, CRC signature length of 32 bits, and maximum error-correction capability of 4 bits, under a representative bit error rate (BER)$P_{e}=10^{-3}$, the BER is reduced from$10^{-3}$to$1.49\times 10^{-8}$, the packet error rate from$6.46\times 10^{-1}$to$8.11\times 10^{-7}$, and the residual error probability from$6.36\times 10^{-11}$to$3.42\times 10^{-16}$, with an average of$2.86\times 10^{3}$guessing attempts per assembled SPDU. These results indicate that the fragmentation/assembling mechanism can substantially improve safety-communication dependability with implementable cost.

In modern smart factories, functional safety protocols are widely used to guarantee the reliable transmission of Safety Protocol Data Unit (SPDU). Using our constructed WirelessHP physical layer protocol and universal software radio peripherals (USRP) as the hardware platform, this paper proposes to improve the reliability of SPDU transmission by adopting the Joint Error Detection and Correction (JEDeC) strategy. Through actual experiments, the performance of JEDeC for decoding CRC-coded SPDUs is investigated in real industrial environments. As a preliminary study, our results demonstrate that the Bit Error Rate (BER) and Packet Error Rate (PER) of SPDU transmission are significantly improved compared to traditional CRC error detection mechanism. We also identify and discuss the challenging issue of high decoding complexity for future research.

In agricultural and food production, sensors are widely used for real-time monitoring of the production process. These sensors transmit data to access points (APs) in wireless sensor networks (WSNs), forming an Internet of Things-empowered advanced production paradigm. Due to limited power, sensors have constrained transmission ranges, necessitating unmanned ground vehicles (UGVs) to assist in timely sensor data collection. A critical problem is the intelligent coordination among multiple UGVs to realize safe path planning, as well as fair and timely data collection. However, it encounters the following challenges: 1) real-time monitoring introduces the dynamics in the volume of sensor data; 2) unknown obstacles, such as mobile packaging containers and vehicles, complicate safe path planning and fair data collection in WSNs; and 3) inefficient action explorations deteriorate action selection. To address these challenges, we propose a multiagent path planning algorithm based on coalition formation game and Bayesian optimization (BO) (MAPP-CFGBO) to optimize UGVs paths and sensor association in industrial WSNs. First, we construct a dynamic data caching model and design a fairness index. Second, a cooperative communication coalition formation (C3F) algorithm is proposed to facilitate cooperation among UGVs. Next, the safe path planning problem is solved with our proposed BO algorithm, which addresses challenges 2 and 3. Extensive simulations are performed. Compared with the benchmark algorithms, the proposed algorithm improves the fairness of communication services by$\rm 39.20{\,}\% $and increases the amount of collected data by$\rm 142.07{\,}\%$.

In modern Industrial Internet of Things (IIoT) ecosystems, functional safety protocols are increasingly utilized to transmit safety protocol data unit (SPDU). Integrating the universal guessing random additive noise decoding (GRAND) algorithm with cyclic redundancy check (CRC)-coded SPDU can minimize SPDU retransmissions. However, it introduces residual error probability (REP) degradation that requires careful consideration. Using the IEC 61784-3 Standard and CRC assumptions, we derive a closed-form REP evaluation formula specific to SPDU length and maximum error correction capability. Our analysis reveals that, under the worst industrial conditions and for an SPDU length of 128 bits, the REP performance degrades by approximately 2:2 × 102times when the CRC signature length is 24 bits and up to one bit is guessing decoded. This degradation becomes more pronounced with increased error correction capability. To address this, we propose to compensate the REP degradation by adopting longer CRC signature. This paper provides a theoretical framework for adopting the GRAND algorithm in functional safety communication, setting a foundation for enhancing reliability in IIoT applications..

Wireless sensing methods for human-vehicle classification (WHVC) offer cost-effective advantages and enhance the detection efficiency of traffic parameters in intelligent transportation systems (ITSs). Existing WHVC methods primarily utilize channel state information (CSI) or received signal strength (RSS) features extracted from the surrounding wireless signals. Although CSI data provides more detailed and accurate channel information compared to RSS data, extracting and processing CSI is more challenging than RSS. Moreover, for applications that do not require fine-grained human-vehicle classification, such as intelligent street lighting systems, RSS-based WHVC has the advantages of easy implementation and low cost. Therefore, investigating the performance of CSI-and RSS-based WHVC methods in different application scenarios could provide valuable insights for the WHVC domain. To address this issue, this paper proposes a deep learning-based WHVC method, which employs deep learning as a tool to evaluate the performance of RSS and CSI methods in various classification tasks. Specifically, this paper collects CSI and RSS data for seven different classification tasks in real traffic road scenarios and evaluates these tasks using a convolutional neural network-based deep learning model designed in this paper. Experimental results demonstrate that for road user categories less than four, RSS-based WHVC achieves higher accuracy than CSI-based WHVC. However, as the number of categories increases, CSI-based WHVC exhibits superior accuracy compared to RSS-based WHVC. Additionally, the developed dataset is publicly available at https://github.com/TZ-mx/mixeddataset.

Abstract For the hard guessing random additive noise decoding Markov order (GRAND-MO) algorithm, it is crucial to develop an efficient noise error patterns (NEPs) generator to facilitate its application in bursty channels. This paper proposes a practical hardware realization by generating the NEPs in a sequential manner. Based on classification of the four types of NEPs, we propose to iteratively calculate the “1" and the “0" permutations in the same time. Then, the novel “0" permutation regularization and bit flipping techniques are employed, through which the generation of the four types of NEPs is uniformed at the same way. Moreover, the proposed NEPs generator can generate all NEPs by using the “1" burst parameters, and is suitable for the guessing decoding of any linear block codes. Built on field programmable gate array (FPGA) implementation and comparison with existing benchmark, we show the proposed NEPs generator is a power-efficient architecture for realization. This work presents a new solution for the hardware implementation of the NEPs generator in GRAND-MO.

In the successive cancellation list decoding of polar codes, the metric sorting is mostly responsible for the total delay. We propose a two-rank-sorter method to save hardware resources and avoid conducting redundant sorting operations on measurements. It includes two rank sorters as well as a clean half sorter. The rank sorter approach differs from previous compare-and-exchange units (CAEUs) in that it produces an ordered output using a logical comparator. The evaluation results demonstrate that when the list size is modest, the suggested design performs better in terms of hardware complexity than the current sorting architecture.

In this paper, we proposed a modified Trellis extended min-sum (T-EMS) algorithm with L truncations for non-binary low density parity check (NB-LDPC) codes. The proposed algorithm achieves to reduce complexity of T-EMS algorithm, especially on large order Galois fields GF(q). In order to improve the error-correcting performance of the algorithm, an elitist selection (ES) strategy is presented, which enlarges the belief value of the selected symbols. As a proof, the simulation results and complexity analysis show that with the increase of q, the complexity decreases obviously. However, in the case of a suitable L value, the LT-EMS algorithm with ES strategy is not inferior to the T-EMS algorithm in performance. In addition, the ES strategy can accelerate the convergence rate and reduce the number of iterations of the decoding process, which will enhance the decoding throughput.

Impulse interference is an important cause of transmission failure in the industry environments targeted by the Wireless High Performance (WirelessHP). As interleavers are commonly used to improve the reliability on the Orthogonal Frequency Division Multiplexing (OFDM) symbol level for long packet transmission, this paper considers the feasibility of applying short-packet bit interleaving to enhance the impulse/burst interference resisting capability on both OFDM symbol and frame level. Using the Universal Software Radio Peripherals (USRP) and PC hardware platform, the Packet Error Rate (PER) performance of interleaved coded short-packet transmission with Convolutional Codes (CC), Reed-Solomon (RS) codes, and RS+CC concatenated codes are tested and analyzed. The IEEE 1613 standard is applied for impulse interference generation, and extensive PER tests of CC$(1/2)$and RS$(31,21)+$CC$(1/2)$concatenated codes are conducted. We prove the effectiveness of bit interleaved coded short-packet transmission in real factory environments with practical experiments. Moreover, we investigate how PER performance depends on the interleavers, codes and impulse interference power and frequency.

In wireless high-performance communications (WirelessHP) target industrial applications, impulse interference is an important source that may cause burst errors in a transmitted packet. By concatenating interleaver with channel coding, this paper investigates the improvement of reliability for short packets transmission in WirelessHP. Based on our constructed hardware platform for WirelessHP protocols, the packets error rate (PER) of interleaved coded packets transmission for convolutional codes (CC) is tested with detailed analysis. Through practical experiments, we shown that interleavers can improve the PER performance in factory environments, the interleaver structure and code rate are also important factors affecting the improvement.

Turbo codes are a family of near Shannon limit error correction coding schemes that usually are adopted for wireless data transmission. To reduce the power dissipation of a long-term evolution (LTE) advanced turbo decoder, in this paper, we propose a reverse calculation based low memory turbo decoder architecture by partitioning the trellis diagram and simplifying the max* operator. The designed forward state metrics calculation architecture is merged with two classical decoding schemes. Through field programmable gate array (FPGA) hardware implementation, the state metrics cache (SMC) capacity is reduced by 65%, the power dissipation of the reverse calculation architecture is significantly reduced for all tested clock frequencies, and the decoding performance is not affected as compared with classical decoding schemes. The proposed reverse calculation architecture is an effective technique to achieve better decoding performance for power-constrained applications.

In recent years, the demand for low latency and high reliability in industrial have been increasing rapidly. To meet these requirements, we propose a new decoding architecture of low-density parity-check convolutional codes (LDPC-CC). The proposed decoding architecture reduces the distance of the neighboring processors by using the rationale of compact pipeline decoder. At the same time, the stopping rule is employed in the process of iteration and the weighting factor is added to reduce the error propagation caused by the update of the parity-check node, thus accelerating the convergence of the decoding. The results show that the proposed algorithm has about 0.3 dB gain compared with the on-demand variable node activation (OVA) algorithm when the bit rate error (BER) is 10-4and maximum number of iterations is 10. Meanwhile, the initial delay and storage requirement are reduced by about 50% and the average number of iterations per decoded bit is also reduced largely.

To meet the extremely low latency constraints of industrial wireless control in critical applications, the wireless high-performance scheme (WirelessHP) has been introduced as a promising solution. The proposed design showed great improvements in terms of latency, but its performance in terms of reliability have not been fully tested yet. While traditional wireless systems achieve high reliability through packet retransmissions, this would impair the latency, and an approach based on channel coding is preferable in industrial applications. In this paper, a set of packet error rate (PER) tests is performed by applying concatenated Reed Solomon and convolutional codes to the WirelessHP physical layer, using a demonstrator based on a universal software radio peripheral platform. The effectiveness of channel coding to achieve 10-7level PER without retransmissions is shown in typical laboratory and factory environments.

In the field of industrial automation research, wireless control in key application scenarios has become a research hotspot. Nevertheless, transmission over wireless channels in industrial environments is prone to interference, resulting in frequent erroneous packet deliveries. Forward Error Correction (FEC) code as an approach is able to effectively improve reliability and reduce the number of retransmissions. Therefore, channel coding needs further analysis to achieve better industrial wireless application. To this aim, this paper compares the decoding performance of convolutional codes and low-density parity-check (LDPC) codes on the condition of short packet transmission. The metrics employed for evaluation are bit error rate (BER) and packet error rate (PER). The logarithmic belief propagation (Log-BP) algorithm and the Viterbi decoding algorithm are adopted to LDPC codes and convolutional codes respectively. The results show that the decoding algorithm of LDPC codes is prominent in short packet transmission.

Ultra-high reliable and low-latency communication (URLLC) is envisaged to support emerging applications with strict latency and reliability requirements. Critical industrial control is among the most important URLLC applications where the stringent requirements make the deployment of wireless networks critical, especially as far as latency is concerned. Since the amount of data exchanged in critical industrial communications is generally small, an effective way to reduce the latency is to minimize the packet's synchronization overhead, starting from the physical layer (PHY). This paper proposes to use a short one-symbol PHY preamble for critical wireless industrial communications, reducing significantly the transmission latency with respect to other wireless standards. Dedicated packet detection and synchronization algorithms are discussed, analyzed, and tuned to ensure that the required reliability level is achieved with such extremely short preamble. Theoretical analysis, simulations, and experiments show that detection error rates smaller than 10-6can be achieved with the proposed preamble while minimizing the latencies.

In this demo, we introduce and implement a power efficient and low-memory capacity Turbo decoding architecture for LTE-Advanced standard on field programmable gate array (FPGA). In addition, the performance comparison and power estimation are presented. As compared with the traditional decoding architecture, the memory capacity is reduced by 67.4%, and the decoding performance is acceptable in practice. Moreover, the overall power consumption is decreased by 34.6% at the frequency of 100MHz.

The next generations of industrial control systems will require high-performance wireless networks (named WirelessHP) able to provide extremely low latency, ultrahigh reliability, and high data rates. The current strategy toward the realization of industrial wireless networks relies on adopting the bottom layers of general purpose wireless standards and customizing only the upper layers. In this paper, a new bottom-up approach is proposed through the realization of a WirelessHP physical layer specifically targeted at reducing the communication latency through the minimization of packet transmission time. Theoretical analysis shows that the proposed design allows a substantial reduction in packet transmission time, down to 1 μs, with respect to the general purpose IEEE 802.11 physical layer. The design is validated by an experimental demonstrator, which shows that reliable communications up to 20 m range can be established with the proposed physical layer.

MOTIVATION: Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients' outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities. RESULTS: In this study, we propose a novel systematic computational tool DRUGCOMBORANKER: to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients' genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations. AVAILABILITY AND IMPLEMENTATION: The program is available on request.

In the implementation of turbo‐like decoder, the size of state metrics cache (SMC) has a predominant impact on the core area and the overall power dissipation. Different from previous reported decoding schemes, in the proposed decoding schemes, a compressing module and a regeneration module are added to the decoder. The compressing module sorts the forward state metrics from the minimum to the maximum, by which an index sequence and the corresponding increase metrics are calculated, and subsequently are stored in the SMC. In the regeneration module, the forward state metrics are estimated with the index sequence and the increase metrics that accessed from the SMC. With the cost of dummy calculation that is performed by the compressing and the regeneration modules, two decoding schemes are proposed. For an eight‐state turbo codes, the linear and the nonlinear estimation based decoding schemes reduce the SMC size by 62.5% and 57.5%, respectively. The bit error rate (BER) simulation is performed for both binary turbo code and duo binary convolutional turbo code, and shows BER of the linear estimation‐based scheme is superior to that of the enhanced max‐log‐MAP (the maximum a posteriori probability) algorithm, whereas BER of the non‐linear estimation‐based decoding scheme is very close to that of the near optimal decoding scheme.

BACKGROUND: In cancer, gene networks and pathways often exhibit dynamic behavior, particularly during the process of carcinogenesis. Thus, it is important to prioritize those genes that are strongly associated with the functionality of a network. Traditional statistical methods are often inept to identify biologically relevant member genes, motivating researchers to incorporate biological knowledge into gene ranking methods. However, current integration strategies are often heuristic and fail to incorporate fully the true interplay between biological knowledge and gene expression data. RESULTS: To improve knowledge-guided gene ranking, we propose a novel method called coordinative component analysis (COCA) in this paper. COCA explicitly captures those genes within a specific biological context that are likely to be expressed in a coordinative manner. Formulated as an optimization problem to maximize the coordinative effort, COCA is designed to first extract the coordinative components based on a partial guidance from knowledge genes and then rank the genes according to their participation strengths. An embedded bootstrapping procedure is implemented to improve statistical robustness of the solutions. COCA was initially tested on simulation data and then on published gene expression microarray data to demonstrate its improved performance as compared to traditional statistical methods. Finally, the COCA approach has been applied to stem cell data to identify biologically relevant genes in signaling pathways. As a result, the COCA approach uncovers novel pathway members that may shed light into the pathway deregulation in cancers. CONCLUSION: We have developed a new integrative strategy to combine biological knowledge and microarray data for gene ranking. The method utilizes knowledge genes for a guidance to first extract coordinative components, and then rank the genes according to their contribution related to a network or pathway. The experimental results show that such a knowledge-guided strategy can provide context-specific gene ranking with an improved performance in pathway member identification.

MOTIVATION: Significant efforts have been made to acquire data under different conditions and to construct static networks that can explain various gene regulation mechanisms. However, gene regulatory networks are dynamic and condition-specific; under different conditions, networks exhibit different regulation patterns accompanied by different transcriptional network topologies. Thus, an investigation on the topological changes in transcriptional networks can facilitate the understanding of cell development or provide novel insights into the pathophysiology of certain diseases, and help identify the key genetic players that could serve as biomarkers or drug targets. RESULTS: Here, we report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. We propose a local dependency model to represent the local structures of a network by a set of conditional probabilities. We develop an efficient learning algorithm to learn the local dependency model using the Lasso technique. A permutation test is subsequently performed to estimate the statistical significance of each learned local structure. In testing on a simulation dataset, the proposed algorithm accurately detected all the genes with network topological changes. The method was then applied to the estrogen-dependent T-47D estrogen receptor-positive (ER+) breast cancer cell line datasets and human and mouse embryonic stem cell datasets. In both experiments using real microarray datasets, the proposed method produced biologically meaningful results. We expect DDN to emerge as an important bioinformatics tool in transcriptional network analyses. While we focus specifically on transcriptional networks, the DDN method we introduce here is generally applicable to other biological networks with similar characteristics. AVAILABILITY: The DDN MATLAB toolbox and experiment data are available at http://www.cbil.ece.vt.edu/software.htm.

MOTIVATION: Unraveling the transcriptional regulatory program mediated by transcription factors (TFs) is a fundamental objective of computational biology, yet still remains a challenge. METHOD: Here, we present a new methodology that integrates microarray and TF binding data for unraveling transcriptional regulatory networks. The algorithm is based on a two-stage constrained matrix decomposition model. The model takes into account the non-linear structure in gene expression data, particularly in the TF-target gene interactions and the combinatorial nature of gene regulation by TFs. The gene expression profile is modeled as a linear weighted combination of the activity profiles of a set of TFs. The TF activity profiles are deduced from the expression levels of TF target genes, instead directly from TFs themselves. The TF-target gene relationships are derived from ChIP-chip and other TF binding data. The proposed algorithm can not only identify transcriptional modules, but also reveal regulatory programs of which TFs control which target genes in which specific ways (either activating or inhibiting). RESULTS: In comparison with other methods, our algorithm identifies biologically more meaningful transcriptional modules relating to specific TFs. We applied the new algorithm on yeast cell cycle and stress response data. While known transcriptional regulations were confirmed, novel TF-gene interactions were predicted and provide new insights into the regulatory mechanisms of the cell.

MOTIVATION: We address the problem of identifying gene transcriptional modules from gene expression data by proposing a new approach. Genes mostly interact with each other to form transcriptional modules for context-specific cellular activities or functions. Unraveling such transcriptional modules is important for understanding biological network, deciphering regulatory mechanisms and identifying biomarkers. METHOD: The proposed algorithm is based on two-stage matrix decomposition. We first model microarray data as non-linear mixtures and adopt the non-linear independent component analysis to reduce the non-linear distortion and separate the data into independent latent components. We then apply the probabilistic sparse matrix decomposition approach to model the 'hidden' expression profiles of genes across the independent latent components as linear weighted combinations of a small number of transcriptional regulator profiles. Finally, we propose a general scheme for identifying gene modules from the outcomes of the matrix decomposition. RESULTS: The proposed algorithm partitions genes into non-mutually exclusive transcriptional modules, independent from expression profile similarity measurement. The modules contain genes with not only similar but different expression patterns, and show the highest enrichment of biological functions in comparison with those by other methods. The usefulness of the algorithm was validated by a yeast microarray data analysis. AVAILABILITY: The software is available upon request to the authors.

MOTIVATION: A major challenge in post-genomic research has been to understand how physiological and pathological phenotypes arise from the networks of expressed genes. Here, we addressed this issue by developing an algorithm to mimic the behavior of regulatory networks in silico and to identify the dynamic response to disease and changing cellular conditions. RESULTS: With regulatory pathway and gene expression data as input, the algorithm provides quantitative assessments of a wide range of responses, including susceptibility to disease, potential usefulness of a given drug, or consequences to such external stimuli as pharmacological interventions or caloric restriction. The algorithm is particularly amenable to the analysis of systems that are difficult to recapitulate in vitro, yet they may have important clinical value. The hypotheses derived from the algorithm were biologically relevant and were successfully validated via independent experiments, as illustrated here in the analysis of the leukemia-associated BCR-ABL pathway and the insulin/IGF pathway related to longevity. The algorithm correctly identified the leukemia drug target and genes important for longevity, and also provided new insights into our understanding of these two processes. AVAILABILITY: The software package is available upon request to the authors.