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In this paper we propose to implements blockchain technology for Internet of Things (IoT) networks that use Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. To eliminate the reliance on a single leader and improve performance, we propose to allow multiple leaders to propose request batches independently and, possibly, concurrently, by using different overlay networks. Nodes participate in parallel consensus rounds without contention, and they only contend with others to reserve the next available spot for the atomic insertion of a new transaction batch or block into the replicated blockchain ledger. We develop an analytical model for the multiple leader PBFT ordering service by using a Discrete-time Markov chain. Our evaluations show that our model outperforms the original multiple entry point PBFT protocol in a wide range of parameter values, and that it scales well with the number of orderer nodes in the PBFT committee and block arrival rate.

There has been an increasing interest in mobile edge computing (MEC) in recent years. Different from the traditional centralized cloud computing, MEC servers are deployed at the edges of networks such as at base stations (BSs) and access points (APs), in order to support computation-intensive and latency-critical applications. In this paper, we consider a multi-user mobile edge computing (MEC) network in which the wireless data transmission/offloading is performed using finite blocklength (FBL) codes to satisfy the latency constraints. The reliability in the communication phase is characterized in the FBL regime, while the event of queue length violation in the computation phase is investigated via exploiting the extreme value theory. We first formulate the overall optimization problem in the scenario of multiple user equipments (UEs) aiming to minimize the maximal end-to-end error probability among all UEs under both the FBL and energy consumption constraints. We further propose a two-level learning-based approach to jointly determine time allocations in the FBL regime, the UEs' offloading decisions and MEC computational resource allocations to solve the overall optimization problem. Simulation results demonstrate that the proposed two-level learning-based algorithm solves the problem efficiently.

Long-term evolution in unlicensed spectrum (LTE-U) can make use of centralized scheduling, interference coordination and other technologies to achieve better spectrum efficiency (SE). As the evolution of cloud computing, mobile edge computing (MEC) sinks the computing and storage capacity from the centralized data center to the edge of the network, which is the key technology to achieve low delay and high speed. However, the traditional centralized scheme in small cell networks (SCNs) structure will bring huge signal overheads. In order to adapt to the complex and changeable network system, this paper proposes a distributed resource and power allocation scheme under MEC environment, which enables small base stations (SBSs) to work autonomously. The SBSs need only little information exchange through the information cloud (IC) and finally obtain the global optimal SE. Simulation results confirm the correctness and effectiveness of the proposed scheme, and demonstrate the superiority of the distributed scheme over the centralized scheme.

Fault Tree Analysis is widely used in the reliability evaluation of critical systems, such as railway and automotive systems, power grids, and nuclear power plants. While there are efficient algorithms for calculating the probability of failure in static fault trees, Mean Time to First Failure (MTFF) evaluation remains challenging due to state space explosion. Recently, structural and symmetry reduction methods were proposed to counteract this phenomenon. However, systems with a large number of different and highly interconnected components preclude the use of reduction techniques. Their MTFF analysis requires the solution of a system of linear equations whose size is exponential in the number of components in the system. In this paper, we propose a solution leveraging Tensor Trains as a compressed vector and matrix representation. We build upon Binary Decision Diagram-based techniques to avoid explicit state space enumeration and use linear equation solvers developed specifically for Tensor Trains to efficiently solve the arising linear systems. As a result, our novel approach complements the existing reduction-based techniques and makes some previously intractable models possible to analyze. We evaluate our approach on an industrial case study adapted from a railway system, and other openly available benchmark models.

The pervasive use of software systems and current threat environment demand that software systems not only survive cyberattacks, but also bounce back better, stronger, and faster. However, what constitutes a modern software system? Where should the security and resilience mechanisms be-in the application software or in the cloud environment where it runs? In this concept paper, we set up a context to pose these questions and present a roadmap to answer them. We describe challenges to achieving resilience and beyond, and outline potential research directions to stimulate discussion in the workshop.

The uneven utilisation of modes of transport has a big impact on traffic in transport pathway infrastrutures. For motor vehicles for instance, this situation explains rapid road deterioration and the large amounts of money invested in maintenance and development due to overuse. There are many approaches to managing this problem; however, the impact of individual users in infrastructural maintenance is mostly ignored. In this position paper, we hypothesise that important changes torwards a more efficient use of the transport network start with its users and their behavioural changes. To this end, we introduce our vision on how to employ data driven, intelligent agent-based modelling, incorporating human factors aspects, as a toolset to understand travellers and to stimulate behavioural changes. The aim is to achieve better balanced and integrated mobility usage within the transport network. The idea is explored with a few guided questions, and a methodology is proposed. We employ 1) cognitive work analysis to investigate the reasons for travellers' mode choice; 2) computational intelligence to extract and represent knowledge from related datasets; 3) agent-based modelling to represent the real-world and to observe both individual and emergent behaviours. Future directions to adapt our methodology to alternative smart mobility projects are also discussed.

Heuristic evaluation is a usability inspection method that has been adapted to evaluate visualization applications through the development of specific sets of heuristics. This paper presents an empirical study meant to assess the capacity of the method to anticipate the usability issues noticed by users when using a visualization application. The potential usability problems identified by 20 evaluators were compared with the issues found for the same application by 46 users through a usability test, as well as with the fixes recommended by the experimenters observing those users during the test. Results suggest that using some heuristics may have elicited potential problems that none of the users noticed while using the application; on the other hand, users encountered unpredicted usability issues.

Research on spatio-temporal visualization is driven by the development of novel visualization and data aggregation techniques. Yet, only little research is conducted on the systematic evaluation of such visualizations. Evaluation of such technology is often conducted in real-life settings and thus lacks fundamental requirements for laboratory-based replication. Replication requires other researchers to independently conduct their own experiments to verify your results. In this position paper, we discuss the requirements for replication studies of spatio-temporal visualization systems. These requirements are often impossible to achieve for highly contextual visualizations such as spatio-temporal visualizations. We argue that reproducibility—allowing other researchers to validate your findings from your data—is a better aim for highly contextual visualizations. We provide a sample workflow to ensure reproducibility for spatio-temporal visualization and discuss its implications.

In this paper, we discuss the challenges one faces when evaluating authoring systems developed to help people design visualization for communication purposes. We reflect on our own experiences in evaluating the visualization authoring systems that we have developed as well as the evaluation methods used in other recent projects. We also examine alternative approaches for evaluating visualization authoring systems that we believe to be more appropriate than traditional comparative studies. We hope that our discussion is informative, not only for researchers who intend to develop novel visualization authoring systems, but also for reviewers assigned to evaluate the research contributions of these systems. Our discussion concludes with opportunities for facilitating the evaluation and adoption of deployed visualization authoring systems.

Replications are rare in visualization research, but if they were more common, it is not unreasonable to believe that they would show a similar rate of unreproducible results as in the psychological and social sciences. While a replication crisis in visualization research would be a helpful wake-up call, examining and correcting the underlying problems in many studies is ultimately more productive.In this paper, we survey the state of replication in visualization research. We examine six threats to the validity of studies in visualization and suggest ways to address them. Finally, we describe possible models for publishing replications that satisfy the novelty criterion that can keep replications from being accepted.

Even though AI/ML is of strategic importance for many companies, it is not easy to come up with an AI life cycle in a multi-cloud world that is being increasingly embraced. In this paper, we present our position on an AI strategy that is future proof and does not force vendor lock in.

Optimal linear transmitter beamformers in multi-antenna multi-user systems are of the Minimum Mean Squared Error (MMSE) type (dual uplink MMSE receivers). MMSE designs make an optimal compromise between noise enhancement and interference suppression and reduce to matched filters at low SNR and zero-forcing at high SNR. We consider a realistic scenario of user channels of varying attenuation and constrain the beamformers to either zero-force or ignore each interference term. This leads to a reduced-order zero-forcing (RO-ZF) design in which the number of interference sources being zero-forced increases with SNR. We apply a simple large systems analysis (applicable to Massive MIMO) to determine the asymptotic performance of RO-ZF designs, determine the optimal ZF orders, and compare to optimal and ZF linear and Dirty Paper Coding (DPC) designs. RO-ZF designs lead to variable reductions of computational complexity and channel state information (CSI) requirements (esp. in future multi-cell extensions), both important considerations in Massive MIMO systems.

Bitcoin blockchain is growing big, yet its transaction capacity hit its limits in 2017. One solution heavily discussed within the bitcoin community is to let it grow faster by increasing size of the blocks. However, making transactions smaller instead of blocks bigger is more sustainable and also brings other additional benefits. In this paper, we focus on space wasted by non-cryptographic use of hashes and uncompressed numbers within transaction inputs. In our analyses, we show that transactions can be made approximately 16% smaller, depending on their complexity.

Machine learning (ML) components are increasingly adopted in many automated systems. Their ability to learn and work with novel input/incomplete knowledge and their generalization capabilities make them highly desirable solutions for complex problems. This has motivated the inclusion of ML techniques/components in products for many industrial domains including automotive systems. Such systems are safety-critical systems since their failure may cause death or injury to humans. Therefore, their safety must be ensured before they are used in their operational environment. However, existing safety standards and Verification and Validation (V&V) techniques do not properly address the special characteristics of ML-based components such as non-determinism, non-transparency, instability. This position paper presents the authors' view on the safety of automotive systems incorporating ML-based components, and it is intended to motivate and sketch a research agenda for extending a safety standard, namely ISO 26262, to address challenges posed by incorporating ML-based components in automotive systems.

Cyber security and data science are two of the fastest growing fields in Computer Science and more recently they are being integrated for various applications. This position paper will review the developments in applying Data science for cyber security and cyber security for data science and then discuss the applications in Social Media.

In the era of emerging Smart Built Environments (SBEs), a smart house, unlike regular houses with static "components", consists of numerous interconnected and often actuated devices, capable of executing tasks independent of user supervision. Living in such a SBE, where for example, the furniture can rearrange itself, and the doors open and close of their own volition, may be difficult and unpredictable. Furthermore, cyber-security attacks and intrusion could allow attackers to assume control of the SBE, damage its components and to potentially harm its inhabitants. Such novel characteristics of SBEs present developers with several unique challenges with regards to implementing the needed safety and security measures and protocols that go along with them. With such environments, therefore, there is a need for a system that is capable of monitoring user activities in real-time, identifying the safety and security hazards to users in their immediate local context, warning users of these hazards, and perhaps even taking preventative and mitigative action against the hazards that it identified. In this paper, we survey some of these challenges and explore the design and implementation of a system designed around the safety and security of SBE inhabitants. We propose an approach to modeling SBE safety that combines the three laws of robotics and the swarm behavior model. We also present a preliminary prototype and discuss a case study.

A paper machine headbox time-delay system is concerned in this study which is controlled by an Internal Model Control (IMC) in the discrete-time case. We note that the chosen system contain a parametric uncertainties. However, the IMC is applied to the considered system to ensure the desired performances adjacent to the complexities of the system and to illustrate the effectiveness of this approach in the face of external perturbations and parametric uncertainties. In the one hand, the Kharitinov theorem is applied as a strategy to verify stability conditions. In the other hand, the Linear Matrix Inequalities (LMI) method is applied to the headbox system to ensure the corrector's stability conditions with the presence of uncertain parameters.

In this position paper, we propose a decentralized architecture for publishing RDF. Unlike traditional client-server architectures, we propose a peer-to-peer (P2P) architecture for SPARQL query processing and RDF knowledge sharing. We present a general architecture and its key problems. Further, we discuss its potential with respect to solving the availability and performance issues that for a long time have been prevailing on existing infrastructures.

This effort presents an analysis of a system intended for wireless crack sensing and based on zero-power harmonic transponders. The sensors, which are fabricated by using a paper substrate, are suitable for both structural health and supply-chain monitoring. In the text, different techniques to recover information from the proposed tags are firstly explored, with a particular focus on the potentialities and trade-offs of each approach. Then, the impact of the presence of multiple-nodes on the performance of the system is considered and, finally, some proposals for crack localization in presence of multiple tags are explored and experimentally evaluated.

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

Research articles especially those in science and technology fields are verbal presentation of scientific and technological innovations. Their publication can help promote the application of science and technology. Highly cited research papers are regarded as the best and the most representative ones of the concerned research field. CGA model is quite mature in theory but not widely used at the moment. To explain and demystify the ways and reasons of the construction of research papers, a three-space model involving textual space, socio-pragmatic space and socio-cultural space operating at the four levels of discourse realization was proposed by Bhatia. It can be used to analyze professional academic research papers. In this paper, a critical genre analysis of 28 highly cited research papers on authentication schemes reveals problem-solution structure is adopted in these papers. The art tradition of cryptography forms the foundation of the way schemes are designed. Practicality and application requirement in daily life are the reasons that researchers advertise the superiority of their proposed schemes.

The early identification of the influential papers is of great significance for assessing the scientific achievements of researchers and institutions as it can help in addressing the processes in an academic and scientific field, such as promotions, recruitment decisions, and funding allocation. This work evaluates features for predicting the most influential paper award that is given by several renowned conferences, ten years subsequent to their publication. The data of five renowned conferences, i.e., ICSE, ICFP, POPL, PLDI, and OOPSLA is used to predict the long-term citations to identify the most influential paper of the respective conference. GD boost model is considered to be better performing among the five different machine learning algorithms. The results show that a three to five years of the time window is good enough to evaluate the most influential paper award. Additionally, the assessment of time window and the citation trajectory of awarded and non awarded papers shows that the citation trajectory of the awarded paper vary from the Citation gain patterns of non-awarded paper.

The experiment of quality assurance of polymeric outdoor insulators service life is necessary to be checked the class of wettability by using the digital image processing tools. The image processing can improve its pictorial information for human interpretation, render it more suitable for autonomous machine perception. This paper provides a survey of the recent technology and theoretical concept to explain the development of the Hydrophobicity Class (HC) of porcelain insulator. The hydrophobic properties of the housing material were found degraded to a different extent between field-aged insulators due to differences in material structure and pollution conditions. This paper contributes to the latest development of reviews relating to image processing for the implementation of porcelain insulator hydrophobic systems testing., and their related studies. We categorized the computer vision mainstream into 2 group such as Machine Learning Methods Applied to Digital Image Processing and Deep Learning Methods Applied to Digital Image Processing. Then compare pros/cons in each type of technique. We also provide brief explanation on the up-to-date information about the techniques and their performance.

Paper influence analysis is essential technology of the literature management system. In recent years, research on influence analysis has received crucial attention from the industry and academia. In this field, there are many new challenges in how to evaluate valuable papers in the paper citation network. Therefore, in this paper, we have proposed an influence model in paper citation networks with integration features of PageRank and Hyperlink-Induced Topic Search (HITS). First, three ranking features are extracted with the combination of PageRank value, hub, and authority. Second, more features are added to our ranking classification method. Third, the ranking-based classification with $P$ ageRank and HITS is proposed to analyze the paper propagation. An influence score is computed to evaluate the importance of a paper. The experiments show that our ranking-based influence model is effective for paper propagation.

In modern academic social network, it is very difficult for scholars to find academic papers consistent with their research direction. Time is a critical factor in paper recommendation. As time goes on, the impact of an academic paper would gradually fade. Likewise, the research interests of users may also change. Therefore, we propose a temporal perceptual neural collaborative filtering model that integrates the multi-dimensional features of papers. We conducted our experiments on the dataset from CiteULike, comparing the recommended results by using four time-decay functions and evaluating our model with multiple evaluation indicators. The satisfactory results show that our model is effective in filtering out the expired papers by considering the characteristics of papers and the changes of scholars' interests.

Summary models, whether extractive or abstractive, have achieved great success recently. For long academic papers, the abstractive model with the encoder-decoder architecture mainly only relies on the attentional context vector for generation, unlike humans who have already mastered the salient information of the source text to have full control over what to write. While the extracted sentences always contain the correct and salient information which can be used to control the abstraction process. Therefore, based on a hierarchical encoder-decoder architecture specifically for academic papers, we proposed a summary model with an Instructor, an encoder in essence by taking the guiding sentences as the input to further control the generating process. In the encoder part, the final hidden state from Instructor is directly added to the basic hierarchical hidden state from the encoder. Experimental results on arXiv/PubMed show that the only encoder-improved model can generate better abstract. In the decoder part, the context vector from Instructor is integrated with the original discourse-aware context vector for the generation. The results show that Instructor is effective for control and our model can generate a more accurate and fluent abstract with significantly higher ROUGE values.

The paper mills are one of the main industries on which the economy of India is based. The paper production process is very complex and comprised of several stages. One of them is the head box which is the key sub process of this entire system. It distributes the stock uniformly over the wire section of the paper mill. The automatic control system design is the focus of this paper. The considered process is very challenging for control system design due the presence of dead time which makes the system unstable. So, a very popular technique, IMC (internal model control), is attempted. Its performance is analyzed. Also, a modern approach, ANFIS (adaptive neuro fuzzy inference system) is adopted for the control system design for the same process. Finally, the control performance of both the approaches has been compared. For simulations, MATLAB is used in this work.

Examination plays a vital role in all over the world. To test the knowledge and ability of a student, examination is the elementary method. For examination, a teacher has to create sets of question paper as per the institutions or university. It is challenging for the teacher to prepares the question paper, and it takes lots of time for educator. Due to huge efforts, sometimes it leads to human fault. The proposed of this project is easy to prepare the question paper process using the bloom's taxonomy and random package. To create the high quality of question paper, it covers six hierarchical levels of bloom's taxonomy. Question will separate in the database based on section wise and user can easily add the question in the database. After clicking generate button the question paper will automatically generate in PDF format and send to the teacher.

Self-service terminal equipment generally, compose of group of integrated systems, which operate together to offer services to human in the economic aspect. The conception of the self-service store terminal equipment facilitates person's life, greatly. The self- service terminal equipment that exist in Yemen are rarely found, because of their extremely expensive cost. Additionally, that material can't deal with all denominations of Yemeni paper currency; just two denominations can be treated. The proposed system is a Mechatronics' system which typifies a prototype of SST equipment which is able to arrangement with all denominations of Yemeni paper currency. The principal function of the proposed scheme is building up an SST prototype with simple social system which is able to recognize and detect four denominations of Yemeni paper currency (1000, 500, 200 and 100 Yr), then store them in the specified box for each denomination. The proposed scheme is composed of four integrated systems as work together concurrently. The entered paper currency is fed into the detection place to be determined whether it is fake or genuine, then if it is genuine, it will be recognized to identify its denomination, finally it is stored in the specified box according to the currency denomination. The identification scheme has been designed to distinguish the dominant colour in each denomination using color sensor, while the detection scheme is designed using the UV - light detector. The developed system has been tested using 10 samples of each denomination for checking the accuracy and speed of operation. The prototype has achieved a high accuracy with short time of processing.

This paper puts forward a novel approach to evaluate an equivalent circuit of oil paper insulation used in transformers from frequency domain spectroscopy measurements. In recent years, there has been considerable attention on the development of diagnostic tools for condition monitoring of power transformer insulation. Frequency Domain Spectroscopy is one such tool. A proper modelling will ensure that the parameters are influenced by insulation deterioration. Hence, if FDS data can be used for such modelling, it may lead to better interpretation of FDS data from the perspective of insulation monitoring. Considering the above-mentioned facts, a method has been proposed in this work that can model the FDS data through an equivalent circuit comprising of resistances and capacitances. The influence of moisture content on the circuit parameters have been investigated. From the investigation, a relation between the DC conductivity and moisture content of the insulation has also been proposed.

With the development of era, network information technology and big data technology, various industries have taken dramatic changes. The arrival of the new media era has brought a huge impact on the traditional media, and made completely change of the scope, mode and channel of traditional media communication. To adapt to the needs of people's life in new media era, traditional media need to timely change the past management mode and concept, and explore new ideas of effective integration and development with new media, to ensure sustainable and stable development. This paper analyzes the necessity and effectiveness of the integration of traditional media and new media by taking Bandao Chenbao, a local traditional paper media in Dalian, as an example. At the same time, combined with the current development status of Bandao Chenbao, it further discusses the effective strategies of the integration development.

Theoretical analyses as well as case studies have established that behavioral models based on a recurrent neural network (RNN) are suitable for transient modeling of nonlinear circuits. After training, an RNN model can be implemented in Verilog-A and evaluated by a SPICE-type circuit simulator. This paper describes hurdles that have prevented wide-scale adoption of the RNN as an IP-obscuring behavioral model for circuits and presents recent advances. A new stability constraint is formulated and demonstrated to guide model training and improve performance. Augmented RNNs that can accurately capture aging effects and represent process variations are presented.

Noise optimization for FET low noise amplifiers (LNAs) are demonstrated through insights gained from analytical formulations derived using noise transformation matrix. The invited paper reviews the status of analytical noise optimization of FET LNAs including single-/dual-band FET LNAs with the lossless/lossy input match networks and broadband FET LNAs with the lossless input networks.

The space-based integrated information network is composed of space-based backbone network, space-based access network and ground-based node network. It is interconnected with the ground Internet and mobile communication network to build a “ global coverage, on-demand access, on-demand service, safe and reliable ” space-based integrated information network system. As an important part of the system, the space-ground laser communication equipment is faced with many problems, such as the integration of multiple modulation systems and the location of different places. In this paper, the related problems are analyzed and verified by experiments. The results show that the system works at 1550nm wavelength, adopts coherent communication system and IM/DD communication system, coherent communication system supports BPSK, QPSK and DPSK modulation format, IM/DD communication system adopts OOK modulation format, the communication rate reaches 5Gbps under the condition of 1E-9 bit error rate.

The traditional space telemetry, tracking, command and communication system works in the radio frequency band. With the improvement on measurement accuracy and communication capacity in space missions, the traditional means have a certain bottleneck in dealing with the development of future space missions. The inherent lightweight, high parallel, high capacity and fine processing capabilities of Optics and microwave photonics show the potential to solve the above problems. This paper summarizes the innovative work of the Southwest China Institute of Electronic Technology in the application of laser measurement and communication integration, microwave photonic RF front-end technology in space telemetry, tracking, command and communication system. The experimental platform is established for verification, the accuracy of ranging and velocity measurement reaches millimeter level at 10Gbps single channel transmission rate with 1E-9 bit error rate under a simulation satellite-ground link. The microwave photonic RF front-end can be compatible with S to Ka band, with a sampling rate of 20Gsps and a signal bandwidth of 5GHz. It is expected to be applied to next generation of space telemetry, tracking, command and communication system or navigation and relay system.

The rapid spread of COVID-19 prompted many to take precautions to control the transmission of the virus. The most crucial of these is face coverings, such as face masks, as the virus is an airborne pathogen capable of transmission via respiratory droplets. While there are developments in smart mask technologies, they only provide passive protection through filtration and internal decontamination against the virus. This paper describes a new approach to smart mask technology that actively monitors for airborne pathogens using a PM sensor and reduces the wearer’s exposure to them by an active mitigation strategy. This strategy involves using a mist spray to increase the pathogen’s particle size, which reduces their ability to linger in the air. This system can be controlled wirelessly via a mobile application, which also displays the monitoring data. Deployment areas of this smart mask include classrooms, hospitals, and workspaces. Our experimental results demonstrate that increasing droplet sizes reduces the settling time for pathogens. Our future work includes a more precise detection of pathogens and improvements in deploying mitigation using fine-grained analysis about the aerosol and artificial intelligence.

As opposed to multiple-antenna solutions, in-band full-duplex (IBFD) systems can utilize a shared antenna for signal transmission and reception to reduce the device form factor. These single-antenna designs, however, must additionally consider the self-interference (SI) that is generated by antenna impedance-mismatch reflections. This paper investigates the effect of different antenna loads and transmission line lengths for commercially-available magnetic circulator and omnidirectional antenna components operating between 2 and 6 GHz. The results indicate that both the load impedance and the distance between the antenna and interface device can drastically impact the composite SI frequency-selectivity, which influences the overall IBFD system bandwidth.

This paper discusses recent advancements in smart connected biophysical signal sensing technology focusing on the development of wearable biosensors and the use of Electro-Quasistatic Human Body Communication and Edge-Analytics in the human state measurement systems for lower power consumption and physical layer security.

Critical flaws continue to exist at the level of domain, requirement, and/or design specification, and specification verification (i.e. to check whether a specification has desirable properties) is still one of the most important challenges in software/system engineering. CafeOBJ is an executable algebraic specification language system and domain/requirement/design engineers can write proof scores in CafeOBJ for improving quality of specifications by the specification verification. This paper describes advances of the proof scores for the specification verification in CafeOBJ from the author’s point of view.

This paper proposes a novel three-dimensional (3D) cluster-based sixth-generation (6G) model for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels. In the proposed model, a novel method based on visibility region (VR) is developed, where the smooth and consistent birth and death of clusters in both the time domain and space domain are successfully captured. By using the developed method, the space-time non-stationarity and time-space consistency of channels can be modeled for the first time. Based on the proposed model, important channel statistical properties are derived and thoroughly investigated. Simulation results demonstrate that the smooth birth and death of clusters, time-space consistency, and space-time non-stationarity are modeled sufficiently.

In this paper, we propose a data augmentation framework for Optical Character Recognition (OCR). The proposed framework is able to synthesize new viewing angles and illumination scenarios, effectively enriching any available OCR dataset. Its modular structure allows to be modified to match individual user requirements. The framework enables to comfortably scale the enlargement factor of the available dataset. Furthermore, the proposed method is not restricted to single frame OCR but can also be applied to video OCR. We demonstrate the performance of our framework by augmenting a 15% subset of the common Brno Mobile OCR dataset. Our proposed framework is capable of leveraging the performance of OCR applications especially for small datasets. Applying the proposed method, improvements of up to 2.79 percentage points in terms of Character Error Rate (CER), and up to 7.88 percentage points in terms of Word Error Rate (WER) are achieved on the subset. Especially the recognition of challenging text lines can be improved. The CER may be decreased by up to 14.92 percentage points and the WER by up to 18.19 percentage points for this class. Moreover, we are able to achieve smaller error rates when training on the 15% subset augmented with the proposed method than on the original nonaugmented full dataset.

Lately, Deep Learning has indicated its capacity by adequately tackling complex learning issues which were not possible previously. Specifically, Convolutional Neural Networks are most generally utilized and have indicated their viability in image detection and recognition problems. Convolutional Neural Networks(CNN) can be used to perform different kinds of tasks. However, to recognize even a single image, billions of calculations are required. For example, ResNet 50 takes 8 billion calculations just to figure out what's in a single image. So we need to find out ways to be able to run that superfast and that's why we need hardware accelerators. In this paper the basic information about Convolutional Neural Networks is provided along with the key operations involved and the brief idea about Field Programmable Gate Arrays(FPGA) is given which enable them to be used for accelerating the inference process of Convolutional Neural Networks. Various techniques which were employed previously for accelerating the Convolutional Neural Networks are discussed. We are focussing on designing a energy efficient Field Programmable Gate Arrays accelerator for accelerating the inference process of Convolutional Neural Networks with reduced latency and increased throughput.

The emerging of information technology (IT)and operational technology (OT) convergence has driven the smart grid technology adoption in the European (EU) energy system for better visibility and automated controllability. On the other hand, the energy system infrastructures can be threaten by the cyber attacks due to the increasing of information and communication technology integration. The cyber vulnerabilities are caused by the increasing of internet connection and the application complexities. It is crucial to identify essential skills in the field of cyber security protection and defense for the students. This paper presents the outcome of a literature review and a workshop with stakeholders from industry and academia about the state of the art and trends in the education of cyber security in smart grids.

Being able to learn from complex data with phase information is imperative for many signal processing applications. Today’s real-valued deep neural networks (DNNs) have shown efficiency in latent information analysis but fall short when applied to the complex domain. Deep complex networks (DCN), in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision-making requirements of many deployable systems dealing with short observations or short signal bursts. Recent, Binarized Complex Neural Network (BCNN), which integrates DCNs with binarized neural networks (BNN), shows great potential in classifying complex data in real-time. In this paper, we propose a structural pruning based accelerator of BCNN, which is able to provide more than 5000 frames/s inference throughput on edge devices. The high performance comes from both the algorithm and hardware sides. On the algorithm side, we conduct structural pruning to the original BCNN models and obtain 20 × pruning rates with negligible accuracy loss; on the hardware side, we propose a novel 2D convolution operation accelerator for the binary complex neural network. Experimental results show that the proposed design works with over 90% utilization and is able to achieve the inference throughput of 5882 frames/s and 4938 frames/s for complex NIN-Net and ResNet-18 using CIFAR-10 dataset and Alveo U280 Board.

Brain-inspired hyperdimensional computing (HDC) is an emerging computational paradigm that has achieved success in various domains. HDC mimics brain cognition and lever-ages hyperdimensional vectors with fully distributed holographic representation and (pseudo)randomness. Compared to the traditional machine learning methods, HDC offers several critical advantages, including smaller model size, less computation cost, and one-shot learning capability, making it a promising candidate in low-power platforms. Despite the growing popularity of HDC, the robustness of HDC models has not been systematically explored. This paper presents a study on the robustness of HDC to errors in associative memory—the key component storing the class representations in HDC. We perform extensive error injection experiments to the associative memory in a number of HDC models (and datasets), sweeping the error rates and varying HDC configurations (i.e., dimension and data width). Empirically, we observe that HDC is considerably robust to errors in the associative memory, opening up opportunities for further optimizations. Further, results show that HDC robustness varies significantly with different HDC configurations such as data width. Moreover, we explore a low-cost error masking mechanism in the associative memory to enhance its robustness.

Coarse-grained reconfigurable arrays (CGRAs) are programmable hardware platforms that can be used to realize application-specific accelerators for higher performance and energy efficiency. A CGRA is a 2D array of configurable logic blocks & interconnect, where the logic blocks are typically large & ALU-like, and the interconnect is word-wide. CGRA-ME is a software framework that enables the modelling and exploration of CGRA architectures, as well as research on CGRA CAD algorithms. With CGRA-ME, an architect can specify a CGRA architecture at a high level of abstraction. A set of applications can be mapped onto the architecture to assess the mappability, power, performance and cost. CGRA-ME also allows one to generate synthesizable Verilog RTL for the modelled CGRA, permitting its implementation as an ASIC or FPGA overlay. In this paper, we describe the CGRA-ME framework [5] and overview its capabilities and current limitations. We discuss ongoing and prior research conducted with the framework, as well as outline future plans. We believe CGRA-ME will be a valuable contribution to the community, enabling new research on CGRA CAD & architectures.

One great challenge of modern networks is how to select paths and react to highly variable and demanding traffic patterns. In recent years, emerging networking languages and architectures, such as P4/PISA, have created unprecedented opportunities for rapidly prototyping disruptive solutions in programmable data planes. In this direction, source routing (SR) is a prominent alternative to conventional table-based routing, since it reduces the convergence time of building distributed routing tables. In particular, PolKA explores the polynomial residue number system (RNS) for a fully stateless SR, i.e., no state update on packet headers needs to be conveyed along the route. This paper reports the deployment of PolKA in a continental testbed composed of P4 programmable switches. Results for end-to-end throughput and forwarding latency show that PolKA matches the data plane performance of traditional approaches, while paving the way for further innovative applications by exploring RNS properties in dynamic scenarios.

People re-identification is a fundamental stage in video surveillance systems. This stage is used for determining the labels of the people’s images usually based on their visual similarities. The appearance salience caused by wearing the clothes and carrying objects, with specific colors can be used for improving the performance of re-identification systems. To address the mentioned point, in this paper, we propose a color-based re-ranking process. In the proposed approach first, the input images are semantically segmented as: person’s body, possible carried objects, and background. Then the most frequent colors of person’s body and possible carried objects are used to re-order the ranked results obtained from two commonly used re-identification approaches i.e., Gaussian of Gaussian (GOG) and Hierarchical Gaussian Descriptors (HGDs). Experimental results on various people re-identification datasets show that our proposed re-ranking approach can improve the performance of re-identification systems.

Due to the increasingly growing demand for user identification on cell phones, PCs, laptops, and so on, face anti-spoofing has risen to significance and is an active research area in academia and industry. The detection of the real face then recognize it present an important challenge regarding the techniques that can be used to spoof any recognition system like masks, printed photos. This paper we present an anti-spoofing face method to solve the real-world scenario that learns the target domain classifier based on samples used for training in a particular source domain. Specifically, with the conventional regression CNN, the Spatial/Channel-wise Attention Modules were introduced. Two modules, namely the Spatial-wise Attention Module and the Channel-wise Attention Module, were used at spatial and channel levels to improve local features and ignore the irrelevant features. Extensive experiments on current collections with benchmarks datasets verifies that the recommended solution will significantly benefit from the two modules and better generalization capability by providing significantly improved results in anti-spoofing.

In this work, capacitive touchpads are presented based on fabrication processes that are inexpensive, lab and solvent-free. Pencil and paper have been used to fabricate the sensors, and these materials are economical, biodegradable, and readily available. A touchpad comprising a combination of five sensors has been developed, and analyzed its working by interface controlling for LED's toggle operation. LCD and android app interface have been used to display the result. The sensor's response has been sensed for a gloved finger touch for sensors. The sensor's performance remained consistent, and the functionality was similar to common buttons and sensors. These sensors are recyclable and can be fabricated with ease. This can contribute to potential developments in smart packaging, medical and healthcare applications, and thin human-machine interfaces.