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Currently, embedded systems can be found everywhere in quotidian life. In the development of embedded systems, information security is one of the important factors. Encryption is an efficient technique to protect information against attacks. However, because of constraints, existing encryption functions are not compatible and do not agree with real-time applications in embedded systems. In this paper, an improved cryptographic approach with a high level of security and high speed is put forward. Our work uses an efficient version of a hybrid scheme comprising an Advanced Encryption Standard (AES) - Elliptic Curve Cryptography (ECC) for medical image encryption, which combines the benefits of the symmetric AES to speed-up data encryption and asymmetric ECC in order to secure the interchange of a symmetric session key. The contribution of this paper consists of the following two main points: First, we put forward an optimized ECC hardware architecture to respect the compromise between area, power dissipation, and speed. Thus, we primarily utilize only two multipliers to develop the Point Addition (PA) block and the Point Doubling (PD) block, which reduces time complexity. Then, a 32-bit multiplier and a 32-bit inverter architecture based on shifts and XORs are proposed to reduce power consumption and area occupancy. Second, for image encryption, we primarily propose to modify the AES by eliminating the mix-columns transformation and replacing it with a permutation based on the shifts of columns, which decreases time complexity while maintaining the Shannon diffusion and the confusion principle. Then, an adjustment of the rearrangement of the general structure is given to enhance the entropy value. The global cryptosystem is implemented using a co-design approach where the modified AES runs on the NIOS II processor, and the scalar ECC multiplication is designed as a hardware accelerator. The suggested cryptographic system spends much less execution time, which is a significant factor for being applied in practice. Security analysis is successfully performed, and our experiments prove that our proposed technique provides the basics of cryptography with more simplicity and correctness. In fact, the results of the evaluation prove the effectiveness, rapidity and high security of the suggested algorithm.

A well-designed Physical-Layer Authentication (PLA) scheme should consider three properties: covertness, robustness, and security. However, the three properties always cause some dilemmas, e.g., higher covertness leading to lower robustness. This paper concerns the problem of improving the covert- ness without sacrificing the robustness. This problem is important because of the following reasons: reducing the errors in recovered source message, improving the security, and ease of constructing a multi-factor authentication system. In this paper, we propose three covert PLA schemes to address the problem. In the first scheme, we improve the covertness by reducing the modification ratio on the source message based on an encoding mechanism. In the second scheme, we improve the covertness by optimizing the superimposing angle, which maximizes the minimum distance between the tagged symbols and the boundary line of the demodulation decision for the source message. In the third scheme, referred to as the hybrid scheme, we further improve the covertness by jointly using the advantages of both the above two schemes. Our experimental results show that when the SNR at a legitimate receiver is 25 dB, as compared with the prior scheme, the first scheme improves the covertness by 17.74%, the second scheme improves the covertness by 28.79%, and the third scheme improves the covertness by 32.09%, while they have similar robustness as that of the prior scheme.

Recent exploit techniques are highly complex, and it is not easy for cybersecurity learners to understand the attack strategies quickly and clearly. For efficient and comprehensive learning, this paper proposes an attack-scheme visualization system that fulfills three requirements: attack progress visualization in real-time, memory and register-level description, and concise description of the attack schemes. This paper exemplifies two cases: stack buffer overflow and ROP attacks, and demonstrates how the system operates and how users can learn that existing defense technologies are effective or ineffective depending on the execution environments.

Facing the challenges of globalisation and unpredictable shocks, manufacturers seek novel methods to maintain the sustainability of their supply chains. Adopting Industry 4.0 (I4.0) technologies facilitates sustainable supply chain management (SSCM) with the precise decision-making of supply chain activities and the realisation of circular development. However, according to the bibliometric analysis and systematic literature review of ar-ticles related to "SSCM", few frameworks with I4.0 technologies are found to empower SSCM under circular economy (CE) logic. Thus, this article proposes a conceptual framework of I4.0 technologies-embedded SSCM, which takes advantage of five kinds of emerging digital technologies, including cloud services, artificial intel-ligence (AI), big data analytics (BDA), blockchain technology (BT), and internet of things (IoT). The CAB2IN framework is based on the technologies mentioned above alongside the design, manufacturing, delivering, using, and end-of-life stages of products and services to meet the requirements of reducing material usage, remanu-facturing, reusing, and recycling. This papers contribution lies in indicating the trends of SSCM in the era of Industry 4.0 and proposing CAB2IN to creatively establish the virtual side of circular SSCM, which leverages the data generated in each stage to assist sustainable decision-making. CAB2IN illuminates several research di-rections for future studies of digitalised SSCM under the perspective of CE. The case of Company S illustrates the application of CAB2IN in the healthcare supply chain. This paper also summarises insightful directions of digi-talised SSCM under the proposed circular framework. Funding Agencies|National Natural Science Founda- tion of China [71971012]

Integrated modular avionics is the core system of modern aircraft, which hosts almost all kinds of electrical functions. The performance of integrated modular avionics has an immediate influence on flight mission. Remaining useful life prediction is an effective manner to guarantee the safety and reliability of airplane. To satisfy the real-time requirement of integrated modular avionics, the prediction algorithm should have fast learning speed. This paper proposes an ensemble enhanced online sequential parallel extreme learning machine to predict the remaining useful life of integrated modular avionics. Firstly, a network with parallel hidden layers is designed to improve feature extraction. Secondly, to enhance the learning stability, the input weights of the network are determined by using extreme learning machine autoencoder. Thirdly, an updating method is developed for online prediction and an adaptive weight is designed to construct the ensemble online sequential prediction method. The effectiveness and superiority of the proposed method are verified through the standard datasets. Finally, this paper regards intermittent faults as the feature of integrated modular avionics and builds a degradation model by using Levy Process. The proposed method is applied to remaining useful life prediction of integrated modular avionics.

Embedded software is increasingly being used with high reliability. However, the fault localization of embedded software is still largely dependent on the experience of engineers. Besides, faults in embedded software programs are not independent individuals; they are related to each other and affect each other, which may lead to more complex interaction behavior. These uncertainties render the traditional methods for single-fault localization with limited practical value. This paper has proposed a multiple-fault localization method to be applied to the embedded software, with emphasis on the cache-based program spectra-acquiring method and the hybrid clustering-based fault partition method. Through case studies on 108 groups of the subject program, it has been proved that the hybrid clustering-based fault partition method has significantly improved the effectiveness of multiple-fault localization in comparison with the traditional fault localization methods. Experiments on three embedded software programs in engineering have revealed that the cache-based program spectra-acquiring method saves nearly half of the running-time cost compared with the traditional spectrum-acquiring method based on real-time transmission. Therefore, the multiple-fault localization method proposed in this paper can be applied in embedded software debugging and testing in engineering.

Fifth generation wireless communication system is implemented by several mobile companies where important technologies are employed such as the massive multiple-input multiple-output (MIMO). Although it improves the spectrum and energy efficiencies, it is not a trivial to propose an efficient detector for massive MIMO systems because a large number of antennas increases the computational complexity. In this paper, approximate message passing (AMP) is exploited in the massive MIMO uplink (UL) data detection. Although the AMP method is suffering from a considerable performance in special cases, an efficient selection of the initial solution affects greatly the convergence rate, the bit-error-rate performance, and the computational complexity. In this paper, three efficient initialization methods of the AMP-based massive MIMO UL detector are proposed based on iterative methods, namely, the successive overrelaxation, the Gauss–Seidel, and the Jacobi. In addition, a stair matrix is exploited to achieve high convergence rate. Numerical results show that the proposed detector achieved a considerable performance improvement and complexity reduction in comparison with the traditional AMP-based detector.

The advent of the Internet in these last years encouraged a considerable traffic of digital images. In the sanitary field, precisely in telemedicine branch, medical images play a very important role for therapeutic diagnoses. Thus, it is necessary to protect medical images data before transmission over the network to preserve their security and prevent unauthorized access. In this paper, a secure algorithm for biomedical images encryption scheme based on the combination of watermarking technique and chaotic function is proposed. In the proposed method, to achieve high security level performances, a non-blind hybrid watermarking technique with audio signal, Discrete Wavelet Transform is used; smoothness is also used as selected criteria; the iterations obtained by the chaotic sequences are essential and allow a good realization of the encryption process. One of the main advantages of chaos-based encryption schemes is the generation of a large number of key spaces to resist brute force attacks from the encryption algorithm. The experimental results presented in this paper attest to the invisibility and robustness of the proposed algorithm combining watermarking and chaos-based encryption.

Though various applications such as flash memory, cache, storage systems, and even indexing for enterprise big data search, adopt hot data identification schemes, relatively little research has been expended into holistically examining alternative strategies. Rather, researchers tend to classify hot data simplistically by considering one or more frequency metrics, thereby disregarding recency, which is also an important consideration. In practice, different workloads mandate different treatment to achieve effective hot data decisions. This paper proposes a dynamic hot data identification scheme that adopts a workload stack distance approximation. Stack distance is a good recency measure, but it traditionally requires high computational complexity as well as additional space. Since stack distance calculation efficiency is a core component for our dynamic feature design, this paper additionally proposes a stack distance approximation algorithm that significantly reduces both computation and space requirements. To our knowledge, the proposed scheme is the first dynamic hot data identification scheme which judiciously assigns more weight to either recency or frequency based on workload characteristics. Our experiments with diverse realistic workloads demonstrate that our stack distance approximation achieves excellent accuracy (up to a 0.1% error rate) and our dynamic scheme improves performance by as much as 49.8%.

This paper discusses open phase faults, which can happen in a system grid-converter-motor drive, and measures to properly handle this type of fault. The authors of this paper provide classification of algorithms used for the detection of the loss of phase and explain the distinctive features of each group of methods. They review existing algorithms, which are based on the analysis of the current signals, discuss their pros and cons and suggest possible areas of usage for each group of methods. The authors also propose one novel method for detection of open phase, which was developed for low-cost systems with low resolution analog-to digital converters (ADC). This paper mainly considers methods for three-phase motors as the most popular machines, however some algorithms can be used in multiphase drives. The authors of the paper also share their more than 20 years’ experience combined, in this area, which was obtained by developing industrial and commercial drives, and focus on the requirements of the IEC/UL 60730 safety standard, where the phase-loss detection algorithm is one of the essential parts of control system.

This paper concluded that parallel storage similar to the human brain must have the following characteristics: 1) The connection mode of its information storage is scattered and parallel. 2) The operating pattern of this system must be similar to that of the nervous system for memory, facilitation, and recall. Because of this similarity, we can “borrow” some concepts from neuroscience to describe Advanced intelligent basic programming the attributes of parallel storage systems. 3) The excitation mode of this system must be selective excitation. 4) Memory interference is one of the most important characteristics of parallel storage system. We can’t eliminate recall interference completely, but there are a number of steps can take to keep within the limits of our tolerance. 5) Memory interference requires only excitatory memory columns in the contact area. 6) The “memory excitation” mode of the parallel storage system for information must be the fuzzy “memory excitation”, which is inevitable of memory interference. Whereafter, This paper describes and discusses how and why to program on the theoretical basis of the first part. Includes an introduction to functions that enable memory, recall, stimulation, excitement. And the relationship between memory, memory, fuzzy excitement, excitement, reward and punishment, attention distribution and their significance in solving memory interference.

Nowadays, the Direction of Arrival (DoA) estimation problem attracts much attention because of emerging use cases for wireless networks. DoA is beneficial for Reconfigurable Intelligent Surfaces, indoor localization, and various navigation and sensing applications, such as gesture recognition and home monitoring. The Multiple Signal Classification (MUSIC) algorithm is very promising for DoA estimation because it provides better accuracy than the other algorithms and remains simple enough to implement in hardware. MUSIC has many modifications designed to achieve better accuracy in indoor environments by combining and smoothing several measurements. However, such modifications have been implemented in equipment with different capabilities. Consequently, the modifications have never been compared under identical conditions. The paper addresses this issue, provides a classification of existing smoothing modifications of MUSIC, and proposes new ones not considered in the literature yet. All of them are compared in real Wi-Fi networks. For that, a testbed is designed that allows automatic measurements in multiple experiments with different positions of devices. A new calibration procedure is created to achieve higher accuracy, and the testbed is validated in an anechoic chamber. Finally, the paper suggests the preferable smoothing modifications of MUSIC for finding the DoA.

Non-orthogonal multiple access (NOMA) is the potential multiple access considered for next generation (5G/6G) mobile communication and most of the research papers have been focused on power-domain NOMA. However, this research paper makes an effort to contribute to understand another aspect of NOMA i.e. code domain NOMA due to its better performance in large system where number of resources and users grows unboundedly. Interleave division multiple access (IDMA) is an established framework for code domain implementation of NOMA transmission and the performance of IDMA depends greatly on the interleavers as they are used to distinguish among the users. So, a unique Deci power interleaver (DPI) has been proposed and analyzed in this paper for the suggested IDMA scheme. The proposed interleaver is based on modulo operation, which reduce the bandwidth requirement and computational complexity without any performance degradation. Simulation experiments have been carried out to compare the DPI with random and other interleavers with coded and uncoded scenario in IDMA system.

This paper presents a new method to automate the sizing of analog circuits. The method emulates the manual design procedure. The sizing task is formulated as a constraint programming problem. Two new algorithms are introduced: First, a hierarchical structural analysis of functional blocks that automatically sets up the analytical equations for the sizing. And second, a heuristic to guide the branching process of a constraint programming solver. We achieve a reproduction of the manual proceeding of designers and at the same time an automation of the set-up of the design problem, reducing the set-up effort to seconds. The method is primarily designed for operational amplifiers. More than 20 circuits, including different variants of two-stage, folded-cascode, telescopic and symmetrical op-amps, are considered at this time. This paper presents a runtime comparison of the newly developed branching heuristic with a generic branching heuristic on 20 different circuits. Furthermore, sizing results for a two-stage op-amp with transistors in weak inversion and a folded-cascode op-amp with common-mode feedback circuit are presented to show the effectiveness of the approach.

Emerging wireless networks possess the potential to achieve levels of connectivity and Quality-of-Service (QoS) that are orders of magnitude higher than today’s networks. Realizing the potential of these networks will require flexible, low cost, and accurate Digital Signal Processing (DSP). Supervised Learning (SL) models employing unknown parameter estimation and classification techniques have experienced widespread use in physical (PHY) layer wireless communication systems since they can achieve low costs via inexpensive forward-pass computations, attain flexible operations due to trainable parameters, and yield accurate results based on the universal approximator attribute. In this survey and tutorial paper, we present a methodical explanation of how SL can be applied to unknown parameter estimation and classification across several different PHY layer components of a wireless communications system. Additionally, via a survey and comparison of popular methods, this paper provides insights on how to perform weight training, weight initialization, loss function regularization, data pre-processing, input feature design, ensemble training, and hyper-parameter validation. In our review of state-of-the-art works, we found significant use of SL algorithms in the following PHY layer applications: Dynamic Spectrum Access (DSA), channel corrections, Automatic Gain Control (AGC), Multiple Input Multiple Output (MIMO) control, Analog to Digital (ADC) conversion, and Automatic Coding and Modulation (ACM). The overarching goal of this survey and tutorial paper is to assist the reader in understanding the motivation and methodologies associated with various SL algorithms applied to PHY layer DSP operations, as well as to provide the reader with the necessary tools and techniques needed for addressing open challenges to be experienced by future wireless networks.

Recently, convolutional neural networks (CNNs) have set latest state-of-the-art on various human activity recognition (HAR) datasets. However, deep CNNs often require more computing resources, which limits their applications in embedded HAR. Although many successful methods have been proposed to reduce memory and FLOPs of CNNs, they often involve special network architectures designed for visual tasks, which are not suitable for deep HAR tasks with time series sensor signals, due to remarkable discrepancy. Therefore, it is necessary to develop lightweight deep models to perform HAR. As filter is the basic unit in constructing CNNs, it deserves further research whether re-designing smaller filters is applicable for deep HAR. In the paper, inspired by the idea, we proposed a lightweight CNN using Lego filters for HAR. A set of lower-dimensional filters is used as Lego bricks to be stacked for conventional filters, which does not rely on any special network structure. The local loss function is used to train model. To our knowledge, this is the first paper that proposes lightweight CNN for HAR in ubiquitous and wearable computing arena. The experiment results on five public HAR datasets, UCI-HAR dataset, OPPORTUNITY dataset, UNIMIB-SHAR dataset, PAMAP2 dataset, and WISDM dataset collected from either smartphones or multiple sensor nodes, indicate that our novel Lego CNN with local loss can greatly reduce memory and computation cost over CNN, while achieving higher accuracy. That is to say, the proposed model is smaller, faster and more accurate. Finally, we evaluate the actual performance on an Android smartphone., Comment: 11 pages, 11 figures

Non-orthogonal multiple access (NOMA) is widely recognized to increase the number of users and enhance the spectral efficiency in fifth-generation (5G) wireless networks and beyond. NOMA is still in the theoretical analysis and simulation phases and fewer experimental works are reported to date. In this paper, we design and implement NOMA in software-defined radio, and evaluate its performance. This includes the real-time realization of the key components of NOMA, i.e., superposition and successive interference cancellation. The main novelty of this paper is to introduce constructing superimposed signals with varying symbol rates to enlarge the achievable rate region of the experimented NOMA. By applying varying symbol rates, the set of possible transmission rate pairs enlarges and we can reach higher data rates compared to existing modulation and coding schemes (MCS). We also propose an algorithm to efficiently find the rate pairs. Simulations and experiments demonstrate that NOMA with a varying symbol rate not only can reach higher data rates than orthogonal schemes such as time division multiple access, but it can also outperform existing MCS-based methods which have a fixed symbol rate. The experiments also show that there is a noticeable gap between NOMA in theory and practice. In addition to the new NOMA experiments, we review the state-of-the-art in experimental NOMA. We also discuss several directions for future experiments that can help bridge the gap between theory and practice and bring NOMA to practical communication systems.

Underwater sensor networks (USNs) are envisioned to enable a large variety of marine applications. Such applications require accurate position information of sensor nodes. However, the openness and inhomogeneity characteristics of underwater medium make it much more challenging to solve the localization issue. This paper is concerned with a privacy-preserving localization issue for USNs in inhomogeneous underwater medium. An honest-but-curious model is considered to develop a privacy-preserving localization protocol. Based on this, a localization problem is constructed for sensor nodes to minimize the sum of all measurement errors, where a ray compensation strategy is incorporated to remove the localization bias from assuming the straight-line transmission. To make the above problem tractable, we consider the unsupervised, supervised and semisupervised scenarios, through which deep reinforcement learning (DRL) based localization estimators are utilized to estimate the positions of sensor nodes. It is noted that, the proposed localization solution in this paper can hide the private position information of USNs, and more importantly, it is robust to local optimum for nonconvex and nonsmooth localization problem in inhomogeneous underwater medium. Finally, simulation studies are given to show the position privacy can be preserved, while the localization accuracy can be enhanced as compared with the other existing works.

Active learning method (ALM) is a powerful fuzzy–based soft computing methodology suitable for various applications such as function modeling, control systems, clustering and classification. Despite considerable advantages, the main computational engine of ALM, ink drop spread (IDS), is memory-intensive, which imposes significant area overheads in the hardware realization of the ALM for real–time applications. In this paper, we propose a compressed model for ALM which greatly alleviates the storage limitations. The proposed approach employs a distinct inference algorithm, enabling a significant reduction in memory utilization from $O(N^{2})$ to $O(2N)$ for a multi–input single–output (MISO) system. Also, the computational costs in both training and inference modes are decreased to only a few additions and multiplications. Furthermore, we develop a memory–efficient digital architecture for the proposed compressed ALM algorithm that can be leveraged for various computing systems through configuring a few registers. Finally, we assess the performance of the proposed approach using various function modeling and classification applications and provide a comparison with conventional ALM and some other well-know approaches. Simulation and hardware implementation results demonstrate that the proposed approach achieves reduced noise sensitivity with $128\times $ reduction in the average memory usage while realizing comparable accuracy compared to the other approaches studied herein.

Signal hiding countermeasures have been extensively investigated in the early side-channel attacks’ literature. Due to design and physical imperfections, their stand-alone use only leads to a limited reduction of the attacks’ complexity. As a result, more algorithmic countermeasures providing a more formal cost vs. security tradeoff (e.g., shuffling and masking) have gained more attention. Yet, since the cost associated with these is high, designers aim at combining countermeasures, leveraging the strength of each. In this manuscript, we demonstrate that by asking less to both signal hiding and algorithmic countermeasures (as stand-alone), we can develop combined countermeasures that indeed provide higher security at lower cost. For this purpose, we show how we can stack signal reduction and amplitude randomization techniques with ultra low cost automatic design flows and standard tools, and reach attractive security levels in combination with masking. Concretely, we examine two natural strategies for signal hiding and their combination: namely WDDL and a simple, local, scalable and easy-to-implement noise generation engine. A 65nm technology ASIC is evaluated with multiple isolated AES cores, leveraging recent information theoretic bounds which are connected to masking security proofs, significantly reducing the side-channel information leakage. We further quantify performance gains for masked designs.

Multivariate or multichannel data have become ubiquitous in many modern scientific and engineering applications, e.g., biomedical engineering, owing to recent advances in sensor and computing technology. Processing these data sets is challenging owing to their large size and multidimensional nature, thus requiring specialized algorithms and efficient hardware designs for online and real time processing. In this paper, we address this issue by proposing a fully FPGA based hardware architecture of a popular multi-scale and multivariate signal processing algorithm, termed as multivariate empirical mode decomposition (MEMD). MEMD is a data-driven method that extends the functionality of standard empirical mode decomposition (EMD) algorithm to multichannel or multivariate data sets. Since its inception in 2010, the algorithm has found wide spread applications spanning different engineering related fields. Yet, no parallel FPGA based hardware design of the algorithm is available for its online and real time processing. Our proposed architecture for MEMD uses fixed-point operations and employs cubic spline interpolation (CSI) within the sifting process. Finally, examples of decomposition of multivariate synthetic and real-world biological signals are provided.

The paper is devoted to the evaluation of energy efficiency of High Performance Computing systems used in a scientific supercomputer center. The authors propose a method for the comparison of energy efficiency of computing systems based on the power consumption and execution time of parallel programs. The paper presents a software tool that allows to determine the energy consumption profile of a parallel program automatically without changing its source code. The paper also presents the results of power consumption comparison of NAS Parallel Benchmarks (BT, EP, IS, and LU) tests on computing systems with codenames Intel microprocessors Broadwell, Cascade Lake, Knights Landing and Skylake).

This special issue of the International Journal of Serious Games is dedicated to the selected best papers of the 2021 edition of the GALA conference. This edition was organized by Francesca De Rosa and her team at NATO Centre for Maritime Research and Experimen-tation (CMRE), La Spezia, Italy. Because of the Covid-19 pandemic, it was held online, for the second year. The three papers published in this special issue were first selected for a content exten-sion, so to make them suitable as journal papers, then underwent the regular IJSG peer-review process, which, on the other hand, discarded other three selected manuscripts. Here is a short introduction to the accepted papers that you will find in the GALA Conf 2021 special issue of the IJSG.

In computer networking, simulations are widely used to test and analyse new protocols and ideas. Currently, there are a number of open real testbeds available to test the new protocols. In the EU, for example, there are Fed4Fire testbeds, while in the US, there are POWDER and COSMOS testbeds. Several other countries, including Japan, Brazil, India, and China, have also developed next-generation testbeds. Compared to simulations, these testbeds offer a more realistic way to test protocols and prototypes. In this paper, we examine some available wireless testbeds from the EU and the US, which are part of an open-call EU project under the NGIAtlantic H2020 initiative to conduct Software-Defined Networking (SDN) experiments on intelligent Internet of Things (IoT) networks. Furthermore, the paper presents benchmarking results and failure recovery results from each of the considered testbeds using a variety of wireless network topologies. The paper compares the testbeds based on throughput, latency, jitter, resources available, and failure recovery time, by sending different types of traffic. The results demonstrate the feasibility of performing wireless experiments on different testbeds in the US and the EU. Further, issues faced during experimentation on EU and US testbeds are also reported.

Computer viruses have become complex and operates in a stealth mode to avoid detection. New viruses are argued to be created each and every day. However, most of these supposedly ‘new’ viruses are not completely new. Most of the supposedly ‘new’ viruses are not necessarily created from scratch with completely new (something novel that has never been seen before) mechanisms. For example, most of these viruses just change their form and signatures to avoid detection. But their operation and the way they infect files and systems is still the same. Hence, such viruses cannot be argued to be new. In this paper, the authors refer to such viruses as derived viruses. Just like new viruses, derived viruses are hard to detect with current scanning-detection methods. Therefore, this paper proposes a virus detection system that detects derived viruses better than existing methods. The proposed system integrates a mutating engine together with neural network to improve the detection rate of derived viruses. Experimental results show that the proposed model can detect derived viruses with an average accuracy detection rate of 80% (this include 91% success rate on first generation, 83% success rate on second generation and 65% success rate on third generation). The results further shows that the correlation between the original virus signature and its derivatives decreases further down along its generations.

Side channel attacks (SCA) are known to be efficient techniques to retrieve secret data. In this context, this paper concerns the evaluation of the robustness of secure triple track logic (STTL) against power and electromagnetic analyses on FPGA devices. More precisely, it aims at demonstrating that the basic concepts behind STTL are valid in general and particularly for FPGAs. Also, the paper shows that this new logic may provide interesting design guidelines to get circuits that are resistant to differential power analysis (DPA) attacks which and also more robust against differential electromagnetic attacks (DEMA).

Time (or clock) synchronization is a large and vital field of research, as synchronization is a precondition for many applications. A few example applications are distributed data acquisition, distributed databases, and real-time communication. First, this survey paper introduces the research area of time synchronization and emphasizes its relation to other research areas. Second, we give an overview of the state-of-the-art of time synchronization. Herein, we discuss both established protocol and research approaches. We analyze all techniques according to three criteria: used estimation algorithm, achievable synchronization accuracy, and the experimental conditions. In our opinion, this analysis highlights potential improvements. The most important question in this survey is as follows: which estimation method can be used to achieve which accuracies under which conditions? The intention behind this is to identify estimation methods that are particularly worth considering, as these already achieve good results in the wireless area but have not yet been examined in the wired area (and vice versa). This survey paper differs from other surveys in particular through the consideration of wireless and wired synchronization and the focus on estimation algorithms and their achievable accuracy.

Background: In model-driven development, model transformation transforms one model to another between different phases of software engineering. In model transformation, metamodel plays a vital role which defines the abstract syntax of models and the interrelationship between their elements. A unified metamodel defines an abstract syntax for both source and target models when they share core elements. Theoretical approaches define language and platform independent representation of models in software engineering. This paper investigates the theoretical foundation to this unified meta-modelling for their consistent transformation. Objective: This paper aims to define the formal foundations to the unified metamodel for generating implementation from design specifications and model reusability. Method: In this paper, the study considers transformation from design to implementation and vice versa using theoretical foundations to build a verified software system. Results: The related tools provide a formal representation of the design phase for verification purpose. Our approach provides a set-theoretical foundation to the unified metamodel for model transformation from USE (UML/OCL) to Spec#. In other words, our approach defines the formal foundation to define a model which consists of all the required properties for verification at the design and implementation phase. Conclusion: This paper introduced a new set of the theoretical framework which acts as an interface between the design and implementation to generate verified software systems.

In the recent days, the multiple input multiple output (MIMO) is considered as the important technology of wireless due to its characteristics. The channel selection and scheduling plays an important roles in MIMO communication systems. For this purpose, various techniques are proposed in the traditional works, but it has some major drawbacks such as increased bit error rate, inefficient channel selection, and reduced spectral efficiency. In order to overcome these issues, this paper aims to develop a new optimization based scheduling technique for a successful MIMO communication. At first, the Rayleigh fading channel is initialized and its parameters are extracted, then the beamforming technique is used to extract the features. After that, the optimal channel is selected from the available number of channels by implementing a joint optimization (JO) technique. Consequently, the power spectral density is estimated before scheduling the channel for communication. Finally, the sub-band expediency based scheduling (SES) technique is implemented for scheduling the channel based on the priority. The novel concept of this paper are, an optimal channel is selected based on the correlation coefficient value, and the priority based channel scheduling is performed for communication. The experimental results evaluate the performance of the proposed JO-SES technique based on the measures of BET, SNR, and average sum rate. Also, some of the existing techniques are considered in this work for proving the betterment of the proposed technique.

Wireless sensor networks (WSN) have gradually integrated into the concept of the Internet of Things (IoT) and become one of the key technologies. This paper studies the optimization algorithm in the field of artificial intelligence (AI) and effectively solves the problem of node location in WSN. Specifically, we propose a hybrid algorithm WOA-QT based on the whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm. It skillfully combines the strengths of the two algorithms, not only retaining the WOA’s distinctive framework advantages but also having QUATRE’s excellent coevolution ability. In order to further save optimization time, an auxiliary strategy for dynamically shrinking the search space (DSS) is introduced in the algorithm. To ensure the fairness of the evaluation, this paper selects 30 different types of benchmark functions and conducts experiments from multiple angles. The experiment results demonstrate that the optimization quality and efficiency of WOA-QT are very prominent. We use the proposed algorithm to optimize the weighted centroid location (WCL) algorithm based on received signal strength indication (RSSI) and obtain satisfactory positioning accuracy. This reflects the high value of the algorithm in practical applications.

Quantization is a promising technique to compress the size of Convolutional Neural Network (CNN) models. Recently, various precision-scalable designs have been presented to reduce the computational complexity in CNNs. However, most of them adopt straightforward calculation scheme to implement the CNN, which causes high bandwidth requirement and low hardware utilization efficiency. This paper proposes a new precision-scalable architecture which can fully reduce the computational complexity in CNN inference and meanwhile has a finely simplified calculation scheme. Based on the proposed scheme, a well-optimized multiplier called Compositional Processing Element (C-PE) is devised. Compared with the previous multipliers, the new C-PE requires less area and power. Furthermore, two levels of optimization are introduced to the design to relieve the bandwidth problem and increase the hardware utilization efficiency. Implemented under the TSMC 90nm CMOS technology, the whole design achieves 6-68.1 fps in various precisions on VGG16 benchmark and a 49.8TOPS/W energy efficiency at 500MHz when scaled to 28nm, which is much better than previous precision-scalable ones.

This paper deals with the formal verification of reconfigurable discrete event control systems (RDECSs) using reconfigurable timed net condition/event systems (R-TNCESs) formalism. A reconfigurable system switches from a mode to another during its working process to adapt its behavior to the related environment. By including such a feature, RDECSs become complex and their verification is often expensive in terms of computation time and memory. In this paper, a new methodology for formal verification of RDECSs is proposed in order to ensure the correctness of these systems with a reduced cost (decreasing the verification time and memory occupation). The proposed contribution includes an improved modeling and verification of RDECSs. The modeling with R-TNCESs is enriched with all reconfiguration forms, and the verification involves an improvement method that avoids any redundancy and cancels unnecessary calculations. In addition, a visual tool called Rec-AG based on the proposed methodology is developed. The performance evaluation of this paper is achieved by measuring computation time and memory for several systems and different sizes of the problem. This paper’s contribution is applied to the benchmark production system FESTO.

Higher education institutions are the main institutions in creating young people who can be ready to enter the world of work and industry, and one of the educational programs that take part in vocational education. Vocational education is higher education that is directed at focusing certain applied skills. Harapan Bersama of Polytechnic is one of the vocational education institutions that provides Human Resources (HR) to become the younger generation as practitioners in industry, companies, or other stoke holders. In the D3 Computer Engineering Study Program, each batch can graduate approximately 300 graduates, where each graduate will produce one scientific work from the final project research conducted. Likewise, with students who do practical work (KP), every student who does KP will produce scientific papers. But all the results of this scientific work, namely the research report of the Final Project and the Job Training Report are still being submitted to the Study Program in one compact disk (CD). This turned out to be a problem for the Study Program because from each batch, around 300 compact disks (CDs) were collected from the results of scientific work from the practical work and 300 compact disks (CDs) from the final project research outputs. Physical data in the form of a compact disk (CD) requires a large space for its placement. Meanwhile, the condition of the D3 Computer Engineering study program room makes it impossible to place the physical form. And along with technological developments, this physical form can be replaced by soft files in the form of e-journals or e-reports. This needs to be done in line with the paradigm shift from conventional libraries to e-libraries. With the existence of SIPESTA or the information system for the completion of this Final Project, the results of writing scientific papers are uploaded and become a digital trace of all scientific papers that have been carried out by all D3 Computer Engineering students. And so if this data is needed it will be easy to find because the database has been well managed.

In this paper, we investigate the performance of various multi-user scheduling algorithms over independent and non-identically distributed generalized fading channel and 3GPP long term evolution (LTE) networks. Multi-user scheduling is an important task to be performed in wireless communication networks to share the bandwidth resource effectively between large number of users. In this paper, novel expressions for average BER, average channel capacity and fairness among users are derived for various scenarios such as (1) independent identical fading parameter, independent non identical average SNR channel conditions and (2) both fading parameter and average SNR are independent and non-identically distributed under absolute SNR (AS) scheduling and threshold SNR scheduling. More over the performance of threshold SNR scheduling is compared with AS scheduling and other existing scheduling techniques to identify a suitable scheduling algorithm for LTE networks. Analytical expressions are simulated and validated through MATLAB, and insightful discussions are provided from the numerical results.

The paper deals with state-of-the-art trends in the field of automated hardware development, specifically development of digital systems using programmable logic integrated circuits, as exemplified by field-programmable logic arrays. The paper suggests a modeling method of development using an algebraic model of design specifications, requirements and binary codes in order to apply formal methods of verification, model-based testing, as well as methods of algebraic matching. The specifications of the algebraic hardware model are behavior algebra determined on a set of actions and behaviors.

Deep neural networks (DNNs) frequently contain far more weights, represented at a higher precision, than are required for the specific task which they are trained to perform. Consequently, they can often be compressed using techniques such as weight pruning and quantization that reduce both the model size and inference time without appreciable loss in accuracy. However, finding the best compression strategy and corresponding target sparsity for a given DNN, hardware platform, and optimization objective currently requires expensive, frequently manual, trial-and-error experimentation. In this paper, we introduce a programmable system for model compression called Condensa. Users programmatically compose simple operators, in Python, to build more complex and practically interesting compression strategies. Given a strategy and user-provided objective (such as minimization of running time), Condensa uses a novel Bayesian optimization-based algorithm to automatically infer desirable sparsities. Our experiments on four real-world DNNs demonstrate memory footprint and hardware runtime throughput improvements of 188x and 2.59x, respectively, using at most ten samples per search. We have released a reference implementation of Condensa at https://github.com/NVlabs/condensa., This is an updated version of a paper published in IEEE Micro, vol. 40, no. 5, pp. 17-25, Sept.-Oct. 2020 at https://ieeexplore.ieee.org/document/9151283

The increasing amount of data generated by space applications poses several challenges due to limited resources available onboard: power, memory, computation, data rate. In this paper, we propose Compressed Sensing (CS) as the key tool to face those challenges via compressive imaging. This signal processing technique, only recently applied to space applications, dramatically simplifies the image acquisition featuring native compression/encryption and enabling onboard image analysis, allowing to design simpler and lighter optical systems. In this paper, we try to answer the following question: To what extent are the potential benefits of CS going to materialize in a realistic “space big data” application scenario? To this purpose, we first review compressive imaging techniques and already existing prototypes and concepts, critically discussing the technological issues involved. Then, we propose a set of instrument concepts in the application domains of space science, planetary exploration and earth observation, most suitable for a CS–based application. For the most promising of them, we go deeper into the analysis showing preliminary reconstruction performance tests.

The term Massive MIMO means, Massive multiple input multiple output also known as (large-scale antenna system, very large MIMO). Massive Multiple-Input-MultipleOutput (MIMO) is the major key technique for the future Fifth Generation (5G) of mobile wireless communication network due to its characteristics, elements and advantages. Massive MIMO will be comprised of five major elements; antennas, electronic components, network architectures, protocols and signal processing. We realize that precoding technique is a processing technique that utilizes Channel State Information Technique (CSIT) by operating on the signals before transmitting them. This technique varies base on the type of CSIT and performance criterion. Precoding technique is the last digital processing block at the transmitting side. In this paper, linear and non-linear Precoding technique was reviewed and we proposed two techniques under each that is Minimum Mean Square Error (MMSE), Block Diagonalization (BD), Tomlinson-Harashima (TH) and Dirty paper coding (DPC). Four Precoding techniques: MMSE, BD, DPC and TH were used in the studies to power consumption, energy efficiency and area throughput for single-cell and multi-cell scenarios. In comparing the proposed techniques, in terms of energy efficiency and area throughput, reuse factor (Reuse 4) performs better than other techniques when there is an imperfect CSI is used

For future Internet of Things (IoT)-based Big Data applications (e.g., smart cities/transportation), wireless data collection from ubiquitous massive smart sensors with limited spectrum bandwidth is very challenging. On the other hand, to interpret the meaning behind the collected data, it is also challenging for edge fusion centers running computing tasks over large data sets with limited computation capacity. To tackle these challenges, by exploiting the superposition property of a multiple-access channel and the functional decomposition properties, the recently proposed technique, over-the-air computation (AirComp), enables an effective joint data collection and computation from concurrent sensor transmissions. In this paper, we focus on a single-antenna AirComp system consisting of $K$ sensors and one receiver (i.e., the fusion center). We consider an optimization problem to minimize the computation mean-squared error (MSE) of the $K$ sensors' signals at the receiver by optimizing the transmitting-receiving (Tx-Rx) policy, under the peak power constraint of each sensor. Although the problem is not convex, we derive the computation-optimal policy in closed form. Also, we comprehensively investigate the ergodic performance of AirComp systems in terms of the average computation MSE and the average power consumption under Rayleigh fading channels with different Tx-Rx policies. For the computation-optimal policy, we prove that its average computation MSE has a decay rate of $O(1/\sqrt{K})$, and our numerical results illustrate that the policy also has a vanishing average power consumption with the increasing $K$, which jointly show the computation effectiveness and the energy efficiency of the policy with a large number of sensors., Paper accepted by IEEE Transactions on Wireless Communications. Copyright may be transferred without notice, after which this version may no longer be accessible

International audience; Sample rate conversion (SRC) is ubiquitous and critical function of software defined radio and other signal processing systems (speech coding and synthesis, computer simulation of continuous-time systems, etc..). In this paper, we present a survey on linear phase finite impulse response (FIR) based sampling rate conversion. Many different FIR-based SRC solutions exist, such as classical FIR, polyphase, Farrow, cascaded-integrator-comb, and Newton structures. Each one of these solutions is presented differently in the literature, and SRC reference books introducing the subject are often missing hardware implementation aspects. The main objective of this paper is to provide a simple and comprehensive overview of main FIR-based SRC techniques from theoretical to hardware implementation aspects. The state of the art of FIR-based SRC filters is summed-up through a concise derivation of the different solutions from a common root: linear phase FIR filters. Each SRC solution is presented from both theoretical and practical implementation points of view. The paper provides a succinct tutorial that introduces SRC, and helps identifying and implementing the appropriate FIR-based SRC architecture for any given applications.

Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs and GPUs. If the application is mapped to an appropriate processing core, then these architectures provide many performance benefits to applications. Typically, programmers map sequential applications to CPU and parallel applications to GPU. The task mapping becomes challenging because of the usage of evolving and complex CPU- and GPU-based architectures. This paper presents an approach to map the OpenCL application to heterogeneous multi-core architecture by determining the application suitability and processing capability. The classification is achieved by developing a machine learning-based device suitability classifier that predicts which processor has the highest computational compatibility to run OpenCL applications. In this paper, 20 distinct features are proposed that are extracted by using the developed LLVM-based static analyzer. In order to select the best subset of features, feature selection is performed by using both correlation analysis and the feature importance method. For the class imbalance problem, we use and compare synthetic minority over-sampling method with and without feature selection. Instead of hand-tuning the machine learning classifier, we use the tree-based pipeline optimization method to select the best classifier and its hyper-parameter. We then compare the optimized selected method with traditional algorithms, i.e., random forest, decision tree, Naïve Bayes and KNN. We apply our novel approach on extensively used OpenCL benchmarks, i.e., AMD and Polybench. The dataset contains 653 training and 277 testing applications. We test the classification results using four performance metrics, i.e., F-measure, precision, recall and $$R^2$$ R 2 . The optimized and reduced feature subset model achieved a high F-measure of 0.91 and $$R^2$$ R 2 of 0.76. The proposed framework automatically distributes the workload based on the application requirement and processor compatibility.

Additive manufacturing (AM) is established as a new class for fabricating 3D physical prototypes layer by layer. Given that a large number of different 3D printers (AM machines) are present now (as a product of renowned manufacturers or custom-made products), the design of a benchmark artifact for evaluation of the AM processes is very important. This implies quality evaluation of the capabilities and limitations of each AM process and also the geometrical testing of the 3D printers. The paper proposes a benchmark artifact, according to the criteria for modeling of the benchmark artifact, with a large number of basic features for its geometrical evaluation. Although there are a large number of commercial software packages, this paper also proposes a low-cost developed measurement method of geometrical deviation that could be also adjustable for requirements in an easy way. Verification of the proposed deviation measurement method has been done by several experiments. The experiments include fabrication of the proposed benchmark artifact on available 3D printers by an FFF (fused filament fabrication) process also known as FDM (fused deposition modeling), and after that its scanning. Verification is done by comparing quality evaluation of each fabricated part by developed method and commercial software.

This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI). It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) to analyze and encrypt data. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs for generating unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of finite binary strings for applications in Cryptanalysis. The aim of the paper is to provide an overview on how AI can be applied for encrypting data and undertaking cryptanalysis of such data and other data types in order to assess the cryptographic strength of an encryption algorithm, e.g. to detect patterns of intercepted data streams that are signatures of encrypted data. This includes some of the authors’ prior contributions to the field which is referenced throughout. Applications are presented which include the authentication of high-value documents such as bank notes with a smartphone. This involves using the antenna of a smartphone to read (in the near field) a flexible radio frequency tag that couples to an integrated circuit with a non-programmable coprocessor. The coprocessor retains ultra-strong encrypted information generated using EC that can be decrypted on-line, thereby validating the authenticity of the document through the Internet of Things with a smartphone. The application of optical authentication methods using a smartphone and optical ciphers is also briefly explored.

WOS:000562987500001 Adaptive algorithms play a vital role in Digital Signal Processing (DSP) and Communication. Quality of Voice over IP (VoIP) Service is an important issue as VoIP service is an affordable alternative to conventional communication methods. The Least Mean Square (LMS) algorithm is one of the most popular adaptive filters. LMS is the core of many active noise control applications. This paper presents a performance comparison between three modified versions of the LMS algorithms for online noise cancellation problem for VoIP applications. The three modified algorithms are all concerned with modifications of the step-size of the algorithm since the step-size has a direct relation to the convergence speed of the algorithm. The algorithms investigated in this paper are the Normalized LMS (NLMS), the Variable Step Size LMS (VSSLMS), the Variable Leaky LMS (VLLMS), and the Variable Step Size LMS (VSSLMS) algorithms. The comparison is based on various performance indices including convergence speed, Mean Square Error (MSE), Signal to Noise Ratio (SNR). The simulation test results show that the VSSLMS algorithm is the most suitable algorithm for this type of application.

In massive multiple-input multiple-output (MIMO) systems, the large size of channel state information (CSI) matrix significantly increases the computational complexity of uplink detection and size of required memory to store the channel data. To address these challenges, we propose to perform detection in the angular domain, where the channel information can be presented in a more condensed way. The underlying idea is to exploit the sparsity of massive MIMO channel in the angular domain to reduce the size of CSI matrix by selecting dominant beams. Then, an angular-domain linear detector followed by a non-linear post-processing scheme is proposed to perform detection using the reduced-size CSI. Evaluated using measured massive MIMO channels, our method results in 35%-73% reduction in complexity and required memory compared to traditional detectors while it achieves better performance. Moreover, this paper provides a framework, which trades between performance, complexity, and size of required memory. As a proof of concept, we implement the angular-domain detector in a 28 nm FD-SOI CMOS for a massive MIMO with 128 antennas communicating with up to 16 users. Synthesis result shows that our design attains a throughput of 2240 Mbps with an area of 829 k gates.

In recent years, the intelligent diagnosis technology of wind turbines has made great progress. However, in practical engineering applications, the operating states of wind turbine are various, accompanied by a large number of noise interference, which leads to the decline of the discrimination accuracy of intelligent diagnosis. In order to solve this problem, inspired by the Google team Inception model, this paper proposes a concurrent convolution neural network (C–CNN), the raw data is fed into the network without any prior knowledge, and the characteristics are learned directly and adaptively from the input. Even if the data is accompanied by noise, the model still has high accuracy and strong generalization ability. The model is composed of a CNN with multiple branches. Meanwhile, the convolutional layer of different branches selects the kernels with different scales in same level, thus improving the learning ability of entire network. In this paper, the feasibility of this method for fault diagnosis of bearings in wind turbines is demonstrated by three bearing datasets. The results show that the proposed method can extract discriminative features and classify bearing data accurately under the disturbance of different rotating speed, different load and random noise.

Hardware acceleration substantially enhances both energy efficiency and performance, but raises major challenges for programmability. This is especially true in the domain of approximate computing, where energy-approximation tradeoffs at the hardware level are extremely difficult to encapsulate in interfaces to the software level. The programmability challenges have motivated co-design of accelerators with program-synthesis frameworks, where the structured computations resulting from synthesis are exploited towards hardware specialization. This paper proposes energy-aware code synthesis targeting heterogeneous architectures for approximate computing. A heterogeneous architecture for embedded sensor inference is employed, demonstrated in custom silicon, where programmable feature extraction is mapped to an accelerator via genetic programming. The high level of accelerator specialization and structured mapping of computations to the accelerator enable robust energy models, which are then employed in a genetic-programming algorithm to improve the energy-approximation Pareto frontier. The proposed algorithm is demonstrated in an electroencephalogram-based seizure-detection application and an electrocardiogram-based arrhythmia-detection application. At the same level of baseline inference performance, the energy consumption of genetic-programming models executed on the accelerator is 57.4% and 21.8% lower, respectively, with the proposed algorithm, compared to a conventional algorithm without incorporating energy models for execution on the accelerator.

Channel emulation is widely used to control wireless channels and replay scenarios for repeatable experimentation and evaluation. Characterizing the propagation of massive Multiple-Input Multiple-Output (MIMO) systems, is challenging due to the rapid scaling of the number of channels needed to create representative scenarios as well as accurately modeling complex interdepedencies that exist within the channel matrix. Nonetheless, having the ability to emulate massive MIMO channels is critical for the development and testing of next-generation cellular protocols and algorithms. To achieve such scale for massive MIMO channel emulation solutions, in this paper, we study the tradeoff in channel emulation fidelity versus the hardware resources consumed using both analytical modeling and FPGA-based implementations. To reduce the memory footprint of our design, we optimize our channel emulation using an iterative structure to generate geometry-based channels at scale. In addition, the effects of varying the channel update rate and word length selection are evaluated in the paper and can significantly improve the implementation efficiency on an FPGA.

Purpose Non-orthogonal multiple access (NOMA) is a much hopeful scheme, which is deployed to enhance the spectral efficiency (SE) significantly, and it also enhances the massive access that has attained substantial concern from industrial and academic domains. However, the deployment of superposition coding (SC) at the receiver side resulted in interference. For reducing this interference, “multi-antenna NOMA” seems to be an emerging solution. Particularly, by using the channel state information at the transmitter, spatial beam forming could be deployed that eliminates the interference in an effective manner. Design/methodology/approach This survey analyzes the literature review and diverse techniques regarding the NOMA-based spatial modulation (SM) environment. It reviews a bunch of research papers and states a significant analysis. Initially, the analysis depicts various transmit antenna selection techniques that are contributed in different papers. This survey offers a comprehensive study regarding the chronological review and performance achievements in each contribution. The analytical review also concerns on the amplitude phase modulation (APM) selection schemes adopted in several contributions. Moreover, the objective functions adopted in the reviewed works are also analyzed. Finally, the survey extends with various research issues and its gaps that can be useful for the researchers to promote improved future works on NOMA-based SM. Findings This paper contributes to a review related to NOMA-based SM systems. Various techniques and performance measures adopted in each paper are analyzed and described in this survey. More particularly, the selection of transmission antenna and APM are also examined in this review work. Moreover, the defined objective function of each paper is also observed and made a chronological review as well. Finally, the research challenges along with the gaps on NOMA-based SM systems are also elaborated. Originality/value This paper presents a brief analysis of NOMA-based SM systems. To the best of the authors’ knowledge, this is the first work that uses NOMA-based SM systems to enhance SE.

This paper addresses the distributed resilient filtering for discrete-time large-scale systems (LSSs) with energy constraints, where their information are collected by sensor networks with a same topology structure. As a typical model of information physics systems, LSSs have an inherent merit of modeling wide area power systems, automation processes and so forth. In this paper, two kinds of channels are employed to implement the information transmission in order to extend the service time of sensor nodes powered by energy-limited batteries. Specifically, the one has the merit of high reliability by sacrificing energy cost and the other reduces the energy cost but could result in packet loss. Furthermore, a communication scheduling matrix is introduced to govern the information transmission in these two kind of channels. In this scenario, a novel distributed filter is designed by fusing the compensated neighboring estimation. Then, two matrix-valued functions are derived to obtain the bounds of the covariance matrices of one-step prediction errors and the filtering errors. In what follows, the desired gain matrices are analytically designed to minimize the provided bounds with the help of the gradient-based approach and the mathematical induction. Furthermore, the effect on filtering performance from packet loss is profoundly discussed and it is claimed that the filtering performance becomes better when the probability of packet loss decreases. Finally, a simulation example on wide area power systems is exploited to check the usefulness of the designed distributed filter.