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A multiprocessor system should be able to identify and eliminate faults in time to avoid the paralysis of a whole system. This paper proposes an improved binary bat algorithm to identify faulty processors in a multiprocessor system. Compared with most existing works based on metaheuristic algorithms, the proposed algorithm employs a random initial population and does not require transfer functions. The exclusive-OR operation in the velocity equation is used to measure the distance between two individuals in binary space. To improve population diversity and avoid local optima, the mutation operator is integrated into the position update equation. A new local search strategy is proposed to strengthen the ability of local search in binary space. Experimental results show that the proposed algorithm based on the Malek model can maintain approximately 100 % diagnostic accuracy in a small random initial population with fewer iterations and less CPU running time. [ABSTRACT FROM AUTHOR]

The 17 papers in this special section focus on resilience in networked robotic systems. This collection of articles aims to provide a deeper understanding of resilience as it pertains to multirobot systems, and to disseminate the current advances in designing and operating networked robotic systems. We understand resilience to be a characteristic that enables amultirobot system to withstand or overcome unexpected adverse conditions or shocks, and unknown, unmodeled disturbances. It refers to the contingent nature of the robots' behaviors that is aimed at preserving their functionality or minimizing the time periods during which their functionality is compromised. The papers explore new algorithmic and mathematical foundations toward resilience. [ABSTRACT FROM AUTHOR]

With the development of the Internet and the emergence of various social-media platforms, designing approximation algorithms for optimization problems such as the influence maximization in social networks has received widespread attention. With considering cost, these problems are typically modeled as the regularized submodular optimization, in which a regularized submodular function is expressed as the difference of a nonnegative submodular revenue function $f(\cdot )$ and a nonnegative modular cost function $c(\cdot )$ . In this paper, we consider two kinds of regularized submodular maximization problems with a $k$ -matroid intersection constraint. When $f(\cdot )$ is additionally monotone, we provide a distorted greedy algorithm with a tight constant bicriteria approximation ratio and a time complexity of $O(n^{2})$ . When $f(\cdot )$ is nonmonotone, we combine the distorted greedy algorithm with a simple stochastic process to design a constant bicriteria approximation algorithm with a time complexity of $O(kn^{2})$ . To our knowledge, this is the first paper to design polynomial-time algorithms for the regularized submodular maximization with a $k$ -matroid intersection constraint.

Data center networks may comprise tens or hundreds of thousands of nodes, and, naturally, suffer from frequent software and hardware failures as well as link congestions. Packets are routed along the shortest paths with sufficient resources to facilitate efficient network utilization and minimize delays. In such dynamic networks, links frequently fail or get congested, making the recalculation of the shortest paths a computationally intensive problem. Various routing protocols were proposed to overcome this problem by focusing on network utilization rather than speed. Surprisingly, the design of fast shortest-path algorithms for data centers was largely neglected, though they are universal components of routing protocols. Moreover, parallelization techniques were mostly deployed for random network topologies, and not for regular topologies that are often found in data centers. The aim of this paper is to improve scalability and reduce the time required for the shortest-path calculation in data center networks by parallelization on general-purpose hardware. We propose a novel algorithm that parallelizes edge relaxations as a faster and more scalable solution for popular data center topologies.

Some electrical parameters of the SIS-type hysteretic underdamped Josephson junction (JJ) can be measured by its current-voltage characteristics (IVCs). Currents and voltages at JJ are commensurate with the intrinsic noise level of measuring instruments. This leads to the need for multiple measurements with subsequent statistical processing. In this paper, the digital algorithms are proposed for the automatic measurement of the JJ parameters by IVC. These algorithms make it possible to implement multiple measurements and check these JJ parameters in an automatic mode with the required accuracy. The complete sufficient statistics are used to minimize the root-mean-square error of parameter measurement. A sequence of current pulses with slow rising and falling edges is used to drive JJ, and synchronous current and voltage readings at JJ are used to realize measurement algorithms. The algorithm performance is estimated through computer simulations. The significant advantage of the proposed algorithms is the independence from current source noise and intrinsic noise of current and voltage meters, as well as the simple implementation in automatic digital measuring systems. The proposed algorithms can be used to control JJ parameters during mass production of superconducting integrated circuits, which will improve the production efficiency and product quality.

• Design a Three-Tier Extended Energy Efficient Clustering Hierarchy (TEEECH) Protocol approach. • TEEECH combines the MFCM and CH threshold selection approach to extend the Heterogeneous WSN lifetime. • Direct communication is used with BS to optimize cluster formulation procedures. • Results show that it provides better performance in terms of the network stability and lifetime. • Improve the cluster head selection and reduce the energy consumption of distant cluster heads. The energy efficiency in wireless sensor networks (WSNs) is a significant challenge. A clustering-based approach is an energy-saving approach in WSN. The practical clustering approach divides the network into various clusters that directly affect the total energy consumption and reduce the network lifetime of the WSN. As a result, this paper introduces novel energy-efficient clustering approaches for cluster head (CH) selection and cluster formulation. The cluster head (CH) selection is designed based on the threshold-based advanced LEACH (ADV-LEACH2) approach. The cluster formulation (sensor node distribution) among the cluster heads is done with modified fuzzy c-mean (MFCM) approaches. The clusters are formed using the MFCM approach, and cluster heads (CHs) are selected using ADV-LEACH2. The cluster formulation and head selection are selected at the beginning of each round. The MFCM approach forms balanced clusters and balances sensor node distribution among the cluster heads. The proposed approach maintains the cluster head energy. The experimental simulation outcome justifies that the proposed approach, TEEECH surpasses the existing clustering approach by an average of 13%. The comparison with DEC, SEP-E, EBCS, MEDDEEC, and EDDEEC presents the TEEECH approach to maintain the CH energy and prolong the network lifetime. The proposed approach, TEEECH, also significantly improves the network stability and lifetime based on Half node dead (HND). [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because it promises to bring vast benefits for consumers and businesses, with considerable benefits promised in productivity growth and innovation. To date, significant accomplishments have been reported in many areas that have been deemed challenging for machines, ranging from computer vision, natural language processing, audio analysis to smart sensing and many others. The technology trend in realizing success has developed towards increasingly complex and large-size AI models to solve more complex problems at superior performance and robustness. This rapid progress, however, has taken place at the expense of substantial environmental costs and resources. In addition, debates on the societal impacts of AI, such as fairness, safety, and privacy, have continued to grow in intensity. These issues have reflected major concerns pertaining to the sustainable development of AI. In this work, major trends in machine learning approaches that can address the sustainability problem of AI have been reviewed. Specifically, the emerging AI methodologies and algorithms are examined for addressing the sustainability issue of AI in two major aspects, i.e., environmental sustainability and social sustainability of AI. Then, the major limitations of the existing studies are highlighted, and potential research challenges and directions are proposed for the development of the next generation of sustainable AI techniques. It is believed that this technical review can help promote a sustainable development of AI R&D activities for the research community. [ABSTRACT FROM AUTHOR]

The Maximum Common Subgraph problem has been long proven NP-hard. Nevertheless, it has countless practical applications, and researchers are still searching for exact solutions and scalable heuristic approaches. Driven by applications in molecular science and cyber-security, we concentrate on the Maximum Common Subgraph among an indefinite number of graphs. We first extend a state-of-the-art branch-and-bound procedure working on two graphs to N graphs. Then, given the high computational cost of this approach, we trade off complexity for accuracy, and we propose a set of heuristics to approximate the exact solution for N graphs. We analyze sequential, parallel multi-core, and parallel-many core (GPU-based) approaches, exploiting several leveraging techniques to decrease the contention among threads, improve the workload balance of the different tasks, reduce the computation time, and increase the final result size. We also present several sorting heuristics to order the vertices of the graphs and the graphs themselves. We compare our algorithms with a state-of-the-art method on publicly available benchmark sets. On graph pairs, we are able to speed up the exact computation by a 2× factor, pruning the search space by more than 60%. On sets of more than two graphs, all exact solutions are extremely time-consuming and of a complex application in many real cases. On the contrary, our heuristics are far less expensive (as they show a lower-bound for the speed up of 10×), have a far better asymptotic complexity (with speed ups up to several orders of magnitude in our experiments), and obtain excellent approximations of the maximal solution with 98.5% of the nodes on average. [ABSTRACT FROM AUTHOR]

Radio Access Network (RAN) disaggregation allows operators to mix-and-match multivendor components and bring RAN services from one end to the other. Despite this goal, issues of resource misuse or performance undershoot may arise because of inflexible RAN function deployment and uncoordinated decision-making across different network segments. To address these issues, this paper considers full flexibility in the synthesis of end-to-end RAN services from a set of disaggregated and uncoordinated components. In particular, five design factors are jointly considered to maximize the overall network spectral efficiency: (1) User association, (2) Remote radio unit clustering, (3) RAN functional split, (4) Fronthaul network routing, and (5) Baseband unit placement. To efficiently deal with the formulated problem, we propose a two-level turbo-based solution and compare its performance with several related works. The simulation results show that our proposed solution can not only achieve a 1.33-times spectral efficiency gain compared with state-of-the-art methods, but also provides 1.27 and 1.74 multiplexing benefits for computing and networking resources, respectively.

In modern computers, complicated signal processing is highly optimized with the use of compilers and high-speed processing using floating-point units (FPUs); therefore, programmers have little opportunity to care about each process. However, a highly accurate approximation can be processed in a small number of computation cycles, which may be useful when embedded in a field-programmable gate array (FPGA) or micro controller unit (MCU), or when performing many large-scale operations on a graphics processing unit (GPU). It is necessary to devise algorithms to obtain the desired calculated values without an accelerator or compiler assistance. The residual correction method (RCM) developed here can produce simple and accurate approximations of certain nonlinear functions with minimal multiply–add operations. In this study, we designed an algorithm for the approximate computation of trigonometric and inverse trigonometric functions, which are nonlinear elementary functions, to achieve their fast and accurate computation. A fast first approximation and a more accurate second approximation of each function were created using RCM with a less than 0.001 error using multiply–add operations only. This achievement is particularly useful for MCUs, which have a low power consumption but limited computational power, and the proposed approximations are candidate algorithms that can be used to stabilize the attitude control of robots and drones, which require real-time processing. [ABSTRACT FROM AUTHOR]

Inpatient gait analysis is an essential part of rehabilitation for foot amputees and includes the ground contact time (GCT) difference of both legs as an essential component. Doctors communicate improvement advice to patients regarding their gait pattern based on a few steps taken at the doctor's visit. A wearable sensor system, called Suralis, consisting of an inertial measurement unit (IMU) and a pressure measuring sock, including algorithms calculating GCT, is presented. Two data acquisitions were conducted to implement and validate initial contact (IC) and toe-off (TO) event detection algorithms as the basis for the GCT difference determination for able-bodied and prosthesis wearers. The results of the algorithms show a median GCT error of −51.7 ms (IMU) and 14.7 ms (sensor sock) compared to the ground truth and thus represent a suitable possibility for wearable gait analysis. The wearable system presented, therefore, enables a continuous feedback system for patients and, above all, a remote diagnosis of spatio-temporal aspects of gait behaviour based on reliable data collected in everyday life. [ABSTRACT FROM AUTHOR]

Digital auscultation is a well-known method for assessing lung sounds, but remains a subjective process in typical practice, relying on the human interpretation. Several methods have been presented for detecting or analyzing crackles but are limited in their real-world application because few have been integrated into comprehensive systems or validated on non-ideal data. This work details a complete signal analysis methodology for analyzing crackles in challenging recordings. The procedure comprises five sequential processing blocks: (1) motion artifact detection, (2) deep learning denoising network, (3) respiratory cycle segmentation, (4) separation of discontinuous adventitious sounds from vesicular sounds, and (5) crackle peak detection. This system uses a collection of new methods and robustness-focused improvements on previous methods to analyze respiratory cycles and crackles therein. To validate the accuracy, the system is tested on a database of 1000 simulated lung sounds with varying levels of motion artifacts, ambient noise, cycle lengths and crackle intensities, in which ground truths are exactly known. The system performs with average F-score of 91.07% for detecting motion artifacts and 94.43% for respiratory cycle extraction, and an overall F-score of 94.08% for detecting the locations of individual crackles. The process also successfully detects healthy recordings. Preliminary validation is also presented on a small set of 20 patient recordings, for which the system performs comparably. These methods provide quantifiable analysis of respiratory sounds to enable clinicians to distinguish between types of crackles, their timing within the respiratory cycle, and the level of occurrence. Crackles are one of the most common abnormal lung sounds, presenting in multiple cardiorespiratory diseases. These features will contribute to a better understanding of disease severity and progression in an objective, simple and non-invasive way. [ABSTRACT FROM AUTHOR]

Emerging applications of collaborative autonomy, such as multitarget tracking, unknown map exploration, and persistent surveillance, require robots plan paths to navigate an environment while maximizing the information collected via on-board sensors. In this article, we consider such information acquisition tasks but in adversarial environments, where attacks may temporarily disable the robots’ sensors. We propose the first receding horizon algorithm, aiming for robust and adaptive multirobot planning against any number of attacks, which we call Resilient Active Information acquisitioN (RAIN). RAIN calls, in an online fashion, a robust trajectory planning (RTP) subroutine that plans attack-robust control inputs over a look-ahead planning horizon. We quantify RTP’s performance by bounding its suboptimality. We base our theoretical analysis on notions of curvature introduced in combinatorial optimization. We evaluate RAIN in three information acquisition scenarios: multitarget tracking, occupancy grid mapping, and persistent surveillance. The scenarios are simulated in C++ and a unity-based simulator. In all simulations, RAIN runs in real time, and exhibits superior performance against a state-of-the-art baseline information acquisition algorithm, even in the presence of a high number of attacks. We also demonstrate RAIN’s robustness and effectiveness against varying models of attacks (worst case and random), as well as varying replanning rates. [ABSTRACT FROM AUTHOR]

The original problem of group testing consists in the identification of defective items in a collection, by applying tests on groups of items that detect the presence of at least one defective element in the group. The aim is then to identify all defective items of the collection with as few tests as possible. This problem is relevant in several fields, among which biology and computer sciences. In the present article we consider that the tests applied to groups of items returns a load, measuring how defective the most defective item of the group is. In this setting, we propose a simple non-adaptative algorithm allowing the detection of all defective items of the collection. Items are put on an n × n grid and pools are organised as lines, columns and diagonals of this grid. This method improves on classical group testing algorithms using only the binary response of the test. Group testing recently gained attraction as a potential tool to solve a shortage of COVID-19 test kits, in particular for RT-qPCR. These tests return the viral load of the sample and the viral load varies greatly among individuals. Therefore our model presents some of the key features of this problem. We aim at using the extra piece of information that represents the viral load to construct a one-stage pool testing algorithm on this idealized version. We show that under the right conditions, the total number of tests needed to detect contaminated samples can be drastically diminished. [ABSTRACT FROM AUTHOR]