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With rapid development and application of artificial intelligence and block chain, the requirement of information and data security is also increased, in which the public-key cryptography, such as Rivest-Shamir-Adleman (RSA) cryptography, plays a significant role. Modular exponentiation is fundamental in computer arithmetic and is widely applied in cryptography, such as ElGamal cryptography, Diffie–Hellman key exchange protocol, and RSA cryptography. The implementation of modular exponentiation in a residue number system leads to high parallelism in computation and has been applied in many hardware architectures. While most residue number system (RNS)-based architectures utilize RNS Montgomery algorithm with two residue number systems, the recent modular multiplication algorithm with sum residues performs modular reduction in only one residue number system with about the same parallelism. In this work, it is shown that the high-performance modular exponentiation and RSA cryptography can be implemented in RNS. Both the algorithm and architecture are improved to achieve high performance with extra area overheads, where a 1024-bit modular exponentiation can be completed in 0.567 ms in Xilinx XC6VLX195t-3 platform, costing 26489 slices, 87357 LUTs, 363 dedicated multipilers of $18 \times 18$ bits, and 65 block RAMs. [ABSTRACT FROM AUTHOR]

Representing deep neural networks (DNNs) in low-precision is a promising approach to enable efficient acceleration and memory reduction. Previous methods that train DNNs in low-precision typically keep a copy of weights in high-precision during the weight updates. Directly training with low-precision weights leads to accuracy degradation due to complex interactions between the low-precision number systems and the learning algorithms. To address this issue, we develop a co-designed low-precision training framework, termed LNS-Madam, in which we jointly design a logarithmic number system (LNS) and a multiplicative weight update algorithm (Madam). We prove that LNS-Madam results in low quantization error during weight updates, leading to stable performance even if the precision is limited. We further propose a hardware design of LNS-Madam that resolves practical challenges in implementing an efficient datapath for LNS computations. Our implementation effectively reduces energy overhead incurred by LNS-to-integer conversion and partial sum accumulation. Experimental results show that LNS-Madam achieves comparable accuracy to full-precision counterparts with only 8 bits on popular computer vision and natural language tasks. Compared to FP32 and FP8, LNS-Madam reduces the energy consumption by over 90% and 55%, respectively. [ABSTRACT FROM AUTHOR]

Owing to its good compression ability, discrete cosine transform (DCT) is widely used in signal processing, including high-efficiency video coding (HEVC). For efficient implementation, approximate DCTs and integer DCTs were proposed, but the challenge in the tradeoff between coding performance and implementation cost always exists. To solve this problem, this article proposes a new algorithm to derive Int-DCT with good coding performance and low hardware cost. To address the multiobjective optimization problem, we extract information from the initially shortlisted transform matrices to derive the weighting factors for the different matrices properties. Those properties having a higher impact to the performance are assigned with higher weights and vice versa. Subsequently, an efficient search algorithm is proposed to shortlist those candidates which can lead to better coding performance evaluated by the proposed measure. Last but not the least, a splitting method is proposed to search the efficient extended double-base number system (EDBNS) representation of the coefficients for hardware implementation and select the solution which encounters the lowest cost. The experimental results in ASIC demonstrate that the implementation area and power cost of designs by the new algorithm are reduced by at least 11.1% and 17.5% for 32-point transform, respectively, over other competing algorithms. Meanwhile, the transforms generated by the proposed algorithm causes negligible impact to coding performance compared to the original transforms in HEVC. [ABSTRACT FROM AUTHOR]

We present a C-band amplifying reconfigurable intelligent surface (ARIS) integrated with power amplifier (PA) to enhance the reflected electromagnetic (EM) signals. An aperture-coupled patch element with two orthogonal hourglass-shaped slots is elaborately constructed to ensure a broad operation bandwidth. A power combining and dividing network is also introduced to combine the 2 $\times2$ elements into a subarray to reduce the number of PAs and the system cost. The simulation results indicate that the achieved gain of ARIS is 7.7–12.2 dB from 5.0 to 6.0 GHz. A prototype of the proposed ARIS is designed, fabricated, and experimentally verified. The measured results agree well with the simulation. The proposed ARIS is promising for applications in future wireless communication systems for signal enhancement and coverage extension. [ABSTRACT FROM AUTHOR]

Long period digital random sequence plays an important role in reliable communications and high security scenarios. This paper improved the method of generating long period digital random sequences based on the Residue Number System (RNS) and the Chinese Remainder Theorem (CRT), and a sequence mapping method after CRT extension. This paper proves that the period of sequence after mapping will not degenerate if the modulus used in the mapping stage is coprime with the period of the original sequence. By using the parallelism of RNS, the proposed method can generate sequences at high speed with fewer hardware resources. The NIST test results show that the pass rate of each test item is above 98.40%, which meets the NIST test confidence requirements, confirming the randomness of the generated sequences. An image encryption test is given as one of the example applications of the generated sequences. On the theoretical basis, by jointly optimizing the sequence mapping and iteration procedure, a hardware implementation architecture is also presented in this paper. The implementation is based on Xilinx XC7Z020CLG484-3 FPGA and compared with the implementations of classical chaotic maps. The results show that the proposed architecture has longer sequence period with less hardware resource consumption and higher generation speed and is more general. Meanwhile, the proposed architecture has fast phase switching ability, which is about 10 clock periods. This is one of the key attributes when the sequence is used in communication systems. [ABSTRACT FROM AUTHOR]

Scheduling algorithms are called “global” if they can migrate tasks between cores. Global scheduling algorithms are the de-facto standard practice for general purpose Operating Systems, to balance the workload between cores. However, the exact schedulability analysis of real-time applications for these algorithms is proven to be weakly NP-hard. Despite such a hardness, the research community keeps investigating the methods for an exact schedulability analysis for its relevance and to tightly estimate the execution requirements of real-time systems. Due to the NP-hardness, the available exact tests are very time and memory demanding even for sets of a few tasks. On another hand, the available sufficient tests are very pessimistic, despite consuming less resources. Motivated by these observations, we propose an exact schedulability test for constrained-deadline sporadic tasks under global multiprocessor fixed-priority scheduling scheduler, which is significantly faster and consumes less memory, compared to any other available exact test. To derive a faster test, we exploit the idea of a state-space pruning, aiming at reducing the number of feasible system states to be examined by the test. The resulted test is multiple orders of magnitude faster with respect to other state-of-the-art exact tests. Our C++ implementation is publicly available. [ABSTRACT FROM AUTHOR]

High-performance multicast packet switching technologies are evolving to meet the growing demand for scalability on the Internet and datacenters, etc. Implementing a high-performance switch/router relies on a polynomial-time group membership query algorithm within the Packet Forwarding Engines (PFEs) to determine whether a packet is forwarded through an egress. Among these, Bloom filter (BF)-based and Residue Number System (RNS)-based are being considered as two representatives of the membership query algorithms. However, both approaches suffer from some fatal weaknesses such as false-positive probability and time inefficiencies, especially for a carrier-grade PFE with high port-density features. According to similar properties of the RNS, we propose a simplified forwarding algorithm in this paper, named Per-Port Prime Filter Array (P3FA). The simulation results indicate that the P3FA can significantly improve space efficiencies under specific lower egress-diversities conditions. Under the same space constraints, P3FA improves multicast and unicast time efficiency by 1 to 4 orders of magnitude in the port-density 16–1024 range compared to previous works. Although it comes at the expense of hardware cost, it is still acceptable compared to recently improved previous work. [ABSTRACT FROM AUTHOR]

The supermarket model refers to a system with a large number of queues, where new customers choose $d$ d queues at random and join the one with the fewest customers. This model demonstrates the power of even small amounts of choice, as compared to simply joining a queue chosen uniformly at random, for load balancing systems. In this work we perform simulation-based studies to consider variations where service times for a customer are predicted, as might be done in modern settings using machine learning techniques or related mechanisms. Our primary takeaway is that using even seemingly weak predictions of service times can yield significant benefits over blind First In First Out queueing in this context. However, some care must be taken when using predicted service time information to both choose a queue and order elements for service within a queue; while in many cases using the information for both choosing and ordering is beneficial, in many of our simulation settings we find that simply using the number of jobs to choose a queue is better when using predicted service times to order jobs in a queue. In our simulations, we evaluate both synthetic and real-world workloads–in the latter, service times are predicted by machine learning. Our results provide practical guidance for the design of real-world systems; moreover, we leave many natural theoretical open questions for future work, validating their relevance to real-world situations. [ABSTRACT FROM AUTHOR]

Fault response of an inverter based resource (IBR) is dictated by its control scheme, making it markedly different from the response of a synchronous source. This has led to misoperation of distance relays when fed by IBRs. Published literature is mostly focused on changing the control design of inverters, but this approach is not practical, as these controls are proprietary and beyond the reach of utilities. To resolve this issue, this paper proposes a method for a source-agnostic distance relay that works in time-domain. Using only single ended measurements, the relay detects, classifies and locates any fault on the line it monitors. The method does not require any polarization technique. It is shown to work for unbalanced lines of any length, for transmission or distribution system with any number (even 100%) of IBRs, is immune to load encroachment, decaying dc offset and prefault currents. With very limited communication, it is also shown to be immune to fault resistance. [ABSTRACT FROM AUTHOR]

Unit Commitment (UC) is an important problem in power system operations. It is traditionally planned for 24 hours with one-hour time intervals. To accommodate the increasing net-load variability, sub-hourly UC has been suggested for improved system flexibility. Such a problem is larger and more complicated than hourly UC because of the increased number of periods and reduced unit ramping capabilities per period. The computational burden is further exacerbated for systems with large numbers of virtual transactions leading to dense transmission constraint matrices. Consequently, the state-of-the-art and practice method, branch-and-cut (B&C), suffers from poor performance. In this paper, our recent Surrogate Absolute-Value Lagrangian Relaxation (SAVLR) is enhanced by embedding ordinal-optimization concepts for a drastic reduction in subproblem solving time. Rather than formally solving subproblems by using B&C, subproblem solutions satisfying SAVLR's convergence condition are obtained by modifying solutions from previous iterations or solving crude subproblems. All virtual transactions are included in each subproblem to reduce major changes in solutions across iterations. A parallel version is also developed to further reduce the computation time. Testing on MISO's large cases demonstrates that our ordinal-optimization embedded approach obtains near-optimal solutions efficiently, is robust, and provides a new way of solving other MILP problems. [ABSTRACT FROM AUTHOR]

While a number of network systems are running under certain stress with a limited lifetime, it is still unknown how to predict the lifetime–stress relation of complex systems. We develop a percolation-based approach to build an operational lifetime–stress model for complex networks, which captures the spatial and temporal reliability characteristics of the system. In this article, the general analytical expression for the entire operational lifetime–stress relation has been presented, which suggests that the load stress and the number of nodes in the network impact the operational lifetime in the same manner. The size effect found here in the lifetime–stress relation is observed for the first time, to our best knowledge. For 2-D lattices, we show that the lifetime–stress relation can be regarded as the combination of two parts—an approximately linear region for small stress, and nonlinear region for large stress. We also analyze the lifetime–stress function of Beijing road network and the western United States power grid. Our article might help to develop acceleration testing methods, which will facilitate better design of reliable complex systems. [ABSTRACT FROM AUTHOR]

This article presents the input-to-state stability (ISS) and decentralized sliding-mode control of polynomial fuzzy interconnected systems by using the small-gain theorem. The contribution of this article is threefold. First, based on the boundedness of the solution of fuzzy interconnected subsystems, the sufficient conditions for the boundedness fuzzy interconnected system are provided. Second, the ISS small-gain theorem is extended to the polynomial fuzzy interconnected system for the number of membership functions being the same and different, and the trajectory-based ISS small-gain theorem is proposed. Third, the sliding surface based on state feedback is designed, and the stability of sliding-mode motion is analyzed for the fuzzy interconnected system. Finally, the effectiveness and superiority of the proposed method are illustrated by using a numerical example, a spring-connected double inverted pendulum system, and comparison with the existing results. [ABSTRACT FROM AUTHOR]

Brain-Machine Interfaces (BMI) offer the potential to modulate dysfunctional neurological networks by electrically stimulating the cerebral cortex via chronically-implanted microelectrodes. Wireless transmitters worn by BMI recipients must operate within electromagnetic emission and tissue heating limits, such as those prescribed by the IEEE and International Commission on Non-Ionizing Radiation Protection (ICNIRP), to ensure that radiofrequency emissions of BMI systems are safe. Here, we describe an approach to generating pre-compliance safety data by simulating the Specific Absorption Rate (SAR) and tissue heating of a multi-layered human head model containing a system of wireless, modular BMIs powered and controlled by an externally worn telemetry unit. We explore a number of system configurations such that our approach can be utilized for similar BMI systems, and our results provide a benchmark for the electromagnetic emissions of similar telemetry units. Our results show that the volume-averaged SAR per 10g of tissue exposed to our telemetry field complies with ICNIRP and IEEE reference levels, and that the maximum temperature increase in tissues was within permissible limits. These results were unaffected by the number of implants in the system model, and therefore we conclude that the electromagnetic emissions our BMI in any configuration are safe. [ABSTRACT FROM AUTHOR]

The addition-min fuzzy relation inequalities could be applied to characterize the peer-to-peer (P2P) network system. This article focuses on the problem that how many arbitrary (or specific) line faults a P2P network system is able to bear at most. In order to find the maximum number of tolerant line faults, we first study the consistency checking of the P2P network system with a given number of arbitrary line faults. Corresponding properties and illustrative example are also presented. Furthermore, the so-called reordered-vector-based (RVB) algorithms I and II are proposed for searching the maximum number of arbitrary and specific line faults in a given P2P network system, respectively. The validness of our proposed RVB algorithms is verified by formally proving and some numerical examples. [ABSTRACT FROM AUTHOR]

Hardware realization of public-key cryptosystems often entails Montgomery modular multiplication (MMM), which is more efficient in residue number systems (RNS). A large pool of co-prime moduli allows for higher number of dynamically changeable moduli-set pairs for the required base extension, leading to ultra-wide key-lengths to accommodate the indispensable resistance to differential power-analysis (DPA) attacks. The moduli are often of the form ${2^r} - {{\delta }}$ 2 r - δ , where $r$ r denotes the width of residue channels. In a previous relevant RNS MMM design, with $r\ = \ 64$ r = 64 , probability of a successful DPA attack is less than ${2^{ - 66}}$ 2 - 66 , where efficient arithmetic is obtained only for a limited set of moduli that are insufficient for key-lengths over 1024 bits. Here we propose a free- ${{\delta }}$ δ RNS MMM scheme, for up-to 8192-bit key-lengths and fast 16-bit residue channels, based on the proposed ${{\delta }}$ δ -independent modulo-(${2^r} - {{\delta }}$ 2 r - δ ) adders and multipliers. Moreover, we propose an especial method for moduli selection that is required for base extension, leading to the same aforementioned DPA-resistance measure and much lower measures for key-lengths over 1024. The implementation results show $82,69,44\ percent$ 82 , 69 , 44 p e r c e n t less RSA delay, for key-lengths $512,1024,2048$ 512 , 1024 , 2048 , respectively of the home designs versus the 512-bit main reference design, and more than $5,100\ percent$ 5 , 100 p e r c e n t for $4096,8192$ 4096 , 8192 key-lengths, respectively, all per 512-bit encrypted messages. [ABSTRACT FROM AUTHOR]

Volt/VAr Optimization (VVO) is becoming increasingly crucial in distribution management systems with renewable sources that require setting their reactive power in coordination with legacy voltage and reactive power control devices. This paper presents a VVO method that operates in complex variables based on the Wirtinger calculus; it employs a penalty method that keeps voltage magnitudes and controller parameters within limits together with a probabilistic rounding technique for handling switched device variables. The complex variable implementation permits using the compensation technique when solving the Karush-Kuhn Tucker (KKT) system, thus achieving a significant speedup due to limiting the number of linear system factorizations. The proposed method is contrasted with a classical VVO implementation employing discrete coordinate search from the current operating point and an enhanced version that uses sensitivity information. Numerical results on distribution networks having up to 3147 nodes show that the proposed method is significantly faster than classical methods and gives operating points free of voltage magnitude violations and lower power loss. [ABSTRACT FROM AUTHOR]

In this paper an analytical model is introduced to describe the impulse response of the diffusive channel between a pointwise transmitter and a given fully-absorbing (FA) receiver in a molecular communication (MC) system. The presence of neighbouring FA nanomachines in the environment is taken into account by describing them as sources of negative molecules. The channel impulse responses of all the receivers are linked in a system of integral equations. The solution of the system with two receivers is obtained analytically. For a higher number of receivers the system of integral equations is solved numerically. It is also shown that the channel impulse response shape is distorted by the presence of the neighbouring FA interferers. For instance, there is a time shift of the peak in the number of absorbed molecules compared to the case without interference, as predicted by the proposed model. The analytical derivations are validated by means of particle based simulations. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is an excellent use case for line-of-sight (LOS) based technologies such as free-space optical (FSO) communications. In this paper, we analyze the performance of RIS-empowered FSO (RISE-FSO) systems by unifying Fisher-Snedecor (${\mathcal{F}}$), Gamma-Gamma ($\cal {GG}$), and Malága ($\cal {M}$) distributions for atmospheric turbulence with zero-boresight pointing errors over deterministic as well as random path-loss in foggy conditions with heterodyne detection (HD) and intensity modulation/direct detection (IM/DD) methods. By deriving the probability density function (PDF) and cumulative distribution function (CDF) of the direct-link (DL) with the statistical effect of atmospheric turbulence, pointing errors and random fog, we develop exact expressions of PDF and CDF of the resultant channel for the RISE-FSO system. Using the derived statistical results, we present exact expressions of outage probability, average bit-error-rate (BER), ergodic capacity, and moments of signal-to-noise ratio (SNR) for both DL-FSO and RISE-FSO systems. We also develop an asymptotic analysis of the outage probability and average BER and derive the diversity order of the considered systems. We validate the analytical expressions using Monte-Carlo simulations and demonstrate the performance scaling of the FSO system with the number of RIS elements for various turbulence channels, detection techniques, and weather conditions. [ABSTRACT FROM AUTHOR]

We propose an analytically tractable Bayesian method to infer parameters in power system dynamic models from noisy measurements of bus-voltage magnitudes and frequencies as well as active- and reactive-power injections. The proposed method is computationally appealing as it bypasses the large number of system model simulations typically required in sampling-based Bayesian inference. Instead, it relies on analytical linearization of the nonlinear system differential-algebraic-equation model enabled by trajectory sensitivities. Central to the proposed method is the construction of a linearized model with the maximum probability of being (closest to) the actual nonlinear model that gave rise to the measurement data. The linear model together with Gaussian prior leads to a conjugate family where the parameter posterior, model evidence, and their gradients can be computed in closed form, markedly improving scalability for large-scale power systems. We illustrate the effectiveness and key features of the proposed method with numerical case studies for a three-bus system. Algorithmic scalability is then demonstrated via case studies involving the New England 39-bus test system. [ABSTRACT FROM AUTHOR]

Due to high leakage current and threshold voltage, Dennard scaling has reached its limit on conventional semiconductor technology. Energy reduction at the transistor level by simply lowering supply voltage has proven to be infeasible for these devices (e.g., MOSFETs). Some recently proposed millivolt switch techniques aim to mitigate these issues, by maintaining a high on/off ratio of drain currents with a much lower supply voltage. However, $V_{dd}$ V d d reduction is constrained by high intermittent error probabilities in millivolt switches. Energy-efficient microarchitectures that are computationally error-tolerant are therefore urgently needed. This article systematically leverages the error correction and checkpointing properties of Redundant Residue Number Systems (RRNS) by varying the number of non-redundant ($n$ n ) and redundant ($r$ r ) residues. The state-of-the-art of RRNS microarchitecture is confined to a fixed configuration point within such a ($n$ n , $r$ r )-RRNS design plane, as it supports single error correction alone. Being able to efficiently handle resilience in this ($n$ n , $r$ r )-RRNS plane significantly improves reliability, allowing further ${V_{dd}}$ V d d reduction to save energy. To this end, first, we propose a scalable RRNS microarchitecture that simultaneously supports both, error-correction, as well as checkpointing with restart capabilities upon detecting uncorrectable errors. Second, we design a novel RRNS-based adaptive checkpointing&restart mechanisms that automatically guarantees reliability while minimizing the energy-delay product (EDP). To the best of our knowledge, these are the first set of checkpointing mechanisms targeting the RRNS infrastructure. Moreover, these mechanisms optimize the usage efficiency of memory capacity. Third, we systematically explore the RRNS design space to find the best ($n$ n , $r$ r ) configuration point. For similar reliability when compared to a conventional binary core without computationally error-tolerant (runs at high $V_{dd}$ V d d ), the proposed RRNS scalable microarchitecture reduces EDP by 53 percent on average for memory-intensive workloads and by 67 percent on average for non-memory-intensive workloads. [ABSTRACT FROM AUTHOR]

In this work we describe and analyze a novel multi-tier architecture for recording Internet of Things (IoT) data. Each tier in the architecture consists of vertically interconnected node clusters that may be operated by different service providers. Blocks of IoT data are accepted by a cluster tier by tier, until they reach the top tier which adds them to the global blockchain and makes them available to client applications. To allow for large geographical coverage and reduce the dependency on any individual node in the cluster, clusters run a multiple entry version of the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. We describe an analytical model of the system and find the optimal allocation of clusters and nodes within clusters for a given number of tiers and system coverage area. Our results show that the total number of orderers for given system coverage and total load are main predictors of the block linking time, and indicate that minimizing block linking time is easier to achieve with a smaller number of tiers and large number of clusters with a smaller number of orderers per cluster. These observations can be used as guidelines for planning and dimensioning of multi-tier cluster architectures that create and maintain blockchain structures in a given coverage area. [ABSTRACT FROM AUTHOR]

In uncertainty analysis, surrogate modeling techniques demonstrate high efficiency and reliable precision in estimating the uncertainty for the finite difference time domain (FDTD) computation. However, building an accurate surrogate model may require a considerable number of system simulations which could be computationally expensive. To reduce such computational cost to build an accurate model, a general framework to build surrogate models for the FDTD computation in the human body based on the least angle regression (LARS) method and the artificial neural network (ANN) is proposed. The LARS method is adapted to dynamically select a number of informative random parameters which are significantly relevant to system outputs. We design a series of convergence criteria for ANN and introduce the adaptive moment estimation (ADAM) optimizer to ANN in order to improve the computational efficiency and accuracy of ANN. This is the first dynamic surrogate modeling technique for the FDTD computation designed by taking both accuracy and computational cost into account. [ABSTRACT FROM AUTHOR]

Indoor optical wireless communication (IOWC) is a key technology complementing radio communication in 6G. Typically, the downlink of IOWC is implemented using visible light and uplink is implemented using infrared. Though the downlink of IOWC has been studied well, the uplink of multiple-access IOWC requires further research. In this paper, power allocation strategies in the uplink of IOWC with multiple non-cooperative users is studied. Optimizing power allocation in IOWC that maximize sum-rate with power constraints becomes a non-convex problem. First, this paper proposes an alternating maximization algorithm to compute near optimal power allocation policies for small number of users. Next, a phenomenon of invariance is observed when the number of users in the system is large. Under invariance, the optimal power allocation policy of a user becomes independent of other users, thereby facilitating fast and decentralized computation of power allocation strategies. From simulations, it was observed that the invariance can occur even with five to ten users. For the first time, a rigorous analysis of the invariance phenomenon is provided. An algorithm to compute the optimal power allocation at invariance is proposed; this algorithm is proven to achieve the global maxima in sum-rate without the channel knowledge of other users. [ABSTRACT FROM AUTHOR]

This brief utilizes the logarithmic number system (LNS) to realize the half-precision division (DIV), square root (SR), and inverse SR (ISR) that are widely used in both the error resilience application and high-performance computing. With the aid of similarities of logarithmic and antilogarithmic functions, the adder tree and multiplexer in the shift-and-add architecture can be shared by the Log and Antilog converter. Moreover, a novel architecture for DIV and SR based on the fused converter is proposed. Compared to the existing works, this new architecture not only achieves a good tradeoff between precision level and hardware efficiency, but also can support more operations (e.g., exponential function, multiplication) with negligible hardware resources. In addition, the proposed architecture can be easily pipelined to further increase the throughput. Furthermore, for some formulas requiring multiple basic operations (e.g., ISR), the advantage of the LNS is more significant. [ABSTRACT FROM AUTHOR]

The use of dc shipboard power distribution networks has been shown to offer several advantages over their ac counterparts. The reduction of the size and number of system components, the increased simplicity of operation and fuel efficiency of the system, and convenient integration of energy storage systems offer significant economical benefits to the ship operator. Solid-state bus-tie switches are widely regarded as being an indispensable component in dc shipboard power distribution networks, for their role as first line of defense against faults. Nevertheless, these devices are not limited exclusively to protection, but can provide additional functionality thanks to their design. This article shows how a solid-state bus-tie switch can be employed for soft-starting an unenergized section of a shipboard power distribution network, eliminating the need for additional components aimed specifically at this task. A technique is suggested and its performance is experimentally verified in a laboratory scaled-down model of a dc shipboard network. The additional functionality is achieved exclusively on a control basis, without modification of the switch topology initially intended purely for system protection. [ABSTRACT FROM AUTHOR]

Context-based binary arithmetic coding (CBAC) is an entropy coder of AVS 2.0, the latest Chinese video compression standard for 8K ultra-high-definition (UHD) video, which employs a logarithmic number system to avoid multiplication operations for range updates for binary arithmetic coding (BAE). In this paper, we propose a high-throughput CBAC hardware encoder for 8K UHD. To enhance its throughput, we introduce a bin-merging technique that converts a subsequence of bins into a merged bin without affecting the functionality of the CBAC encoder. Moreover, we also employ a 4-stage pipeline architecture for CBAC and lookup tables for context updates. Consequently, the proposed CBAC encoder can process about 3.94 bins per cycle and achieve a throughput of 2.9 Gbins per second. When synthesized in a TSMC 65nm LP process, it operates up to 735 MHz and the total gate count is 182.5 K. To the best of our knowledge, this is the highest throughput for a CBAC encoder. [ABSTRACT FROM AUTHOR]

In this work, we focus on the optimization of charging scheduling policy for shared electric vehicles (EVs) integrated with wind power generation. This problem is of significant importance nowadays because of the large adoption of EVs in modern societies and the increasing penetration of renewables. A particular challenge of the problem is the large action space, the size of which may increase exponentially with respect to the number of EVs in the system. This makes the problem difficult to solve in practice. A lot of efforts have been made to overcome the above difficulty. The previous study proposed least-laxity-longer-processing-time-first (LLLP) principle, a rule-based algorithm to schedule EVs’ battery charging. The LLLP principle assigns higher priority to vehicles with less laxity and longer processing time. We extend the LLLP principle and further study the structural property of the charging scheduling problem. The main contributions in this work are as follows. First, we show that the LLLP applies to our problem and may be used to narrow down the action space while preserving the global optimality. Second, we provide a modified LLLP algorithm that may construct a policy in $O(NT)$ , where $N$ is the number of the EVs and $T$ is the number of time steps in the scheduling problem. Third, we use numerical experiments to show that the new algorithm performs better than other existing algorithms, including the least-laxity-shorter-processing-time-first (LLSP) principle, the earliest-deadline-first (EDF) principle, and the latest-deadline-first (LDF) principle. The new algorithm finds near-optimal policies (within 1% performance loss) and is at least 40 times faster than CPLEX. We hope that this work provides insight into the charging scheduling of shared EVs in general. [ABSTRACT FROM AUTHOR]

This article examines multiagent relay tracking systems with the varying number of tracking agents. We first establish an accurate mathematical model to describe a relay tracking process with communication delays, jumps in the tracking errors and time-varying number of tracking agents. Then, a novel impulse-time-dependent average Lyapunov function is proposed for designing a multiagent relay tracking system. Sufficient conditions for the existence of a relay tracking controller are derived in terms of linear matrix inequalities. Finally, the effectiveness of our approaches is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has recently emerged as a promising technology for 6 G wireless systems, due to its capability to reconfigure the wireless propagation environment. In this paper, we investigate a Rate-Splitting Multiple Access (RSMA) for IRS-assisted downlink system, where the base station (BS) communicates with single-antenna users with the help of an IRS. RSMA relies on rate-splitting (RS) at the BS and successive interference cancellation (SIC) at the users and provides a generalized multiple access framework. We derive a new architecture called IRS-RS that leverages the interplay between RS and IRS. For performance analysis, we utilize an on-off control technique to control the passive beamforming vector of the IRS-RS and derive the closed-form expressions for outage probability of cell-edge users and near users. Moreover, we also analyze the outage behavior of cell-edge users for a sufficiently large number of reflecting elements. Additionally, we also analyze the outage performance of cooperative RS based decode-and-forward (DF)-assisted framework called DF-RS. Through simulation results, it is shown that the proposed framework outperforms the corresponding DF-RS, RS without IRS and IRS-assisted conventional non-orthogonal multiple access (NOMA) schemes. Furthermore, the impact of various system's parameters such as the number of IRS reflecting elements and the number of users on the system performance is revealed. [ABSTRACT FROM AUTHOR]

Global sensitivity analysis (GSA) of voltage to uncertain power injection variations plays an important role for appropriate Volt-VAR optimization. This paper proposes a data-driven GSA method for large-scale distribution systems with a large number of uncertain sources. Specifically, the deep Gaussian process is used to identify the mapping relationship between uncertain power injections and voltages. This allows resorting to the analysis of variance framework to calculate the Sobol indices for GSA. Unlike the existing polynomial chaos expansion and Gaussian process-based approaches, our proposed method has much better scalability. Test results on the EPRI 1747-node K1 circuit with different numbers of uncertain sources with various uncertain levels and different PV distributions demonstrate that the proposed method can achieve accurate GSA. [ABSTRACT FROM AUTHOR]

In this paper, we propose a mixture gamma distribution based analytical framework for NOMA wireless systems over composite fading channels. We analyze the outage probability (OP), delay-limited throughput (TP) and effective capacity (EC) in uplink NOMA with imperfect successive interference cancellation (SIC) due to the presence of residual hardware impairments and delay constraints. A mixture gamma distribution is used to approximate the probability density functions of fading channels. Based on this, we obtain closed-form expressions in terms of Meijer-G functions for the OP, the TP and the EC. We also perform asymptotic analysis of these metrics to characterize system behaviors at the high signal-to-noise ratio regime. Moreover, upper-bounds for the EC is derived. Efficacy of NOMA over orthogonal multiple access is analytically examined. Unlike the existing works, our analytical expressions hold for NOMA systems with an arbitrary number of users per cluster over a wide range of channel models, including lognormal-Nakagami−m, KG, η−μ, Nakagami−q (Hoyt), κ−μ, Nakagami-n (Rician), Nakagami−m, and Rayleigh fading channels. This unified analysis facilitates evaluations of impacts of the residual interference, the power allocation among users, the delay quality-of-service exponent as well as the shadowing and small-scale fading parameters on the performance metrics. Simulation results are provided to validate theoretical analysis. [ABSTRACT FROM AUTHOR]

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy. [ABSTRACT FROM AUTHOR]

We consider the problem of distributed dynamic rate and channel selection in a multi-user network, in which each user selects a wireless channel and a modulation and coding scheme (corresponds to a transmission rate) in order to maximize the network throughput. We assume that the users are cooperative, however, there is no coordination and communication among them, and the number of users in the system is unknown. We formulate this problem as a multi-player multi-armed bandit problem and propose a decentralized learning algorithm that performs almost optimal exploration of the transmission rates to learn fast. We prove that the regret of our learning algorithm with respect to the optimal allocation increases logarithmically over rounds with a leading term that is logarithmic in the number of transmission rates. Finally, we compare the performance of our learning algorithm with the state-of-the-art via simulations and show that it substantially improves the throughput and minimizes the number of collisions. [ABSTRACT FROM AUTHOR]

Microblogging services such as Twitter are important, up-to-date, and live sources of information on a multitude of topics and events. An increasing number of systems use such services to detect and analyze events in real-time as they unfold. In this context, we recently proposed ArmaTweet—a system developed in collaboration among armasuisse and the Universities of Oxford and Fribourg to support semantic event detection on Twitter streams. Our experiments have shown that ArmaTweet is successful at detecting many complex events that cannot be detected by simple keyword-based search methods alone. Building up on this work, we explore in this paper several approaches for event detection on microposts. In particular, we describe and compare four different approaches based on keyword search (Plain-Seed-Query), information retrieval (Temporal Query Expansion), Word2Vec word embeddings (Embedding), and semantic retrieval (ArmaTweet). We provide an extensive empirical evaluation of these techniques using a benchmark dataset of about 200 million tweets on six event categories that we collected. While the performance of individual systems varies depending on the event category, our results show that ArmaTweet outperforms the other approaches on five out of six categories, and that a combined approach offers highest recall without adversely affecting precision of event detection. [ABSTRACT FROM AUTHOR]

Various scheduling algorithms have been proposed for real-time parallel tasks modeled as a Directed Acyclic Graph (DAG). The capacity augmentation bound is a quantitative metric widely used in this field to compare the algorithms. Among the existing algorithms, the lowest capacity augmentation bound for DAG tasks with implicit deadlines is 2, which has been achieved by federated scheduling. To improve the schedulability and lower the capacity augmentation bound, this paper proposes DAG-Fluid, an algorithm based on fluid scheduling. We prove that DAG-Fluid has a capacity augmentation bound of $2-\frac{1}{m+1}$ 2 - 1 m + 1 , in which $m$ m is the number of processors in the system. Experiments show that DAG-Fluid performs better than the state of the art scheduling algorithms. [ABSTRACT FROM AUTHOR]

In Diffusive Molecular Communication (DMC), information is transmitted by diffusing molecules. Synaptic signaling, as a natural implementation of this paradigm, encompasses functional components that, once understood, can facilitate the development of synthetic DMC systems. To unleash this potential, however, a thorough understanding of the synaptic communication channel based on biophysical principles is needed. Since synaptic transmission critically depends also on non-neural cells, such understanding requires the consideration of the so-called tripartite synapse. In this paper, we develop a comprehensive channel model of the tripartite synapse encompassing a three-dimensional, finite-size spatial model of the synaptic cleft, molecule uptake at the presynaptic neuron and at glial cells, reversible binding to individual receptors at the postsynaptic neuron, and spillover to the extrasynaptic space. Based on this model, we derive analytical time domain expressions for the channel impulse response (CIR) of the synaptic DMC system and for the number of molecules taken up at the presynaptic neuron and at glial cells, respectively. These expressions provide insight into the impact of macroscopic physical channel parameters on the decay rate of the CIR and the reuptake rate, and reveal fundamental limits for synaptic signal transmission induced by chemical reaction kinetics and the channel geometry. Adapted to realistic parameters, our model produces plausible results when compared to previous experimental and simulation studies and we provide results from particle-based computer simulations to further validate the analytical model. The proposed comprehensive channel model admits a wide range of synaptic configurations making it suitable for the investigation of many practically relevant questions, such as the impact of glial cell uptake and spillover on signal transmission in the tripartite synapse. [ABSTRACT FROM AUTHOR]

A power system with a large number of wind farms may consist of several thousand or more wind turbine generators (WTGs). Owing to the high dimension of the linearized model of such a power system, applying conventional modal analysis may be impossible due to the numerical complexity. This study proposes a reduced-order method of modal analysis to detect the risk and trace the sources of oscillatory instability within the power system. The proposed method does not use the representation of wind farms by aggregating WTGs to reduce model dimension. The maximum dimension of the matrix involved in the reduced-order modal analysis is either the total number of WTGs in the power system or the order of dynamic models of individual WTGs. Hence, the proposed method effectively avoids the numerical complexity of applying modal analysis to the high-dimensional model. Improvements of the reduced-order method in practical applications are suggested, particularly for situations in which detailed parametric information for every WTG is not available. Initially, a few representative WTGs are selected to derive their parametric models via field or laboratory tests. Then, reduced-order modal analysis is applied to detect the risk and trace the sources of oscillatory instability. [ABSTRACT FROM AUTHOR]

The electrical grid is evolving from a network consisting of mostly synchronous machines to a mixture of synchronous machines, and inverter-based resources such as wind, solar, and energy storage. This transformation has led to a decrease in mechanical inertia, which necessitate a need for the new resources to provide frequency responses through their inverter interfaces. In this paper we proposed a new strategy based on model predictive control to determine the optimal active-power set-point for inverters in the event of a disturbance in the system. Our framework explicitly takes the hard constraints in power, and energy into account, and we show that it is robust to measurement noise, limited communications, and delay by using an observer to estimate the model mismatches in real-time. We demonstrate the proposed controller significantly outperforms an optimally tuned virtual synchronous machine on a standard 39-bus system under a number of scenarios. In turn, this implies optimized inverter-based resources can provide better frequency responses compared to conventional synchronous machines. [ABSTRACT FROM AUTHOR]

The training of Deep Neural Networks (DNNs) brings enormous memory requirements and computational complexity, which makes it a challenge to train DNN models on resource-constrained devices. Training DNNs with reduced-precision data representation is crucial to mitigate this problem. In this article, we conduct a thorough investigation on training DNNs with low-bit posit numbers, a Type-III universal number (Unum). Through a comprehensive analysis of quantization with various data formats, it is demonstrated that the posit format shows great potential to be employed in the training of DNNs. Moreover, a DNN training framework using 8-bit posit is proposed with a novel tensor-wise scaling scheme. The experiments show the same performance as the state-of-the-art (SOTA) across multiple datasets (MNIST, CIFAR-10, ImageNet, and Penn Treebank) and model architectures (LeNet-5, AlexNet, ResNet, MobileNet-V2, and LSTM). We further design an energy-efficient hardware prototype for our framework. Compared to the standard floating-point counterpart, our design achieves a reduction of 68, 51, and 75 percent in terms of area, power, and memory capacity, respectively. [ABSTRACT FROM AUTHOR]

The increased phase number of multiphase systems enables us to exploit more degrees of freedom, such as the shape of phase carriers in pulsewidth modulation (PWM). The sawtooth carrier-based PWM (SCPWM) techniques are proposed in this article to reduce common-mode voltage (CMV) in both the peak-to-peak amplitude and the changing frequency, and it can be easily extended to symmetrical multiphase two-level inverters with any odd phase number. Theoretical analysis reveals that the switching between mirror-symmetrical carriers within one phase narrows the range of the sum of switching states in all phases, which leads to the reduction of CMV amplitude. Meanwhile, the overlapping of sawtooth carriers’ straight edges among different phases slows down the change of the sum of switching states, which results in the decrease of CMV changing frequency. Moreover, the effects of voltage harmonics injection and switches’ dead-time settings on the CMV reduction performance under the proposed SCPWM techniques are investigated. Finally, the experiment results in a five-phase induction machine and multiphase RL loads verify the improved CMV performance and the extensibility of the proposed SCPWM methods. [ABSTRACT FROM AUTHOR]

The Residue Logarithmic Number System (RLNS) offers fast multiplication and division, but poses challenges for implementing addition and subtraction because the underlying integer Residue Number System (RNS) has slow sign detection. The conventional Binary Logarithmic Number Systems (BLNS) has benefited from interpolation and cotransformation. We propose a dual-path ALU that speculates about the sign detection to adapt interpolation and cotransformation to the limitations of RLNS. Synthesis shows for the same precision and technology, the area of the proposed RLNS circuit is similar to BLNS and much smaller than prior RLNS methods. We also compare against Floating Point (FP). [ABSTRACT FROM AUTHOR]

In this paper, the asymptotic performance of a full-duplex (FD) millimeter wave (mmWave) two-way relay (TWR) system with a massive multiple-input multiple-output (MIMO) structure is analyzed. After applying conjugate array response vector-based precoding to a hybrid analog and digital MIMO structure in the mmWave band, the asymptotic spectral efficiency of the system as the number of antennas approaches infinity is derived. Based on the asymptotic performance and different system configurations, power scaling schemes are investigated. Considering different power scaling strategies at each node, eight power scaling cases are discussed, and the corresponding asymptotic spectral efficiencies are derived. The results show that interference and noise can be fully eliminated by increasing the number of antennas. A high spectral efficiency can be achieved in a system wherein all the nodes are equipped with large antenna arrays. If the numbers of antennas of node A and node B are both limited, then the system can achieve considerable spectral efficiency and energy efficiency. For both configurations, the power of node A and node B can be scaled down without spectral efficiency loss. However, if only one node is equipped with limited antennas, only the node with a massive array can achieve a higher spectral efficiency. Moreover, we analyze the effect of the FD self-interference (SI) levels. We show that the spectral efficiency decreases rapidly as the SI intensity increases, which is related to the capability of SI cancellation. Under ideal conditions, the FD system can achieve twice the spectral efficiency of its conventional half-duplex (HD) counterpart. However, the FD SI and the finite number of antennas degrade the spectral efficiency of the FD system. [ABSTRACT FROM AUTHOR]

In today's technology, a sheer number of Internet of Things applications use hardware security modules for secure communications. The widely used algorithms in security modules, for example, digital signatures and key agreement, are based upon elliptic curve cryptography (ECC). A core operation used in ECC is the point multiplication, which is computationally expensive for many Internet of things applications. In many IoT applications, such as intelligent transportation systems and distributed control systems, thousands of safety messages need to be signed and verified within a very short time-frame. Considerable research has been conducted in the design of a fast elliptic curve arithmetic on finite fields using residue number systems (RNS). In this article, we propose an RNS-based ECC core hardware for the two families of elliptic curves that are short Weierstraß and twisted Edwards curves. Specifically, we present RNS implementations for SECP256K1 and ED25519 standard curves. We propose an RNS hardware architecture supporting fast elliptic curve point-addition (ECPA), point-doubling (ECPD), and point-tripling (ECPT). We implemented different ECC point multiplication algorithms on the Xilinx FPGA platform. The test results confirm that the performance of our fully RNS ECC point multiplication is better than the fastest ECC point multiplication cores in the literature. [ABSTRACT FROM AUTHOR]

The letter proposes an algorithm for big data generation based on realistic selection of a set of contingencies for power systems described by undirected graphs. Every contingency is created by eliminating a certain number of elements in the system represented by graph edges. The number of elements as well as the distance between elements of the contingency is randomly selected according to a geometric probability distributions based on historical data. The duration of a fault that starts the contingency as well as the time intervals between elements of the contingency are chosen by sampling from a gamma distribution. In addition, the absence of islands in the system is assessed by analyzing the connectedness of the graph with deleted edges, which is quantified by computing the number of zero eigenvalues of the Laplacian matrix of the resulting graphs. The algorithm is validated on the Nordic 44-bus power system. [ABSTRACT FROM AUTHOR]