We propose a zero-order-hold triggered control for a chain of integrators with an arbitrary sampling period. We analytically show that our control scheme globally asymptotically stabilizes the considered system. The key feature is that the pre-specified sampling period can be enlarged as desired by adjusting a gain-scaling factor. An example with various simulation results is given for clear illustration.

A new extended normalized least-mean-square (ENLMS) algorithm is proposed. A novel non-linear time-varying step-size (NLTVSS) formula is derived. The convergence rate of ENLMS increases due to NLTVSS as the number of data-reuse L is increased. ENLMS does not involve matrix inversion, and, thus, avoids numerical instability issues.

During the COVID-19 pandemic, a robust system for masked face recognition has been required. Most existing solutions used many samples per identity for the model to recognize, but the processes involved are very laborious in a real-life scenario. Therefore, we propose “CPNet” as a suitable and reliable way of recognizing masked faces from only a few samples per identity. The prototype classifier uses a few-shot learning paradigm to perform the recognition process. To handle complex and occluded facial features, we incorporated the covariance structure of the classes to refine the class distance calculation. We also used sharpness-aware minimization (SAM) to improve the classifier. Extensive in-depth experiments on a variety of datasets show that our method achieves remarkable results with accuracy as high as 95.3%, which is 3.4% higher than that of the baseline prototype network used for comparison.

Secret sharing is a cryptographic scheme to encode a secret to multiple shares being distributed to participants, so that only qualified sets of participants can restore the original secret from their shares. When we encode a secret by a secret sharing scheme and distribute shares, sometimes not all participants are accessible, and it is desirable to distribute shares to those participants before a secret information is determined. Secret sharing schemes for classical secrets have been known to be able to distribute some shares before a given secret. Lie et al. found a ((2, 3))-threshold secret sharing for quantum secrets can distribute some shares before a given secret. However, it is unknown whether distributing some shares before a given secret is possible with other access structures of secret sharing for quantum secrets. We propose a quantum secret sharing scheme for quantum secrets that can distribute some shares before a given secret with other access structures.

In this study, we focus on evaluating the false-positive probability of the Demirci-Selçuk meet-in-the-middle attack, particularly within the context of configuring precomputed tables with multisets. During the attack, the adversary effectively reduces the size of the key space by filtering out the wrong keys, subsequently recovering the master key from the reduced key space. The false-positive probability is defined as the probability that a wrong key will pass through the filtering process. Due to its direct impact on the post-filtering key space size, the false-positive probability is an important factor that influences the complexity and feasibility of the attack. However, despite its significance, the false-positive probability of the multiset-based Demirci-Selçuk meet-in-the-middle attack has not been thoroughly discussed, to the best of our knowledge. We generalize the Demirci-Selçuk meet-in-the-middle attack and present a sophisticated method for accurately calculating the false-positive probability. We validate our methodology through toy experiments, demonstrating its high precision. Additionally, we propose a method to optimize an attack by determining the optimal format of precomputed data, which requires the precise false-positive probability. Applying our approach to previous attacks on AES and ARIA, we have achieved modest improvements. Specifically, we enhance the memory complexity and time complexity of the offline phase of previous attacks on 7-round AES-128/192/256, 7-round ARIA-192/256, and 8-round ARIA-256 by factors ranging from 20.56 to 23. Additionally, we have improved the overall time complexity of attacks on 7-round ARIA-192/256 by factors of 20.13 and 20.42, respectively.

We study the mixed-integer optimization (MIO) approach to feature subset selection in nonlinear kernel support vector machines (SVMs) for binary classification. To measure the performance of subset selection, we use the distance between two classes (DBTC) in a high-dimensional feature space based on the Gaussian kernel function. However, DBTC to be maximized as an objective function is nonlinear, nonconvex and nonconcave. Despite the difficulty of linearizing such a nonlinear function in general, our major contribution is to propose a mixed-integer linear optimization (MILO) formulation to maximize DBTC for feature subset selection, and this MILO problem can be solved to optimality using optimization software. We also derive a reduced version of the MILO problem to accelerate our MILO computations. Experimental results show good computational efficiency for our MILO formulation with the reduced problem. Moreover, our method can often outperform the linear-SVM-based MILO formulation and recursive feature elimination in prediction performance, especially when there are relatively few data instances.

This paper presents a multi-channel biomedical sensor system with system-level chopping and stochastic analog-to-digital (A/D) conversion techniques. The system-level chopping technique extends the input-signal bandwidth and reduces the interchannel crosstalk caused by multiplexing. The system-level chopping can replace an analog low-pass filter (LPF) with a digital filter and can reduce its area occupation. The stochastic A/D conversion technique realizes power-efficient resolution enhancement. A novel auto-calibration technique is also proposed for the stochastic A/D conversion technique. The proposed system includes a prototype analog front-end (AFE) IC fabricated using a 130 nm CMOS process. The fabricated AFE IC improved its interchannel crosstalk by 40 dB compared with the conventional analog chopping architecture. The AFE IC achieved SNDR of 62.9 dB at a sampling rate of 31.25 kSps while consuming 9.6 μW from a 1.2 V power supply. The proposed resolution enhancement technique improved the measured SNDR by 4.5 dB.

This paper proposes an analytical model of maximum operating frequency of class-D zero-voltage-switching (ZVS) inverter. The model includes linearized drain-source parasitic capacitance and any duty ratio. The nonlinear drain-source parasitic capacitance is equally linearized through a charge-related equation. The model expresses the relationship among frequency, shunt capacitance, duty ratio, load impedance, output current phase, and DC input voltage under the ZVS condition. The analytical result shows that the maximum operating frequency under the ZVS condition can be obtained when the duty ratio, the output current phase, and the DC input voltage are set to optimal values. A 650 V/30 A SiC-MOSFET is utilized for both simulated and experimental verification, resulting in good consistency.

Stochastic resonance is a representative phenomenon in which the degree of synchronization with a weak input signal is enhanced using additive stochastic noise. In systems with multiple chaotic attractors, the chaos-chaos intermittent behavior in attractor-merging bifurcation induces chaotic resonance, which is similar to the stochastic resonance and has high sensitivity. However, controlling chaotic resonance is difficult because it requires adjusting the internal parameters from the outside. The reduced-region-of-orbit (RRO) method, which controls the attractor-merging bifurcation using an external feedback signal, is employed to overcome this issue. However, the lower perturbation of the feedback signal requires further improvement for engineering applications. This study proposed an RRO method with more sophisticated and less perturbed feedback signals, called the double-Gaussian-filtered RRO (DG-RRO) method. The inverse sign of the map function and double Gaussian filters were used to improve the local specification, i.e., the concentration around the local maximum/minimum in the feedback signals, called the DG-RRO feedback signals. Owing to their fine local specification, these signals achieved the attractor-merging bifurcation with significantly smaller feedback perturbation than that in the conventional RRO method. Consequently, chaotic resonance was induced through weak feedback perturbation. It exhibited greater synchronization against weak input signals than that induced by the conventional RRO feedback signal and sustained the same level of response frequency range as that of the conventional RRO method. These advantages may pave the way for utilizing chaotic resonance in engineering scenarios where the stochastic resonance has been applied.

This paper discusses the synchronization of two identical canard-generating oscillators. First, we investigate a canard explosion generated in a system containing a Bonhoeffer-van der Pol (BVP) oscillator using the actual parameter values obtained experimentally. We find that it is possible to numerically observe a canard explosion using this dynamic oscillator. Second, we analyze the complete and in-phase synchronizations of identical canard-generating coupled oscillators via experimental and numerical methods. However, we experimentally determine that a small decrease in the coupling strength of the system induces the collapse of the complete synchronization and the occurrence of a complex synchronization; this finding could not be explained considering four-dimensional autonomous coupled BVP oscillators in our numerical work. To numerically investigate the experimental results, we construct a model containing coupled BVP oscillators that are subjected to two weak periodic perturbations having the same frequency. Further, we find that this model can efficiently numerically reproduce experimentally observed synchronization.

Data resource sharing can guarantee the reliable and safe operation of distribution power grid. However, it faces the challenges of low security and high delay in the sharing process. Consortium blockchain can ensure the security and efficiency of data resource sharing, but it still faces problems such as arbitrary master node selection and high consensus delay. In this paper, we propose an improved practical Byzantine fault tolerance (PBFT) consensus algorithm based on intelligent consensus node selection to realize high-security and real-time data resource sharing for distribution power grid. Firstly, a blockchain-based data resource sharing model is constructed to realize secure data resource storage by combining the consortium blockchain and interplanetary file system (IPFS). Then, the improved PBFT consensus algorithm is proposed to optimize the consensus node selection based on the upper confidence bound of node performance. It prevents Byzantine nodes from participating in the consensus process, reduces the consensus delay, and improves the security of data resource sharing. The simulation results verify the effectiveness of the proposed algorithm.

Power line communication (PLC) provides a flexible-access, wide-distribution, and low-cost communication solution for distribution network services. However, the PLC self-organizing networking in distribution network faces several challenges such as diversified data transmission requirements guarantee, the contradiction between long-term constraints and short-term optimization, and the uncertainty of global information. To address these challenges, we propose a backpressure learning-based data transmission reliability-aware self-organizing networking algorithm to minimize the weighted sum of node data backlogs under the long-term transmission reliability constraint. Specifically, the minimization problem is transformed by the Lyapunov optimization and backpressure algorithm. Finally, we propose a backpressure and data transmission reliability-aware state-action-reward-state-action (SARSA)-based self-organizing networking strategy to realize the PLC networking optimization. Simulation results demonstrate that the proposed algorithm has superior performances of data backlogs and transmission reliability.

With the popularization of software defined networks, switch migration as an important network management strategy has attracted increasing attention. Most existing switch migration strategies only consider local conditions and simple load thresholds, without fully considering the overall optimization and dynamics of the network. Therefore, this article proposes a switch migration algorithm based on global optimization. This algorithm adds a load prediction module to the migration model, determines the migration controller, and uses an improved whale optimization algorithm to determine the target controller and its surrounding controller set. Based on the load status of the controller and the traffic priority of the switch to be migrated, the optimal migration switch set is determined. The experimental results show that compared to existing schemes, the algorithm proposed in this paper improves the average flow processing efficiency by 15% to 40%, reduces switch migration times, and enhances the security of the controller.

Dependent Quantization (DQ) is a new quantization tool introduced in the Versatile Video Coding (VVC) standard. While it provides better rate-distortion calculation accuracy, it also increases the computational complexity and hardware cost compared to the widely used scalar quantization. To address this issue, this paper proposes a parallel-dependent quantization hardware architecture using Verilog HDL language. The architecture preprocesses the coefficients with a scalar quantizer and a high-frequency filter, and then further segments and processes the coefficients in parallel using the Viterbi algorithm. Additionally, the weight bit width of the rate-distortion calculation is reduced to decrease the quantization cycle and computational complexity. Finally, the final quantization of the TU is determined through sequential scanning and judging of the rate-distortion cost. Experimental results show that the proposed algorithm reduces the quantization cycle by an average of 56.96% compared to VVC’s reference platform VTM, with a Bjøntegaard delta bit rate (BDBR) loss of 1.03% and 1.05% under the Low-delay P and Random Access configurations, respectively. Verification on the AMD FPGA development platform demonstrates that the hardware implementation meets the quantization requirements for 1080P@60Hz video hardware encoding.

Spatial modulation (SM) is a type of multiple-input multiple-output (MIMO) technology that provides several benefits over traditional MIMO systems. SM-MIMO is characterized by its unique transmission principle, which results in lower costs, enhanced spectrum utilization, and reduced inter-channel interference. To optimize channel estimation performance over frequency-selective channels in the spatial modulation system, cross Z-complementary pairs (CZCPs) have been proposed as training sequences. The zero correlation zone (ZCZ) properties of CZCPs for auto-correlation sums and cross-correlation sums enable them to achieve optimal channel estimation performance. In this paper, we systematically construct CZCPs based on binary Golay complementary pairs and binary Golay complementary pairs via Turyn’s method. We employ a special matrix operation and concatenation method to obtain CZCPs with new lengths 2M + N and 2(M + L), where M and L are the lengths of binary GCP, and N is the length of binary GCP via Turyn’s method. Further, we obtain the perfect CZCP with new length 4N and extend the lengths of CZCPs.

Emerging data-intensive services in distribution grid impose requirements of high-concurrency access for massive internet of things (IoT) devices. However, the lack of effective high-concurrency access management results in severe performance degradation. To address this challenge, we propose a cloud-edge-device collaborative high-concurrency access management algorithm based on multi-timescale joint optimization of channel pre-allocation and load balancing degree. We formulate an optimization problem to minimize the weighted sum of edge-cloud load balancing degree and queuing delay under the constraint of access success rate. The problem is decomposed into a large-timescale channel pre-allocation subproblem solved by the device-edge collaborative access priority scoring mechanism, and a small-timescale data access control subproblem solved by the discounted empirical matching mechanism (DEM) with the perception of high-concurrency number and queue backlog. Particularly, information uncertainty caused by externalities is tackled by exploiting discounted empirical performance which accurately captures the performance influence of historical time points on present preference value. Simulation results demonstrate the effectiveness of the proposed algorithm in reducing edge-cloud load balancing degree and queuing delay.

In this work, the core size estimation technique of magnetic nanoparticles (MNPs) using the static magnetization curve obtained from a high-Tc SQUID magnetometer and a metaheuristic inversion technique based on the Particle Swarm Optimizer (PSO) algorithm is presented. The high-Tc SQUID magnetometer is constructed from a high-Tc SQUID sensor coupled by a flux transformer to sense the modulated magnetization signal from a sample. The magnetization signal is modulated by the lateral vibration of the sample on top of a planar differential detection coil of the flux transformer. A pair of primary and excitation coils are utilized to apply an excitation field parallel to the sensitive axis of the detection coil. Using the high-Tc SQUID magnetometer, the magnetization curve of a commercial MNP sample (Resovist) was measured in a logarithmic scale of the excitation field. The PSO inverse technique is then applied to the magnetization curve to construct the magnetic moment distribution. A multimodal normalized log-normal distribution was used in the minimization of the objective function of the PSO inversion technique, and a modification of the PSO search region is proposed to improve the exploration and exploitation of the PSO particles. As a result, a good agreement on the Resovist magnetic core size was obtained between the proposed technique and the non-negative least square (NNLS) inversion technique. The estimated core sizes of 8.0484 nm and 20.3018 nm agreed well with the values reported in the literature using the commercial low-Tc SQUID magnetometer with the SVD and NNLS inversion techniques. Compared to the NNLS inversion technique, the PSO inversion technique had merits in exploring an optimal core size distribution freely without being regularized by a parameter and facilitating an easy peak position determination owing to the smoothness of the constructed distribution. The combination of the high-Tc SQUID magnetometer and the PSO-based reconstruction technique offers a powerful approach for characterizing the MNP core size distribution, and further improvements can be expected from the recent state-of-the-art optimization algorithm to optimize further the computation time and the best objective function value.

In this study, we propose a novel dataset distillation method based on parameter pruning. The proposed method can synthesize more robust distilled datasets and improve distillation performance by pruning difficult-to-match parameters during the distillation process. Experimental results on two benchmark datasets show the superiority of the proposed method.

The steady-state and convergence performances are important indicators to evaluate adaptive algorithms. The step-size affects these two important indicators directly. Many relevant scholars have also proposed some variable step-size adaptive algorithms for improving performance. However, there are still some problems in these existing variable step-size adaptive algorithms, such as the insufficient theoretical analysis, the imbalanced performance and the unachievable parameter. These problems influence the actual performance of some algorithms greatly. Therefore, we intend to further explore an inherent relationship between the key performance and the step-size in this paper. The variation of mean square deviation (MSD) is adopted as the cost function. Based on some theoretical analyses and derivations, a novel variable step-size algorithm with a dynamic limited function (DLF) was proposed. At the same time, the sufficient theoretical analysis is conducted on the weight deviation and the convergence stability. The proposed algorithm is also tested with some typical algorithms in many different environments. Both the theoretical analysis and the experimental result all have verified that the proposed algorithm equips a superior performance.

Assortment optimization is one of main problems for retailers, and has been widely studied. In this paper, we focus on vending machines, which have many characteristic issues to be considered. We first formulate an assortment optimization problem for vending machines, next propose a model that represents consumer’s decision making, and then show a solution method based on partially observable Markov decision process (POMDP). The problem includes incomplete state observation, stochastic consumer behavior and policy decisions that maximize future expected rewards. Using computer simulation, we observe that sales increases compared to that by heuristic methods under the same condition. Moreover, the sales approaches the theoretical upper bound.