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.

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.

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.

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 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.

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.

Wafer-level performance prediction techniques have been increasingly gaining attention in production LSI testing due to their ability to reduce measurement costs without compromising test quality. Despite the availability of several efficient methods, the site-to-site variation commonly observed in multi-site testing for radio frequency circuits remains inadequately addressed. In this manuscript, we propose a wafer-level performance prediction approach for multi-site testing that takes into account the site-to-site variation. Our proposed method is built on the Gaussian process, a widely utilized wafer-level spatial correlation modeling technique, and enhances prediction accuracy by extending hierarchical modeling to leverage the test site information test engineers provide. Additionally, we propose a test-site sampling method that maximizes cost reduction while maintaining sufficient estimation accuracy. Our experimental results, which employ industrial production test data, demonstrate that our proposed method can decrease the estimation error to 1/19 of that a conventional method achieves. Furthermore, our sampling method can reduce the required measurements by 97% while ensuring satisfactory estimation accuracy.

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.

Homomorphic encryption (HE) is a promising approach for privacy-preserving applications, enabling a third party to assess functions on encrypted data. However, problems persist in implementing privacy-preserving applications through HE, including 1) long function evaluation latency and 2) limited HE primitives only allowing us to perform additions and multiplications. A homomorphic lookup-table (LUT) method has emerged to solve the above problems and enhance function evaluation efficiency. By leveraging homomorphic LUTs, intricate operations can be substituted. Previously proposed LUTs use bit-wise HE, such as TFHE, to evaluate single-input functions. However, the latency increases with the bit-length of the function’s input(s) and output. Additionally, an efficient implementation of multi-input functions remains an open question. This paper proposes a novel LUT-based privacy-preserving function evaluation method to handle multi-input functions while reducing the latency by adopting word-wise HE. Our optimization strategy adjusts table sizes to minimize the latency while preserving function output accuracy, especially for common machine-learning functions. Through our experimental evaluation utilizing the BFV scheme of the Microsoft SEAL library, we confirmed the runtime of arbitrary functions whose LUTs consist of all input-output combinations represented by given input bits: 1) single-input 12-bit functions in 0.14 s, 2) single-input 18-bit functions in 2.53 s, 3) two-input 6-bit functions in 0.17 s, and 4) three-input 4-bit functions in 0.20 s, employing four threads. Besides, we confirmed that our proposed table size optimization strategy worked well, achieving 1.2 times speed up with the same absolute error of order of magnitude of -4 (a × 10-4 where 1/$\sqrt{10}$ ≤ a < $\sqrt{10})$ for Swish and 1.9 times speed up for ReLU while decreasing the absolute error from order -2 to -4 compared to the baseline, i.e., polynomial approximation.

Secure computation is a kind of cryptographic techniques that enables to compute a function while keeping input data secret. Komano and Mizuki (International Journal of Information Security 2022) proposed a model of coin-based protocols, which are secure computation protocols using physical coins. They designed AND, XOR, and COPY protocols using so-called hand operations, which move coins from one player’s palm to the other palm. However, hand operations cannot be executed when all players’ hands are occupied. In this paper, we propose coin-based protocols without hand operations. In particular, we design a three-coin NOT protocol, a seven-coin AND protocol, a six-coin XOR protocol, and a five-coin COPY protocol without hand operations. Our protocols use random flips only as shuffle operations and are enough to compute any function since they have the same format of input and output, i.e., committed-format protocols.

Lightweight block ciphers have gained attention in recent years due to the increasing demand for sensor nodes, RFID tags, and various applications. In such a situation, lightweight block ciphers Piccolo and TWINE have been proposed. Both Piccolo and TWINE are designed based on the Generalized Feistel Structure. However, it is crucial to address the potential vulnerability of these structures to the impossible differential attack. Therefore, detailed security evaluations against this attack are essential. This paper focuses on conducting bit-level evaluations of Piccolo and TWINE against related-key impossible differential attacks by leveraging SAT-aided approaches. We search for the longest distinguishers under the condition that the Hamming weight of the active bits of the input, which includes plaintext and master key differences, and output differences is set to 1, respectively. Additionally, for Tweakable TWINE, we search for the longest distinguishers under the related-tweak and related-tweak-key settings. The result for Piccolo with a 128-bit key, we identify the longest 16-round distinguishers for the first time. In addition, we also demonstrate the ability to extend these distinguishers to 17 rounds by taking into account the cancellation of the round key and plaintext difference. Regarding evaluations of TWINE with a 128-bit key, we search for the first time and reveal the distinguishers up to 19 rounds. For the search for Tweakable TWINE, we evaluate under the related-tweak-key setting for the first time and reveal the distinguishers up to 18 rounds for 80-bit key and 19 rounds for 128-bit key.

At CT-RSA 2022, Bossert et al. proposed Pholkos family, an efficient large-state tweakable block cipher. In order to evaluate the security of differential attacks on Pholkos, they obtained the lower bounds for the number of active S-boxes for Pholkos using MILP (Mixed Integer Linear Programming) tools. Based on it, they claimed that Pholkos family is secure against differential attacks. However, they only gave rough security bounds in both of related-tweak and related-tweakey settings. To be more precise, they estimated the lower bounds of the number of active S-boxes for relatively-large number of steps by just summing those in the small number of steps. In this paper, we utilize efficient search methods based on MILP to obtain tighter lower bounds for the number of active S-boxes in a larger number of steps. For the first time, we derive the exact minimum number of active S-boxes of each variant up to the steps where the security against differential attacks can be ensured in related-tweak and related-tweakey settings. Our results indicate that Pholkos-256-128/256-256/512/1024 is secure after 4/5/3/4 steps in the related-tweak setting, and after 5/6/3/4 steps in the related-tweakey setting, respectively. Our results enable reducing the required number of steps to be secure against differential attacks of Pholkos-256-256 in related-tweak setting, and Pholkos-256-128/256 and Pholkos-1024 in the related-tweakey setting by one step, respectively.

Permutation polynomials have been studied for a long time and have important applications in cryptography, coding theory and combinatorial designs. In this paper, by means of the multivariate method and the resultant, we propose four new classes of permutation quadrinomials over 𝔽q3, where q is a prime power. We also show that they are not quasi-multiplicative equivalent to known ones. Moreover, we compare their differential uniformity with that of some known classes of permutation trinomials for some small q.

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.

In this paper, we first recall the concept of 2-tuples distribution matrix, and further study its properties. Based on these properties, we find four special classes of 2-tuples distribution matrices. Then, we provide a new sufficient and necessary condition for n-variable rotation symmetric Boolean functions to be 2-correlation immune. Finally, we give a new method for constructing such functions when n=4t - 1 is prime, and we show an illustrative example.

In recent years, high-resolution 77 GHz band automotive radar, which is indispensable for autonomous driving, has been extensively investigated. In the future, as vehicle-mounted CS (chirp sequence) radars become more and more popular, intensive inter-radar wideband interference will become a serious problem, which results in undesired miss detection of targets. To address this problem, learning-based wideband interference mitigation method has been proposed, and its feasibility has been validated by simulations. In this paper, firstly we evaluated the trade-off between interference mitigation performance and model training time of the learning-based interference mitigation method in a simulation environment. Secondly, we conducted extensive inter-radar interference experiments by using multiple 77 GHz MIMO (Multiple-Input and Multiple-output) CS radars and collected real-world interference data. Finally, we compared the performance of learning-based interference mitigation method with existing algorithm-based methods by real experimental data in terms of SINR (signal to interference plus noise ratio) and MAPE (mean absolute percentage error).

The concept of dual function radar-communication (DFRC) provides solution to the problem of spectrum scarcity. This paper examines a multiple-input multiple-output (MIMO) DFRC system with the assistance of a reconfigurable intelligent surface (RIS). The system is capable of sensing multiple spatial directions while serving multiple users via orthogonal frequency division multiplexing (OFDM). The objective of this study is to design the radiated waveforms and receive filters utilized by both the radar and users. The mutual information (MI) is used as an objective function, on average transmit power, for multiple targets while adhering to constraints on power leakage in specific directions and maintaining each user’s error rate. To address this problem, we propose an optimal solution based on a computational genetic algorithm (GA) using bisection method. The performance of the solution is demonstrated by numerical examples and it is shown that, our proposed algorithm can achieve optimum MI and the use of RIS with the MIMO DFRC system improving the system performance.

With the increase of the number of Mobile User Equipments (MUEs), numerous tasks that with high requirements of resources are generated. However, the MUEs have limited computational resources, computing power and storage space. In this paper, a joint coverage constrained task offloading and resource allocation method based on deep reinforcement learning is proposed. The aim is offloading the tasks that cannot be processed locally to the edge servers to alleviate the conflict between the resource constraints of MUEs and the high performance task processing. The studied problem considers the dynamic variability and complexity of the system model, coverage, offloading decisions, communication relationships and resource constraints. An entropy weight method is used to optimize the resource allocation process and balance the energy consumption and execution time. The results of the study show that the number of tasks and MUEs affects the execution time and energy consumption of the task offloading and resource allocation processes in the interest of the service provider, and enhances the user experience.

Edge devices with strict safety and reliability requirements, such as autonomous driving cars, industrial robots, and drones, necessitate software verification on such devices before operation. The human cost and time required for this analysis constitute a barrier in the cycle of software development and updating. In particular, the final verification at the edge device should at least strictly confirm that the updated software is not degraded from the current it. Since the edge device does not have the correct data, it is necessary for a human to judge whether the difference between the updated software and the operating it is due to degradation or improvement. Therefore, this verification is very costly. This paper proposes a novel automated method for efficient verification on edge devices of an object detection AI, which has found practical use in various applications. In the proposed method, a target object existence detector (TOED) (a simple binary classifier) judges whether an object in the recognition target class exists in the region of a prediction difference between the AI’s operating and updated versions. Using the results of this TOED judgement and the predicted difference, an automated verification system for the updated AI was constructed. TOED was designed as a simple binary classifier with four convolutional layers, and the accuracy of object existence judgment was evaluated for the difference between the predictions of the YOLOv5 L and X models using the Cityscapes dataset. The results showed judgement with more than 99.5% accuracy and 8.6% over detection, thus indicating that a verification system adopting this method would be more efficient than simple analysis of the prediction differences.

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.