To meet the key performance requirements of permanent magnet synchronous motor (PMSM) speed control systems―such as high-precision control, fast response, and strong anti-disturbance, a improved power exponential reaching law (IPERL) is proposed. This approach incorporates an inverse cotangent function to adaptively adjust the power term coefficient, effectively balancing the trade-off between convergence speed and sliding mode chattering. Additionally, a dynamic linear sliding mode surface is designed based on the system error, which effectively mitigates overshoot and further enhances both the dynamic and steady-state performance of the system. Finally, the effectiveness of the proposed control strategy is validated through simulations, demonstrating superior stability and convergence speed compared to conventional methods.

This paper presents a continuous-time pulse compressor (CTPC) for ultrasound imaging systems with enhanced signal intensity and axial resolution. Conventional pulse compression using a chirp signal employs matched filtering, which performs cross-correlation calculations in the digital domain. However, the calculation requires a large amount of computing resources. The proposed CTPC performs pulse compression directly in the analog domain by frequency-dependent delay. In the proposed CTPC, a second-order all-pass filter (APF) with a relatively high quality factor (Q) of 1.8 achieves a long group delay (GD) to reach a sufficiently long chirp period in the ultrasonic band. Furthermore, cascading other second-order APFs with different resonance frequencies approximately linearizes the total GD with respect to frequency. We investigated the implementation of the proposed CTPC using discrete operational amplifiers and passive components. Measurements confirmed a 6.7 dB signal enhancement from compression effects with an ideal electrical chirp signal. Further acoustic measurements using an 8 × 8 channel 2-D matrix array probe and an imaging prototype demonstrated improved signal enhancement and compression factor of 3.2.

Defect pattern detection in wafer bin maps (WBMs) is crucial for enhancing wafer quality, as it prevents the escalation of defects and the squandering of resources. To address this, we introduce a quantum-based Support Vector Machine (SVM) leveraging the quantum approximate optimization algorithm, termed QAOA_SVM. We employ Inception V3 to extract efficient and compact features from WBMs and apply QAOA_SVM for training and testing the data. When recognizing WBMs with two mixed defect types, our method outperforms the original mixup approach by over 7.4%, and achieves a 0.7% accuracy improvement compared to the Inception V3+SVM method. For mixed defect samples containing more than two types of defects (three-mixed and four-mixed), we observe gains of at least 4.2% (relative to SVM) and 0.9% (relative to Inception V3+SVM), respectively. Additionally, our method surpasses other state-of-the-art Convolutional Neural Network (CNN) methods in both single-type and mixed-type defect pattern recognition. Furthermore, QAOA_SVM requires only O(Nd) time, which is significantly more efficient than the O(N³) time complexity of traditional SVM. In summary, QAOA_SVM achieves higher accuracy with significantly reduced computational time.

This work studies the fundamental properties of self-learning observers (LOs), which simultaneously estimate states and parameters resource-efficiently. LOs have a simple structure, the capacity to estimate system uncertainties using only one algebraic equation, and decent performance. We explore the exponential convergence property of LOs in-depth and present an explicit exponential convergence rate for the first time using Halanay's inequality technique. This work further contributes by providing fewer conservative conditions, thereby decreasing the equality condition that must be satisfied in previous studies on LOs. The LO parameters are acquired by solving linear matrix inequalities (LMIs), and the rules for parameter tuning under the new constraints are provided.

This letter proposes an ANC system without an error microphone introducing primary path estimation. The proposed system enables adaptively update of a noise control filter to realize the noise reduction at a desired position without placing an error microphone at the target point, by estimating the primary path to the desired position. A computer simulation using impulse responses generated by the image method demonstrates that the proposed system achieves a noise reduction of more than 10 dB at the desired position, even when the noise source moves.

This paper proposes stable virtual sensing algorithm for active noise control with sequential online modeling of the auxiliary filter and the secondary path. The two online modelings prevents system divergence and maintain high noise reduction when there is a secondary path change such as microphone relocation. The online modeling of the secondary path adjusts mismatch in the noise-control filter. The online modeling of the auxiliary filter compensates for the mismatch therein caused by the secondary path change. A simulation result with recorded signals shows that the proposed method maintains a noise reduction of 22 dB even when a secondary-path change takes place.

The distributed space-based coherent aperture radar (DSCAR) is a radar system that utilizes a space-based platform to form a formation of radar unit (RU) for joint target detection, inspired by the concept of distributed coherent radar. However, DSCAR based on uniform linear formation (ULF) suffers from numerous grating lobe which severely impair the beamforming performance. Due to the fact that conventional grating lobe suppression method based on non-uniform spacing is not suitable for uniform formation, this letter proposes a method based on randomized angle yaw to address the issue of grating lobe suppression in DSCAR. We present the formula for the DSCAR joint pattern and use particle swarm optimization (PSO) to optimize the peak side lobe level (PSLL). Simulation results demonstrate that randomized angle yaw method has better grating suppression effect than non-uniform spacing method, and increasing the number of RUs and the number of RU antenna elements will further improve the optimal PSLL.

In this letter, we consider an asymptotic stabilization problem for a system under saturated input which has uncertain and time-varying bounds. With our gain-scaling controller, we analytically show that the controlled system is asymptotically stabilized and the domain of operation can be arbitrarily enlarged by increasing the gain-scaling factor. Then, our proposed method is extended to a class of feedforward systems. Two numerical results are provided to show the validity of our results.

Secure multiparty computation (MPC) is a cryptographic technology to perform some computation on multiple parties' input data while concealing the individual inputs from other parties. For the case of semi-honest adversaries, the security definition in Goldreich's famous book is widely used as a standard definition. In this paper, however, we point out that there is an MPC protocol where a semi-honest adversary receives only a ciphertext of one-time pad with unknown key but the protocol is not secure under the standard security definition, which may look inconsistent with the perfect secrecy of one-time pad that its ciphertext leaks no information at all. We propose a variant of the security definition that resolves this issue. We also show that a somewhat restrictive version of the Composition Theorem holds for our modified security definition.

With the expansion of industrial applications and data volumes, building a user-edge-cloud collaborative network and efficiently distributing computing tasks has emerged as a critical solution to alleviate terminal computing burdens and ensure quality of service (QoS). However, existing studies often overlook extra delays in the offloading process, including queuing, propagation, and wired transmission delays, significantly impacting task delay and offloading strategies. To this end, this paper proposes a multi-slice task offloading and resource allocation scheme for user-edge-cloud networks, targeting delay minimization while considering various delay factors. This scheme jointly optimizes offloading mode, offloading ratio, user association, and resource allocation under task delay constraints. To address the problem's non-convexity, an alternating optimization framework is employed to decompose the problem into offloading mode selection and resource allocation subproblems. Specifically, a deep reinforcement learning (DRL)-based algorithm is developed for offloading mode selection, while convex optimization techniques are applied to determine optimal offloading ratios and resource allocation. Additionally, a matching theory-based algorithm establishes optimal connections between users and base stations (BSs). Simulations validate the effectiveness of the proposed scheme, showing that the three-layer offloading mode, i.e., collaborative computing across user, edge, and cloud reduces latency compared to single-layer and two-layer modes for large-scale tasks.

Automatic Question Generation (AQG) aims to generate natural and relevant questions based on a given context and optional answers. It is a significant and challenging task in the field of natural language processing. However, existing AQG models often produce a single type of question with repetitive content, which hinders the diversity of the generated questions. In this paper, we introduce a Diversify Question Generation model based on the Diffusion Model (DQG-DM). Our model effectively incorporates latent variables and fine-grained question types to ensure both the relevance and diversity of the generated questions. Experiments conducted on two benchmark datasets demonstrate that our proposed model outperforms the state-of-the-art results.

In this letter, we construct a class of binary parity-check matrices with column weight 3 and show that these matrices have full-rank. Then we prove that the stopping distance of each binary parity-check matrix is equal to the minimum distance of the code specified by the parity-check matrix. Taken together, we obtain a new class of binary linear codes with optimal stopping redundancy.

For linear state estimation problems involving Brownian motion process noise, this paper proposes a novel adaptive Kalman filter that leverages online assessment of the power spectral density (PSD) for continuous-time dynamic noise. Unlike existing adaptive filters that estimate the entire noise covariance matrix, this work proposes to directly evaluate the noise PSD according to a analytical derivation for process noise covariance. As the key innovation, the proposed adaption scheme significantly reduces the number of scalar unknowns and results in enhanced accuracy for estimating the PSD of Brownian motion noise. As the resulted advantage, the new adaptive Kalman filter mitigates the crucial reliance on noise statistics without extra computation. Numerical examples of target tracking demonstrate the new adaptive Kalman filter's filtering adaptability, accuracy, and simplicity.

Recently, IoT (Internet-of-Things) devices are very widely used in our daily lives and their design and manufacturing are often outsourced to third parties to make them at a low cost. Meanwhile, malfunctions may be inserted into them intentionally by malicious third parties. Utilizing power waveforms measured from IoT devices is one of the effective ways to detect its anomalous behaviors. Most IoT devices regularly consume steady-state power due to the operating system and/or hardware components and we have to remove it from the total power to detect anomalous behaviors. However, the existing methods manually or semi-manually remove the steady-state power and further they utilize the pre-determined features in the power waveform to detect anomalies. Hence, they cannot well detect them automatically. In this paper, we propose a method, called Gen-Power2, to detect anomalous behaviors in IoT devices utilizing the generative machine-learning model. The proposed method generates an application power waveform by inferring the steady-state power by machine-learning from the observed total power waveform. Then, the anomalous application behaviors are detected by automatically extracting the latent features from the generated application power waveform. Experimental evaluations show that Gen-Power2 detects anomalous application behaviors successfully, while the recent state-of-the-art method cannot detect them.

Permutations on the vector spaces $\mathbb{F}_{q}^n$ are few at present. Inspired by the work of Chi, Li and Qu [1], we construct two classes of permutations with 3-homogeneous structures in trivariate form over $\mathbb{F}_{2^m}^3$. To establish their permutation properties, we formulate a system of equations and analyze it using techniques such as resultants, multivariate methods, and the method of undetermined coefficients.

Cache tiling or recursive data layouts for two-dimensional (2-D) data access has been proposed to ameliorate the poor data locality caused by conventional layouts like row-major and column-major. However, cache tiling and recursive data layouts require non-conventional address computation, which involves bit-level manipulations that are not supported in current processors, there is also a significant overhead in execution time due to software-based tiling address calculation. In this paper, we design a cache memory with hardware-based tile/line accessibility support for 2-D data access and a tile-set-based tag comparison (TSTC) scheme to optimize overall hardware scale overhead. Our technique captures the benefits of locality of the sophisticated data layouts while avoiding the cost of software-based address computation. Simulation results show the proposed method improves the performance of matrix multiplication (MM) over conventional data layout and Z-Morton order layout by reducing L1 cache, L2 cache and Translation Lookaside Buffer (TLB) misses, especially at larger matrix sizes. We implement the proposed cache with a SIMD-based data path by using 40 nm Complementary Metal-Oxide-Semiconductor (CMOS) technology. The entire hardware overhead of the proposed TSTC method was reduced to only 10% of that required for a conventional cache without performance degradation.

Indoor localization is essential for navigation, tracking, and path planning applications. Traditional systems, based on sensors like inertial devices and LiDAR, offer high accuracy but are costly and prone to cumulative errors. We propose a cost-effective multi-sensor fusion system specifically tailored for two-dimensional localization of two-wheeled mobile robots, combining Wi-Fi channel state information (CSI) and odometry, using an extended Kalman filter (EKF) and an adaptive Monte Carlo localization (CSI-AMCL) algorithm to enhance accuracy. Our innovative 1D convolutional neural network (1D-CNN) based on residual networks effectively processes CSI data, improving adaptability in complex environments by addressing the vanishing gradient issue. Our approach increases accuracy by 56% compared to Wi-Fi fingerprinting. Tests show a 20.1% improvement over WIO-EKF and a 36.3% improvement over Fusion-dhl. This demonstrates the potential of our method for enhancing multi-sensor fusion systems.

The skeleton modality provides an efficient representation of human pose. However, its lack of appearance information can lead to poor performance in tasks requiring such information. To address this, we propose a multimodal skeleton representation that integrates intermediate feature maps from a pose estimation network, called Pose Feature Map Enhanced Skeleton Representation (PFMESR). Specifically, we estimate the joint positions of the human body in the video and locate the local features related to each joint from the feature maps of the pose estimation network. These local features are then aligned and fused with the skeleton features in the action recognition network. We believe that the feature maps from the pose estimation network contain rich appearance information that complements the skeleton information. Experiments on multiple datasets demonstrate that this approach significantly improves action recognition performance and yields favorable results in the Action-Identity Recognition task, proving the effectiveness of incorporating appearance information from pose estimation feature maps. We also investigated the relationship between PFMESR's performance and sampling depth and range to explore its effectiveness under different parameters. Additionally, we validated the generality of PFMESR by applying it to various skeleton-based methods. Our method surpasses the state-of-the-art on multiple skeleton-based action recognition benchmarks, achieving accuracies of 94.6 % on the NTU RGB+D 60 cross-subject split, 97.7 % on the NTU RGB+D 60 cross-view split, and 93.1 % on the NTU RGB+D 120 cross-subject split.

For a regularized least squares estimation of discrete-valued signals, we propose a Linearly involved Generalized Moreau Enhanced (LiGME) regularizer, as a nonconvex regularizer, of designated isolated minimizers. The proposed regularizer is designed as a Generalized Moreau Enhancement (GME) of the so-called sum-of-absolute-values (SOAV) convex regularizer. Every candidate vector in the discrete-valued set is aimed to be assigned to an isolated local minimizer of the proposed regularizer while the overall convexity of the regularized least squares model is maintained. Moreover, a global minimizer of the proposed model can be approximated iteratively by using a variant of the constrained LiGME (cLiGME) algorithm. To enhance the accuracy of the proposed estimation, we also propose a pair of simple modifications, called respectively an iterative reweighting and a generalized superiorization. Numerical experiments demonstrate the effectiveness of the proposed model and algorithms in a scenario of multiple-input multiple-output (MIMO) signal detection.

This paper presents an iterative algorithm for computing an eigenvalue close to a user-specified value and its corresponding eigenvector of a nonlinear eigenvalue problem. This algorithm iterates two parts alternately. The first part is the existing algorithm called the successive approximation algorithm, where the Taylor expansion of a matrix is used to transform the nonlinear problem to the linear problem. By solving the linear problem, an approximate eigenvalue and an approximate eigenvector of the nonlinear problem are computed. The second part refines the approximate eigenvalue computed by the first part. To this end, we approximately compute the Rayleigh functional, which is the solution of the nonlinear equation defined by the approximate eigenvector, and use it as a new approximate eigenvalue. Experimental results show that a combination of the successive approximation algorithm and the Rayleigh functionals converges within fewer iterations and requires less computational time in comparison with the existing successive approximation algorithms.

There are some research projects on the analysis of traffic states by using mathematical models. In this article, we propose a new method based on a distributed constant circuit, namely an electrical circuit based on distributed elements. In this method the distributed constant circuit is regarded as a traffic circuit. The voltage on the distributed constant circuit is regarded as traffic pressure, like the previous works based on the lumped elements on the lumped constant circuit. However, these previous works have some difficulties on the definition of the traffic pressure. In virtue of the distributed elements composed of not Resistance, but Inductance and Capacitance, the input voltage does not augment unreasonably. The analogy between traffic circuit and electrical circuit becomes more reasonable. Moreover, the status of traffic light is also taken into account. The validity of our proposed method was confirmed by some simulations based on distributed constant circuits.

This paper presents two novel keyed hashing modes, KHC1 and KHC2, designed to construct hash functions that guarantee both collision resistance and pseudorandomness. These modes employ compression functions alongside unique encoding schemes, enabling efficient handling of variable-length inputs. The proposed constructions achieve collision resistance, provided that the underlying compression function satisfies the extended notion of collision resistance, which ensures that it is intractable to find distinct input pairs whose output difference falls within a small set. They are also proven to be secure pseudorandom functions (PRFs) under the assumption that the underlying compression function is a secure PRF under related-key attacks. They accept a 256-bit key as input and guarantee 128-bit security against quantum key recovery when instantiated with the SHA-256 compression function. Furthermore, we implemented KHC1 and KHC2 instantiated with the SHA-256 compression function and evaluated their performance. The results confirm that both constructions achieve the efficiency expected by the theoretical evaluation and outperform HMAC-SHA-256 for short messages.

We propose a probabilistic interpretation of the Gap Shortest Vector Problem by leveraging Södergren's analysis of random lattices. By linking shortest vector norms with the Gaussian heuristic, our study characterizes GapSVP behavior in high dimensions and informs secure parameter choices in lattice-based cryptography.

Symmetrical Z-complementary code sets (SZCCSs), as a novel type of training sequences, have demonstrated exceptional channel estimation performance when applied to generalized spatial modulation (GSM) systems. This paper addresses the current limitations in the design methods and parameter range of SZCCSs by proposing three schemes. Initially, by analyzing the complementary properties of Discrete Fourier Transform (DFT) matrix elements, we construct a class of optimal SZCCSs using the Kronecker product of unit modulus sequences and DFT matrices. Subsequently, to diversify the parameter of code quantity, we perform the Kronecker product with complete complementary codes (CCCs) and orthogonal matrices, resulting in a class of optimal SZCCSs. Also, if a sequence with low autocorrelation replace the orthogonal matrix, the resulting SZCCS exhibits low autocorrelation sidelobes outside the zero correlation zone and complete complementary cross correlation characteristics. The study offer a broad range of parameters not previously identified in the literature, significantly enriching the parameter space of SZCCSs. Simulation results validate that the proposed SZCCSs show superior resistance to multipath interference in GSM systems compared to traditional sequences, highlighting their potential advantages in channel estimation.

Private information retrieval (PIR) allows a client to obtain records from a database without revealing the retrieved index to the server. In the single-server model, it has been known that (plain) PIR is vulnerable to selective failure attacks, where a (malicious) server intends to learn information of an index by getting a client's decoded result. Recently, as one solution for this problem, Ben-David et al. (TCC 2022) proposed verifiable PIR (vPIR) that allows a client to verify that the queried database satisfies certain properties. However, the existing vPIR scheme is not practically efficient, especially when we consider the multi-query setting, where a client makes multiple queries for a server to retrieve some records either in parallel or in sequence.

In this paper, we introduce a new formalization of multi-query vPIR and provide an efficient scheme based on authenticated PIR (APIR) and succinct non-interatctive arguments of knowledge (SNARKs). More precisely, thanks to the nice property of APIR, the communication cost of our multiquery vPIR scheme is O(n · |a| + |π|), where n is the number of queries, |a| is the APIR communication size, and |π| is the SNARK proof size. That is, the communication includes only one SNARK proof. In addition to this result, to show the effectiveness of our multi-query vPIR scheme in a real-world scenario, we present a practical application of vPIR on the online certificate status protocol (OCSP) and provide a comprehensive theoretical evaluation on our scheme in this scenario. Especially in the setting of our application, we observe that integrating SNARK proofs (for verifiability) does not significantly increase the communication cost.

Key-insulated encryption (KIE) is one of the countermeasures against the exposure of secret keys in public-key cryptography. In KIE, a user can update secret keys with a helper key to ensure that even if many secret keys, where each of them corresponds to each time period, are leaked, the security for other time periods is not compromised. However, KIE does not have resilience against the partial exposure of secret keys. Although there is public key encryption resilient to such partial exposure, unlike KIE, it cannot ensure security against the exposure of a whole secret key. In this paper, we introduce leakage-resilient key-insulated encryption (LR-KIE) that satisfies resilience against both partial and whole exposure of secret keys. We show three LR-KIE schemes from any leakage-resilient identity-based encryption scheme and/or any leakage-resilient secret sharing scheme.

In this paper, we present a new classification method for the defining sets of η-constacyclic codes of length $n=\frac{q^4+1}{2}$. This classification can simplify the calculation of the dimensions of the Hermitian hulls for these codes, while deriving the numbers of sharing pre-shared maximally entangled states of the corresponding entanglement-assisted quantum error-correcting codes under various design distances.

Flying Ad-hoc Networks (FANETs) frequently suffer from widespread disconnections due to unpredictable external disruptions, consequently leading to network partitioning into segments in which UAVs become isolated from the base station. To address this issue, this letter proposes the Infection-based Leader Election (ILE) algorithm to autonomously restore network connectivity after disruptions. The ILE identifies and elects a leader within each disconnected segment, which subsequently guides the restoration process. By limiting information exchange to neighboring nodes, the ILE achieves significant reductions in both computational complexity and communication overhead. After the leadership election process is completed, the elected leader coordinates nodes within the partition to execute cascading movements, enabling rapid restoration of network connectivity. The simulation results validate the effectiveness of our algorithm, demonstrating notable improvements in communication overhead and restoration time.

In online social media, various fake news highly impact experience quality (QoE) of consumers. Nowadays, researchers in this area concentrated on either semantic analysis or contextual awareness. However, social media is a spatiotemporal place, and much important information such as social activities, social connection and social attributes, can make sense. Hence, this work proposes an intelligent fake news detection method by leveraging semantic spatiotemporal fusion. It uses the RoBERTa model to process posts made by users to understand their semantic content. Then, the processed data is fed into a Long Short-Term Memory network (LSTM) to capture the temporal features of the posts. Additionally, the study constructs a spatial model using Graph Attention Networks (GATs) to simulate the spread of fake news. Performance of the proposed method is testified on a realworld dataset specifically collected by our research team. And the results reveal that this innovative approach not only improves the accuracy of fake news detection but also optimizes consumer service quality.

In the constant dimension codes (CDCs) case, to explore the largest possible value A_q(n, d, k) is one of the most fundamental topics in subspace coding. Echelon-Ferrers is an approach proposed in 2009 for CDCs' construction, and has received extensive attentions. In this letter, in order to further improve the constructions, we study the method which combines linkage construction and echelon-Ferrers construction. This allows us to produce some improvements to lower bounds for constant dimension codes, including A_q(13, 4, 4),A_q(17, 4, 4), A_q(19, 6, 6) etc.

Symmetric 2-adic complexity stands out as a better measure for appraising the strength of a periodic sequence against the rational approximation attack. In this letter, we determine the symmetric 2-adic complexity of a class of interleaving cascade binary sequences with period 4N (where N ≡ 3 (mod 4)), which was introduced recently. Our results indicate that the symmetric 2-adic complexity of these sequences is not less than 2N.

Quantization is an effective way to reduce memory and computational costs in the inference of convolutional neural networks. However, it remains unclear which model can achieve higher recognition accuracy while minimizing memory and computational costs: a large model (with a large number of parameters) quantized to an extremely low bit width (1 or 2 bits) or a small model (with a small number of parameters) quantized to a moderately low bit width (3, 4, or 5 bits). In this paper, we define a metric that combines the numbers of parameters and computations with the bit widths of quantized weight parameters. By utilizing this metric, we demonstrate that Pareto-optimal performance, where the best accuracy is attained at a given memory or computational cost, is achieved when a small model is moderately quantized, not when a large model is extremely quantized. Based on this finding, we empirically show that the Pareto frontier is improved by 4.3 × in a post-training quantization scenario for a quantized ResNet-50 model using the ImageNet dataset.

Accurate prediction of transportation carbon emissions is crucial for the government to formulate transportation strategies. However, due to the characteristics of the traffic carbon emission data, such as nonlinearity, uncertainty, and abrupt changes, accurately predicting traffic carbon emissions faces significant challenges. In this paper, we propose the LNSSA-BiLSTM-MHA model, which combines the improved Sparrow Search Algorithm based on the logistic-tent-cosine chaotic mapping and the Narrowed Opposition-Based Learning Strategy (LNSSA) with the BiLSTM-MHA model to improve the accuracy of transportation carbon emission prediction. Firstly, the LNSSA algorithm is proposed for model hyperparameter search, which combines Logistic-Tent-Cosine chaotic mapping with Narrowed Opposition-Based Learning strategy to improve the SSA algorithm. Among them, the logistic-tent-cosine chaotic mapping is used to uniformly initialize the population, and the Narrowed Opposition-Based Learning strategy is used to reduce the solution space. Secondly, the BiLSTM model is used to analyze the temporal data features in both forward and reverse directions. Finally, we combine the multihead attention mechanism to learn data features from different subspaces. The private data set of six intersections of roads in Xiangyang City from 2023 to 2024 is collected. We perform model training and experimental analysis on this private dataset. Using the evaluation metrics RMSE, MSE, and MAE, the results showed that the LNSSA-BiLSTM-MHA model outperformed the comparison models LSTM, GRU, LSTM-Attention, GRU-Attention, CNN-LSTM-Attention, CNN-GRU-Attention and ResNet-GRU-Attention by 32%, 54% and 35%, respectively, in six datasets.

While COVID-19 poses significant global challenges, this study proposes leveraging deep learning models to predict adverse events post-vaccination. Using the Vaccine Adverse Event Reporting System (VAERS) dataset, which includes potential side effects of Pfizer, Janssen, and Moderna vaccines, the study partitions the data to predict vaccination adverse events (VAE) such as "died," "hospitalized," and "recovered." The proposed DeepCNNBDLSTM model combines deep neural network layers with convolutional and bidirectional long short-term memory layers. Baseline models include deep neural networks, bidirectional long short-term memory, convolutional neural networks, and long short-term memory. Performance metrics such as confusion matrix, AUCROC, accuracy, F1-score, precision, and recall are evaluated. Experimental results show the proposed model outperforms by achieving an accuracy of 89.05% for predicting 'recovered' VAE (trained on Pfizer dataset), 99.03% accuracy for predicting 'hospitalized' VAE, and 99.86% accuracy for predicting 'died' VAE (both trained on Moderna dataset). These insights may aid doctors in selecting the most effective COVID-19 vaccine for patient protection.

Partial transmit sequence (PTS) is an effective technique to mitigate the peak-to-average power ratio (PAPR) problem in orthogonal frequency-division multiplexing (OFDM) systems. Some past research proposed a Q-section coded PTS scheme with the progressive edge-growth (PEG) search algorithm for construction of the employed quasi-cyclic low-density parity-check (QC-LDPC) codes. In this paper, a new approach of the Q-section PTS scheme for PAPR reduction and error correction is devised based on algebraically constructed QC-LDPC codes with masking. The corresponding Q-section constraint is then turned into one only on the masking matrix which is easy to satisfy, based on which QC-LDPC codes can be flexibly constructed and designed without extra search complexity. Our method allows for a single algebraically constructed base matrix to generate a family of Q-section QC-LDPC codes with different lengths and rates. The performance of the constructed codes can be guaranteed by employing analysis such as density evolution. The corresponding efficient ways of encoding and decoding applicable to general QC-LDPC codes are also proposed. Our scheme need not transmit extra side information and has no extra cost compared with the traditional PTS scheme. The PAPR control patterns are embedded in specific bit positions of the codeword so that the information bits can be recovered without attempting all possible control patterns during decoding. Finally, simulation results show that our design can provide better error performance than previous codes and have similar PAPR reduction performance.

In recent years, the performance of phase-aware speech enhancement neural networks has steadily improved. However, dealing with complex-valued Short-Time Fourier Transform (STFT) spectrograms involves complex operations and phase estimation, which increases the complexity and parameter number of the model. To address this, we have built upon the foundation of DCTCRN and introduced real-valued Short-Time Discrete Cosine Transform (STDCT) spectrograms as input features, which avoids the complexities associated with phase estimation and modeling amplitude-phase relationships. To further enhance skip connections without increasing parameters, we have incorporated the SimAM attention mechanism. Additionally, we have added dual-path RNN modules between the encoder and decoder to capture long dependencies in both time and frequency dimensions. We have also introduced Hardtanh as the new scaling function. Through comparative experiments and ablation studies, we have confirmed the effectiveness of using STDCT spectrograms, attention mechanism and Hardtanh scaling function. Our approach demonstrates higher competitiveness in objective performance metrics compared to recent speech enhancement models. Notably, it achieves this while maintaining a relatively low parameter number, thus raising the performance ceiling of the DCTCRN series models.

Large-scale interactive systems require efficient performance and scalability to accommodate user demands. The response time of individual requests can be a limiting factor for overall performance. One effective solution is to employ a consistent hashing algorithm for request distribution, which alleviates the load on each server and enhances response speed. However, existing consistent hash algorithms often fail to account for the heterogeneity of resources, which may degrade performance and result in underutilized resources. To solve this problem, this paper proposes a solution based on the study of cluster load balancing using a consistent hash algorithm to increase the resource utilization. Firstly, The scheme adopts a two-dimensional spatial coordinate positioning. Partitions are formed by dividing the ring created by the value range of the CRC32 or CRC64 hash function. The partition and relative position for a key are determined by its vertical and horizontal coordinate, respectively. Secondly, nodes with different performance are assigned with different weights, and each partition also has a corresponding weight which determined by the number and weight of nodes within it. Experiments have shown that this scheme can achieve a search rate of more than 30 million keys per second, with a resource utilization rate of over 80% within the cluster, and maintain stability when changing the number of nodes, thereby improving the overall performance of the system.

In this study, blood vessel shape was accurately measured to evaluate the viscoelastic properties of the radial artery. For a robust measurement, the shape parameters of the elliptical blood vessel were determined by integrating the brightness over the entire vessel instead of using the brightness gradient at the vessel boundary. The usefulness of the proposed method is demonstrated through simulations and in vivo experiments. The results obtained exhibit considerable potential for estimating the viscoelastic properties of elliptically deformed blood vessels.

In this letter, we extend the analysis of outage probability of decode-and-forward dual-hop system over Nakagami-m fading to include non-integer fading parameters, which addresses a limitation of previous studies that were restricted to integer values. By deriving a generalized expression, we can provide a unified framework applicable to more general and practical scenarios. Extensive numerical results are presented to validate that our proposed closed-form formula can embrace more general Nakagami-m fading environments, obviously showing its compactness and accuracy.

Approximate computing (AC) has been increasingly used to meet the growing demand for energy-efficient and high-performance computing in error-tolerant applications, particularly in the fields of machine learning, signal processing, and multimedia applications. Approximate synthesis tools are an essential component in approximate circuit design and optimization as they enable designers to trade-off accuracy for reduced power consumption, area, and delay. However, these approximate synthesis tools are quite vulnerable to adversarial attacks as it is quite challenging to distinguish between a change in the functionality of the given circuit due to an approximation or a Hardware Trojan (HT). In this paper, we propose an approach for HT detection using Triple modular Redundancy in APproximate Synthesis (HT-TRAPS) tools. The approach also utilizes functional and side channel power and timing analysis to distinguish approximations from HTs and is applicable for either identifying a clean netlist or detecting the presence of a HT at the gate-level post-approximate synthesis stage. We implement the HT-TRAPS approach using a full stack of open-source and commercial tools, including BLASYS, ABACUS, SCOAP and Xilinx Vivado, and thoroughly evaluate its effectiveness by detecting various Trojan benchmarks, such as B19-T100, B19-T200, and B19-500 Trojans, inserted in several circuits, such as the approximated Absolute Difference calculator, Butterfly filter, a classifier, x2, a 16-bit Multiplier and a 32-bit Adder.

A system of zero-knowledge proofs on graph signatures has been proposed, where a graph can be signed, and the owner of the graph signature can prove a graph relation such as the connectivity and isolation of any two vertexes on the graph without disclosing all information about the graph. The correctness of the graph information is guaranteed by the signature. One of the applications is a virtualized infrastructure, where an infrastructure provider manages a distributed system, and each tenant is allocated a specific portion of this infrastructure for use. Tenants need to check with the provider that their resources are properly connected (connectivity) and that their resources are properly separated from the resources of other tenants (isolation). On the other hand, the provider cannot simply disclose the entire infrastructure topology to each tenant. Using the zero-knowledge proof system on graph signatures, both requirements can be addressed. Previously, an efficient zero-knowledge proof system on graph signatures using a bilinear-map accumulator has been proposed, where the verification time and the size of the proof data do not depend on the number of graph vertexes and edges. However, this system has two problems. First, since the proof does not include labels, it is not possible to prove the connectivity considering network bandwidth and cost. Second, since it assumes undirected graphs, it cannot handle applications on directed graphs such as network flows. In this paper, we extend the previous system and propose a zero-knowledge proof system of the connectivity for directed graphs where each edge has labels. We implemented our system on a PC using a pairing library and evaluate it by measuring the processing times. Compared to the conference version of this paper, we show the formal definitions and the security proofs of our proposed system, and add implementation-based evaluations reflecting the application to the virtualized infrastructure.

This letter studies the robust transceiver designs for multiple-input multiple-output (MIMO) interference channels with imperfect channel state information (CSI). The sum power minimization problem is considered subject to signal-to-interference-plus-noise ratio (SINR) constraint under CSI mismatch with statistical error model. Since the problem is a joint design of transmitter and receiver beamformers, an alternating approach is developed by exploiting the primal decomposition. Specifically, closed-form expressions are derived as a solution for the robust transceiver design. Numerical results are provided to demonstrate the robustness of the proposed algorithm and the performance efficiency in combating channel errors.

Precise and automatic segmentation of pulmonary lesions is crucial for assisting pulmonologists in accurate diagnosis and decision-making. Despite advances in deep learning, segmenting pulmonary nodules remains challenging due to factors like small lesions, irregular boundaries, and data imbalance. We propose an edge detail enhancement method (EDC-UNet) for pulmonary nodule segmentation, which integrates deformable convolutional layers to improve flexibility for various lesion morphologies and dilated convolution-based residual blocks to enhance feature extraction. Additionally, a Sobel-based detail supervision module in the decoder helps capture low-level spatial details, improving segmentation of blurred edges. Extensive experiments on the LIDC-IDRI and LUNA16 datasets demonstrate that EDC-UNet outperforms other models, highlighting its potential for medical image analysis.

Automatic Micro-Expression (ME) spotting is a fundamental yet essential task in Micro-Expression analysis from videos. Due to the brief and subtle of ME, the ME spotting task is challenging and the spotting performance need to be further improved. However, current works generally neglect the correlation between expression proposals in a video. In this work, we propose a two-stage relation-aware graph convolutional network (MES-RANet) to locate the temporal positions of macro- and micro-expression. First, we adopt a temporal evaluation module (TEM) to predict the frame-level probabilities from the spatial-temporal feature sequences and generate candidate proposals for the subsequent module. Specifically, in relation-aware module (RAM), we formulate video proposals as graph nodes, and proposal-proposal correlations as edges to construct graphs. Then apply the relation-aware network to model the relations among proposals and learn powerful representations for the boundary regression. Comprehensive experiment results show that MES-RANet is effective and achieves competitive performance compared with state-of-the-art methods on two public benchmark datasets CAS(ME)² and SAMM-LV. Codes are available at https://github.com/hahaluluyo/MES-RANet.

Automatic modulation recognition in low-computation edge devices under noisy environments has become increasingly critical. To address the dual challenges of lightweight design and noise resistance, this paper proposes WCTFormer, a novel lightweight network that integrates discrete wavelet transform for frequency-domain noise suppression and a Transformer for global feature extraction, enabling robust performance in low SNR conditions. Experiments on open-source datasets demonstrate that WCTFormer achieves superior recognition accuracy, with 92.40% accuracy at 0 dB SNR, requiring only 60K parameters. WCTFormer combines high recognition performance and computational efficiency, making it suitable for deployment in resource-constrained edge devices.

The stream version of asymmetric binary systems (ABS) invented by Duda is an entropy coder for information sources with a finite alphabet. It has the state parameter l of a nonnegative integer and the probability parameter p with 0 < p < 1. First we observe that the edge shift X_G associated with the stream version of ABS has the topological entropy h(X_G) = log 2. Then we define the edge shift X_H associated with output blocks from the stream version of ABS, and show that h(X_H) = h(X_G), which implies that X_G and X_H are finitely equivalent. The encoding and decoding algorithms for the stream version of ABS establish a bijection between X_G and X_H. We consider the case where p = 1/β with the golden mean β = (1 + √5)/2. Eventually we show that X_G and X_H are conjugate for l = 7, and that they are almost conjugate for l = 10.

For a given forward transmission scheme, the backward transmission in reverse direction is often wanted in practical communications. This letter proposes an optimal scheme for the backward transmission of the propagation-delay (PD) based general X channel with two receivers, which is reciprocal to forward transmission. With cyclic interference alignment, the desired messages are shown to be allocated at specified time-slots without interference, while other interfering messages are aligned in the remaining time-slots. Thus the backward transmission achieves the maximal degrees of freedom (DoF) with the reciprocal PD channel, which supports optimal bidirectional transmission of the X channel with two receivers.

Codebooks possessing optimal correlation property are utilized to differentiate signals among various users in code-division multiple-access (CDMA) systems. In this paper, a class of codebooks is proposed based on additive characters over finite fields and is verified to be optimal with respect to the Levenshtein bound. By shortening the length of the presented optimal codebooks, a kind of asymptotically optimal codebooks is obtained. To our knowledge, the presented asymptotically optimal codebooks have new parameters. Further more, these optimal and asymptotically optimal codebooks are highly desirable in synchronous Direct-Sequence CDMA systems, as they facilitate the expansion of subscriber capacity within the constrained spectral resources.

In this letter, we optimize the weight distribution of the polar codes by exchanging elements in the information set and the frozen set. Based on the work of M. Rowshan et al., we propose two improvements. First, the rows with the twice weight of the minimum weight row are usually selected by the original scheme for the added rows. By our improvement, we select rows with the largest weights such as 4 or 8 times of the minimum weight row. This modification optimizes the weight distribution of polar codes. Furthermore, if there is a case where the selected row weights are the same, the row with higher reliability is selected. Second, we consider the influence of balancing rows for the removed rows. In the original scheme, one core row is removed and then an added row is selected at each step. Our strategy is to fix the position of several added rows, and then remove the core rows or balancing rows globally. This modification can not only reduce the number of Min-Weight codewords but also optimize the channel reliability of the removed rows. Simulation results show that the decoding performance of modified codes has been greatly improved at the practical block error rate of 10^-2 - 10^-3 without changing the code rate of the polar codes.

Accurate scene representation holds practical significance for autonomous driving and virtual reality. This letter proposes a network to optimize images encoding and features learning for better scene representations. Experimental results show that this method can render high-quality novel images on both synthetic and real-world datasets.