Analog in-memory computing (AIMC) executes matrix-vector multiplications (MVMs) inside memory to alleviate the von Neumann bottleneck and improve energy efficiency. This tutorial classifies AIMC circuits in a memory-agnostic way, namely, current-domain, charge-domain, charge-redistribution, capacitive-division, resistive-division, and time-domain IMC. We explain each type of AIMC circuit with simple mathematical models. Furthermore, we review key device and circuit nonidealities (e.g., process variation, IR drop, sneak paths, and I/O quantization/nonlinearity) with practical mitigation strategies in circuitry and peripherals. Finally, we organize hardware-aware training into three complementary families — probabilistic/precise modeling, physical modeling, and hardware-in-the-loop techniques — providing a mathematically grounded bridge between circuits and learning for robust, scalable AIMC accelerators.

Interfacing physical world with digital world is essential in recent days to enrich our cyber physical systems. Low-power system architecture design is particularly important because of the available resource limitations, especially available power and communication bandwidth. As an efficient way to cope with limited resources the authors have proposed to “do most of the things before changing energy domains”. Local computational sensing and direct use of energy such as kinetic energy for sensing and computation have a potential to drastically reduce electrical power consumption. This article summarizes such new attempts of integrated Micro Electro Mechanical Systems (MEMS) low-power computing and sensing, with theoretical prediction towards miniaturization to High Aspect Ratio Nano Systems (and/or Structures, HARNS).

We propose a method for reliability research at the circuit-level chips. The degradation model used combines the Wiener process and the Arrhenius acceleration model. The degradation data analyzed is the pin leakage current sampled during the constant stress acceleration degradation test of Flash memory chips at different temperatures. This method has low testing costs while providing a comprehensive reflection of the degradation conditions of the tested samples. In this work, we established the model through mathematical derivation, and then estimated the distribution of the model parameters by generating bootstrap samples. Then, under the premise of completing the model accuracy test, we completed the estimation of the remaining life of the sample through Monte Carlo simulation.

In this article, the authors propose an electronic, linear, continuously tunable phase shifter (PS) with an adjustable phase range of leading +180° to delay -180° within the phase shift bandwidth (PSBW) and a maximum adjustable phase range of +240° to -240° within the half power bandwidth (HPBW). This PS enables flexible design of its operating frequency and PSBW and maintaining a constant linear slope of -116.6° per susceptance (jb) for any design PSBW. The PS consists of two different wavelengths of transmission lines and four identical LC parallel resonators (LC tanks), with phase variation achieved by adjusting the capacitance values, susceptance values, or resonance frequencies within the LC tanks. The authors also present an analysis of the electrical theory underlying this PS. Theoretically, the transmission coefficient T ranges from 0 dB to -0.973 dB, and the reflection coefficient Γ peaks at -6.99 dB. Additionally, the authors provide design guidelines for the PS. Based on these guidelines and available lumped components, a PS operating at 3 GHz with a PSBW of 400 MHz was developed. Finally, measurement results confirm a generally consistent between theoretical predictions and practical implementation. This demonstrates that the proposed PS can be designed with arbitrary center frequencies and PSBW, exhibiting excellent S-parameters and linear continuously tunable phase shifting performance.

Many researchers expect to apply microrobots in narrow environments for tasks such as exploration and maintenance. However, digital control, which is the primary robot control method, faces computational cost and circuit miniaturization challenges. The authors have been studying neuromorphic circuits, which mimic biological neural functions for robot control, acting as a central pattern generator (CPG) as a driving circuit to perform the walking motion. Previously, we constructed a neuromorphic circuit on an integrated circuit and we successfully implemented the neuromorphic integrated circuit in millimeter-scale microrobots. However, the microrobot lacked sensory input, which prevented the robot from adapting to the robot’s movement in response to external environmental changes. This paper proposes a neuromorphic integrated circuit capable of adaptively switching the gait of an insect-type microrobot in response to light stimuli. The proposed circuit incorporates a receptor cell model that mimics biological sensory neurons, enabling the transformation of external light input into electrical signals using photovoltaic cells (PV cells). The electrical signals are processed through synaptic and CPG models to switch locomotion patterns. The authors systematically measured gait patterns to evaluate the operating range while varying the power supply voltage of the receptor cell model and the output voltage of PV cells. We observed the results, which clarified the regions where stable wave gait, tripod gait, or unstable outputs. Furthermore, we measured the I-V characteristics of a PV cell. Also, we confirmed that its output voltage matches the designed switching threshold of the proposed circuit, enabling optical control without additional signal conditioning. These findings demonstrate that the proposed circuit can be a low-power, sensor-responsive gait controller for future autonomous microrobots.

Although a sampling rate and a resolution of a time-interleaved A/D converter (ADC) have improved remarkably by recent advance of digital calibration, it is still difficult to achieve an effective resolution larger than 12 bits for sampling rate higher than 1 GS/s. This limitation is mainly caused by higher-order effect of sampling-timing mismatch among unit converters. To overcome the fundamental limitation, fully digital calibration of a time-interleaved ADC with cascaded higher-order sampling-timing correction is presented. In the proposed correction method, by using a reference ADC as an only additional analog component, analog tuning is eliminated, allowing mismatch effects to be corrected solely through post-digital processing. Due to its fully digital nature and unlimited correction in principle provided by the cascaded processing, accuracy is only limited by digital implementation cost, which is mitigated significantly with CMOS scaling. The extension to a sub-sampling time-interleaved ADC is also presented for a broad range of applications. Effectiveness of the proposed calibration was verified by extensive simulation with the 3rd-order sampling-timing correction for both standard and sub-sampling time-interleaved ADCs as well as measurement of a prototype time-interleaved ADC, which proved 11.5-bit effective resolution (71.2-dB SNDR) at 1 GS/s with the 2nd-order correction.

Frequency-hopping multiple access (FHMA) systems offer significant advantages such as jamming resistance, multi-user capability, and enhanced security, making them crucial for secure communications and wireless networks. This letter establishes new theoretical bounds on the maximum periodic Hamming autocorrelation (PHAC) and periodic partial Hamming autocorrelation (PPHAC) for multi-timeslot wide-gap frequency-hopping sequences (MTWGFHSs), along with the maximum periodic partial Hamming correlation (PPHC) for MTWGFHS sets. These advancements address the challenges posed by dynamic channels and intelligent jamming, contributing to a robust FHMA system.

Hopfield networks have been extended to two dimensional multistate models such as complex-valued Hopfield networks. They have been applied to storage of multilevel information, such as image data. Few 3D models of Hopfield networks have been proposed. First, vector product Hopfield networks (VPHNs) were proposed. However, VPHNs have two disadvantages, the resolution and learning algorithms. Since the activation function is defined based on the regular polyhedrons, the resolution factor is limited. The projection rule, which is a practical learning algorithm, is not applicable for VPHNs. Quaternionic vector product Hopfield networks (QVPHNs), which are extensions of VPHNs, are proposed to solve the second disadvantage. Nevertheless, the QVPHNs require more weight parameters than the VPHNs. Recently, Hopfield networks were extended using group theory. In this paper, cyclic-group-ring-valued Hopfield networks are introduced, such that the projection rule is applicable without increase in weight parameters. We also determine the stability conditions for projection rules. The noise tolerance and memory costs for weight parameters are considered to be the trade-off. Our computer simulations show that the CGRVHNs achieve the noise tolerance according to the memory costs.

This paper proposes a distributed dual decomposition algorithm for solving mixed-integer linear programming (MILP) problems in multi-agent systems. In the proposed approach, a MILP problem is transformed into an approximated problem by linear programming relaxation, which enables each agent to independently solve their local subproblems. The theoretical analysis provides guarantees on both the feasibility error bounds and the optimality gap of the solutions obtained by the proposed method. The effectiveness of the proposed method is shown through a numerical example of a fairness-aware multiple traveling salesman problem.

In this paper, a switching control method for pedestrian flows using the discrete Hughes model with signal temporal logic is proposed. In the discrete Hughes model, the space is given by an undirected graph, and pedestrian flows are considered as changes of the density at each vertex. To specify various properties of the system, we introduce signal temporal logic. In the proposed method, pedestrian flows are controlled by switching the graph structure. For each graph structure, a safety penalty is set. The switching times of the graph structure that minimize the penalty are calculated under constraints on a given signal temporal logic formula including the evacuation rate defined in this paper. A numerical example shows that the proposed method is effective for evacuation planning and exit control.

In this study, we propose Modern Complex-Valued Hopfield Network (Modern CVHN), a novel associative memory model designed for continuous data with inherent periodic structure. The model operates on a toroidal state space — constructed as the Cartesian product of complex unit circles — and performs memory encoding and retrieval via a softmax-based energy function that intrinsically incorporates periodicity. Through numerical experiments, we demonstrate that Modern CVHN achieves superior memory capacity and robustness to noise compared to both conventional Complex-Valued Hopfield Networks and Modern Hopfield Network, across discrete phase patterns and continuous periodic data. These findings underscore the effectiveness of energy-based modeling on toroidal manifolds for associative memory involving periodic structures. This approach offers a promising foundation for future applications in complex information processing tasks characterized by periodicity.

This study examines data-driven attack detectors that utilize maximum likelihood estimation for cyber-physical systems. The proposed methodology optimally estimates the input-output trajectories of these systems using both pre-experimental data and real-time measurements, in accordance with the principles of maximum likelihood. The attack detector identifies the presence of an attack by comparing the estimated trajectories with actual measured trajectories. The theoretical contributions of this study include the demonstration of a fundamental limitation, specifically, the set of undetectable attacks when disturbances are negligible. Furthermore, when disturbances can not be disregarded, the proposed method can detect attacks with a specified false rate based on the detectable condition derived through a χ² test.

A distributed network system is a dynamical system in which multiple subsystems are connected through a physical/communication network. There are many applications such as power systems. In this paper, we propose a method of event-triggered model predictive control for distributed network systems with switching topologies. In the proposed method, each subsystem is controlled by state-feedback using its state and the state of neighbors. Its gain is calculated by the upper controller (supervisor), only when a certain event-triggering condition is satisfied. The design problem of state-feedback gains is reduced to an LMI (linear matrix inequality) optimization problem. Finally, the proposed method is demonstrated by two numerical examples.

As the global energy mix shifts toward renewable energy, microgrids, as a key enabler for efficient energy utilization and the integration of distributed generation, face complex and ever-changing energy management challenges. The uncertainty of the output of distributed power sources (such as solar and wind power), the interactions between devices, and the multi-objective optimization requirements within microgrids make it difficult for traditional energy management methods to achieve accurate forecasting and efficient scheduling. To address these challenges, this paper proposes the TGD-RL model, an innovative approach that combines deep learning techniques. This model integrates three advanced techniques: the Transformer, the Graph Neural Network (GNN), and the Deep Q-Network (DQN). The Transformer module utilizes a multi-head attention mechanism to capture long-term temporal dependencies in microgrid data, making it suitable for processing data with time-series characteristics. The GNN uses graph convolution operations and node embedding techniques to model the topology and dynamic interactions of each device in the microgrid. The DQN uses a state-action-reward mechanism to continuously optimize energy management strategies and achieve efficient scheduling decisions. Experimental results on two public datasets, PJM and MISO electricity market price data and NREL wind and solar data, demonstrate that the TGD-RL model outperforms other baseline models in energy forecast accuracy, achieving mean absolute percentage errors (MAPEs) of 6.5% and 5.8%, respectively, representing reductions of 36.3% to 39.0% compared to the optimal baseline model. The operating costs of the microgrids were reduced to 1,250 yuan and 1,180 yuan, respectively, representing decreases of 17.4% to 37.8%, while energy self-sufficiency increased to 78% and 82%, respectively, representing increases of 10.0% to 26.0%. Ablation experiments further validated the essential role of each component in the model’s performance. This research demonstrates that the TGD-RL model can effectively address complex energy management issues in microgrids, providing a new technical path for improving the economic efficiency, stability, and energy self-sufficiency of microgrids. It also holds significant implications for the development of smart grids and the efficient use of renewable energy.

Recent advancements in Convolutional Neural Networks (CNNs) have led to significant increases in computational complexity and power consumption. Approximate computing, especially through the use of approximate multipliers, has emerged as a promising approach to realize the reduction of power, area and delay by sacrificing some computational precision. Approximate multipliers produce results with errors, thus their effects on CNN accuracies need to be evaluated. However, the lack of efficient software tools for simulating approximate multipliers hinders their widespread adoption. This paper proposes ApproxTorch 2.0, a high performance simulation framework based on PyTorch and CUDA for evaluating 8-bit signed integer based approximate multipliers for CNNs. To realize higher performance, CUDA and Nvidia GPUs are used to accelerate the simulation process. Two approximate layers are implemented: 2D Convolution layer and Linear layer. The simulation of approximate multiplications is done by accessing pre-defined look-up tables (LUTs) stored in GPU memory. By dedicated optimization in the customized CUDA approximate GEMM kernel, ApproxTorch 2.0 achieves 171× speedup compared with CPU simulation and 17.2× speedup compared with previous ApproxTorch 1.0 on running ResNet50. Lastly, gradient estimation is added to support the retraining of approximate CNNs with quantized values and proved to be effective for recovering the accuracy loss after approximation.

In stabilizer-based quantum secret sharing schemes, it is known that some shares can be distributed to participants before a secret is given to the dealer. This distribution is known as advance sharing. It is already known that a set of shares is advance shareable only if it is a forbidden set. However, it was not known whether any forbidden set is advance shareable. We provide an example of a set of shares such that it is a forbidden set but is not advance shareable in the previous scheme. Furthermore, we propose an advance sharing scheme for stabilizer-based quantum secret sharing of quantum secrets such that any forbidden set is advance shareable.

Compression by Substring Enumeration (CSE), which is one of the lossless data compression algorithms, and various versions of CSE have been proposed. In encoding of CSE, substrings of given fixed length and their frequencies within circular string for an input string are output as a codeword. The circular string is made by connecting the first symbol and the last symbol of an input string. In decoding of CSE, the circular string is reconstructed from its substrings and their frequencies. Furthermore, the minimum length of substrings for which the decoding does reconstruct the circular string has been proved, together with a reconstruction algorithm. However, the algorithm requires substrings to have no errors. Therefore, in this paper, we propose an error correcting algorithm which can detect one of the substrings having one bit-flipping, one bit-insertion, or one bit-deletion error and correct the bit error. By applying the proposed algorithm, we can reconstruct a circular string from a set of substrings and their frequencies including only one substring which has at most one bit error.

Private information retrieval (PIR) is a mechanism for retrieval of a message while keeping its index secret. Tian’s and Sun’s methods are representative PIR methods, which are mainly evaluated in terms of download rate. In this paper, we propose a new method based on Shamir’s secret sharing. Furthermore, a comprehensive evaluation including communication cost, computation time, and message size is performed; our results demonstrate that the proposed method outperforms conventional ones.

Infrasound monitoring is regarded as promising for the early detection of disasters because infrasound, which is often generated by strong natural events, propagates across long distances through the atmosphere at the speed of sound. An important obstacle is that no available sensor device can measure the entire frequency range of infrasound at low cost, which hinders the construction of a sensor network for infrasound monitoring. Fusion sensing might be one workaround to alleviate this hurdle to future development. This paper presents a proposal for the fusion of a MEMS pressure sensor and a miniature infrasound microphone, with subsequent investigation of optimal signal processing to merge the outputs of these two sensors having different characteristics. The proposed method is based on a gradient descent algorithm combined with deep unfolding. After the hyperparameters of the gradient descent algorithm are optimized using deep learning under suitable constraints, the filter coefficients are derived through parallel observation with a high-precision reference sensor using an arbitrary signal containing a wide frequency band. These study findings demonstrated that the proposed fusion sensing achieves approximately 26% reduction in RMSE compared with a simple combination of low-pass and high-pass filters or a direct sum of the two sensor outputs.

Specific emitter identification plays a crucial role in the field of information security. To improve identification performance in complex electromagnetic environments, this letter proposes a dual-branch network, FADC-Transformer, which combines frequency-aware dynamic convolution (FADC) and Transformer. It can adaptively fuse frequency-domain features from FADC and time-domain features from the Transformer. Specifically, FADC introduces a frequency-band attention mechanism and dynamic kernel generation, which can dynamically adjust convolutional kernel parameters according to the inputs, resulting in better robustness. Experimental results show that the accuracy of FADC is improved by 16% compared with static convolution, and the dual-branch structure significantly enhances identification performance.

Accurately predicting energy consumption of the heating ventilating and air conditioning system is crucial for achieving building energy conservation. To overcome the limitations of traditional methods in terms of generalization capacity, this study proposed a novel prediction method for the HVAC system energy consumption based on the ensemble learning model. In this method, the operation variables of chilled and cooling water systems (i.e., supply/return temperature and flowrate) and environmental variables (i.e., temperature and humidity) are utilized as inputs, three single models (i.e., support vector regression, extreme learning machine, and decision tree) are employed as the base models, and the Gaussian process regression is utilized as the stacked model. Experiments are conducted on the HVAC system of the public building, and the performance of the ensemble learning model is verified using practical measured data. Results show that the ensemble learning model is superior to the single models in predicting cooling capacity, heat dissipation and total power. More importantly, the ensemble learning model can provide reliable confidence intervals, effectively quantifying the uncertainty of the prediction results.

Cayley hash functions, which are hash functions based on walks on Cayley graphs of groups, are a well-studied class of hash functions with provable security (under assumptions on computational hardness of some group-theoretical problems). In a recent work by Aikawa, Jo, and Satake (Transactions on Mathematical Cryptology, 2023), they proposed a variant of Cayley hash functions called left-right Cayley hash functions, whose design intended the problem of finding a collision to be as difficult as the problem of finding collisions of two Cayley hash functions simultaneously. In this paper we show, as opposed to the expectation, that finding a collision of their hash function is reduced to finding a collision of a single Cayley hash function. We also propose a possible countermeasure against this issue.

In order to terminate iterations earlier of the linear programming (LP) decoder based on the alternating direction method of multipliers (ADMM) (ADMM-LP), this letter proposes an early termination (ET) criterion based on the number of parity-check constraints of iterative solution vector. The criterion can be used to detect erroneous codewords in advance and thereby avoid unnecessary iterations, without increasing the additional computational complexity. Compared with existing termination criteria of ADMM-LP decoding algorithm, the proposed ET criterion can significantly reduce the average number of iterations at low signal-to-noise ratio (SNR) regions with little effect on the decoding performance.

Integrated sensing and communication (ISAC) enables simultaneous wireless communication and environmental sensing, where joint beamforming design is key to balancing both tasks. To address the complexity and trade-off challenges of joint beamforming design in multi-user multi-target ISAC systems, this letter proposes the cross-attentive Pareto transformer (CAPT), an end-to-end deep learning framework that integrates enhanced spatial embeddings and cross-attention to jointly optimize beamforming. By leveraging Pareto multi-task learning, CAPT efficiently generates the Pareto front of solutions in a single inference. Simulation results show that CAPT achieves better Pareto front quality and generalization than weighted minimum mean square error (WMMSE) and convolutional neural network (CNN)-based baselines.

Modulation is crucial for multiplexing, reducing bandwidth, and improving transmission efficiency. By incorporating neural networks, we can optimize modulation for specific communication channels. We propose two neural network optimized modulation schemes: a regular constellation mapping for 16-ary modulation and an irregular mapping for 2^m-ary modulation. These maintain gradient flow during backpropagation, allowing adjustments to constellation points to minimize bit error rates (BER) while keeping system complexity manageable. The results show our polar-coded modulation schemes outperform traditional uniform QAM with about a 0.5 dB gain under low SNR. Additionally, these schemes can also be applied to LDPC-coded modulation systems to improve BER performance.

With the development of artificial intelligence technology, the demand for multi-domain pose estimation in complex scenes is growing. The existing single model solution faces the problem of insufficient ability in cross domains of multi-scene, which is caused by the different numbers of keypoints and complex features of samples. To solve this problem, we propose a new method that has a four-stage pose estimation framework. This framework applies methods such as object detection, domain classification and pose estimation. As the experimental results show, on the testing set of mixed domains, the accuracy of our method is 5.1% higher than the best one of the existing methods, which ensures high performance pose estimation in many applications.

This paper introduces a latent port-Hamiltonian framework using deep learning to improve the robustness for vision-based control. Although reinforcement learning and deep learning are promising solutions to control system states with differentiable policies, physics-free methods usually suffer from unstable and low-confident results with respect to the system dynamics. We propose a vision-based control architecture by employing a port-Hamiltonian model in the latent space of autoencoder (AE) to achieve physically consistent control. Specifically, we apply a variational autoencoder (VAE) to encode visual observations into a low-dimensional latent space, where the port-Hamiltonian energy structure is learned. Moreover, we introduce AI-Pontryagin, which generates control signals similar to optimal control inputs through a neural network inspired by optimal control theory. The experimental results show that our method achieves more accurate and stable control performance compared to baseline approaches.