The kT/C cancellation technique can effectively reduce the DAC size for SAR ADCs and thus relax the burden for input drivers and ref-erence buffers. However, the prior kT/C cancellation technique suffers from a hard trade-off between the noise, amplifier bandwidth and linearity. In this work, an improved kT/C noise cancellation technique is proposed to break the trade-off. It uses presampling to hold the input signal unchanged during the noise cancelling phase, leading to sig-nificantly relaxed requirements on the bandwidth and linearity of the amplifier. The proposed technique is verified in a 14-bit 200MS/s SAR ADC with the DAC size of 128 fF. Simulation results show that this work achieves 75.6 dB SNDR and consumes 1.56 mW power.

In order to maximize the promotion effect of renewable energy policies, this paper proposes a capacity allocation optimization method of wind power generation, solar power and energy storage in power grid planning under different policy objectives. Firstly, based on the policy quantification, grey relation analysis is used to calculate the correlation degree of the policy indicators on the planning capacity of renewable energy. Further, a multi-objective capacity estimation model is comprehensively presented. Some highly correlated policy indicators are transformed into the special constraints. And the economy and the stability of the power grid are integrated as the objective function. Meanwhile, the carbon trading and punishment for wind power and solar power abandonment is considered. Finally, the proposed model is solved by NSGA-Ⅱ-PSO algorithm. The novelty of the algorithm is that the crossover operation of NSGA-Ⅱ is replaced by the position updating of particle swarm. The calculation result of the case study can effectively evaluate the optimal planning capacity of renewable energy under different policies, while ensure the economic and the stability of the power system. The paper can provide the reasonable basis and the valid analytical method for the policy formulation and the renewable energy development.

In a drone system with dual data links, this paper presents a redundant data processing algorithm that can minimize flight control instability without increasing the weight and price of the aircraft by software processing of duplicate received messages.

Emerging forecasting-aided state estimation (FASE) frequently encounters complicated parameter analysis and observation calculation tasks, especially when confronted with intricate and uncertain scenarios. To this end, a concise FASE estimator is developed by combining the precise depiction of dynamic state change and linear power flow approximation. Designing the dynamic system state as a voltage perturbation vector around the nominal value, the forecasted state is firstly derived from the linear approximation of power injection equation solutions. The state forecasting model relies solely on nodal impedance information as the state transition matrix, eliminating the onerous parameter tuning effort. After that, the optimal filtered state is efficiently obtained utilizing line power flow measurements, with branch admittance information to construct the approximate observation matrix. Numerical simulation comparisons on a symmetric balanced 56-node distribution system verify the performance of the proposed estimator in terms of accuracy and robustness.

Power system unreliability tracing model allocates the system’s reliability index to individual components, identifying potential weaknesses. This study expands its scope by considering the impact of storage resources. Unreliable factors leading to load shedding are categorized into two groups: objective factors inherent to the component and insufficient storage resources. The latter requires a retrospective analysis of other components that caused unreliability previously. When allocating responsibility for load shedding at a certain time, it begins by allocating it among components based on differences between fixed expected output and actual supply. Expected output insufficiency is considered the unreliable factor. This insufficiency due to insufficient storage resources is then decomposed into segments, each caused by excessive output in earlier instances of the same component. The expected output excess is attributed to the expected output insufficiency of other components in previous times, for which responsibility has been allocated to each component. Consequently, the expected output insufficiency at a particular time can be traced back based on a temporal recursive model, with the load shedding further allocated to components before that time. Case studies based on several systems demonstrate that the proposed model’s allocation results are reasonable and more accurate than the traditional model.

Retinopathy of Prematurity (ROP) mainly occurs in premature infants with low birth weight, and it is the leading cause of childhood blindness. Early and accurate ROP diagnosis is imperative for appropriate treatment. However, less research concentrates on early-stage ROP diagnosis based on limited-labeled images in an imbalanced dataset. To address the dilemma, this study proposed a novel self-supervised network, MOCO-MIM, for early ROP grading. The proposed classification network was evaluated on a total of 553 labeled fundus images from 89 preterm infants. The trained network achieved a test accuracy of 98.29% and an AUC score of 97.6% for three stages of grading. The adopted method is verified that the proposed method can be detected early stages of ROP more efficiently and grade the severity more accurately based on limited-labeled fundus images, which is superior to the existing state-of-the-art methods.

Silent Speech Recognition (SSR) based on Surface Electromyography (sEMG) is a voice interaction technology proposed for scenarios requiring silent operations. In this article, we abstract the SSR task based on sEMG into a short-term image sequence classification task. We perform time-frequency domain feature extraction and data reconstruction on the muscle activity segment data. Additionally, we analyze the temporal and spatial dimensions to capture the intrinsic correlation representation of muscle activity. We propose the SVIT-SSR model based on the Vision Transformer (VIT) framework. Finally, we design experiments to identify 33 types of typical silent speech commands in the SSR dataset. The results demonstrate that the proposed model achieves an accuracy of 96.67±1.15%, outperforming similar algorithms.

Feature extraction from the normalized transformation domain is a key technique in target detection. Traditional normalization approaches assume that matrix elements follow a normal distribution, but any deviations from this assumption can lead to significant systematic errors. This article presents a novel method that modifies the normalization process in the fractional Fourier transform (FRFT) domain by incorporating a threshold mechanism to counteract the effects of non-normal distributions. Three modified FRFT features are then extracted from this modified FRFT domain. Furthermore, we propose a target detection method that utilizes these three adjusted features. Experimental results based on measured data indicate that the modified FRFT features exhibit superior classification capabilities for sea clutter and targets compared to the original ones. Additionally, the experiments also demonstrate that under the same conditions, the proposed detection method outperforms traditional FRFT feature detector and the tri-feature based detector.

In modern power systems, the randomness and uncertainty of the generation and loads greatly increase the balance discrepancy between the power supply and demand, which introduces major potential security hazards to the stable operation of power systems. In particular, out-of-bounds frequencies emerge. To control the system frequencies, a frequency control method based on the state space model for power systems under small, uncertain disturbances is proposed in this paper. Considering the general model of the power system frequency response with the aim of improving the input‒output finite-time stability of the system, the frequency deviation of the power system is controlled in a closed loop. The Monte Carlo simulation of different systems shows that the proposed method can effectively control the frequency deviation of the power system. When the closed-loop system is disturbed by uncertain power fluctuations, its frequency will always remain within the allowable range to ensure the secure and stable operation of the power system.

A document by Matthew Chen. Click on the document to view its contents.

In this paper, we investigate joint dictionary representation for Massive MIMO satellite channel and discuss the representation performance. What kind of the dictionary model is satisfied for channel representation is still an unknown field. This paper mainly focuses on the analysis of the joint dictionary for channel representation including uplink and downlink. The main contributions are as follows. Firstly, the conditional constraints for satellite channel representation have been established with joint dictionary, including both uplink constraints and downlink constraints. Secondly, the maximum boundary that dictionary learning can represent channel characteristics is determined, that is, the optimal approximation of channel dictionary was achieved. Finally, channel sparse representation method for joint SVD decomposition at dictionary boundary conditions is proposed.

Traditional deep convolutional networks (ConvNets) have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single ConvNets to extract the underlying relationship and complementary features between these two kinds of modalities. In this paper, we proposed a novel two stream ConvNet for multi-modality action recognition by joint optimization learning to extract global features from RGB and depth sequences. Specifically, a non-local multi-modality compensation block (NL-MMCB) is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi-modality human action datasets, including NTU RGB+D 120 and PKU-MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed NL-MMCB can learn complementary features and enhance the recognition accuracy.

Point cloud data is a large collection of high dimensional 3D points with 3D coordinates and attributes, which has been one of the mainstream representations for emerging 3D applications, such as virtual reality, autonomous vehicles and robotics. Due to the large-scale unstructured high-dimensional nature of point clouds, point cloud processing, transmitting and analysing has been challenging issues in multimedia signal processing and communication. Deep learning is a powerful tool to learn statistical knowledge from massive data. Advances in artificial intelligence, especially deep learning models are offering new opportunities for point cloud processing, compression and analysis. This special issue aims at promoting cutting-edge research on deep learning-based point cloud processing, including object detection, segmentation, registration, compression, and visual quality assessment.

Leveraging the ultra-wideband advantages of the Terahertz (THz) band, Integrated Sensing and Communication (ISAC) facilitates high-precision sensing demands in human smart home applications. ISAC channel characteristics are the basis for ISAC system design. Currently, the ISAC channel is divided into target and background channels. Existing researches primarily focus on the attributes of human target itself, e.g., radar cross-section and Micro-Doppler effect. However, the impact of human target on neither the pathloss characteristic of background channel nor the multipath propagation characteristic of target channel is considered. To address the gap, we conduct indoor channel measurements at 105 GHz to investigate the ISAC channel characteristics with the impact of human target. Firstly, by analyzing the power angular delay profiles with and without human target, we observe the changes in quantity and power of multipath components (MPCs). Then, we propose a parameter called power control factor (PCF) to evaluate the human target impact on pathloss, thereby modifying the existing pathloss model of background channel. Eventually, we extract the MPCs belonging to target channel within target-oriented power delay profile to count the power proportion of each bounce MPCs of the target-Rx link, which supports the necessity of multi-bounce (indirect) paths modeling in target channel.

To solve the problem of negative-sequence and reactive power in electrified railway, this paper proposes a comprehensive compensation method based on V/v transformers and electromagnetic single-phase var compensator (ESVC) for co-phase power supply. First, based on the principles of static var generator (SVG) and single-phase rotary phase shifting transformer (SRPST), the topology and model of ESVC have been established, and the compensation mechanism has been analyzed. Second, the topological structure and mathematical model of co-phase power comprehensive compensation device (CPCD) are put forward based on the principles of V/v transformers and ESVC, and the operation mode of CPCD in multiple scenarios is designed. Then the strategy of CPCD with double closed loop control is analyzed: corresponding compensation mode is selected according to negative-sequence unbalance degree and expected power factor value. In this strategy, the compensated current output by ESVC is taken as the quantity of external loop control, and the rotation angle of ESVC is taken as the quantity of internal loop control to realize double-closed loop control of CPCD. Finally, the comprehensive CPCD compensation model is built on the simulation platform and validated the accuracy of the mathematical model and the effectiveness of the control strategy.

This paper introduces a millimeter-wave Integrated Substrate Gap Waveguide (ISGW) filtering antenna featuring four adjustable radiation nulls, with two located in each of its upper and lower stop bands. These nulls are tunable by altering the dimensions of stepped-impedance resonators, complementary U-slots, and passive coplanar parasitic patches. The antenna offers the benefits of individually adjustable radiation nulls and the flexible modification of selectivity and gain curve roll-off. Simulation results indicate the antenna operates at a central frequency of 25.4 GHz, with a relative bandwidth of 14.3% (23.76–27.04 GHz), and attains an average gain of 7.6 dBi.

This paper proposes a unilateral Adaptive update strategy based Interval Prediction (AIP) model for short-term load prediction, which is developed based on lower and upper bound estimation (LUBE) architecture. In traditional LUBE interval prediction model, the model training is usually trained by heuristic algorithms. In this paper, the model training is formulated as a bi-level optimization problem with the help of proposed unilateral adaptive update strategy and cost function. In lower-level problem, a simplified biased convex cost function is developed to supervise the learning direction of basic prediction engines. The basic prediction engine utilizes Gated Recurrent Unit (GRU) to extract features and Full Connected Neural Network (FNN) to generate interval boundary. In upper-level problem, a unilateral adaptive update strategy with unilateral coverage rate is put forward. It iteratively tunes hyper-parameters of cost function during training process. Comprehensive experiments based on residential load data are implemented and the proposed interval prediction model outperforms the tested state-of-the-art algorithms.

For citrus trees cultivated by dwarf dense planting, the fruits are randomly distributed in space, which poses difficulties for mechanized picking. To improve picking efficiency, a citrus picking system based on dual robot collaboration is designed and a global scanning picking scheme that scans the entire fruit tree to achieve orderly fruit picking is proposed in this research. The dual-robot calibration with the iterative method and closed-form method is completed in the research. The picking problem is attributed to the single traveling salesman problem, and the trajectory planning and the picking task are completed with genetic algorithm in the research. The picking experiments are designed in the research. As a result, in the picking experiments, the average time for planning the picking sequence of citrus was 0.1184 seconds. In each group of citrus picking experiments, the total picking time averaged 158.9 seconds, the picking success rate is 82%. The picking results show that the built dual-machine system can effectively complete the picking task. The proposed dual-robot picking system can provide a reference for the establishment of other picking robot system.