This paper proposes a methodology of delivering the emulation hardware of several step-down converter power stages. The generalized emulator design methodology follows these steps: first, the power stage is described using an ordinary differential equation system; second, the ordinary differential equation system is solved using Euler’s method, and thus an accurate time-domain model is obtained; next, this time-domain model can be described using either general-purpose programming language (MATLAB, C, etc.) or hardware description language (VHDL, Verilog, etc.). As a result, the emulator has been created; validation of the emulator may be carried out by comparing it to SPICE transient simulations. Finally, the validated emulator can be implemented on the preferred target technology, either in a general-purpose processor or a field programmable gate array. As the emulator relies on the ordinary differential equation system of the power stage, it has better behavioral accuracy than the emulators based on average state space models. Moreover, this paper also presents the design methodology of a manually tuned proportional–integrative–derivative controller deployed on a field programmable gate array. Full article

Quantum dots (QDs) are zero-dimensional (0D) nanomaterials with charge confinement in all directions that significantly impact various applications. Metal-free organic quantum dots have fascinating properties such as size-dependent bandgap tunability, good optical absorption coefficient, tunability of absorption and emission wavelength, and low-cost synthesis. Due to the extremely small scale of the materials, these characteristics originated from the quantum confinement of electrons. This review will briefly discuss the use of QDs in solar cells and quantum dots lasers, followed by a more in-depth discussion of QD application in photodetectors. Various types of metallic materials, such as lead sulfide and indium arsenide, as well as nonmetallic materials, such as graphene and carbon nanotubes, will be discussed, along with the detection mechanism. Full article

A CMOS widely tunable second-order $G_m$-C bandpass filter (BPF), intended to be used in multi-sine bioimpedance applications, is presented. The filter incorporates a tunable transconductor in which the responses of two linearized voltage-to-current converters are subtracted. As a result, the effective transconductance can be continuously adjusted over nearly three decades, which allows a corresponding programmability of the center frequency of the BPF. The circuit was designed and fabricated in 180 nm CMOS technology to operate with a 1.8 V supply, and the experimental characterization was carried out over eight samples of the silicon prototype. The simulated transconductance of the cell can be tuned from 5.3 nA/V up to 19.60 $\mathsf{\mu }$A/V. The measured range of the experimental transconductance varied, however, between 1.42 $\mathsf{\mu }$A/V and 20.57 $\mathsf{\mu }$A/V. Similarly, the center frequency of the BPF, which in the simulations ranged from 500 Hz to 342 kHz, can be programmed in the silicon prototypes from 22.4 kHz to 290 kHz. Monte Carlo and corner simulations were carried out to ascertain the origin of this deviation. Besides, the extensive simulation and experimental characterization of the standalone transconductor and the complete BPF are provided. Full article

Digital image transmission plays a very significant role in information transmission, so it is very important to protect the security of image transmission. Based on the analysis of existing image encryption algorithms, this article proposes a new digital image encryption algorithm based on the splicing model and 1D secondary chaotic system. Step one is the algorithm of this article divides the plain image into four sub-parts by using quaternary coding, and these four sub-parts can be coded separately. Only by acquiring all the sub-parts at one time can the attacker recover the useful plain image. Therefore, the algorithm has high security. Additionally, the image encryption scheme in this article used a 1D quadratic chaotic system, which makes the key space big enough to resist exhaustive attacks. The experimental data show that the image encryption algorithm has high security and a good encryption effect. Full article

An approach is proposed to the filtering of an additive mixture of ultra-wideband chaotic signals and white Gaussian noise, in order to retrieve the useful signal in the receiver. The role of the filter is performed by a passive frequency-selective circuit, identical to the one involved in the formation of oscillations in the chaos generator. A mathematical model of a modulating chaos generator, detecting and receiving a sequence of ultra-wideband chaotic radio pulses in a noisy channel is designed. For the receiver of sequences of symbols encoded by chaotic radio pulses with 2- and 4-position modulation, the bit error ratio as a function of the noise level and the pulse duration is estimated numerically. Full article

The rapid development of the automobile industry has made life easier for people, but traffic accidents have increased in frequency in recent years, making vehicle safety particularly important. This paper proposes an improved YOLOv5s algorithm for vehicle identification and detection to reduce vehicle driving safety issues based on this problem. In order to solve the problems of a disappearing model training gradient in the YOLOv5s algorithm, difficulty in recognizing small objects and poor recognition accuracy caused by the boundary frame regression function, it is necessary to implement a new function. These aspects have been enhanced in this article. On the basis of the traditional YOLOv5s algorithm, the ELU activation function is used to replace the original activation function. The attention mechanism module is then added to the YOLOv5s algorithm’s backbone network to improve the feature extraction of small and medium-sized objects. The CIoU Loss function replaces the original regression function of YOLOv5s, thereby enhancing the convergence rate and measurement precision of the loss function. In this paper, the constructed dataset is utilized to conduct pertinent experiments. The experimental results demonstrate that, compared to the previous algorithm, the mAP of the enhanced YOLOv5s is 3.1% higher, the convergence rate is 0.8% higher, and the loss is 2.5% lower. Full article

Currently, neighborhood linear discriminant analysis (nLDA) exploits reverse nearest neighbors (RNN) to avoid the assumption of linear discriminant analysis (LDA) that all samples from the same class should be independently and identically distributed (i.i.d.). nLDA performs well when a dataset contains multimodal classes. However, in complex pattern recognition tasks, such as visual classification, the complex appearance variations caused by deformation, illumination and visual angle often generate non-linearity. Furthermore, it is not easy to separate the multimodal classes in lower-dimensional feature space. One solution to these problems is to map the feature to a higher-dimensional feature space for discriminant learning. Hence, in this paper, we employ kernel functions to map the original data to a higher-dimensional feature space, where the nonlinear multimodal classes can be better classified. We give the details of the deduction of the proposed kernel reverse neighborhood discriminant analysis (KRNDA) with the kernel tricks. The proposed KRNDA outperforms the original nLDA on most datasets of the UCI benchmark database. In high-dimensional visual recognition tasks of handwritten digit recognition, object categorization and face recognition, our KRNDA achieves the best recognition results compared to several sophisticated LDA-based discriminators. Full article

The initiative of users to participate in power grid operation is a key factor in realizing the optimal allocation of power. Demand response (DR) management mechanisms based on real-time pricing (RTP) can effectively promote the enthusiasm of users, stimulate the efficiency of microgrids for power dispatch, and achieve the goasl of power peak shifting and valley filling. In this paper, we consider a microgrid composed of several energy providers (EPs) and multiple users, and each user is equipped with several electric vehicles (EVs). It should be noted that EVs may be attacked by networks in the process of data exchange when EVs connect to the MG. In this environment, we establish a multi-time slots social welfare maximization model that reflects the common interests of EPs and users. To simplify the problem, we decompose this multi-time slots model into a set of single-time slot optimization problems by the relaxation method. Furthermore, the mechanisms of identification and processing (MIP) for EVs under cyber-attack are proposed. The problem is decoupled to EPs and users by duality decomposition. Then, through integration with MIP, a distributed RTP algorithm based on the dual subgradient algorithm is designed to obtain the optimal electricity price. Finally, the simulation results verify the feasibility of the model and the effectiveness of the proposed algorithm. Through comparative analysis, the necessity of identifying EVs under cyber-attack is fully embodied. Full article

The Internet of Things (IoT) has become increasingly popular, and opened new possibilities for applications in various domains. However, the IoT also poses security challenges due to the limited resources of the devices and its dynamic network topology. Routing attacks on 6LoWPAN-based IoT devices can be particularly challenging to detect because of its unique characteristics of the network. In recent years, several techniques have been proposed for detecting routing attacks, including anomaly detection. These techniques leverage different features of network traffic to identify and classify routing attacks. This paper focuses on routing attacks that target the Routing Protocol for Low-Power and Lossy Networks (RPL), which are widely used in 6LoWPAN-based IoT systems. The attacks discussed in this paper can be categorized as either inherited from Wireless Sensor Networks or exploiting vulnerabilities unique to RPL (known as RPL-specific attacks). The paper describes various RPL attacks, including Flood Attacks, Data-DoS/DDoS Attacks, Wormhole Attacks, RPL Rank Attacks, Blackhole Attacks, Version Attacks, and Sinkhole Attacks. In this paper, a novel Hybrid Intrusion Detection System (HIDS) that combines a decision tree classifier and a one-class Support Vector Machine classifier is proposed to detect routing attacks. The HIDS draws on the strengths of both a Signature Intrusion Detection System (SIDS) and an Anomaly-based Intrusion Detection System (AIDS) to identify routing attacks with a high degree of accuracy and a low false alarm rate. The routing dataset, which features genuine IoT network traffic and various kinds of routing attacks, was used to test the proposed HIDS. According to the findings, the hybrid IDS proposed in this study outperforms SIDS and AIDS approaches, with higher detection rates and lower false positive rates. Full article

Timing-mismatch errors among channels in time-interleaved analog-to-digital converters (TIADCs) greatly degrade the whole performance of the system. Therefore, techniques for calibrating timing mismatch are indispensable, and a new fully-digital calibration technique is presented in this article. Based on a Hilbert filter, modified moving averagers (MMAs) and inverse cosine functions, the proposed estimation algorithm is fast (within 1200 sample points) and accurate. Meanwhile, the coordinate rotational digital computer (CORDIC) algorithm, which is used to implement inverse cosine functions, is also improved, giving it higher precision. In addition, a compensation method based on second-order Taylor series approximation with less hardware resource consumption is provided. Through analyses and simulations, this calibration technique proved to be suitable for TIADCs with an arbitrary number of channels, in which the signal-to-noise and distortion ratio (SNDR) and the spurious-free dynamic range (SFDR) were, respectively, improved from 24.06 dB and 24.57 dB to 67.96 dB and 85.69 dB. Full article

Tone mapping (TM) aims to display high dynamic range scenes on media with limited visual information reproduction. Logarithmic transformation is a widely used preprocessing method in TM algorithms. However, the conventional logarithmic transformation does not take the difference in image properties into account, nor does it consider tone mapping algorithms, which are designed based on the luminance or gradient-domain features. There will be problems such as oversaturation and loss of details. Based on the analysis of existing preprocessing methods, this paper proposes a domain-aware adaptive logarithmic transformation AdaLogT as a preprocessing method for TM algorithms. We introduce the parameter p and construct different objective functions for different domains TM algorithms to determine the optimal parameter values adaptively. Specifically, for luminance-domain algorithms, we use image exposure and histogram features to construct objective function; while for gradient-domain algorithms, we introduce texture-aware exponential mean local variance (EMLV) to build objective function. Finally, we propose a joint domain-aware logarithmic preprocessing method for deep-neural-network-based TM algorithms. The experimental results show that the novel preprocessing method AdaLogT endows each domain algorithm with wider scene adaptability and improves the performance in terms of visual effects and objective evaluations, the subjective and objective index scores of the tone mapping quality index improved by 6.04% and 5.90% on average for the algorithms. Full article

The traditional spectrum sensing algorithm based on deep learning requires a large number of labeled samples for model training, but it is difficult to obtain them in the actual sensing scene. This paper applies self-supervised contrast learning in order to solve this problem, and a spectrum sensing algorithm based on self-supervised contrast learning ($SSCL$) is proposed. The algorithm mainly includes two stages: pre-training and fine-tuning. In the pre-training stage, according to the characteristics of communication signals, data augmentation methods are designed to obtain the pre-trained positive sample pairs, and the features of the positive sample pairs of unlabeled samples are extracted by self-supervised contrast learning to obtain the feature extractor. In the fine-tuning stage, the parameters of the feature extraction layer are frozen, and a small number of labeled samples are used to update the parameters of the classification layer, and the features and labels are connected to get the spectrum sensing classifier. The simulation results demonstrate that the $SSCL$ algorithm has better detection performance over the semi-supervised algorithm and the traditional energy detection algorithm. When the number of labeled samples used is only 10% of the supervised algorithm and the $SNR$ is higher than −12 dB, the detection probability of the $SSCL$ algorithm is higher than 97%, which is slightly lower than the supervised algorithm. Full article

In this paper, the human-like motion issue for anthropomorphic arms is further discussed. An Intelligent Human-like Motion Planner (IHMP) consisting of Movement Primitive (MP), Bayesian Network (BN) and Coupling Neural Network (CPNN) is proposed to help the robot generate human-like arm movements. Firstly, the arm motion model is decoupled in the aspects of arm structure and motion process, respectively. In the former aspect, the arm model is decoupled into different simple models through the Movement Primitive. A Hierarchical Planning Strategy (HPS) is proposed to decouple a complete motion process into different sub-processes. Based on diversified arm motion models, the Bayesian Network is used to help the robot choose the suitable motion model among these arm motion models. Then, according to the features of diversified arm motion models, the Coupling Neural Network is proposed to obtain the inverse kinematic (IK) solutions. This network can integrate different models into a single network and reflect the features of these models by changing the network structure. Being a major contribution to this paper, specific focus is on the improvement of human-like motion accuracy and independent consciousness of robots. Finally, the availability of the IHMP is verified by experiments on a humanoid robot Pepper. Full article

Source code clone detection, which can identify code fragments with similar functions, plays a significant role in software development and quality assurance. Existing methods either extract single syntactic or semantic information, or ignore the associated information between code statements in different structures. It is difficult for these methods to effectively detect clone pairs with similar functions. In this paper, we propose a new model based on a dual graph convolutional network (GCN) and interval-valued hesitant fuzzy set (IVHFS), which we named DG-IVHFS. Specifically, we simplified and grouped the abstract syntax tree (AST) of source code to obtain the group representations. The group representations of the AST, as well as the control flow graph (CFG) representations, were transformed into graph structures, and then we applied GCNs on them to learn dependencies between nodes. In addition, we introduced IVHFS into the model for a more comprehensive evaluation of similarity. Our experimental results demonstrated that the precision, recall, and F1-scores of DG-IVHFS on the BigCloneBench and GoogleCodeJam datasets reached 98, 97 and 97% and 98, 93 and 95%, respectively, exceeding current state-of-the-art models. Moreover, our model performed well in terms of time consumption. Full article

With extensive access to distributed power sources and the rising electricity load, the structure and tide of distribution networks are becoming increasingly large and complex, leading to great challenges for fault location methods. In this paper, the power coupling phenomenon of the T-section in the distribution network is studied, and a hierarchical optimization model for fault location is proposed based on the port equivalence principle, which divides the fault location into two levels—area location and section location—to reduce the fault search dimension. Then, an improved binary particle swarm optimization algorithm (IBPSO) applied to the area location is proposed to improve the convergence accuracy and speed by optimizing the convergence criterion and integrating the chaotic mapping and mutation strategies. Finally, based on the topological characteristics of the sections in the fault area, a fault candidate scenario screening method based on the fault confidence factor is proposed to realize a second dimensionality reduction in the section location link. Simulation tests show that the proposed method demonstrates a good dimensionality reduction effect for large-scale, active distribution networks; additionally, the accuracy rate is improved by 25.7% and the location speed is improved by 300 ms when compared with traditional fault location methods. Full article