Journal of Circuits, Systems and Computers

Image sharpening detection, as a crucial branch of image forensics research, has attained a satisfactory level of performance with the assistance of deep learning. However, due to the nature of convolutional neural network (CNN) models, adversarial examples synthesized by generative adversarial networks (GANs) can easily attack existing forensics models. Therefore, deep learning-based forensics faces new challenges. In this paper, a novel architecture inspired by ConvNext is proposed to detect synthesized adversarial USM sharpening images. Through practical demonstration, our proposed technique achieves satisfying performance in recognizing adversarial samples that outperform previous sharpened image forensic systems. In addition, we have undertaken an ablation analysis of our suggested network topology and analyzed the efficacy of different enhancements.

Micro coal-fired units (MCFU) and combined heat and power plants (CHP) in integrated energy system (IES) will emit a large amount of carbon dioxide when providing loads to customers, which will lead to higher operating costs of IES. To solve this challenge, an optimal dispatch model of power-to-gas (P2G) and methane reactor (MR) considering the reward and punishment costs based on carbon emission trading mechanism is proposed, to reduce carbon emissions of IES and enhance the accommodation of renewable energy (RE). Subsequently, considering the uncertainty of RE, a combination optimization method for MCFU and CHP units is developed based on the Lagrange multiplier method. Finally, considering the mechanism of electricity spot market (ESM), a transaction strategy for IES participating in ESM is proposed to further enhance the accommodation of RE. The effectiveness of the proposed framework is demonstrated through simulations.

Clock attacks and power attacks are used as common means to inject faults in embedded devices. Critical analysis of the properties and the components engaged with these fault injection methods is of high importance to assess the related dangers and furthermore to enhance the process of designing suitable counter-measures. This paper provides critical analysis of induced faults on combinational and sequential circuits and study effects of various attacks (over-clock, under clock, power attack). While doing analysis, it was revealed that clock attacks i.e., under and over clock attacks have similarity index of (80–90%). Also it was found that power attacks have results similar (60–70%) to clock attacks but they cause propagation delay in circuits affecting the output. Cadence virtuoso 45 nm technology node, Keil and Multi-sim software’s were used to generate various attacks. The comparative analysis reveals that the attack timing and frequency of the glitch is the key while inducing fault injection attacks. While performing this work, it is observed the sequential circuits get less impacted by attacks comparing to combinational circuits. This research is conducted from an attacker’s perspective instead of mitigation perspective.

This paper deals with the design, simulation, and practical modeling of metamaterial-based multiband conformal bandpass filter (BPF) for various wireless communication applications with improved quality factors. The novel metamaterial in the form of a split ring resonator is loaded on the ground plane face of the proposed BPF. The overall dimension of the designed BPF is only $28\times 28{\mathrm{\text{mm}}}^2$. The proposed BPF is tuned initially for quality factor enhancement based on the thickness of the substrate, physical parameters of the f transmission line, ground plane, externally loaded elements, and the gap in the metamaterial loading. The suggested filter operates at triple band covering the frequency bands from 1.4 to 2.2, 3.6 to 3.9, and 4.8 to 5.9GHz, which are suitable for sub-6GHz 5G and other wireless applications. The insertion loss is observed as 1dB, which is suitable for the proposed BPF. The conformal behavior of the filter is judged through bending deformation analysis at various bending positions like (15${}^{\circ }$, 30${}^{\circ }$, 45${}^{\circ }$, 60${}^{\circ }$, and 90${}^{\circ }$). The proposed BPF retains triple pass band characteristics at various bending deformations, which makes it suitable to be used in curved structures or flexible circuitry. The theory of equivalent circuits and quality factor $(Q)$ of the designed BPF is discussed in this paper. The results are analyzed experimentally through ANRITSU-MS2037C combinational analyzer. The proposed BPF is suitable for sub-6 GHz 5G, WLAN, and Wi-Max applications.

Drilling data communication mainly includes mud pulse telemetry and electromagnetic wave telemetry. These methods have the advantages of economy and convenience, but they also have the disadvantages of low data transmission rate and large signal attenuation. A data transmission system while drilling was designed based on the key technologies and basic principles of Orthogonal Frequency Division Multiplex (OFDM). The hardware of the system includes the Universal Software Radio Peripheral (USRP) motherboard and daughter board, power amplifier circuit and piezoelectric transducer. The software used in the system is C and Python. Compared with traditional methods, new methods using acoustic signals have many advantages. This new wireless transmission system was designed by combining down-hole data transmission with wireless communication. This method has advantages in oil exploration and development.

This paper proposes a new four-dimensional hyper-chaotic system capable of generating multi-wing chaotic attractors by introducing active magnetron memristors, multi-segmented square functions and trigonometric functions. The dynamical properties of this new hyper-chaotic system, such as equilibrium point, dissipation, Lyapunov exponential spectrum, bifurcation diagram and Poincaré cross-section and attraction basin, are analyzed theoretically and simulated numerically, and the complexity of this system with different parameters is analyzed. It is observed that this hyper-chaotic system has periodic, chaotic and hyper-chaotic variations with an infinite number of equilibria and coexisting attractors under different parameter conditions. The circuit simulation was performed using Multisim and the results obtained were consistent with the numerical analysis of the dynamics, and the chaotic circuit system is designed by FPGA to verify the realizability of the system. Finally, an image encryption algorithm is designed in conjunction with the DNA algorithm to enable a new system chaotic sequence for image encryption. The results show that the hyper-chaotic system has rich dynamical behavior and has high-security performance when applied to image encryption with strong chaotic key and plaintext sensitivity and large key space in image encryption.

Object detection occupies a very important position in the fishing operation and autonomous navigation of underwater vehicles. At present, most deep-learning object detection approaches, such as R-CNN, SPPNet, R-FCN, etc., have two stages and are based on anchors. However, the previous methods generally have the problems of weak generalization ability and not high enough computational efficiency due to the generation of anchors. As a well-known one-stage anchor-free method, CenterNet can accelerate the inference speed by omitting the step of generating anchors, whereas it is difficult to extract sufficient global information because of the residual structure at the bottom layer, which leads to low detection precision for the overlapping targets. Dilation convolution makes the kernel obtain a larger reception field and access more information. Multi-branch structure can not only preserve the whole area information, but also efficiently separate foreground and background. By combining the dilation convolution and multi-branch structure, multi-branch dilation convolution is proposed and applied to the Hourglass backbone network in CenterNet, then an improved CenterNet named multi-branch dilation convolution CenterNet (MDC-CenterNet) is built, which has a stronger ability of object detection. The proposed method is successfully utilized for detection of underwater organisms including holothurian, scallop, echinus and starfish, and the comparison result shows that it outperforms the original CenterNet and the classical object detection network. Moreover, with the MS-COCO and PASCAL VOC datasets, a number of comparative experiments are performed for showing the advancement of our method compared to other best methods.

Image semantic segmentation technology is one of the core research contents in the field of computer vision. With the improvement of computer performance and the continuous development of deep learning technology, researchers have more and more enthusiasm to study the actual effect and performance of image semantic segmentation. The results of deep semantic segmentation allow computers to have a more detailed and accurate understanding of images, and have a wide range of application needs in the fields of autonomous driving, intelligent security, medical imaging, remote sensing images, etc. However, the existing image semantic segmentation algorithms have the disadvantages of easy discontinuous results and insufficient prediction accuracy. In this paper, we take deep learning-based image semantic segmentation technology as the research object to explore the improvement of the image semantic segmentation algorithm and its application in road scenarios. First, this paper proposes MCU-Net method based on residual fusion and multi-scale contextual information. MCU-Net uses residual fusion module to deepen the network structure and improve the ability of U-Net to acquire deeper features. Then a top-down and bottom-up path is constructed for feature information between different levels, and the spatial and semantic information contained in shallow and deep features in the network is fully utilized by fusing features from different levels. In addition, an enhanced void space pyramid pooling module is added for feature information between the same levels, which enables the output features to have a larger range of semantic information. Second, this paper proposes the DAMCU-Net method based on attention mechanism and edge detection based on MCU-Net. DAMCU-Net extracts global contextual information by the attention mechanism optimization module, while fusing features using dense jump connections to facilitate the network to recover more spatial detail information during upsampling, and uses the FReLU activation function to improve the segmentation capability of the network for complex targets. For the edge information lost in the feature extraction process, the edge detection branch is added to supplement the feature information of the main path by feature fusion to achieve the optimization of the edge information.

Dynamic binary translation (DBT) is a core technology that enables the migration of legacy software to different instruction set architectures while maintaining the original semantics. However, the development and maintenance of an efficient cross-DBT system are challenging. Key challenges include memory access overhead, inefficient instruction simulation, and frequent context switches. In this paper, we propose three novel optimization techniques. First, we formalize a register mapping cost model and investigate a hierarchical register mapping approach to bridge the memory access overhead. Second, we accelerate floating point (FP) emulation by surrounding the use of hardware FP unit with high-efficiency non-FP code. Third, we present a function inlining approach to alleviating the overhead associated with indirect control lookup. On the system side, we implement our approach on ARM64 and SW64 architectures based on QEMU and extensively evaluate the effectiveness with the SPEC2006 benchmark suite. The experimental results show that an average of 1.28× performance speedup and 13.41% code size reduction can be achieved on SW64. Similarly, on ARM64, we achieve an average of 1.15× performance speedup and 11.48% code size reduction.

In data acquisition scenario of edge computing, the optimization of UAV (Unmanned Aerial Vehicle) deployment is of great significance for making use of resources of UAV. We establish an optimization model of UAV cluster deployment in the edge data acquisition system. The model takes the height of UAV as the solving variable, which is more in line with the realistic characteristics. DEVIPSK-SA-FWA is proposed according to the characteristics of this model. The algorithm uses a novel coding mechanism, and uses K-Means to accelerate the convergence process of the algorithm. A variety of differential evolution mutation operators are used to form a self-adaptive strategy pool mechanism to carry out variable scale variation of population, which complete the global search well. Then fireworks algorithm searches the population locally after each round of global search. In our algorithm, global search and local search are well balanced and local optimal is effectively escaped. Finally, experimental results indicate that DEVIPSK-SA-FWA is capable of solving the model with good results, and the superiority of DEVIPSK-SA-FWA is verified through the Wilcoxon rank sum test method. In the best case, the proposed algorithm reduces energy consumption of edge data acquisition system by 32.87$\%$.

In the current nanoscale regime, fin field effect transistor (FinFET) technology overcomes the limitations of metal oxide semiconductor field effect transistor (MOSFET) technology. Robust and low-power static random access memory (SRAM) design is a demanding task for memory designers, especially in the nanoscale regime. Therefore, this paper proposes a 10 transistor (10T)-based SRAM cell design using low-power FinFET technology. The proposed approach not only reduces the leakage current, but also improves cell stability in different states. The proposed SRAM cell is simulated and analyzed at a 10nm technology node using a multi-gate predictive technology model (PTM) for the transistors with a power supply of 0.7V. The comparison analysis is also presented with the existing designs. The read and write static noise margins, and SRAM electrical quantity matrix (SEQM) of the proposed SRAM cell are improved by 3.54×, 1.71× and 26.41×, respectively, compared with the conventional 6T (C6T) design. The reliability investigations and comparison of the proposed SRAM cell have been carried out using Monte Carlo simulations with $\pm 10$% deviations in the process parameters. The reliability analysis shows that the proposed SRAM cell is less sensitive to process variations.

Nowadays, kidney stone disease is one of the most common health issue which needs more attention for early diagnosis. Several imaging modalities are used for the detection of kidney stone. The gold standard CT scans are valuable for kidney stone detection. For kidney stone detection, machine and deep learning-based algorithms are widely used. In order to enhance the performance of earlier techniques, two techniques are developed. Initially, an AlexNet-based model is developed in this work. By using the enhanced recognition capability of Random Forest (RF), we developed a hybrid AlexNet-RF model. Both the models are tested against Kidney Stone Detection dataset. The performance of the proposed model proved that in terms of accuracy and loss the hybrid AlexNet-RF model secured reliable higher detection rate of approximately 97.1% to 97.5%. This showed that embedding RF in the Softmax layer of AlexNet significantly improves the prediction rate of kidney stone.

In the current technological era, the reliability growth model depicts the failure in software that is employed for estimating the reliability of software. Conventional software reliability growth model (SRGMs) usually presume that faults in software are immediately corrected once a software fault is found and no new faults are introduced. This consideration can be impractical. However, introduction of several new faults is possible throughout debugging, which is called imperfect debugging. During such a process of debugging and introduction of new faults, consumption of testing effort also plays an important role. Hence, in this paper, two models are proposed that incorporate testing effort along with imperfect debugging during fault correction. Additionally, the combined testing effort is the addition of two efforts, specifically detection effort as well as correction effort. The formulated models are validated for real data set and contrasted with additional famous SRGMs. These inferences indicate that Model 2 performs best in terms of fitting and prediction capability.

In this paper, the controller modelling and design for the Active Switched Capacitor/Switched Inductor Quasi-Z-Source with Multilevel Inverter (ASC/SL-QZSI) related three-phase grid-tied photovoltaic (PV) power system is proposed. The ASC/SL-QZSI control method consists of two phases: these are assessed with the use of the proposed effective controller. The proposed control system is the hybridization of the Student Psychology-Based Optimization (SPBO) and the Radial Basis Function Neural Network (RBFNN), hence it is named SPBO–RBFNN control scheme. The ASC/SL-QZS offers greater boost capability, uses fewer passive components, like inductors and capacitors, and reduces the voltage stress across main inverter switching devices. The expandability of this topology is another advantage. Extra cells can simply be cascaded at the network’s impedance if a higher boost rate is required by adding an inductor and three diodes. In the proposed control scheme, SPBO is developed for determining the total PV voltages. The input PV reference voltages and gain parameters of the SPBO are created as output for optimal tuning of the Proportional Integral (PI) controller. RBFNN is trained with offline process and it is used to extract the reference currents of the grid, and the output of RBFNN is provided with SPBO. It delivers the corresponding tuning parameters to accomplish the grid current. With this proper control, the input power is reduced and the current, voltage and frequency conditions of DC-link are regulated. Finally, the performance of the QZS-CMI is executed in MATLAB and the performance is compared with existing methods.

In order to verify the functional correctness of quantum circuits or algorithms, a prominent approach is to specify them as quantum programs and semi-automatically deduce them in a theorem prover. It is indispensable to first formalize the semantics of the basic quantum language. We formalize in Coq an imperative language which allows for classical and quantum information interactions. We define small-step operational semantics and state-based denotational semantics. Then we prove a consistency theorem between these two semantics. A distribution-based denotational semantics is also defined and related to the state-based one. Finally, we describe a few typical quantum algorithms and utilize the distribution-based denotational semantics to verify their correctness.

While using polynomial functions to define window functions is an initial approach in studying the memristor element, it is susceptible to generating imaginary results. However, using window functions, including the trigonometric function, is a current field of research on the memristor element. This paper uses the trigonometric Blackman window function to present a new memristor element model and investigates its nonlinear ion drift model properties. The motivation of this study is the usage of the trigonometric Blackman window function, which presents a more detailed definition and leads to more accurate results in windowing operations. The Blackman window function can address the issues of border locking and terminal state. Numerical simulations have verified this proposed structure. Additionally, the analog realizations of the memristor element constructed with the Blackman window function have been achieved on a Field Programmable Analog Array, which offers fast prototyping, serving as an alternative approach for emulating memristors.

This paper focuses on deep vision sensing-assisted gesture recognition for athletes in dynamic scenes. Although many research attention had been devoted to this field in recent years, most of existing works failed to fully take characteristics of dynamic scenes into consideration. To deal with this challenge, this paper proposes a diffusion convolution neural network-based multiview gesture recognition approach in dynamic scenes. For one thing, the dynamic spatiotemporal slice position selection based on the body mask heatmap is adopted to calculate positions of horizontal and vertical slices. Thus, the dynamic selection of slice positions in two directions can be realized, and then the extraction of bi-directional spatiotemporal slice images can be completed. For another, action sequences through the 3D residual neural network are learned, and the spatiotemporal information among frames are mined through recurrent networks. Through their combination, a multi-view gesture recognition approach for athletes is constructed. In the experiments, two standard datasets UCF101 and HMDB51 are utilized to establish simulation environment. The proposed method can reach the accuracy beyond 95% on the two datasets. Compared with several typical recognition methods, the proposed method shows higher accuracy.

The automobile driving axles contain many types of transmission gears, as well as complex dimensional constraints among them. This makes it a cumbersome work for design of the whole vehicle. To improve design efficiency and reduce consumption of expert labors, it is necessary to search digital design for manufacturing optimization of automobile driving axles. As a result, this paper proposes a modular mathematical modeling method to realize smart design and manufacturing of automobile driving axles. First, with use of modular segmentation, design process of automobile driving axles can be abstracted as mathematical expressions according to their basic functions. Through solution of the developed model objective, main performance indexes of automobile driving axles under various utilization conditions can be automatically output by computers. In addition, to further analyze and optimize noise-vibration-harshness performance of designed schemes, computers-aided engineering-based simulation technology is utilized to make modal analysis and vibration noise analysis toward designed automobile driving axles. Such comprehensive evaluation works can contribute to determination of better design schemes for manufacturing optimization.