Journal of Circuits, Systems and Computers

Planar networks are an important special class of networks that arise in several application contexts. Because of the special structure of planar networks, network flow algorithms often run faster on these networks than they do on more general networks. In first part (Secs. 2–5), we present an algorithm for finding maximum cut and an algorithm for finding minimum flow in directed $1-n$ planar static networks with arcs and nodes capacities. This part is presented in Ciurea et al. [Proc. 5th Int. Conf. Control Decision and Information Technologies (CoDIT2018), Thessaloniki, Greece (2018), pp. 110–115]. In the second part (Secs. 6–8), we present this problem in directed $1-n$ planar dynamic networks with arcs and nodes capacities.

In embedded multicore shared memory systems, processing elements (PEs) are mutually untrusted since they carry different computing tasks independently. Therefore, the sharing of secret constants (SCs) between PEs, which is applied in the existing confidentiality protection schemes, will lead to the leakage of nonshared data. Besides, for integrity protection, tree construction checking over the whole counter space leads to the increase of both memory occupation and the average delay of verification. In this paper, we propose a ciphertext sharing confidentiality protection scheme based on certificateless proxy re-encryption and an integrity protection scheme based on a multigranularity scalable hash tree for secure data sharing between untrusted processing elements (SDSUP). With our schemes, the SC does not need to be shared and the scale of the checking tree is reduced, thus solving the leakage of nonshared data and reducing the high cost in integrity check. The results from the Rice Simulator for ILP Multiprocessors (RSIM) multicore simulator show that compared with the unprotected system, the performance degradation from applying the confidentiality protection scheme is 17.3% on average. Moreover, the performance degradation of the integrity protection scheme is 12.89%, which is superior to 35.36% for the bonsai Merkle tree (BMT), 29.49% for the multigrained hash tree (MGT) and 21.82% for the multigranularity incremental hash tree (MIT).

In this paper, an ultra-low power CMOS voltage reference capable of operating at sub-1V input supply is proposed. Four transistors biased in weak inversion are used to generate the required complementary-to-absolute-temperature (CTAT) and proportional-to-absolute-temperature (PTAT) voltages of the proposed circuit. Self-biasing of nature of the proposed configuration in the form of operational amplifier (opamp)-free ensure nano-power operation and eliminate the need for lateral bipolar junction transistors (BJTs) and offset cancelation techniques. A prototype of the circuit is designed and simulated in a standard 0.18-$\mu$m CMOS process. Post-layout simulation results show that the circuit generates a reference voltage of 494mV with temperature coefficient (TC) of 58.4ppm/^∘C across $-40^{\circ }$C to 85^∘C; while the consuming power is lowered to 3.48nW at the minimum supply of 0.8V. The line sensitivity is 0.7%/V for the supply voltages from 0.8V to 1.8V, whereas the power supply ripple rejection (PSRR) is $-$49.06dB at 1Hz. Monte Carlo simulation results of the voltage reference show a mean value of 497.2mV with $\sigma$/$\mu$ of 1.7%, demonstrating the robustness of the generated reference voltage against the process variations and mismatch.

This paper introduces a charge-controlled memristor based on the classical Chuas circuit. It also designs a novel four-dimensional chaotic system and investigates its complex dynamics, including phase portrait, Lyapunov exponent spectrum, bifurcation diagram, equilibrium point, dissipation and stability. The system appears as single-wing, double-wings chaotic attractors and the Lyapunov exponent spectrum of the system is symmetric with respect to the initial value. In addition, symmetric and asymmetric coexisting attractors are generated by changing the initial value and parameters. The findings indicate that the circuit system is equipped with excellent multi-stability. Finally, the circuit is implemented in Field Programmable Gate Array (FPGA) and analog circuits.

Manufacturing product innovation is increasingly showing a green, low-carbon, systematic ecological innovation development trend. In this paper, using CiteSpace analysis method and assisted by BICOMB2 software, the literature of CNKI (1992–2019) and WOS (1995–2019) database was analyzed to construct the knowledge mapping of manufacturing product innovation, and the research findings were as follows: (i) The research is mainly concentrated in China, the United States, the United Kingdom, Germany and other countries. The number of Chinese scholars’ research is the largest, but the American scholars’ research is the first in novelty and prospective, and its sigma value is as high as 294.17. (ii) In recent years, Shanghai Jiaotong University, Xi’an University of Technology, Hunan University and other research institutions have recently produced more research results on product innovation. Among them, Shanghai Jiaotong University can be ranked first. (ii) “Green product innovation” and “product innovation performance” can continue to be the hot spots for future manufacturing product innovation and development.

In order to improve the decision-making and control effect of autonomous vehicles, in this paper, combined with literature research and process analysis, the control algorithm of autopilot vehicle is analyzed, and the driving process is analyzed combined with the flow method. In order to improve the effect of autonomous driving, with the support of improved algorithms, an integrated decision-making control system for autonomous vehicles under multi-task constraints in intelligent traffic scenarios is constructed, and system performance is improved by simulating autonomous driving decisions in a variety of complex situations. Moreover, this paper designs the road driving model according to actual needs, sets the functional modules of the entire system, and build the overall framework of the system. Finally, in order to study the integrated decision-making effect of this system, this paper conducts test research by designing a simulation test method. From the simulation test results, it can be seen that the intelligent decision-making system for autonomous vehicles constructed in this paper has certain effects.

Image segmentation is an important processing technology, which is the basis of image recognition and has been widely used in many fields. In this paper, we propose a method, termed coarse-to-fine strategy-based image segmentation (CSIS), for color image segmentation. The basic idea is to segment an image by three phases: (1) the original image is first segmented into several distinct regions by using the mean shift method; (2) the segmented regions are converted to a weighted region adjacency graph (RAG), and a new graph cut method, called multi-cut algorithm, is proposed to partition the RAG into multiple regions; (3) a one-step Chan–Vese algorithm is applied to smooth the boundaries of the segmented objectives. In each of the last two phases, a method is applied to refine the result obtained in the previous phase. By carefully balancing the efforts used in each phase, CSIS could segment color images both efficiently and effectively. These advantages are demonstrated by applying the proposed method to a variety of test instances, and the statistical results also show that it is comparable with some state-of-the-art methods.

Microgrids (MGs) are the most sought out and feasible solution for the present energy crisis. MG is a group of Distributed Generators (DGs) interacting with each other to provide energy to a defined local area. The inclusion of DGs into the conventional power system at various voltage levels has altered the topology of the power system and their control techniques. Hence, the MGs can no longer be considered as a traditional radial network but rather a meshed network. The control and operation of such practical MGs become a challenge, especially when operated in the islanded mode. This research paper considers a realistic meshed MG operating in an islanded mode for study. In an islanded MG, the issues of real and reactive power sharing among DGs are addressed so that the power contribution of each DG is proportional to its rating, thus preventing overload and ensuring reliable operation. A communication-based virtual impedance estimation is proposed in addition to the droop controller for proportionate real and reactive power sharing among DGs in a meshed MG. With the increased complexity of meshed MG, the proposed communication-based control scheme offers an efficient reactive power sharing between DGs without the feeder and network impedance requirements. A MATLAB simulation study proves the effectiveness of the proposed control strategy for a meshed MG with equal DG ratings and unequal DG ratings under changing load conditions.

The energy-efficient circuits, though important in IoT and biomedical applications, are vulnerable to soft errors due to their low voltages and small node capacitances. This paper presents an energy-efficient low-area double-node-upset-hardened latch (EEDHL). The proposed latch enhances the radiation hardness by employing a restorer circuit based on a Muller C-element and a memory element. The post-layout simulations show that the EEDHL improves the area–energy–delay product (AEDP) by $\sim$80% compared to the newly reported double-node-upset-resilient latch (DNURL) in STMicroelectronics 65-nm CMOS technology. Synopsys TCAD mixed-mode simulations in 32-nm CMOS technology framework are also used to validate the proposed DNU-hardened latch. The proposed EEDHL effectively mitigates the DNU at the strike with a linear energy transfer (LET) equal to 160$\mathrm{\text{MeV}}\cdot {\mathrm{\text{cm}}}^2$/mg in 32-nm CMOS technology.

With the popularization of the Internet of Things technology and the improvement of 5G communication technology, the influence of mobile devices on the network structure is increasing. The devices in the network are usually regarded as social users that transmit information. Because the movement of users is dynamic and random, it is more difficult for complex networks to grasp the changing rules of their topological structure. The data transmission model established by considering only the historical behavior of users can no longer meet the demand for fast transmission of large-capacity data. Based on this, this paper proposes a dynamic personalized data transmission model (GRDPS) that considers the recurrent neural network and attention mechanism. First, it uses a recurrent neural network to build users’ personalized preferences and model the user’s historical behavior. Then, GRDPS introduces an attention mechanism to dynamically weight historical user behaviors based on the user’s current message transmission. It is different from the previous methods of modeling user historical behaviors. Based on the requirements of user dynamics, GRDPS effectively considers the temporal characteristics of user historical behaviors and automatically learns the evolution law of user behaviors. Based on the demand of user randomness, GRDPS fully considers the characteristic correlation between the user’s historical behavior and current transmission demand. Finally, GRDPS combines these two points to obtain a personalized ranking of users. The simulation results show that the delivery rate of GRDPS is up to 0.95. Moreover, its data transmission delay and network overhead are better than other methods in the experiment.

Localizing images by visual information is a very challenging task in image-based travel recommendations. Travelers take a large number of pictures every day and share them on social networks (Facebook, Sina Weibo, Yelp, etc.). Many of these images are associated with the location where they are taken. But for images that do not associate with geographic location information, how to estimate where they are taken? With the rapid development of social media, the increasing number of shared geographic-labeled images brings an opportunity to address this problem. Using geographic-labeled images to estimate the location of unlabeled images is a popular approach. In this paper, we propose an image geographic location estimation model via multi-task learning (GLML). It combines the classification task and retrieval task to calculate the similarity between the query image and dataset images. Additionally, it fuses multi-global features through multiple global pooling techniques to enhance feature extraction. Each part of the proposed GLML model is flexible and extensible. Experiments on seven public datasets show the effectiveness of the proposed model.

In this paper, pilot-based time-domain channel estimation (CE) along with peak-to-average power ratio (PAPR) reduction is proposed for universal filtered multicarrier (UFMC) system. The pilots that are inserted in time domain not only estimate the channel behavior but also can be used in the reduction of PAPR. For PAPR reduction, a linear companding scheme, which can treat amplitudes of the UFMC signal separately with a different scale, is proposed. The proposed companding scheme offers more design flexibility and better performance gains by using two inflexion points. However, the proposed companding scheme requires side information (SI) to perform de-companding at the receiver. The transmission of SI decreases the data efficiency, so a pilot-assisted UFMC system that can perform both data recovery and PAPR reduction without the requirement of SI transmission is proposed. In pilot-assisted UFMC system, the inserted time-domain pilots can enable SI cancellation inherently. Furthermore, a hybrid transform, which improves PAPR performance by employing clipping scheme to the linear companded signal, is proposed. Simulation results confirm that the proposed joint CE with linear companding scheme achieves an improved net gain of 6.5dB. Additionally, the proposed hybrid scheme with clipping threshold of 1.4 provides an improved PAPR reduction of 8.8dB and enhanced net gain of 7.3dB. Moreover, the proposed joint time-domain CE with hybrid PAPR reduction scheme of UFMC system is validated over real time by employing wireless open-access research platform (WARP) board.

Power quality issues and their effective mitigation invariably play a crucial role in a microgrid system. Such power quality problems are often resolved by employing multiple power electronics-based components in the utility grid. This paper is focused on the optimal enhancement of power quality under islanded mode of operation in a microgrid, with a deep Convolutional Neural Network (CNN) with Long Short-term Memory (LSTM) algorithm using distribution static compensator (DSTATCOM). The objective of the research is centered on the reactive power control in DSTATCOM using deep CNN with LSTM for voltage enhancement, minimization of current distortion and reduction of harmonics on a microgrid. This objective can be achieved by the proposed Simulink design model of DSTATCOM intended for improving the power quality in a microgrid. The renewable energy-based power compensator is used for an enhanced and effective control strategy like voltage and current control of the microgrid circuit and uses LSTM-based deep CNN for achieving superior time consumption indicators. Due to varying loads in the microgrid, the reactive power and harmonic voltage and current may be distorted. This problem can be rectified by controlling the microgrid using the LSTM-based deep CNN. This approach consequently reduces the negative-sequence frequency range with the aid of this filtering method in the proposed microgrid circuit. The microgrid is thereafter subjected to different testing conditions and the corresponding simulation results are discussed in relation to existing approaches. The proposed framework was observed to have successfully accomplished harmonic substance and voltage profile enhancement.

In medical image examination, image segmentation is the broadly used method. Currently, the efficient segmentation of mammogram images is the main challenge. Many methods were presented for segmenting the mammogram images, but the results are not satisfactory. In this paper, an efficient segmentation of mammogram images-based Multilevel Thresholding (MLT) method is proposed. Initially, the preprocessing step is executed for eliminating the unnecessary noises. For gaining the useful features from the mammogram images, mammogram image segmentation is carried out using multilevel thresholding method. In this paper, a novel Multi-Objective Emperor Penguin Optimization (MOEPO) algorithm is proposed for searching the multilevel greatest thresholds that segment the images into background and objects. The objective functions of the MLT are Otsu’s method, Kapur and Tsallis entropy. The effectiveness of the proposed method is analyzed using several performances evaluating metrics, like PSNR, FSIM and SSIM. The experimental outcomes show that the performance of the proposed technique is superior to other state-of-the-art methods. The proposed technique is likened to three existing models, viz. ScPSO-MT, Double Threshold and IWO-SUSAN. The SSIM of the proposed technique is 24.99%, 27.83% and 26.95% better than ScPSO-MT, Double Threshold and IWO-SUSAN existing approaches. The PSNR of the proposed technique is 25.27%, 40% and 50.74% better than ScPSO-MT, Double Threshold and IWO-SUSAN approaches. The FSIM of the proposed technique is 28.57%, 34.12% and 34.12% better than ScPSO-MT, Double Threshold and IWO-SUSAN methods.

Metro Vehicle Door System (MVDS) is one of the most frequently used parts in rail transit. In this work, we propose an effective and visualized state detection technique of MVDS by utilizing feature extraction and matching, which is helpful to improve the safety and reliability of the door system. Five states of MVDS, the normal opening, opened, closing and closed state, as well as the anti-extrusion (also named as anti-drag) state, are to be detected in our work. In particular, we design an improved Speeded Up Robust Features (SURF) method to reduce the mismatching pairs by exploiting local space distance, which is crucial for practical usage. Furthermore, we present a scheme to detect the five states of MVDS mentioned above via our improved SURF. Finally, we conduct experiments in subjective and objective, and the resultant performance shows the effectiveness of our method.

As a core component of the Space Telemetry, Track and Command (TT&C) system, the TT&C software’s quality is the key factor to ensure the successful implementation of space TT&C missions. Due to the complexity of space TT&C missions, incremental development is adopted in which frequent testing is required. Many problems of the original manual testing are exposed, such as large consumption of human resources, low efficiency of testing and quality highly relying on human expertise. Automatic testing method is urgently required. However, the testing of the TT&C software highly depends on the domain knowledge of space TT&C missions, which is complex and professional. This hinders the direct application of existing software automatic testing methods to the TT&C software. Therefore, in this paper, we propose an automatic testing method of the TT&C software based on domain knowledge. The domain knowledge description of the space TT&C domain is defined and a set of guidance principles for eliciting domain knowledge elicitation is given. Testing cases are automatically generated and executed by combining the domain knowledge and the image recognition results. Evaluations show that our method can realize the automatic testing of space TT&C software increments with higher accuracy. Its time cost is reduced by more than 50% compared with the manual testing, and will not increase rapidly with growing software maintenance scale. The time cost in domain knowledge elicitation will not affect the testing efficiency.

Station passenger float forecasting is a normal spatio-temporal statistics forecasting problem. Effectively capturing comprehensive spatio-temporal correlations in such data plays a key role in solving such problems. This paper proposes Spatio-Temporal Graph Convolutional Neural Network Based on Periodic Component (Periodic ST-GCN) to predict the passenger glide at public transportation stations. The model now not solely captures the spatio-temporal traits of visitors’ facts through the spatio-temporal convolution block with a sandwich shape composed of one spatial-dimensional convolution and two temporal dimensional convolutions. Also, it effectively considers the periodicity of passenger flow at public transportation stations through the recent, daily and weekly periodic components and, because the graph convolution in the spatial dimension uses pure convolution operations, it reduces the model’ training parameters and converges faster. Through the experiment of predicting the Origin–Destination (OD) of passenger flow at public transportation stations in Chongqing, it is found that Periodic ST-GCN achieves better results in two evaluation indicators, mean absolute error (MAE) and root mean square deviation (RMSE).

The design complexity and outsourcing trend of modern integrated circuits (ICs) have increased the chance for adversaries to implant hardware Trojans (HTs) in the development process. To effectively defend against this hardware-based security threat, many solutions have been reported in the literature, including dynamic and static techniques. However, there is still a lack of methods that can simultaneously detect and diagnose HT circuits with high accuracy and low time complexity. Therefore, to overcome these limitations, this paper presents an HT detection and diagnosis method for gate-level netlists (GLNs) based on different machine learning (ML) algorithms. Given a GLN, the proposed method first partitions it into several circuit cones and extracts seven HT-related features from each cone. Then, we repeat this process for the sample GLN to construct a dataset for the next step. After that, we use K-Nearest Neighbor (KNN), Decision Tree (DT) and Naive Bayes (NB) to classify all circuit cones of the target GLN. Finally, we determine whether each circuit cone is HT-implanted through the label, completing the HT detection and diagnosis for target GLN. We have applied our method to 11 GLNs from ISCAS’85 and ISCAS’89 benchmark suites. As shown in experimental results of the three ML algorithms used in our method: (1) NB costs shortest time and achieves the highest average true positive rate (ATPR) of 100%; (2) DT costs longest time but achieve the highest average true negative rate (ATNR) of 98.61%; (3) Compared to NB and DT, KNN costs a slightly longer time than NB but the ATPR and ATNR values are approximately close to DT. Moreover, it can also report the possible implantation location of a Trojan instance according to the detecting results.

Field programmable gate arrays (FPGAs) have become very widely used devices in space applications, and their runtime reconfigurable architecture allows for the area and power acceleration for complex applications. However, FPGAs are increasingly susceptible to aging effects and failures due to harsh space environments and long operation cycles, which reduce the reliability and lifetime of such devices. Although offline aging-aware layout-based methods are effective in aging mitigation, existing studies ignore the fault tolerance needs of the task and the layout strategy will completely fail after a hard failure occurs. This paper presents a reliability framework AM&FT for SRAM-based FPGAs in space applications to support on-chip aging mitigation and fault tolerance. We use an Integer Linear Programming (ILP) model to solve mapping relationships between tasks and reconfigurable blocks (Rbs) in the offline phase to achieve the aging and reliability-aware layout strategy. Second, the ILP model is incorporated into the Design Space Exploration (DSE) to generate a set of layout strategies to tolerate hard faults. Moreover, the state model is used for runtime fault management to handle the impact of different types of faults on the device. Experimental results demonstrate that our framework achieves FPGA on-chip aging mitigation and fault tolerance. Compared with the existing methods, AM&FT can guarantee the fault tolerance requirements of tasks and give priority to guarantee the Quality of Service (QoS) of critical tasks under the condition of hard faults accumulation. In addition, our framework delivers up to $6.5\times \mathrm{\text{higher}}$ mean time to failure (MTTF) than the baseline.