Journal of Circuits, Systems and Computers

Crowdsourcing is an emerging approach in software development, where software is developed by unknown, heterogeneous people around the globe, via an open call by the employer. Crowdsourcing, when used with agile, faces challenges because of the different development methodologies. Agile development methodology has practices that involve face-to-face frequent interaction, whereas crowdsourcing is a distributed development with no or limited face-to-face interaction. Both concepts are actively being used in the software development industry due to their benefits. However, there is a need to explore the integration of both methodologies for an effective and efficient software development. In this regard, the first step is to identify the challenges of integrating crowdsourcing and agile. In this paper, we intend to identify the possible challenges that may be faced while executing agile along with crowdsourcing. For this, we have conducted a Systematic Literature Review. Five main categories of challenges are presented which are: team issues, coordination and communication issues, organizational issues, project-related issues and task-related issues. A list of possible challenges of Agile–Crowd Software Development (ACSD) is presented in this study.

Electrocardiogram (ECG) is considered as the important diagnostic tests in medical field for detecting the cardiac anomalies. But, the ECG signals are polluted with numerous noise from power line intrusion, muscle noise, baseline wander, motion artifacts, low frequency noise signals, high frequency noise signals and T-wave, which automatically affects the QRS profile. The existing method provides the result in lesser accuracy with higher rate of error detection. To overcome these issues, QRS detector using modified maximum mean minimum (MoMaMeMi) filter optimized with mayfly optimization algorithm (QRS-MoMaMeMi-MOA) is proposed in this paper for less computational cost along with resource requirements. The proposed filter design consists of two phases for detecting QRS detector, such as filtering process associated to the enhancement and detection phase. Initially, the ECG data are taken from MIT/BIH arrhythmia dataset (MIT-AD). For eradicating the baseline wander in ECG data, MaMeMi filter is used. For expanding the performance of the modified MaMeMi filter, filter parameters, such as $\sigma$ and $\mathrm{\Delta }$ are optimized by MOA to accomplish the best values and measure the performance of the whole QRS detector. For high frequency noise suppression in ECG data, the range function, noise subtractors, modified triangular detector are used. Then, heart beat detection can be done with the help of adaptive thresholding technique. The proposed filter design is carried out in MATLAB and implemented on field programmable gate arrays (FPGAs). The proposed QRS-MoMaMeMi-MOA filter design had 0.93%, 0.12% and 0.19% higher accuracy and 89.32%, 50% and 62% low detection error rate, compared to the existing filters, like Kalman filtering based adaptive threshold algorithm for QRS complex detection (QRS-KF-ATA), QRS detection of ECG signal utilizing hybrid derivative with MaMeMi filter by efficiently removing the baseline wander (QRS-HD-MaMeMi), and knowledge-based QRS detection operated by cascade of moving average filters (QRS-CAF). Then, the device utilization of the proposed FPGA implementation of the QRS-MoMaMeMi-MOA filter provides 95.556% and 71.428% lower power usage compared with the existing algorithms, like Kalman filtering based adaptive threshold algorithm for QRS complex detection in FPGA (FPGA-QRS-KF-ATA), and efficient architecture for QRS detection in FPGA utilizing integer Haar wavelet transform (FPGA-QRS-IHWT).

In this paper, a single-phase 13-level switching capacitor multilevel boost inverter (SCMLBI) with less switches and a voltage boost gain of six times is presented. The main focus of this work is to present a single DC source SCMLBI topology, which can provide an AC output voltage with low harmonic distortion by suitable sinusoidal pulse modulation (SPWM) technique. The given SCMLBI boost topology is designed to provide 13 output voltage levels utilizing a single DC source and 14 power electronic switches, and it includes intrinsic capacitor self-voltage balancing. The presented SCMLBI inverter does not require any magnetic elements like inductors which do not make the system complex in IC fabrication. In this work, the DC source is provided with a solar PV array with maximum power point tracking (MPPT) algorithm. The proposed SCMLBI topology provides an output voltage larger than the input voltage by appropriately converting the capacitors in series and parallel combinations. Sinusoidal PWM technique is used to control the switches in SCMLBI. A solar PV array of 100 W with incremental and conductance MPPT algorithm is used in this work. The verification of the presented SCMLBI topology is simulated on MATLAB software. Hardware results are also presented for the validation of SCMLBI topology.

The usage of the Internet of Things (IoT) in the field of transportation appears to have immense potential. Intelligent vehicle systems can exchange seamless information to assist cars to ensure better traffic control and road safety. The dynamic topology of this network, connecting a large number of vehicles, makes it vulnerable to several threats like authentication, data integrity, confidentiality, etc. These threats jeopardize the safety of vehicles, riders, and the entire system. Researchers are developing several approaches to combat security threats in connected and autonomous vehicles. Artificial Intelligence is being used by both scientists and hackers for protecting and attacking the networks, respectively. Nevertheless, wirelessly coupled cars on the network are in constant peril. This motivated us to develop an intrusion detection model that can be run in low-end devices with low processing and memory capacity and can prevent security threats and protect the connected vehicle network. This research paper presents an Attention-enabled Hierarchical Deep Neural Network (AHDNN) as a solution to detect intrusion and ensure autonomous vehicles’ security both at the nodes and at the network level. The proposed AHDNN framework has a very low false negative rate of 0.012 ensuring a very low rate of missing an intrusion in normal communication. This enables enhanced security in vehicular networks.

Phase Locked Loop (PLL) is an on-chip clock generator for timing-centric electronic systems. Voltage Controlled Oscillator (VCO) is the key element for high-performance PLLs. A detailed qualitative explanation has been given to describe VCO operation. It is shown from simulation results that the variation of small signal transconductance ($g_m$) is the main dominant source of frequency and gain ($K_{\mathrm{\text{VCO}}}$) variation in a VCO. In this work, simulation results for the conventional ring oscillator are presented which demonstrates $\approx 3$ times variation in $K_{\mathrm{\text{VCO}}}$ across Process Voltage Temperature (PVT) corners. Such huge sensitivity to PVT is undesirable for high bandwidth PLL design. To mitigate this sensitivity, a constant-gm bias circuit is proposed in this paper, with a detailed mathematical analysis. A prototype of 4-stage ring oscillator with center frequency of 5GHz is developed in 65nm TSMC CMOS technology, and post-layout simulation results are carried out. Results show that maximum $K_{\mathrm{\text{VCO}}}$ variation of 28% and frequency variation of 17% at a given control voltage. Temperature sensitivity has been decreased from 19.3% to 7% using the proposed biasing technique. Proposed solution consumes 2.4mW power from 1V power supply.

A simple circuit based on modified extra-X second-generation current conveyor (MEXCCII), which is capable of realizing the following grounded immittance functions: a lossless capacitor, a lossy capacitor, a lossy inductor and a lossy frequency-dependent negative conductance, is introduced in this paper. The circuit employs one MEXCCII and three passive components. The use of single active block makes the circuit structure simpler. No component’s matching constraint is needed in the proposed circuit. The nonideal study of the proposed grounded immittance circuit is also included. The circuit’s performance is examined using 0.18-$\mu$m technology-based PSPICE simulations. Experimental results which are performed using off-the-shelf integrated circuits (ICs) and bread board are also included. The proposed circuit is negligibly affected by the temperature variation and process variation. A single pole high-pass filter as an application of the realized lossy capacitor and a band-pass filter as an application of the realized lossy inductor are also presented in this paper. The realized filters offer the feature of ease of cascadability.

This paper represents a new integrable trans-admittance mode universal filter employing an active building block, i.e., Voltage Differencing Current Conveyor, and all grounded passive elements. The proposed circuit can easily realize all five, widely used, filter responses, i.e., bandpass, high pass, low pass, all pass and band-reject. The designed circuit configuration provides favorable impedances at the input as well as output ports. Independent tunability of its gain parameter and orthogonal tunability of its quality factor and operating frequency are some of the noteworthy features of the designed circuit. The simulations of the designed filter are verified by 180-nm Complementary Metal Oxide Semiconductor (CMOS) technology in the SPICE simulation environment. Validation of the proposed design is done by experimental work along with the regular mathematical analysis.

The hyperparameters of the metaheuristic algorithm are difficult to determine when solving optimization problems. The existing methods mainly adjust hyperparameters through preset rules or traditional RL. The performance of the above methods is unsatisfactory and the generalization is poor. This work proposes a deep Q-learning network (DQN)-based dynamic setting framework for combinatorial hyperparameters, and applies it to a Genetic algorithm (GA) to improve its performance. By defining the four elements of the environment, state, action and reward required for learning strategy in advance, the parametrized strategy can be trained offline and different DQN models can be studied. Our method was compared with other algorithms and achieved the shortest path on 14 of 15 public TSP instances. Meanwhile, the test results on our simulation TSP validation dataset revealed that Category DQN achieved the best performance. This means the proposed method can effectively solve the problem of combinatorial hyperparameters setting, and bring more solving advantages to the GA.

In this paper, an improved voltage-mode constant on-time (COT) control DC–DC buck converter is designed, which makes the DC–DC buck converter have good stability. Firstly, an improved compensation scheme is introduced through the analysis and derivation of the traditional voltage-mode COT controlled buck converter model. Then, according to the application requirements of the design, Simplis software modeling is used to verify the correctness of the theoretical analysis. The 0.13$\mu$m BCD technology is used to tape out the DC–DC converter of the improved voltage mode COT. The input voltage is 3.7–5.5V and the output voltage is 3.3V. The chip test mainly includes heavy-load condition in CCM operation, light-load condition in DCM operation, and load transient response. The test results show that the chip can remain stable under heavy-load and light-load conditions. The overshoot and undershoot in transient response can be quickly restored to steady state during the recovery process. Finally, compared with chips of the same type, it shows that the DC–DC chip for portable electronic equipment designed in this paper adopts an internal compensation structure, which has good stability, simple design, small main chip area, simple peripheral equipment, and good application value.

Ultrasound imaging is commonly used to diagnose internal anomalies. Imaging for abnormality detection is a challenging process in today’s world. Even though there is an advancement in technology, tele-radiographers face difficulty in the accurate diagnosis of abnormalities. In order to resolve this issue, tele-radiology has paved a new way for doctors around the world to access the Internet to share the radiological images from one location to another. But frequent online access is one of the bottleneck issues. In order to overcome this drawback, Computer Assisted Diagnosis (CAD) is preferred in this proposed study and it uses VIRTEX-6 FPGA to clearly identify abnormality in the platform and also manual control is minimized in this condition. The proposed algorithm includes five steps: pre-processing, segmentation, feature extraction, selection and classification. The classification is performed using the Iterative K-Nearest Neighbor (IKNN) classifier based on the selected features. Unlike popular KNN, the proposed IKNN algorithm performs the similarity measurement on selective neighbors for a number of times where the number of neighbors has been dynamically selected at each iteration. Also, at each iteration, the method would select a subset of features in a random way. For the features selected and with the neighbors selected, the method computes the similarity value of Hist-sim which is being measured according to the features selected from the histogram features where the method computes the Haralick similarity with the features selected from the Haralick features. Using the features selected, the method computes the value of cumulative class drive similarity (CCDS). At each iteration the class with maximum similarity is selected and finally, the class being selected for the most number of times is selected as a result of classification. This improves the performance of classification. While comparing with the existing algorithms such as Support Vector Machine (SVM) with the linear, Radial Basis Function (RBF) and polynomial kernels, greater accuracy is achieved via IKNN classification. The specificity is found to be 95, 80 and 75 for normal, cystic and stone kidneys.

The aim of this paper is to develop and test the features of reconfigurable architecture for its suitability for a secured medical image encryption. The image security has been ensured using three-level zigzag confusion, bit-level confusion, self-invertible matrix and DNA diffusion. The zigzag pixel-level and circular shift-based bit-level confusion offer a good scrambling to overcome the correlation among the pixels whereas the self-invertible matrix is used to have a better substitution effect. The DNA operations are performed to get new random-valued sequence for diffusion thereby the encryption process has been designed to get good protection with simple and effective coding. The concept of secured medical image encryption has been designed using verilog HDL and implemented using field programmable gate array (FPGA) with an intention to create a standalone embedded hardware for encryption. The analysis of the encryption includes correlation coefficient, entropy, Chi-square analysis, NPCR and UACI and so on, which gives a better result and conveysto us that it can resist the statistical and differential attacks. The synthesis report of the embedded hardware has been presented to understand the utilization of the FPGA.

The aim of this paper is to solve the problem of a large amount of redundant data in the process of large-scale acquisition of Internet of Things sensors. This paper proposes a frequency adaptive data sensing method based on revolving gate algorithm for STM32 power safety data acquisition system. The method realizes the intelligent control of current protection terminal through three-stage current protection algorithm and frequency adaptive data acquisition algorithm. First, the three-stage current protection algorithm is used to protect the circuit, which can trip quickly in case of overload, and reduce the peak current caused by some equipment when starting up, so as to avoid damage to the equipment. Second, the arithmetic mean of each statistic is compared with the last value reported to the server. If the absolute value of the difference between the two exceeds the specified threshold range, the reported value is updated. Otherwise, it is filtered out, and the data smoothness is calculated according to the rules. The data collection interval is dynamically adjusted according to the data smoothness, which can greatly reduce the collection of redundant data and the traffic consumption of Internet of Things devices. On the server side, the inverse algorithm is used for interpolation and reconstruction to recover the collected data. This method is applied to the developed electricity safety equipment, and the electricity consumption is monitored and collected continuously for 24 h. After filtering by the frequency adaptive algorithm, only 108 pieces of data records are reported for 28,713 pieces of original data, and the compression ratio reaches 99.49$\%$. Compared with other data collection strategies, this method significantly reduces the amount of redundant data collection and the energy consumption of terminal nodes. Furthermore, this method realizes the real-time perception of line data and environmental data through current, residual current, temperature sensor and electric energy metering chip, and controls the opening and closing of wire controlled micro circuit breaker through PC817 optocoupler to protect the safety of the circuit.

Due to stochastic wireless environment, the process of modulation classification has become a challenging task. Because of its powerful feature extraction ability and promising performance over the conventional schemes, deep learning (DL) models are employed to automatic modulation classification (AMC) problems. Most of the conventional models proposed are tested for the limited set of modulation schemes transmitted over additive white Gaussian noise (AWGN) channels without considering the effect of multipath fading and Doppler shift. The next-generation networks use adaptive and higher-order quadrature amplitude modulation (QAM) schemes for higher spectral efficiency. The classification accuracy of conventional DL-based AMC schemes drastically reduces, when different order QAM modulation schemes are accommodated. In this work, different scaling factors are selected for the generation of $M$-QAM frames. The combination of scaling factors, which maximize the classification accuracy is chosen. A convolutional neural network (CNN) with six stages is employed for AMC. The simulation results show that the classification accuracy of proposed scheme is higher than the conventional DL-based schemes under various signal-to-noise ratio (SNR) conditions. The proposed scheme shows at least 4% improvement in classification accuracy over the other DL-based schemes.

This paper presents a new optimized high-speed divide-by-8/9 dual modulus prescaler. Simulation results show 54% reduction in power consumption, 40% of speed improvement and almost 48% area reduction as compared to the conventional architecture. Power consumption in the proposed prescaler is reduced by eliminating one True-Single-Phase Clocked (TSPC) D Flip-Flop (DFF) from the standard divide-by-2/3 prescaler, replacing it with Pulse Extension Logic (PEL) circuit. Redundant stages from asynchronous divide-by-2 units were also removed to save more power and reduce more delay. The simulation results show that the prescaler is capable of running at 5.5GHz of maximum frequency with 1.9mW power consumption. The divider is implemented in 0.18$\mu$m CMOS technology with 1.8V power supply.

Solar photovoltaic (PV) systems have gained significant attention due to their easy implementation and availability, where proper energy management should be highly concentrated for a successful PV power utilization. In the traditional works, various controlling techniques have been developed for reactive power compensation. But, it lacks with the issues of reduced system performance, increased loss, and high harmonics. Hence, this paper aims to develop a new controlling methodology, named the Synthesis Segmental Multilevel Converter (SSMC) for reactive power compensation in a three-phase grid system. Initially, it extracts the maximum amount of power from the solar PV systems by using an Enhanced Perturb and Observe (EPO) method. Then, the panel separation is performed and the three-phase power input is given to the SSMC converter, where the synchronization and switching pulse generation processes are performed. During synchronization, integrated techniques such as Proportional Integral (PI), Fuzzy Logic Controller (FLC), and Improved Artificial Neural Network (IANN) are utilized to maintain the voltage, magnitude and phase angle in the same level. Consequently, the Inductance Capacitance (LC) filtering technique is applied to reduce the harmonics distortion in the signal. After that, the Park transformation is used to perform the dq0 to abc transformation, which is implemented for reducing the high volume of error. Finally, the error-free signal is fed to the three-phase grid system with reduced harmonics. During experimentation, both the simulation and analytical results have been taken for analyzing the performance of the proposed technique. Moreover, it is compared to the existing algorithms for proving the betterment of the proposed methodology.

Quantitative training virtualization can be applied in the fields of human–computer interaction, virtual reality and motion analysis, and has attracted much attention. Based on the virtual reality theory, this paper constructs a training quantitative evaluation method, evaluates the trainer’s ergonomics, and finally compares the evaluation method with the simulation results. According to the collected training data, this paper uses the methods of model reuse and feature parameter adjustment in the Jack software to quickly generate a 3D training model with the required percentages, and performs data reorganization and analysis on the captured training data, which solves the problem of training quantitative evaluation and the problem of uncertainty of results. During the simulation process, according to its reorganized data, a large number of comparative experiments and evaluations were performed on the performance of the model proposed in this paper on multiple indicators of multiple public data sets. The experimental results show that the cycle is reduced by 10 times, the mini-batch is 32, and the sequence length is 16. In this way, the spatial characteristics of the channel coupling relationship can be better analyzed, and the effect of spatial cognitive training can be effectively evaluated. The training pose and shape estimation model and the corresponding data set and multiple indicators have reached the performance of the existing state-of-the-art models. The integrated model of accurate training pose and shape sequence in the camera coordinate system can be reconstructed, which effectively enhances the effectiveness of the virtual scene parameter adjustment strategy.

With the rapid development of modern information technology represented by the Internet, cloud computing and big data, education and teaching have gradually realized the deep integration of the Internet, which has changed people’s way of life, study and work to a certain extent. Intelligence $+$ education provides a new information-based teaching method for the development of education. From the perspective of improving the accuracy of data fusion results, this paper proposes a new multi-source data fusion method based on a set pair analysis connection degree for the situation that multiple sensors with unknown prior knowledge detect the same target feature parameters multiple times. By using the advantages of the set pair analysis feature function, the degree of opposition, the identity and difference between the measurement data are mined to adjust the degree of connection between the data. According to the existing signal-to-noise ratio weighting method in the fusion process, the weight of the measurement data is reasonably allocated to realize the weighted fusion of multi-source data, and the effectiveness and reliability of the algorithm are verified through simulation experiments. Through the summary of the questions, it can be seen that the learners are very satisfied with the mobile piano learning mode based on the intelligent teaching system, and they believe that this learning mode is conducive to the learner’s mastery of the basic knowledge of piano, and effectively improves the learners’ learning ability. It also improves the individual’s self-directed learning ability and academic performance. Through the research, it is concluded that the students’ piano mobile learning mode based on the intelligent teaching system is more conducive to the learner’s mastery of knowledge, and improves the learning interest and academic performance.

At present, the informatization of basic education quality assessment has become a hot topic in the field of education and is playing an increasingly important role. Based on the theory of deep convolutional neural network, this paper adopts the methods of mathematical analysis and experimental research to construct a regional basic education quality assessment model. The model solves the data informatization problem of education quality assessment. In the simulation process, two key modules of data self-assessment and expert assessment of the deep convolutional neural network are realized by ASENET+SQL SERVER, and the assessment results are integrated by using the weighted average method and the fuzzy comprehensive assessment method. The experimental results show that the quantitative analysis of the quality assessment is carried out by using the logic and support relationship, and the results of comprehensive qualitative analysis and quantitative analysis are realized and segmented when the threshold level is 9, the MIOU obtains the highest value of 0.7501, and the MIOU of the multi-stage method of the quality evaluation model proposed in this paper is 0.8116, which is 6.15% points higher than the traditional multi-stage algorithm, which effectively improves the current stage area quality of basic education.