Journal of Circuits, Systems and Computers

A cap-less voltage spike detection and correction circuit for flipped voltage follower (FVF)-based low dropout regulator (LDO) is proposed in this paper. The transients in the output voltage are controlled by the pull-up currents $I_{\mathrm{\text{UP1}}}$ and $I_{\mathrm{\text{UP2}}}$ and pull-down currents $I_{\mathrm{\text{DN1}}}$ and $I_{\mathrm{\text{DN2}}}$. These currents are dynamic current sources which are activated only during transient period and noise contributed by these current sources at steady state is zero. These currents increase/decrease based on the intermediate FVF node voltage $V_X$. The proposed circuit detects the output voltage via $V_X$ and controls the power MOSFET gate and output capacitances by changing the pull-up and pull-down currents whenever the load changes. The proposed circuit consumes small additional bias current in the steady state and achieves less settling time and output spike voltage. This LDO is simulated using 180nm technology and the simulation result shows that the LDO has good load transient response with 190ns settling time and 170mV voltage spike over 1mA to 100mA load current range.

The world is witnessing a transformation from the conventional electrical grid into the smart grid. The smart grid can provide an effective solution to alarming problems associated with a conventional grid with increased reliability, efficiency, and sustainability. Integration of distributed energy resources (DERs) comprising of renewable energy sources (RESs) is a vital component of the smart grid. DERs not only can provide a viable solution for environmental concerns arising due to conventional fossil fuel-based plants, but can also contribute towards the system reliability. However, the integration of DERs is associated with several challenges.  Thus, the successful deployment of DERs in smart grid framework calls for a comprehensive analysis. This paper presents an exhaustive review of various challenges associated with increased penetration of DERs. An organized classification of various technical challenges along with their mitigation measures has been critically reviewed. Smart inverters equipped with advanced control structure are emerging as a potential solution to address these challenges effectively. Hence, a review of smart inverter along with its functional capabilities has also been discussed in this paper.

Model stealing attack may happen by stealing useful data transmitted from embedded end to server end for an artificial intelligent systems. In this paper, we are interested in preventing model stealing of neural network for resource-constrained systems. We propose an Image Encryption based on Class Activation Map (IECAM) to encrypt information before transmitting in embedded end. According to class activation map, IECAM chooses certain key areas of the image to be encrypted with the purpose of reducing the model stealing risk of neural network. With partly encrypted information, IECAM can greatly reduce the time overheads of encryption/decryption in both embedded and server ends, especially for big size images. The experimental results demonstrate that our method can significantly reduce time overheads of encryption/decryption and the risk of model stealing compared with traditional methods.

Cloud computing model allows service-oriented system that fulfills the needs of the consumers. Capable resource management and task allocation are the important issues in cloud computing. Performance of the task scheduling method directly interrupts the utilization of cloud computing resources and the quality of experience of users. For that reason, reasonable virtual machine (VM) allocation and task scheduling are extremely important. In this paper, an efficient resource allocation model is proposed. Initially, the virtual machines are clustered with the help of Kernel Fuzzy $C$-Means Clustering (KFCM) algorithm to reduce the complexity. After the clustering process, the user tasks are allocated to the particular VM using artificial fish swarm optimization (AFSO) algorithm. A multi-objective function is designed to achieve an optimal resource allocation. The performance of the suggested technique is tested in terms of different metrics.

As one of the most popular frameworks for large-scale analytics processing, Hadoop is facing two challenges: both applications and storage devices become heterogeneous. However, existing data placement and job scheduling schemes pay little attention to such heterogeneity of either application I/O requirements or I/O device capability, thus can greatly degrade system efficiencies. In this paper, we propose ASPS, an Application and Storage-aware data Placement and job Scheduling approach for Hadoop clusters. The idea is to place application data and schedule application tasks considering both application I/O requirements and storage device characteristics. Specifically, ASPS first introduces novel metrics to quantify I/O requirements of applications. Then, based on the quantification, ASPS places data of different applications to the preferred storage devices. Finally, ASPS tries to launch jobs with high I/O requirements on the nodes with the same type of faster devices to improve system efficiency. We have implemented ASPS in Hadoop framework. Experimental results show that ASPS can reduce the completion time of a single application by up to 36% and the average completion time of six concurrent applications by 27%, compared to existing data placement policies and job scheduling approaches.

In mobile cloud computing, the computing resources of mobile devices can be integrated to execute complicated applications, in order to tackle the problem of insufficient resources of mobile devices. Such applications are, in general, characterized as workflows. Scheduling workflow tasks on a mobile cloud system consisting of heterogeneous mobile devices is a NP-hard problem. In this paper, intelligent algorithms, e.g., particle swarm optimization (PSO) and simulated annealing (SA), are widely used to solve this problem. However, both PSO and SA suffer from the limitation of easily being trapped into local optima. Since these methods rely on their evolutionary mechanisms to explore new solutions in solution space, the search procedure converges once getting stuck in local optima. To address this limitation, in this paper, we propose two effective metaheuristic algorithms that incorporate the iterated local search (ILS) strategy into PSO and SA algorithms, respectively. In case that the intelligent algorithm converges to a local optimum, the proposed algorithms use a perturbation operator to explore new solutions and use the newly explored solutions to start a new round of evolution in the solution space. This procedure is iterated until no better solutions can be explored. Experimental results show that by incorporating the ILS strategy, our proposed algorithms outperform PSO and SA in reducing workflow makespans. In addition, the perturbation operator is beneficial for improving the effectiveness of scheduling algorithms in exploring high-quality scheduling solutions.

In grid-connected solar PV system, power quality gets affected seriously due to the variable real power penetration. The transformer is mandatory to integrate and isolate the solar PV and powered network. However, due to presence of this transformer, size and cost of the entire solar PV station get increased and it requires more space while supplying poor quality of power. In order to overcome this drawback, this paper proposes a double boost integrated multilevel inverter to inject the power into utility grid. In addition, the optimal control strategy is used to tap maximum power generation from solar PV and injected into grid with improved power quality. This paper presents an adaptive digital hysteresis current control approach to penetrate optimal power into grid. To validate the proposed work, the simulation results are carried out using PSCAD/EMDTC software. Furthermore, to confirm theoretical study and simulation study, the experimental test was performed and detailed results with validation were presented in this paper.

Orthogonal frequency division multiplexing (OFDM) is a multicarrier transmission system that can achieve high data rate over wireless channels. At the same time, multiple input multiple output OFDM (MIMO-OFDM) in wireless communication systems has been exposed to offer significant improvement over wireless technology by providing transmit diversity. It has become a promising technique for high-performance 5G broadband wireless communications. However, the main problem associated with MIMO-OFDM is that its signal exhibits high peak-to-average power ratio (PAPR), which causes nonlinear distortion and consequently performance degradation. Besides, PAPR carries weaknesses such as an increase in power consumption of high power amplifier (HPA) and analog to digital converter (ADC). Thus, 5G base stations will push up power requirements because energy consumption grows with the number of transceiver elements. So, mobile operators must find the right compromise that, on the one hand, guarantees a certain level of performance to a data flow, and, on the other hand, the energy cost generated for the deployment of the network. For this, as part of the management of power consumption, we propose MIMO constant envelope OFDM (MIMO-CE-OFDM) technique. In this work, we used MIMO-CE-OFDM to mitigate the nonlinear effect of HPA and ADC. To perform practical simulations, we have used COST 2100 MIMO channel model. In this paper, a MIMO-CE-OFDM system has been presented and analyzed under COST 2100 channel model conditions. Simulation results are given to illustrate the performance of $2\times 2$ MIMO-CE-OFDM in the presence of both HPA and ADC nonlinearity. This work shows that the effect of nonlinearity is shown to be negligible on MIMO-CE-OFDM signal.

This work researches the problem of searching for multiple homogeneous polynomial Lyapunov functions (HPLFs) for heterogeneous switched linear systems. First, a nonconvex optimization condition is constructed to study the stability property of heterogeneous switched systems, where each Lyapunov function candidate reduces dimension to their corresponding matrix eigenvalue. Based on the stability analysis condition, a controller-dependently multiple HPLFs condition is introduced to determine controllers and explores locally minimum mode-dependent average dwell time (LMMDADT). Additionally, the existing properties condition and solvable properties condition of controllers are given in the form of HPLFs. At last, a practical example and a contrast example are both presented to show feasibility of the proposed results.

Recently, many organizations and industries are using the cloud computing technologies for exchanging the resources and their confidential data. For this purpose, many cloud services are available and also provide the facility to categorize their users as private and public users for accessing their own data from private cloud and public cloud. The combination of these two clouds is called federated cloud which facilitates to allow both kinds of cloud users for accessing their own data on same cloud database. In this scenario, the authorization and authentication process is becoming complex task on cloud. For providing the facility to access their own data only from federated cloud, a new secured data storage and retrieval algorithm called AES and Triple-DES-based Secured Storage and Retrieval Algorithm (ATDSRA) are proposed for storing the private and public cloud user’s data securely on cloud database. Here, the TDES is used for encrypting the input data, data merging and aggregation methods were used for grouping the encrypted input data. Moreover, a new dynamic data auditing scheme called CRT-based Dynamic Data Auditing Algorithm (CRTDDA) is proposed for conducting the cloud data auditing over the federated cloud and also restricting the data access. The proposed new auditing mechanism that is able to protect the stored data from access violence. In addition, the standard Table64 is used for encryption and decryption processes. The experimental results of this work proves the efficiency of the proposed model in terms of security level.

This paper proposes an integrated system neutrosophic C-means-based attribute weighting-kernel extreme learning machine (NCMAW-KELM) for medical data classification using NCM clustering and KELM. To do that, NCMAW is developed, and then combined with classification method in classification of medical data. The proposed approach contains two steps. In the first step, input attributes are weighted using NCMAW method. The purpose of the weighting method is twofold: (i) to improve the classification performance in the classification of the medical data, (ii) to transform from nonlinearly separable dataset to linearly separable dataset. Finally, KELM algorithm is used for medical data classification purpose. In KELM algorithm, four types of kernels, such as Polynomial, Sigmoid, Radial basis function and Linear, are used. The simulation result on our three datasets demonstrates that the sigmoid kernel is outperformed to ELM in most cases. From the results, NCMAW-KELM approach may be a promising method in medical data classification problem.

In this paper, an evolutionary computation-based optimal design of low power, high gain inductive source degenerated CMOS cascode low noise amplifier (LNA) circuit is presented for 2.4GHz frequency. The main challenge for the design of radio frequency (RF) LNAs at nanometer range is the thermal noise generated in the short-channel MOSFETs. The short-channel effects (SCEs), such as velocity saturation and channel-length modulation, are considered for the design of CMOS LNA. The evolutionary algorithm taken for this work is Moth-Flame Optimization (MFO) algorithm. MFO is utilized for the optimization of noise figure (NF) while satisfying all the other design performance parameters like gain, matching parameters at input/output, power dissipation, linearity, stability. Optimal values of the sizes of the transistors and other design parameters in designing the LNA circuit are also obtained from the MFO algorithm. The CMOS LNA circuit is designed by using MFO-based optimal design parameters in CADENCE software with a standard 0.18$\mu$m CMOS process. The designed LNA shows a gain of 15.28dB, NF of 0.376dB, the power dissipation of 936$\mu$W and IIP3 of $-4.36$dBm at 2.4GHz. The designed LNA achieves better trade-off which results in an FOM of 42.3mW${}^{-1}$ and may be useful in the receiver module of IEEE 802.15.4 for WLAN applications.

This paper presents the debug process of a 28GHz low noise amplifier (LNA) circuit layout. This study is guided utilizing an electromagnetic (EM) simulation program where inductive coupling, the parasitics of dc voltage line and ground line are extracted and simulated, their impacts on LNA performance are also quantitatively characterized. For validation, the circuit was designed and fabricated using GF8HP 0.13um SiGe BiCMOS process. The measurement shows that the gain S21 is 23.22dB, S11 and S22 are $-18$ and $-26$dB, respectively, and the noise figure is 4.26dB. The power consumption is 14.25mW, the chip area including pads is 540$\mathrm{\text{um}}\times 810$um.

In the last couple of years, quantum computing has come out as emerging trends of computation not only due to its immense popularity but also for its commitment towards physical realization of quantum circuit in on-chip units. At the same time, the process of physical realization has faced several design constraints and one such problem is nearest neighbor (NN) enforcement which demands all the operating qubits to be placed adjacent in the implementable circuit. Though SWAP gate embedment can transform a design into NN architecture, it still creates overhead in the design. So, designing algorithms to restrict the use of SWAPs bears high importance.

Considering this fact, in this work, we are proposing a heuristic-based improved qubit placement strategy for efficient implementation of NN circuit. Two different design policies are being developed here. In the first scheme, a global reordering technique based on clustering approach is shown. In the second scheme, a local reordering technique based on look-ahead policy is developed. This look-ahead strategy considers the impact over the gates in the circuit and thereby estimates the effect using a cost metric to decide the suitable option for SWAP implementation. Furthermore, the joint use of both the ordering schemes also has been explored here. To ascertain the correctness of our design algorithms, we have tested them over a wide range of benchmarks and the obtained results are compared with some state-of-the-art design approaches. From this comparison, we have witnessed a considerable reduction on SWAP cost in our design scheme against the reported works’ results.

This paper presents a 6.4-GS/s 16-way 10-bit time-interleaved (TI) SAR ADC for wideband wireless applications. A two-stage master–slave hierarchical sampling network, which is immune to the time skew of multi-phase clocks, is introduced to avoid the time-skew calibration for design simplicity and hardware efficiency. To perform low distortion and fast sampling at acceptable power consumption, a linearity- and energy efficiency-improved track-and-hold (T&H) buffer with current-feedback compensation scheme is proposed. Accompanied by its low-output-impedance feature, the buffer obtains adequate bandwidth which can cover the entire ADC Nyquist sampling range. Moreover, the split capacitor DAC combined with a novel nonbinary algorithm is adopted in single-channel ADC, enabling a shorter DAC settling time as well as less switching energy. Capacitor mismatch effect with related design trade-off is discussed and behavior models are built to evaluate the effect of capacitor mismatch on ENOB. An asynchronous self-triggered SAR logic is designed and optimized to minimize the delay on logic paths to match up the acceleration on DAC and comparator. With these proposed techniques, the 10-b sub-ADC achieves a 400-MHz conversion rate with only 3.5-mW power consumption. The circuit is designed and simulated in TSMC 28 HPC process and the results show that the overall ADC achieves 54.6-dB SNDR and 58.1-dB SFDR at Nyquist input while consuming 127-mW power from 1-V/1.5-V supply and achieving a Walden FoM of 45fJ/conv-step.

A novel electromagnetic energy harvester (EMEH) based on double-ring core for power line energy harvesting is proposed in this paper. Due to large magnetic reluctance caused by the inherent air gap at the opening of core, the magnetic flux leakage in magnetic core severely limits the output power of EMEH. A double-ring core with lower magnetic flux leakage is developed. The internal magnetic reluctance of the double-ring core is reduced by changing the distribution of the air gap with a fixed volume. The simulation results show that the double-ring core can produce the highest average magnetic induction, which is 2.42 times, 1.82 times and 1.7 times that of the single-ring opening, stepped opening and V-shaped opening, respectively. In order to improve the output performance of the EMEH, the resonance matching is used for the power management. The power management unit through the resonant matching can increase the acquisition efficiency by 2.23 times and achieve a maximum output power of 32.78mW at a 10A current across the power line. The EMEH can drive a low-power wireless node operating for 29min. A valuable solution is provided for high-efficiency self-powered systems in smart applications.

Each person describes unique patterns during gait cycles and this information can be extracted from live video stream and used for subject identification. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. In this paper, a method to enhance the appearance-based gait recognition method by also integrating features extracted from depth data is proposed. Two approaches are proposed that integrate simple depth features in a way suitable for real-time processing. Unlike previously presented works which usually use a short range sensors like Microsoft Kinect, here, a long-range stereo camera in outdoor environment is used. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing popular gait recognition methods.

This paper proposes an architecture to enhance coding efficiency (CE) of the Delta Sigma Modulator (DSM) transmitters. In this architecture, a complex–low pass delta sigma modulator (LPDSM) is used instead of existing Cartesian–LPDSM and polar–low pass envelope delta sigma modulator (LPEDSM). Simulation results show that for Uplink long-term evolution (LTE) signal with 1.92MHz bandwidth and 7.8-dB peak to average power ratio (PAPR), the CE for the complex–LPDSM-based transmitter is equal to 41.7% in compare to 9.7% CE for Cartesian–LPDSM transmitter. Also, due to the resolving of noise convolution problem, the complex–LPDSM-based transmitter baseband part needs lower oversampling ratio (OSR) and clock speed than polar–LPEDSM transmitter baseband part to achieve the same signal-to-noise and distortion ratio (SNDR). In the next step, a quantization noise reduction loop is implemented in this architecture. By using this technique for an Uplink LTE signal with 1.92MHz bandwidth, with the same PAPR and OSR of 16, the CE is improved from 41.7% to 56.1% with 40dB SNDR.