Journal of Circuits, Systems and Computers

The increased demand for clustering objects of unlabeled data into similarity group lies in determining a number of clusters. In addition, the performance of the cluster should be analyzed to provide the precise clustering of objects. Available clustering algorithm depends on the number of clusters C to search with threshold. The proposed method of this paper is Enhanced Visual Assessment of Cluster Tendency, which robotically identifies the number of object groups or clusters in unlabeled datasets. The proposed algorithm relies on visual assessment of cluster tendency (VAT) that intermingles Euclidean, Mahalanobis distance measures, and common image processing techniques. Enhanced VAT produces a binary image, which can be visually assessed for the cluster tendency. However, VAT becomes disrupting for huge datasets. Enhanced VAT reduces the amount of computation and performs dissimilarities with different measures of metrics that are used for an effective visual evaluation process. Validation of our algorithm is performed on several UCI datasets and HIV real world datasets.

This paper focuses on a comparative study of a reduced order linear quadratic regulator control (ROLQR) and conventional variable frequency hysteresis controller (HC) for power factor correction, and their performances are compared. A prototype of front-end AC–DC converter followed by DC–DC Cuk converter controlled by a dSPACE signal processor was set up for 60 watts. Experimental results are presented to validate the simulation results. Simulation and experimental results reveal that ROLQR control can achieve good output voltage regulation and also provide improved robustness in shaping the input current in the presence of load variations when compared to conventional HC.

With the increasing deployment of Wi-Fi devices in portable embedded systems, the low power design at system level has attracted considerable research attention in the recent past. In this paper, based on hardware features and software architecture of the embedded Wi-Fi devices, we focus on dynamic power management, dynamic frequency scaling, and their influences upon the system power and performance. We propose effective and realizable system power management solution and application modes under various application requirements, such as response, bandwidth, and speed. Experimental results show that the proposed solutions can achieve significant energy savings.

Fault tolerance and energy have become important design issues in multiprocessor system-on-chips (SoCs) with the technology scaling and the proliferation of battery-powered multiprocessor SoCs. This paper proposed an energy-efficient fault tolerance task allocation scheme for multiprocessor SoCs in real-time energy harvesting systems. The proposed fault-tolerance scheme is based on the principle of the primiary/backup task scheduling, and can tolerate at most one single transient fault. Extensive simulated experiment shows that the proposed scheme can save up to 30% energy consumption and reduce the miss ratio to about 8% in the presence of faults.

It is difficult to measure the surface roughness of microheterogeneous surface in deep-hole parts due to the limitation of measurement space. In this paper, we propose a new method based on microscopic vision to detect the surface roughness of R-surface in the valve. First, the clear microscopic image of R-surface is obtained by the established microscopic system, which is mainly fabricated by the long working distance lenses of digital microscopic camera. Thereafter, based on genetic algorithm (GA) and feed-forward back propagation artificial neural network (BP-ANN), a hybrid method is proposed to predict the surface roughness. In this method, the microscopic image features of R-surface are taken as the inputs to the hybrid model. GA is employed to search the optimal initial weights and thresholds of BP-ANN, which resolves the problem that training methods of BP-ANN are much sensitive to initial weight values and thresholds. In addition, in virtue of a three-dimensional surface profiler, the targets of hybrid model are calibrated by the actual roughness values of R-surface in the sample valves, where the sections of sample valves over R-surface are cut. Finally, experiments on the microscopic image acquisition and roughness calibration are conducted, as well as the prediction experiments. Moreover, the analysis results indicate that the proposed measurement method based on GA and BP-ANN exhibits high precision and stability for evaluating the microcosmic surface roughness of microheterogeneous surface in deep-hole parts.

This paper presents a parameterized version of the stochastic information gradient (SIG) algorithm, in which the error distribution is modeled by generalized Gaussian density (GGD), with location, shape, and dispersion parameters. Compared with the kernel-based SIG (SIG-Kernel) algorithm, the GGD-based SIG (SIG-GGD) algorithm does not involve kernel width selection. If the error is zero-mean, the SIG-GGD algorithm will become the least mean p-power (LMP) algorithm with adaptive order and variable step-size. Due to its well matched density estimation and automatic switching capability, the proposed algorithm is favorably in line with existing algorithms.

In this paper three embedded switched capacitor based DC–DC converters targeting Vdd/2, 2Vdd/3, and Vdd/3 output voltages have been designed with improved power efficiency and output voltage ripple. The performance of each of the converter is improved by nonoverlapped rotational time interleaving (NRTI) switching scheme. Current regulation scheme is included with each of the above NRTI switched capacitor converter to achieve better load and line regulation. The proposed converters are designed and simulated in a 0.18 μm n-well CMOS process with the total flying capacitance of 330 pF and load capacitor of 50 pF. The capacitance values are kept within on-chip implementable range. The maximum power efficiency and the output voltage ripple of the integrated NRTI DC–DC converters targeted for Vdd/2, 2Vdd/3 and Vdd/3 output generation are 71.5% and 5 mV, 69.23% and 13.27 mV and 58.09% and 10.5 mV, respectively.

In this paper, four baseband chain architectures used in multistandard (UMTS–WLAN) reconfigurable receivers will be introduced, simulated and, compared. The architectures are realized using 0.25 μm CMOS technology operating with 1.2 V supply voltage. The baseband chain consists of three stages: the first and the last stage are programmable gain amplifiers and the intermediate stage is an active Gm-RC LPF filter. The proposed architectures are compared in terms of DC-gain, noise, linearity, SFDR, and power consumption. The best receiver architecture is then derived based on system level analysis and based on a defined figure-of-merit. The best baseband chain bandwidth is controlled by the active G_m-RC filter with a value 2.2 MHz for UMTS and 11 MHz for WLAN. The baseband gain can be programmed in the range of -6÷68 dB, while the input-referred noise density is less 20 nV/√Hz for UMTS and 25 μV/√Hz for WLAN.

Recently, growing privacy concerns have received more and more attention and it becomes a significant topic on how to preserve private-sensitive information from being violated in distributed cooperative computation. In this paper, we first propose a novel-general privacy-preserving online analytical processing model based on secure multiparty computation. Then, based on the new model, two schemes to privacy-preserving count aggregate query over both horizontally partitioned data and vertically partitioned data are proposed. Additionally, we also propose several efficient subprotocols that serve as the basic secure buildings. Furthermore, we analyze correctness, security, communication cost, and computation complexity of our proposed protocols, and show that the new schemes are secure, having good linear complexity and that the query results are exactly accurate.

Classification of transconductance amplifiers (TA) based on the number of input ports and output ports is given. A systematic generation method of TA based on using nullator, norator elements, and pathological mirror elements is used to provide pathological realizations of different types of TA. Four pathological realizations of the single input balanced output TA each using two grounded G are given. Four pathological realizations of the differential input single output TA each using two grounded G are given.

Six pathological realizations of the differential input balanced output TA known as BOTA are given. Three pathological realizations for each of the two types of the differential input double output TA are also given. Finally, four alternative ideally equivalent realizations of the BOTA using two current conveyors (CCII) or two inverting current conveyors (ICCII) are generated from the pathological realizations.

Control of induction motor due to its nonlinear property is one of the most popular research topics recently. Vector control based algorithms which are used for proper functioning estimate the rotor speed and magnetic flux. In many applications, flux and speed are estimated without using sensors. Thus, in these cases, it is necessary to obtain speed and flux by measuring voltage and current. In this paper, a nonlinear observer for estimating motor parameters based on Lyapunov function is presented. Using the Lyapunov theory, inputs regulating system and control system are earned which improve system performance. Simulation and experimental results are included to validate the effectiveness of the proposed control scheme under possible uncertainties and different reference trajectories. These results show the advantages of the proposed observer in control of induction motor transient performances. The merits of the proposed control system are also indicated comparing with a traditional optimal control system.

In this paper, we present a novel method for eye tracking, in detail describing the eye contour and the visible iris center. Combining the IVT (Incremental Visual Tracking) tracker, the proposed online affine manifold model, in which the sequentially learning shape and texture are modeled in the first stage and noniterative recovering estimation in the second stage, tracks the eye contour in video sequences. After that, an adaptive black round mask is generated to match the visible iris center. Experimental results of eye tracking indicate that our tracker works well in the PC or domestic camera captured image streams with considerable head and eyeball rotation.

In this paper, a general electronically tunable resistorless biquad, which realizes current ratio transfer function using current backward transconductance amplifiers is proposed. The biquad contains only two active components and two grounded capacitors, which make it convenient for production by the integrated circuit technology. The proposed biquad realizes all of the basic second order filter characteristics: low pass, high pass, band pass, band reject, and finally all pass. It is convenient for cascading with other similar biquads as well as with any two-port circuit with low impedance input to achieve higher order filter characteristics. The simulations that are performed using PSPICE simulator exhibit satisfactory results coherent with the theory.

This paper presents a data estimation scheme for wide band multichannel charge sampling filter bank receivers together with a complete system calibration algorithm based on the least mean squared (LMS) algorithm. A unified model has been defined for the receiver containing all first order mismatches, offsets, imperfections, and the LMS algorithm is employed to track these errors. The performance of this technique under noisy channel conditions has been verified. Moreover, a detailed complexity analysis of the calibration algorithm is provided which shows that sinc filter banks have much lower complexity than traditional continuous-time filter banks.