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This paper studiesan unmanned aerial vehicle (UAV)-assisted wireless network, where a UAV is dispatched to gather information from ground sensor nodes (SN) and transfer the collected data to a depot. The information freshness is captured by the age of information (AoI) metric, whilst the energy consumption of the UAV is seen as another performance criterion. Most importantly, the AoI and energy efficiency are inherently competing metrics, since decreasing the AoI requires the UAV returning to the depot more frequently, leading to a higher energy consumption. To this end, we design UAV paths that optimize these two competing metrics jointly and reveal the Pareto frontier. To formulate this problem, a multi-objective mixed integer linear programming (MILP) is proposed with a flow-based constraint set and we apply Bender’s decomposition on the proposed formulation. Numerical results show that the proposed method allows deriving non-dominated solutions among two competing metrics when designing the UAV path. [ABSTRACT FROM AUTHOR]

We present a method of near-infrared image reconstruction based on spatial modulation instability in a photorefractive strontium barium niobate crystal. The conditions that lead to the formation of modulation instability at near-infrared are discussed depending on the theory of modulation instability gain. Experimental results of scattered image reconstruction at the 1064 nm wavelength show the maximum cross-correlation coefficient and cross-correlation gain are 0.57 and 2.09 respectively. This method is expected to be an aid for near-infrared imaging technologies. [ABSTRACT FROM AUTHOR]

The superiority of convolutional neural network (CNN) has been proven in various object recognition tasks and has received much attention. However, the modern CNN approaches usually that assume the testing data and the training data belong to an identical category. Therefore, the current CNN approaches are not efficient for some applications with multisource data (i.e., the input data come from different sources), such as remote sensing scene. To increase the adaptability of the involved CNN approach, we first propose a $K$ -means-assisted scenario-aware reconfigurable convolutional neural network (KASR-CNN) mechanism. The KASR-CNN is composed of a fully convolutional autoencoder-based $K$ -means clustering (FCA-KC) and a reconfigurable convolutional neural network (RCNN), which are used to perform the coarse-grained classification and fine-grained classification to the input data, respectively. Furthermore, a Lego-like architecture design methodology is proposed to reduce the design complexity and improve computing flexibility. To show the adaptability, the KASR-CNN mechanism has been applied to different CNN models. In addition, the KASR-CNN has been verified on the Xilinx Zynq-ZC706 field-programmable gate array (FPGA) and implemented with TSMC 40-nm technology. Compared with the conventional approaches, the proposed KASR-CNN can help the involved CNN model to improve 3.73%–36.65% classification accuracy with only 0.38%–0.74% area overhead. [ABSTRACT FROM AUTHOR]

As the complexity of multicore system grows with respect to the technology development, the Network-on-Chip (NoC) provides flexible and scalable interconnection for the multicore systems. However, as the complexity of the network increases, the large workload diversity and the time-varying workload distribution result in large power density, which leads to severer thermal problems and makes the temperature distribution of the system become time-varying. To prevent the multicore systems from overheating, in a practical way, many thermal sensors are embedded in the system. However, due to the manufacturing cost constraints, it is not a viable option to involve a massive number of embedded thermal sensors. Therefore, searching for the appropriate locations in offline design phase to allocate the number-limited thermal sensors, which will be used to sense the time-varying system temperature behavior at runtime, is a design challenge. On the other hand, full-chip temperature distribution tracking based on the restricted temperature sensing information affects the efficiency of the involved temperature management. In this paper, we first present a novel thermal sensor allocation methodology by considering the time-varying temperature behavior on the chip according to different applications. Besides, a linear-regression-based reconstruction algorithm is proposed to estimate the full-chip temperature distribution according to the number-limited thermal sensing results. At last, a framework of temperature management with restricted temperature sensing information is introduced. Compared with the conventional methods, the proposed approach can reduce 28% to 92% average error of full-chip temperature estimation, which helps to improve the average system throughput by 60% to 70%. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for locating short-circuit faults on untransposed transmission lines. Various fault-location algorithms have been proposed previously for transposed lines, which may not always be the case in practice. Existing methods usually require measurements recorded from one or two terminals of the faulted line, which may not be always available. To complement existing methods, this paper proposes a novel, general fault-location method for untransposed lines using sparse wide-area measurements, which is also applicable to transposed lines. Phasor measurement units are utilized to obtain the sparse wide-area synchronized measurements, which may be captured from buses far away from the faulted line. This method fully considers inherent unbalances of untransposed lines, and fully models shunt capacitances of lines based on a distributed parameter line model. Optimal estimation theory is exploited to make the most of available measurements for enhanced estimation. The method is able to locate faults on single-circuit and double-circuit line configurations, and is immune to fault resistances. Another feature of this method is that it does not require the fault type to be known. Extensive simulation studies using the Electromagnetic Transients Program have been performed, and quite accurate results have been achieved. [ABSTRACT FROM PUBLISHER]

This paper primarily addresses the subject of reducing outages or reducing the outage duration, auto-restoration applications for transmission substations, (where “transmission” is defined as 69 kV and higher), and the subject of proactively preventing outages. This paper is a summary of the report of K3-WG for transmission systems. The contribution of this paper includes the presentation of the state-of-the-art, practical techniques and measures for preventing and reducing outages in transmission substations which, if properly adopted, can significantly improve transmission system reliability. [ABSTRACT FROM AUTHOR]

Driver distraction has been identified as one major cause of unsafe driving. The existing studies on cognitive distraction detection mainly focused on high-speed driving situations, but less on low-speed traffic in urban driving. This paper presents a method for the detection of driver cognitive distraction at stop-controlled intersections and compares its feature subsets and classification accuracy with that on a speed-limited highway. In the simulator study, 27 subjects were recruited to participate. Driver cognitive distraction is induced by the clock task that taxes visuospatial working memory. The support vector machine (SVM) recursive feature elimination algorithm is used to extract an optimal feature subset out of features constructed from driving performance and eye movement. After feature extraction, the SVM classifier is trained and cross-validated within subjects. On average, the classifier based on the fusion of driving performance and eye movement yields the best correct rate and F-measure ( \textcorrect \textrate=\mbox95.8\pm \mbox4.4\%; for stop-controlled intersections and \textcorrect\ \textrate=\mbox93.7\pm \mbox5.0\%; for a speed-limited highway) among four types of the SVM model based on different candidate features. The comparisons of extracted optimal feature subsets and the SVM performance between two typical driving scenarios are presented. [ABSTRACT FROM PUBLISHER]

In this paper, a single instruction multiple data (SIMD) arithmetic logic unit (ALU)-based architecture is proposed to improve hardware efficiency in a $4 \times 4$ frequency-domain multiple input multiple output–orthogonal frequency division multiplexing modem based on a space-time block code (STBC). The majority of mathematic units in the proposed architecture are centralized so that any mathematic unit can be shared with any algorithm. Six advanced instructions are also defined in the ALU: 1) complex multiplication; 2) complex division; 3) correlation; 4) channel estimation; 5) $4 \times 4$ matrix inversion; and 6) STBC-based decoding. A scheduler is essential to handle all data paths and signaling flows smoothly with the use of an SIMD ALU. As a result, it is relatively easy to reconfigure the proposed design for different specifications. The very large scale integration implementation of this chip, using an in-house 65-nm CMOS process, consumes a total of 1.87 M gates and draws 33.7 mW at a supply voltage of 1 V. [ABSTRACT FROM AUTHOR]

A new fault-location algorithm for series-compensated double-circuit transmission lines utilizing two-terminal unsynchronized voltage and current measurements is presented in this paper. The mutual coupling between the parallel lines in the zero-sequence network is fully considered. The distributed parameter line model is adopted to fully take into account the shunt capacitance of the line. By formulating voltages and currents at the fault point in terms of the unknown fault location, boundary conditions under different fault types are used to derive the fault location. Two subroutines assuming the fault occurs on the left or right side of the series compensator are developed and the principle to identify the correct fault-location estimate is described. Matlab SimPowerSystems is employed to generate cases under diverse fault conditions for validating the proposed fault-ocation algorithm. Evaluation studies have shown that the proposed algorithm has achieved quite accurate results. [ABSTRACT FROM PUBLISHER]

For pure Ohmic drive both plasma breakdown and startup on HL-2A are on the low field side where the field null configuration is located, and the intensity of D\alpha emission confirms the plasma formation area. The current of vertical field coils is an important factor to facilitate plasma breakdown and a certain value is required for prompt plasma formation. The effect of the prefill pressure on plasma breakdown has been studied by scanning the prefill pressure. High and low prefill pressure will cause failure of the discharge and only in an appropriate range of the pressure can obtain a reproducible prompt discharge. A new plasma startup scenario has been introduced for bad wall conditions using early formation of a divertor configuration, the result demonstrates that it can be successful startup even if the plasma facing components have absorbed a lot of impurities. [ABSTRACT FROM PUBLISHER]

Fault location is considered as one of the essential techniques enabling a smart grid. This paper presents a novel fault location approach for double-circuit transmission lines. Various existing algorithms usually require measurements recorded from one or two terminals of the faulted line. However, when such measurements are not available, these methods are not applicable. To fill this gap, this paper proposes a fault location method by harnessing current measurements from one or more branches, which may not be necessarily taken from the faulted section. The currents from multiple branches are synchronized and can be obtained from phasor measurement units installed in the system. The bus impedance matrix technique is exploited to develop the new method. The shunt capacitance of the line is fully considered by utilizing the distributed parameter line model. The network data are assumed to be available. When multiple synchronized current measurements are available, an optimal estimator capable of identifying possible bad measurement data and, thus, further enhancing the fault location is also proposed. Quite accurate results have been achieved based on simulation studies. [ABSTRACT FROM PUBLISHER]

To satisfy the requirement of application heterogeneities, the latest H.264/AVC based video coding standard called scalable video coding additional includes temporal, SNR, and spatial scalabilities for frame rate, quality, and frame resolution adaptation. However, these inclusions significantly increase chip design difficulties such as decoding time, memory bandwidth, and area cost. This paper presents an H.264/AVC scalable high profile decoder realization with several optimization techniques to provide high throughput video decoding. For decoding flow, this paper proposes an one-pass macroblock-based quality layer decoding flow for SNR scalability and 71% of external memory bandwidth and 66% of macroblock processing cycles can be saved. For texture padding in interlayer intra prediction, the modified padding flow can save 26% of decoding time. For interlayer predictor design, this paper proposes a centralized concept for accumulation-based calculation of corresponding spatial position, simplified poly-phase interpolator, and efficient motion vector generator to save area cost and decoding time. Furthermore, the residual reconstruction path with the parallel-pipeline architecture is also proposed to cope with the additional decoding complexity and thus leads to 54% of gate count savings compared to the traditional serial-pipeline architecture. Finally, the proposed H.264/AVC scalable high profile decoder design is implemented with 90 nm CMOS technology and it costs 542 k gate count and 39.66 Kbytes on-chip memory while is capable to decode 60 frames/s for CIF+SD480p+HD1080p resolution with three quality layers at 135 MHz operating frequency. [ABSTRACT FROM PUBLISHER]

In this paper, a high throughput context-based adaptive binary arithmetic coding decoder design is proposed. This decoder employs a syntax element prediction method to solve pipeline hazard problems. It also uses a new hybrid memory two-symbol parallel decoding in order to enhance performance as well as to reduce costs. The critical path delay of the two-symbol binary arithmetic decoding engine is improved by 28% with an efficient mathematical transform. Experimental results show that the throughput of our proposed design can reach 485.76 Mbins/s in the high bit-rate coding and 446.2 Mbins/s on average at 264MHz operating frequency, which is sufficient to support H.264/AVC level 5.1 real-time decoding. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach for locating short-circuit faults on a double-circuit transmission line. Various algorithms have been proposed previously that usually require measurements recorded from one or two buses of the faulted line. However, such measurements may not always be available in some scenarios, rendering inapplicability of existing methods. To complement existing methods, this paper proposes a novel, general fault-location method by harnessing voltage measurements at one or more buses, which may not be taken from the faulted line. The bus impedance matrix of each sequence network with the addition of a fictitious bus at the fault point can be derived as a function of the fault location. The fault location can then be obtained based on the bus impedance matrix and voltage measurements. The distributed parameter line model is utilized. The network data are assumed to be available so that the bus impedance matrix can be constructed. When multiple voltage measurements are available, an optimal estimator capable of identifying bad measurement data is also proposed for enhanced fault location. Since field data are not available at this time, simulated data are utilized for evaluation studies, and quite accurate results have been achieved. [ABSTRACT FROM PUBLISHER]

Various methods have been proposed in the past for locating faults on distribution systems, which generally entail iterative procedures. This paper presents novel fault-location algorithms for overhead distribution systems that provide a unified solution that eliminates or reduces iterative procedures applicable to all types of faults. Two types of methods, respectively, for nonradial systems and radial systems have been proposed by utilizing voltage and current measurements at the local substation. The proposed methods are based on the bus impedance matrix, through which the substation voltage and current quantities can be expressed as a function of the fault location and fault resistance, a solution to which yields the fault location. The methods are developed in phase domain and, consequently, are naturally applicable to unbalanced systems. The assumptions made are that the distribution network parameters and topology are known so that the bus impedance matrix can be developed. Simulation studies have demonstrated that both types of methods are accurate and quite robust to load variations and measurement errors. [ABSTRACT FROM AUTHOR]

Diverse transmission line fault location algorithms have been proposed in the past depending on measurements available. Existing algorithms usually require measurements captured from buses of a faulted line. By taking advantage of the bus-impedance matrix technique, this paper presents a possible fault location approach for single-circuit lines utilizing only voltage measurements from one or two buses, which may be distant from the faulted line. With the addition of a fictitious bus where the fault occurs, the transfer impedances of this bus and other buses are revealed as a function of the fault location. Based on the relationship between the bus voltage change due to fault and the transfer impedance, the fault location can be derived. Shunt capacitance of the line is ignored first and then fully considered based on distributed parameter line model. ElectroMagnetic Transients Program simulation studies have shown quite encouraging results. [ABSTRACT FROM AUTHOR]

Presents the findings of a study for Mandarin syllable recognition by modular recurrent neural networks. Proposal of 1280 highly confusable Mandarin syllables; Methodology used in the study; What was revealed by experimental results.

The papers in this special section was conceived with an aim of stimulating forward-looking and transformative ideas and methods that can lead to significant advances in power system protection. Protection is one of the fastest operating functions of a power system, and critical to safety of equipment, and stability of the system. Designed and refined through more than a century of experience, insight, and technological advances, design and implementation of protection has kept power systems exceptionally reliable, considering the fact that the power grid is the largest nonlinear dynamic system in the world, a large part of which is continuously exposed to weather. In recent times, advances in sensing, communication, and computing have potential to refine, or even redefine the conception and implementation of power system protection. [ABSTRACT FROM PUBLISHER]