An efficient video summarization for surveillance system using normalized k-means and quick sort method

There has been a vast augmentation in quantity of Video Content (VC) generated amid the last some years. The Video Summarization (VS) approach is introduced for managing the VC. Prevailing VS techniques have endeavored to render the VS but the systems have Execution Time (ET) as well as condensing the video's content in domain specific manner. To triumph over such disadvantages, this paper proposed efficient VS for surveillance system using normalized k-means along with quick sort method. The proposed technique comprises eight stages, like split video into frames, pre-sampling, provide ID number, feature extraction, Feature Selection (FS), clustering, extract frames, video summary. Initially, the video frames are pre-sampled utilizing the proposed Three Step Cross Searching Algorithm (TSCS) technique. Then, give the ID number for every frame. Next, the features are extracted as of the frames. Then, the necessary features are selected using Entropy based Spider Monkey Algorithms (ESMA). In next stage, the features are grouped using Normalized K-Means (N-Kmeans) algorithm for identifying best candidate frames. Then select the minimum distance value based cluster set is the Key Frame (KF) selection. At last, the video is orderly summarized using quick sort method. Finally, in experimental evaluation the proposed work is compared with the prevailing methods. The proposed VS gave better outcome than the existing approaches.     

A design of a robust discrete-time controller

In this paper, we proposed a robust discrete-time controller. This control system, which is derived from the idea of the normalized plant, does not include plant parameters. Thus, we obtain a control system independent of plant parameters and that has the same structure as a conventional optimal servo control system. Simulation and experimental results show that the proposed method is fairly robust to plant parameter variations and external disturbances.     

Influence of deposition parameters on surface morphology and application of CuInS2 thin films in solar cell and photocatalysis

Copper indium disulfide (CuInS₂) thin films as an absorption layer for solar cell and photocatalytic degradation of organic pollutants, were successfully electrodeposited on the FTO coated glass substrate using the simple and inexpensive electrodeposition method and a sulfurization process. The effects of the Cu/In molar ratio, annealing temperature and kind of Cu²⁺ precursor (Cu(salen) and Cu(acac)₂ as novel Cu²⁺ precursors) on the structural and morphological properties of samples were examined. The XRD diffraction patterns and energy dispersive spectroscopy measurements exhibit that high-quality film with superior crystalline structure was formed in the presence of Cu(salen) as Cu²⁺ precursor. Also, we found that a suitable heat treatment temperature could suppress the CuS phases and form well-crystallized CIS. As we know, this is the first reported efficiency for any CuInS₂ superstrate solar cell to date that fabricated using Cu(salen) as Cu²⁺ precursor. In addition, the photocatalytic degradation of organic dye in the presence of as-synthesized CuInS₂ thin films was studied. The as-prepared semiconductor photocatalysts have a good reusability; it can be successfully reused for 5 times recycling photoactivity tests.     

一种军棋机器博弈的多棋子协同博弈方法

针对在军棋博弈不完全信息对弈中，面对棋子不同价值、不同位置、不同搭配所产生的不同棋力，传统的单子意图搜索算法，不能满足棋子之间的协同性与沟通性，同时也缺乏对敌方的引诱与欺骗等高级对抗能力。本文提出一种结合UCT搜索策略的高价值棋子博弈方法，实现高价值棋子协同博弈的策略。实战经验表明:高价值多棋子军棋协同博弈策略优于单棋子军棋博弈策略。     

Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.     

An iterative method for suboptimal control of linear time-delayed systems

This article presents a new approach for solving the Optimal Control Problem (OCP) of linear time-delay systems with a quadratic cost functional. The proposed method can also be used for designing optimal control time-delay systems with disturbance. In this study, the Variational Iteration Method (VIM) is employed to convert the original Time-Delay Optimal Control Problem (TDOCP) into a sequence of nonhomogeneous linear two-point boundary value problems (TPBVPs). The optimal control law obtained consists of an accurate linear feedback term and a nonlinear compensation term which is the limit of an adjoint vector sequence. The feedback term is determined by solving Riccati matrix differential equation. By using the finite-step iteration of a nonlinear compensation sequence, we can obtain a suboptimal control law. Finally, Illustrative examples are included to demonstrate the validity and applicability of the technique.     

Compiler compatible 5.66 Mb/mm2 8T 1R1W register file in 14 nm FinFET technology

1-read/1-write (1R1W) register file (RF) is a popular memory configuration in modern feature rich SoCs requiring significant amount of embedded memory. A memory compiler is constructed using the 8T RF bitcell spanning a range of instances from 32 b to 72 Kb. An 8T low-leakage bitcell of 0.106 μm² is used in a 14 nm FinFET technology with a 70 nm contacted gate pitch for high-density (HD) two-port (TP) RF memory compiler which achieves 5.66 Mb/mm² array density for a 72 Kb array which is the highest reported density in 14 nm FinFET technology. The density improvement is achieved by using techniques such as leaf-cell optimization (eliminating transistors), better architectural planning, top level connectivity through leaf-cell abutment and minimizing the number of unique leaf-cells. These techniques are fully compatible with memory compiler usage over the required span. Leakage power is minimized by using power-switches without degrading the density mentioned above. Self-induced supply voltage collapse technique is applied for write and a four stack static keeper is used for read Vmin improvement. Fabricated test chips using 14 nm process have demonstrated 2.33 GHz performance at 1.1 V/25 °C operation. Overall V_min of 550 mV is achieved with this design at 25 °C. The inbuilt power-switch improves leakage power by 12x in simulation. Approximately 8% die area of a leading 14 nm SoC in commercialization is occupied by these compiled RF instances.     

Machine learning techniques for scheduling jobs with incompatible families and unequal ready times on parallel batch machines

This research is motivated by a scheduling problem found in the diffusion and oxidation areas of semiconductor wafer fabrication facilities, where the machines can be modeled as parallel batch processors. Total weighted tardiness on parallel batch machines with incompatible job families and unequal ready times of the jobs is attempt to minimize. Given that the problem is NP hard, a simple heuristic based on the Apparent Tardiness Cost (ATC) Dispatching Rule is suggested. Using this rule, a look-ahead parameter has to be chosen. Because of the appearance of unequal ready times and batch machines it is hard to develop a closed formula to estimate this parameter. The use of inductive decision trees and neural networks from machine learning is suggested to tackle the problem of parameter estimation. The results of computational experiments based on stochastically generated test data are presented. The results indicate that a successful choice of the look-ahead parameter is possible by using the machine learning techniques.