A comprehensive survey on computer vision based concepts,methodologies, analysis and applications for automatic gun/knife detection

The ability to detect gun and gun held in hand or other body parts is a typical human skill. The same problem presents an imperative task for computer vision system. Automatic observer independent detection of hand held gun or gun held in the other body part, whether it is visible or concealed, provides enhance security in vulnerable places and initiates appropriate action there. Compare to the automatic object detection systems, automatic detection of gun has very few successful attempts. In the present scope of this paper, we present an extensive survey on automatic detection of gun and define a taxonomy for this particular detection system. We also describe the inherent difficulties related with this problem. In this survey of published papers, we examine different approaches used in state-of-the-art attempts and compare performances of these approaches. Finally, this paper concludes pointing to the possible research gaps in related fields.     

An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization

The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep‐neural‐network‐based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back‐end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.     

Detector physics and simulation of resistive plate chambers

We present a simulation model suited to study efficiency, timing and pulse-height spectra of Resistive Plate Chambers. After discussing the details of primary ionisation, avalanche multiplication, signal induction and frontend electronics, we apply the model to timing RPCs with time resolution down to 50 ps and trigger RPCs with time resolution of about 1 ns.     

交互控制程序编程方法

交互控制是计算机程序的重要功能 ,也是实现人—机对话的基本途径。VisualBasic编程语言以其易学习掌握、开发灵活的特点而成为目前流行的程序开发工具 ,本文以VisualBasic语言为例介绍了交互控制程序编程方法 ,并对具体的每一种方法编写了相应的示例。     

地面油管漏失定量测试方法探讨

对油管漏失进行定量测试的方法确定具体漏失位置。方法运用双频道回声探仪探测动液面的原理和工作方法，根据油管漏失形成的死油坏在测试曲线上出现特殊波位置判断油管漏失位置。结果：经过７０多井次的现场试验，该方法具有较高的准确性和可靠性。结论：利用特殊波可以有效监测油管漏失，并具有推广使用价值。     

压差式漏率测试仪的实验研究

将薄膜电容真空计的测量室接于被测密闭容器，静态真空室接于一个比较容器，即组成一台压差式漏率测试仪。首先使两容器压力平衡，真空计读数为零。当被测容器存在漏孔时，真空计薄膜两侧形成压差，真空计指示读数，继而计算出该容器的漏率。应用商品真空计在抽真空测试时，检测的最小可测漏率达10－4～10-5Pa·L／s；而在充压测试时，因受气体温度变化的影响，灵敏度会降低几个量级。该仪器有可能具备寻找漏孔位置和确定漏孔漏率的功能。     

数据采集与监控软件Genie

Genie是具有国际先进水平的数据采集与监控软件，它提供了一个面向对象的图形化用户界面，因此简化了数据采集过程、控制应用程序开发和操作员界面。本文介绍了Genie的特点、功能、基本用途以及VISUALBA－SIC在设计这类监控软件中的作用。