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Peripheral Vision
In Portugal between 2005 and 2010, \"modernization through technology\" was the major political motto used to develop and improve the country's peripheral and backward condition. This study reflects on one of the resulting, specific aspects of this trend-the implementation of public video surveillance. The in-depth ethnography provides evidence of how the political construction of security and surveillance as a strategic program actually conceals intricate institutional relationships between political decision-makers and common citizens. Essentially, the detailed account of the major actors, as well as their roles and motivations, serves to explain phenomena such as the confusion between objective data and subjective perceptions or the lack of communication between parties, which as this study argues, underlies the idiosyncrasies and fragilities of Portugal's still relatively young democratic system.
eBook
Optimal of Placement for Battery Energy Storage System Installation Using Fuzzy Expert System in Thailand: A Case Study of Critical Closed-Circuit Television Positions
This paper presents placement optimization for battery energy storage system installation using a fuzzy expert system. Nowadays, the Bangkok Metropolitan Administration (BMA) has installed CCTV cameras for surveillance, deterrence, and to record events as evidence for legal proceedings. However, in some areas, there is no BESS, so when the power goes out, recording cannot continue. This article uses a Fuzzy Logic Expert System to assess critical areas for the consideration of future BESS installation in Bangkok. The key factors include (1) the number of CCTV image requests from the Bangkok Metropolitan Administration, (2) the duration of power outages from the BMA, and (3) the total power consumption of the CCTV in each subdistrict. The study results show that the fuzzy expert system can effectively handle ambiguous data and improve decision-making. The Latkrabang and Lamphlatiew subdistricts have the most critical points where investment in BESS installation is most appropriate. The size of the BESS was determined based on the maximum recorded power outage duration of 57 min, with the backup power design for the BESS set at 1 h. The DIgSILENT program was used to determine the size of the BESS at each critical point, which was calculated to be 160.2 Wh.
Journal Article
Human Activity Recognition: Review, Taxonomy and Open Challenges
Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities. Several reviews and surveys on HAR have already been published, but due to the constantly growing literature, the status of HAR literature needed to be updated. Hence, this review aims to provide insights on the current state of the literature on HAR published since 2018. The ninety-five articles reviewed in this study are classified to highlight application areas, data sources, techniques, and open research challenges in HAR. The majority of existing research appears to have concentrated on daily living activities, followed by user activities based on individual and group-based activities. However, there is little literature on detecting real-time activities such as suspicious activity, surveillance, and healthcare. A major portion of existing studies has used Closed-Circuit Television (CCTV) videos and Mobile Sensors data. Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Support Vector Machine (SVM) are the most prominent techniques in the literature reviewed that are being utilized for the task of HAR. Lastly, the limitations and open challenges that needed to be addressed are discussed.
Journal Article
The Value of CCTV Surveillance Cameras as an Investigative Tool: An Empirical Analysis
There has been extensive research on the value of closed-circuit television (CCTV) for preventing crime, but little on its value as an investigative tool. This study sought to establish how often CCTV provides useful evidence and how this is affected by circumstances, analysing 251,195 crimes recorded by British Transport Police that occurred on the British railway network between 2011 and 2015. CCTV was available to investigators in 45% of cases and judged to be useful in 29% (65% of cases in which it was available). Useful CCTV was associated with significantly increased chances of crimes being solved for all crime types except drugs/weapons possession and fraud. Images were more likely to be available for more-serious crimes, and less likely to be available for cases occurring at unknown times or in certain types of locations. Although this research was limited to offences on railways, it appears that CCTV is a powerful investigative tool for many types of crime. The usefulness of CCTV is limited by several factors, most notably the number of public areas not covered. Several recommendations for increasing the usefulness of CCTV are discussed.
Journal Article
Human Violence Detection Using Deep Learning Techniques
The world’s average annual fatality rate from human violence is 7.9 per 10,000 people. Most of this human violence takes place in an isolated area or of sudden. The information delay here is a major impediment in stopping these acts. To thrive on this issue, the detection technique is used in this study. Detecting moving objects from CCTV is one of the most effective computer vision algorithms. CCTV cameras are now in every streets which are extremely helpful in solving cases. Some techniques of deep learning are used as computer vision to predict and detect the action, properties from video. In real-time police reach violent destinations and start checking CCTV cameras, and investigate to proceed further. This study is deliberately designed to detect violent acts from CCTV cameras. The Inception – v3 and Yolo – v5 models detect the violent act, the number of persons involved, and also the weapons used in the situation. The study consists of these deep learning models, which are used to form a video detection system. This model can be used in real-time as an application programming interface (API) or software. The study results showed the proposed model achieves an accuracy of 74%.
Journal Article
Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning
This work proposes the use of an unmanned aerial vehicle (UAV) with an autopilot to identify the defects present in municipal sewerage pipes. The framework also includes an effective autopilot control mechanism that can direct the flight path of a UAV within a sewer line. Both of these breakthroughs have been addressed throughout this work. The UAV's camera proved useful throughout a sewage inspection, providing important contextual data that helped analyze the sewerage line's internal condition. A plethora of information useful for understanding the sewerage line's inner functioning and extracting interior visual details can be obtained from camera‐recorded sewerage imagery if a defect is present. In the case of sewerage inspections, nevertheless, the impact of a false negative is significantly higher than that of a false positive. One of the trickiest parts of the procedure is identifying defective sewerage pipelines and false negatives. In order to get rid of the false negative outcome or false positive outcome, a guided image filter (GIF) is implemented in this proposed method during the pre‐processing stage. Afterwards, the algorithms Gabor transform (GT) and stroke width transform (SWT) were used to obtain the features of the UAV‐captured surveillance image. The UAV camera's sewerage image is then classified as “defective” or “not defective” using the obtained features by a Weighted Naive Bayes Classifier (WNBC). Next, images of the sewerage lines captured by the UAV are analyzed using speed‐up robust features (SURF) and deep learning to identify different types of defects. As a result, the proposed methodology achieved more favorable outcomes than prior existing approaches in terms of the following metrics: mean PSNR (71.854), mean MSE (0.0618), mean RMSE (0.2485), mean SSIM (98.71%), mean accuracy (98.372), mean specificity (97.837%), mean precision (93.296%), mean recall (94.255%), mean F1‐score (93.773%), and mean processing time (35.43 min). This work describes the construction of an image analysis‐based intelligent information analysis method that will identify issues within municipal sewerage pipes employing an autopilot‐controlled unmanned aerial vehicle (UAV).
Journal Article
Therapeutic potential of brain stimulation techniques in the treatment of mental, psychiatric, and cognitive disorders
Treatment for brain diseases has been disappointing because available medications have failed to produce clinical response across all the patients. Many patients either do not respond or show partial and inconsistent effect, and even in patients who respond to the medications have high relapse rates. Brain stimulation has been seen as an alternative and effective remedy. As a result, brain stimulation has become one of the most valuable therapeutic tools for combating against brain diseases. In last decade, studies with the application of brain stimulation techniques not only have grown exponentially but also have expanded to wide range of brain disorders. Brain stimulation involves passing electric currents into the cortical and subcortical area brain cells with the use of noninvasive as well as invasive methods to amend brain functions. Over time, technological advancements have evolved into the development of precise devices; however, at present, most used noninvasive techniques are repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS), whereas the most common invasive technique is deep brain stimulation (DBS). In the current review, we will provide an overview of the potential of noninvasive (rTMS and tDCS) and invasive (DBS) brain stimulation techniques focusing on the treatment of mental, psychiatric, and cognitive disorders. Diagrams of representative non‐invasive and invasive brain stimulation techniques. (A) represents the standard figure‐eight transcranial magnetic stimulation (TMS) coil, which is connected to intensity and pulse regulator. (B) exhibits bipolar transcranial direct current stimulation (tDCS) electrodes and current regulator. (C) shows deep brain stimulation (DBS) microelectrodes inserted in brain and connected to a stimulator implanted elsewhere.
Journal Article
THE BANALITY OF SECURITY: The Curious Case of Surveillance Cameras
23JulyWhy do certain security goods become banal (while others do not)? Under what conditions does banality occur and with what effects? In this paper, we answer these questions by examining the story of closed circuit television cameras (CCTV) in Britain. We consider the lessons to be learned from CCTV's rapid—but puzzling—transformation from novelty to ubiquity, and what the banal properties of CCTV tell us about the social meanings of surveillance and security. We begin by revisiting and reinterpreting the historical process through which camera surveillance has diffused across the British landscape, focusing on the key developments that encoded CCTV in certain dominant meanings (around its effectiveness, for example) and pulled the cultural rug out from under alternative or oppositional discourses. Drawing upon interviews with those who produce and consume CCTV, we tease out and discuss the family of meanings that can lead one justifiably to describe CCTV as a banal good. We then examine some frontiers of this process and consider whether novel forms of camera surveillance (such as domestic CCTV systems) may press up against the limits of banality in ways that risk unsettling security practices whose social value and utility have come to be taken for granted. In conclusion, we reflect on some wider implications of banal security and its limits.
Journal Article
Blockchain and Interplanetary File System (IPFS)-Based Data Storage System for Vehicular Networks with Keyword Search Capability
Closed-circuit television (CCTV) cameras and black boxes are indispensable for road safety and accident management. Visible highway surveillance cameras can promote safe driving habits while discouraging moving violations. According to CCTV laws, footage captured by roadside cameras must be securely stored, and authorized persons can access it. Footages collected by CCTV and Blackbox are usually saved to the camera’s microSD card, the cloud, or hard drives locally but there are concerns about security and data integrity. These issues may be addressed by blockchain technology. The cost of storing data on the blockchain, on the other hand, is prohibitively expensive. We can have decentralized and cost-effective storage with the interplanetary file system (IPFS) project. It is a file-sharing protocol that stores and distributes data in a distributed file system. We propose a decentralized IPFS and blockchain-based application for distributed file storage. It is possible to upload various types of files into our decentralized application (DApp), and hashes of the uploaded files are permanently saved on the Ethereum blockchain with the help of smart contracts. Because it cannot be removed, it is immutable. By clicking on the file description, we can also view the file. DApp also includes a keyword search feature to assist us in quickly locating sensitive information. We used Ethers.js’ smart contract event listener and contract.queryFilter to filter and read data from the blockchain. The smart contract events are then written to a text file for our DApp’s keyword search functionality. Our experiment demonstrates that our DApp is resilient to system failure while preserving the transparency and integrity of data due to the immutability of blockchain.
Journal Article