Applications In Security Systems Research Articles

Ultralong Room-Temperature Phosphorescence in Ca(II) Metal-Organic Frameworks Based on Nicotinic Acid Ligands.

In recent years, metal-organic framework (MOF) materials with long persistent luminescence (LPL) have inspired extensive attention and presented various applications in security systems, information anticounterfeiting, and biological imaging fields. However, obtaining LPL materials with ultralong lifetime remains challenging. Halogen atoms, as nonmetallic elements existing in the frameworks, can not only induce the heavy-atom effect, effectively enhancing spin-orbit coupling and promoting intersystem crossing (ISC) processes, but also suppress non-radiative transition of the triplet states through the intra- and intermolecular interactions. Specifically, fluorine atoms with the strongest electronegativity may form intermolecular aggregate interlockings through halogen-bonding interactions that restrict molecular motions and vibrations, thereby improving phosphorescent lifetime. With the aforementioned considerations, two distinct types of MOFs with/without fluorine atoms (namely, Ca-MOF and 5FCa-MOF) were synthesized. Notably, by introducing fluorine atoms into MOFs, fluorine-induced intermolecular aggregate interlockings effectively enhanced the phosphorescent lifetime of 5FCa-MOF exceeding 264 ms compared to that of Ca-MOF (103.94 ms). Remarkably, both MOFs displayed bright LPL to the naked eye after removal of the irradiation source, especially 5FCa-MOF which can last for about 2 s. By introducing fluorine atoms, 5FCa-MOF exhibits greatly enhanced ISC with a rate constant up to 4.1 × 106 s-1 and suppressed non-radiative decay down to 3.73 s-1, thereby extending the LPL time. The thus obtained LPL provides potential in information encryption, security systems, optical anticounterfeiting, and so on.

Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification

Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people’s security and promote their well-being.

Integration of Artificial Intelligence in Facial Recognition Systems for Software Security

Facial recognition technology, a cornerstone in modern software security, has seen significant advancements through the integration of Artificial Intelligence (AI). This research focuses on enhancing facial recognition systems by incorporating sophisticated machine learning algorithms and deep neural networks. By doing so, the goal is to increase the accuracy and reliability of these systems in security applications. The study uses a variety of facial datasets to train AI models that are adept at extracting facial features and recognizing patterns. These models are subjected to rigorous testing to evaluate their performance in terms of identification accuracy, processing speed, and adaptability to different environmental conditions. One of the key challenges addressed in the research is the system's vulnerability to errors and potential misuse. Ethical considerations and privacy concerns are at the forefront of the study. The research highlights the importance of designing AI-based facial recognition systems that respect user privacy and are resistant to biases, thus fostering trust and acceptance among users. The results of the study show a marked improvement in system performance, demonstrating enhanced recognition accuracy and speed, while maintaining robustness across different conditions. By offering practical recommendations for the development of secure, ethical, and privacy-aware facial recognition systems, this research contributes valuable insights into the integration of AI in software security. It underscores the importance of continuous innovation and ethical responsibility in the deployment of facial recognition technologies, shaping the future landscape of technological security measures

Cosmic Muon Rates and Neutron Background in a Backpack Radiation Detection System

Radiation detection systems for security applications are often equipped with neutron sensors and required to promptly respond with an alarm if the neutron flux exceeds the background level. As the ambient neutron flux is essentially caused by cosmic radiation, this background could be evaluated by assessing the rate of muons crossing the gamma-ray detector which is comprised in such systems anyway. A study performed with a commercial Backpack Radiation Detection System (BRD) demonstrates the feasibility of this approach, supposed the gamma spectrometer is capable of discriminating muon signals from energetic events caused by the gamma-ray cascades following neutron captures. This requires a dynamic range exceeding the usual 3-4 MeV. The instrument used provided standard spectroscopy up to 10 MeV and facilitated charge-loss compensated spectroscopy up to 1 GeV, which allowed a separate assessment of normal gamma events (< 3 MeV), neutron-capture gamma events (3-7 MeV), and muon events (> 10 MeV). In this case, the rate of neutron-capture gamma events can be used as a neutron indicator on its own, while the muon rate signals the ambient neutron background level.

Sistem Keamanan Perumahan Menggunakan Face Recognition

The residential environment system is a system that can be used to assist security officers and neighborhood residents to carry out environmental monitoring of crimes both coming from outside the environment and from the environment itself. This residential security system application uses the face recognition method carried out by residential security. The security system is an important point, which makes it the most important goal of researchers. Data collection methods used such as, observation, observation and literature study. The results of this study are to create a security system application using android-based facial recognition. The conclusion of this research is that this residential security system application is designed with simple systems and features to make it easier for users or users, with the Face Recognition method it makes it easier for security guards to carry out security for people who come out of housing, and the security system application is an application designed to help community and security officers so as not to lose the motorized vehicles of residential residents

Camera-based security system application at Perumahan Taman Embong Anyar 1, Dau District, Malang

At Taman Embong Anyar 1 Housing Complex in Dau District, Malang, there is a high crime risk due to residents' activity patterns and open access to residential areas. One of the community's biggest issues is a lack of understanding of using security technology. As an alternative to monitoring the situation in residential areas, CCTV installation and operation assistance are needed to increase environmental security. Beginning with a survey of the areas where CCTV must be installed, the CCTV system must then be selected, installed, and its operation explained to all users. The residents can use and maintain the CCTV system as a result of the entire series of activities. The environment is made more secure and the workload of security officers is minimized with the installation of CCTV.

Selection of the Minimum Number of EEG Sensors to Guarantee Biometric Identification of Individuals.

Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security systems. The extraction of information from the electrical biosignal of the human brain has received a great deal of attention in recent years. Analysis of EEG signals has been widely used over the last century in medicine and as a basis for brain-machine interfaces (BMIs). In addition, the application of EEG signals for biometric recognition has recently been demonstrated. In this context, EEG-based biometric systems are often considered in two different applications: identification (one-to-many classification) and authentication (one-to-one or true/false classification). In this article, we establish a methodology for selecting and reducing the minimum number of EEG sensors necessary to carry out effective biometric identification of individuals. Two methodologies were applied, one based on principal component analysis and the other on the Wilcoxon signed-rank test in order to reduce the number of electrodes. This allowed us to identify, according to the methodology used, the areas of the cerebral cortex that would allow selection of the minimum number of electrodes necessary for the identification of individuals. The methodologies were applied to two databases, one with 13 people with self-collected recordings using low-cost EEG equipment, EMOTIV EPOC+, and another publicly available database with recordings from 109 people provided by the PhysioNet BCI.

The method of the real-time human detection and tracking

Today, data collected from video surveillance systems require processing. Video content analysis (VCA) or video analytics (VA) has found applications in security systems, retail, the automotive industry, smart home technologies, etc. The tasks of video analytics include the detection and tracking of objects. Usually, video analytics systems are specialized software for analyzing video data collected from webcams and intelligently assessing the situation. There are also separate video surveillance cameras with built-in video analytics functions. Software approaches to human detection and tracking are quite diverse, special applications and web applications or separate software modules are created. This work presents an approach to solving these problems using convolutional neural networks. The aim of the study is to increase the efficiency of human detection and tracking in video sequences. For this purpose, an overview of existing methods of detecting objects in images was conducted, in particular: the Viola-Jones algorithm, the histogram of oriented gradients. The choice of convolutional neural networks for solving the subtask of object detection is substantiated. The working principles, advantages and disadvantages of Faster R-CNN, YOLO, SSD and RetinaNet networks are considered. Their comparative analysis was carried out according to the indicators of speed and accuracy of recognition on the HABBOF dataset. A hybrid neural network for human detection and tracking has been developed: a convolutional neural network of the YOLO type has been improved. The created network achieved an accuracy of 39.2% at 43 frames per second. Experiments were carried out with the created network in order to evaluate the operation of the network in various conditions. It has been established that it works well in poor lighting conditions, but the issue of detecting small objects remains open.

Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance

The most crucial component of any smart city traffic management system is traffic flow prediction. It can assist a driver in selecting the most efficient route to their destination. The digitalization of closed-circuit television (CCTV) systems has resulted in more effective and capable surveillance imaging systems for security applications. The number of automobiles on the world’s highways has steadily increased in recent decades. However, road capacity has not developed at the same rate, resulting in significantly increasing congestion. The model learning mechanism cannot be guided or improved by prior domain knowledge of real-world problems. In reality, symmetrical features are common in many real-world research objects. To mitigate this severe situation, the researchers chose adaptive traffic management to make intelligent and efficient use of the current infrastructure. Data grow exponentially and become a complex item that must be managed. Unstructured data are a subset of big data that are difficult to process and have volatile properties. CCTV cameras are used in traffic management to monitor a specific point on the roadway. CCTV generates unstructured data in the form of images and videos. Because of the data’s intricacy, these data are challenging to process. This study proposes using big data analytics to transform real-time unstructured data from CCTV into information that can be shown on a web dashboard. As a Hadoop-based architectural stack that can serve as the ICT backbone for managing unstructured data efficiently, the Hadoop Distributed File System (HDFS) stores several sorts of data using the Hadoop file storage system, a high-performance integrated virtual environment (HIVE) tables, and non-relational storage. Traditional computer vision algorithms are incapable of processing such massive amounts of visual data collected in real-time. However, the inferiority of traffic data and the quality of unit information are always symmetrical phenomena. As a result, there is a need for big data analytics with machine learning, which entails processing and analyzing vast amounts of visual data, such as photographs or videos, to uncover semantic patterns that may be interpreted. As a result, smart cities require a more accurate traffic flow prediction system. In comparison to other recent methods applied to the dataset, the proposed method achieved the highest accuracy of 98.21%. In this study, we look at the construction of a secure CCTV strategy that predicts traffic from CCTV surveillance using real-time traffic prediction analysis with generative adversarial networks (GAN) and HDFS.

Wi-Fi Door Lock Using Esp32 Cam and Blynk

This paper describes a smart Wi-Fi door lock system developed using the esp32 cam and BLYNK app. the system aims to enhance home security by providing real-time notifications and visual verification of visitors. When the doorbell rings, the homeowner receives a notification on their smartphone along with a captured image of the person at the door. This allows the homeowner to remotely verify the individual's identity before granting access by unlocking the door through the door security system application. by bridging traditional door locks with advanced technology, this solution offers convenience and control to homeowners, ensuring the safety of their homes even when they are not present.

Iot Based Home Security System Using Arduino

As home security has become an important aspect in life and is necessary to be in every home due to the rising crime rates and robbery that is happening in out day to day life. In this work in progress paper we evaluate an application of home security system using Arduino. This project is based on the implementation in the IOT domain home security system which will allow to make homes secure from anywhere in the world. The main objective of this project is it should be cost effective and at the same time also secure, and this can be achieved by the use of IOT devices and other low cost electronic components and using them in such a way to build a secure home security system. This project was remove some of the limitations in the previous home security system applications. Home security is very useful application of the IOT domain and we will be using it to build a low cost security system that can be affordable by almost everyone.

Invisible touch sensors-based smart and disposable door locking system for security applications

Nowadays, security is one of the living essentials, and there is a dire need for reliable, secure, and smarter locking systems. The stand-alone smart security systems are of great interest as they do not involve keys, cards, or unsecured communication in order to prevent carrying, loss, duplication, and hacking. Here, we report an invisible touch sensors-based smart door locking system (DLS). The passive transducer-based touch sensors are fabricated through a facile do-it-yourself (DIY) based fabrication process by pasting the hybrid geometry copper electrodes on cellulose paper. The employment of biodegradable, and non-toxic materials like paper and copper tape makes this configuration a good candidate for green electronics. For additional security, the keypad in the DLS is made invisible by covering it with paper and spray paint. One can only open the door by knowing the password as well as the location of each key on the sensor keypad. The system can efficiently recognize the exact pattern of passwords without any false values. Invisible touch sensors-based locking systems can easily contribute to the security applications in homes, banks, automobiles, apartments, lockers, and cabinets.

Perancangan Sistem Keamanan Perumahan Menggunakan Face Recognition Berbasis Android

Environmental security is an important issue that must be considered, relying on the physical strength of security officers to stand guard 24 hours is not enough, an information and communication technology-based system is needed to assist security officers and neighborhood residents in monitoring the environment and its surroundings from possible disturbances of good crime. from outside as well as from within the environment itself. Based on these environmental security issues, the authors were inspired to design a residential security system application using the face recognition method. The main objective of this research is to help the environmental security system in housing. Data collection methods used are interviews, observation and literature study. The results of this study are residential security system applications using Android-based face recognition. This residential security system application is designed with simple systems and features to make it easier for the user. The Face Recognition method makes it easier for security officers to carry out their duties to recognize people who enter and leave housing. This security system application is designed to help residential residents, especially security officers, to be able to detect known and unknown people in housing.

Designing and developing IoT based security system for smart home

This paper represents Internet of Things (IoT) based security system for smart home. As technology has progressed and the use of IoT has grown, digital door locks have become increasingly common. Door Security System application uses ESP8266 and IoT technology to monitor the status of the door. Blynk is a communication protocol that allows a smartphone to communicate with a door lock system. The Door Security System is available on Android and iOS. With this automation system, users may utilize an IR (Infrared remote) remote to manage their home security system from afar. We attempted to connect door security system in our home. Obviously, we will be able to solve that problem in the future. WiFi modules connect to our home internet router and link to our IoT server. Our Android and Web applications communicate with the server as well. This program allows users to check the current state of their home appliances and control them from anywhere. Also MQ3 indicates higher overall performance than the MQ2 in phrases of distance and reaction time.

Aplikasi Panic Buton Untuk Keamanan Warga Berbasis Android

Citizens highly expect environmental security, but criminality is inevitable. The level of crime in the community brings unrest and discomfort, so a security system is needed that is connected with the officers concerned. This study aims to build a security system application connected to environmental security officers. This system can help the public provide reports quickly through the panic button application embedded in Android. System development using the spiral model method. A spiral model is an evolutionary software process model assembling the interactive nature of the prototype using control and systematic aspects of a linear sequential model. Meanwhile, the system design stage uses the Unified Modeling Language (UML). The application is in Android Studio for the design stage, a unique Integrated Development Environment (IDE) that runs on the Android platform. The panic button application is a security system designed to assist the public in providing reports and make it easier for security officers to follow up on the messages given. Accelerate the follow-up process of crimes because it is based on Android.

Constraints of implementation of e-banking security system configuration at people's credit bank in north Sumatera

The purpose of this paper is to look at the Constraints of Implementing an E-Banking Security System Configuration at Rural Banks in North Sumatra, Indonesia. This type of research is qualitative research, namely research that intends to understand the phenomena experienced by the research subjects such as behavior, perceptions, motivations, actions, etc. holistically, and by means of descriptions in the form of words and language, in a special context that naturally and by utilizing various natural methods. The results of this study conclude that the obstacles in the implementation of the E-Banking security system in BPR, in the form of preventive measures and also risk analysis on the implementation of the E-Banking security system application in BPR.

A donor-acceptor luminogen serving as a haptic film sensor for identity recognition

Mechanosensation monitoring is advancing rapidly with the development of haptic sensors. In this study, a donor-acceptor (D-A) luminogen CDBA is reported, which exhibits a transition from intramolecular charge transfer (ICT) state to locally-excited (LE) state in aqueous solution, as well as a stimuli-responsive self-assembly in water vapor. The internal mechanism of the photophysical behavior was probed by spectroscopic analysis and quantum chemical calculation. Based on the remarkable emission turn-on effect in response to water vapor, a haptic film sensor was fabricated. It is highly sensitive to contact pressure, capable of recording the fingerprint with low fingertip pressure. Close inspection using digital single-lens reflex camera and fluorescence microscope demonstrates that the full-scale fingerprint details, including the first-level information (ridges, furrows and skin wrinkles), the second-level information (cores, bifurcations, terminations and islands) and even the third-level information (sweat pores), can be readily collected. In a scenario-based experiment, the pre-placed haptic film sensor succeeded in recording the fingerprint information of a volunteer, suggesting the feasibility for application in security system.

Design of a novel chaotic neural network based encryption system for security applications

ABSTRACT Cryptography is one of the vital domains in computer systems. It is continually evolving with a tremendous amount of active research. With the widespread use of technology, there is a constant requirement for faster, secure cryptosystems. The advance of neural networks has provided us with a wide range of opportunities to optimize cryptosystems. Chaotic neural networks (CNN) are sensitive to their initial conditions. This unique property allows us to improve the security of cryptosystems. In this paper, a chaotic neural network is used to design encryption and decryption algorithms. The algorithms are experimented on using a cipher block chaining mechanism. The cryptographic algorithm was optimized by thoroughly evaluating performance parameters such as autocorrelation, cross-correlation and throughput. This paper experiments with various chaotic maps such as 1-D Logistic map, complex squaring map and cubic map to identify the most efficient map for the outlined CNN in this paper. This paper provides a symmetrical chaotic neural network for a cryptographic application that works for a wide range of inputs. The inputs are not sensitive dependent on characters. Also, the network works seamlessly with UTF-8-based characters.

Aplikasi Pengenalan Wajah dengan Metode Adaptive Resonance Theory (ART)

Image processing technology (image processing) at this time can be done using a computer. An image that can be recognized by a computer is called a digital image. One application of image processing (image processing) is the field of face detection (face detection). Today's face detection system is one of the most important technologies. Because, facial recognition systems are closely related to security systems, where facial recognition systems can be applied in various places and situations, namely: security system applications, facial disguise recognition applications, and database information system applications for an institution. In this study, we will discuss facial recognition using the Principal Component Analysis (PCA) method and the Artificial Neural Network (ANN) used is Adaptive Resonance Theory (ART). The application used to conduct this research is Matlab.

Rancang Bangun Sistem Kontrol Keamanan Gerbang Menggunakan Raspberry Pi B+ Berbasis Web

Computer and information technology from time to time are always experiencing developments, both in terms of hardware, software and the internet being one of the most important things atthis time. The increasing number of applications of computer technology will make the role of computers increasingly important to be more advanced (modernization). The security system isstill not effective and efficient enough, so that a control using a website is needed to help the security control process at the gate. This system is not only used in offices, but can be used inhome security, etc. So we need a security system, where the security control system uses a tool to monitor realtime security via a web browser, namely a webcam (Web Camera). A web-basedgate control system can be designed using the Raspberry Pi B +. From the explanation mentioned above, the researcher made a gate control device that was given to the Microcontroller using a Web Browser in order to open and close the gate. So it is hoped that this security system application can provide a sense of comfort, in addition, of course, with this system application it can reduce the number of crimes that occur both crimes and other crimes.