Management, Security, and Further Applications

Satellite services for disaster management and security applications

Web application security as part of an ISO27001-compliant information security management system As cyber security threats proliferate and attacks escalate, and as applications play an increasingly critical role in business, organisations urgently need to focus on web application security to protect their customers, their interests and their assets. SMEs in particular should be very concerned about web application security: many use common, off-the-shelf applications and plugins - such as Internet Explorer, Java, Silverlight, and Adobe Reader and Flash Player - which often contain exploitable vulnerabilities. Application Security in the ISO27001 Environment explains how organisations can implement and maintain effective security practices to protect their web applications - and the servers on which they reside - as part of a wider information security management system by following the guidance set out in the international standard for information security management, ISO27001. The book describes the methods used by criminal hackers to attack organisations via their web applications and provides a detailed explanation of how you can combat such attacks by employing the guidance and controls set out in ISO27001. This second edition is updated to reflect ISO27001:2013 as well as best practices relating to cryptography, including the PCI SSC's denigration of SSL in favour of TLS. Application Security in the ISO27001 Environment is written by Vinod Vasudevan, Anoop Mangla, Firosh Ummer, Sachin Shetty, Sangita Pakala and Siddharth Anbalahan. Together, the authors offer a wealth of expertise in ISO27001 information security, risk management and software application development.

With the rapid growth of domestic economy, science and technology level unceasing enhancement, the rapid development of electronic information technology, the computer database technology is widely used in various fields, playing an increasingly important role. Computer database technology in the application of information management, greatly improving the efficiency of information management, met the requirement of the complexity of the information management. However, before such a convenient and efficient technology, some domestic enterprises and institutions will appear stretched gaffes, therefore, the focus on the development of electronic information technology at the same time, more should focus on the cultivation of the talent, improve staff quality, so as to achieve mastering many skills while specializing in professionalism, and combined with computer database technology, to realize information management of high efficiency and rate of the query. Introduction The development of modern society, and the application of network technology in people's life is very extensive, companies are using the network information technology for data collection management, computer information base in the country's economic and cultural industries have great application, has brought great convenience to people's life [1]. Computer information management is the main way of data management, so the computer database technology is very important to people in the application of information management, only to strengthen the management of the application of the computer database technology, can increase work efficiency of people in the life of information management, information management become more accurate. Database and information management system are complementary to each other, are inseparable whole, the combination of the two is the basis of the information construction and the safeguard, also is the important power to promote the development of science and technology [2]. With the deepening of the database and computer information management application, we also found some problems to be solved, such as database security protection needs to be improved, the updating of the database theory needs further in practice to prove, our ongoing research is needed to promote better and faster development of database technology, promote the information management better, more secure applications. Database technology in information management application flexibility and to promote the efficiency of use, will promote its to the larger data capacity and intelligent direction. As the technology level of ascension and the rapid expansion of the scope of application, the database technology in the user information management cost and the integration of database engine performance will be further improved. Application of database technology in the information management The application of database in enterprise management With the development of network technology, network communication technology plays an important role in enterprise management. Large enterprises use database technology and network communication technology, the scattered around the branch link effectively, guarantee the company as a whole, from the aspects of global management [3]. At the same time, the company want to

Recently RFID (Radio Frequency Identification) technology has drawn great attention in inventory and production management such as WMS and ERP, and security applications such as access control and safety management. However, an RFID service may infringe on its owner's privacy. We designed a secure RFID application model for IBS (Intelligent Building management Service) using an RFID and sensor network. The proposed RFID based access control service supports secure and intelligent building management applications. It will be a secure application of RFID and sensor networks in a ubiquitous environment.

In this paper, we introduce an energy monitoring system composed of a mixed-signal field-programmable gate array (FPGA) device and a custom designed energy measurement circuit. The energy measurement circuit is designed as a current integrator over a fixed interval using the switched-capacitor (SC) technique. The circuit is implemented using the 65 nm CMOS technology. Simulation results show that our circuit provides energy measurement results with a precision of up to 3% for irregular input waveforms. The circuit is designed to accommodate the low sampling rate of mixed-signal FPGA devices in order to support both power management and security applications for embedded devices.

In recent years video surveillance has gained significant attention due to its versatile applications in security and traffic flow management. Vehicle Re-Identification (Re-ID) is a complicated task in Intelligent Transportation Systems (ITS) where vehicles are tracked by multiple cameras in non-overlapping views. In re-identification the targeted vehicle is matched with the query image from the gallery of vehicle images. There are many uncertainties involved in Re-ID due to inter- class and intra-class similarity because of the viewpoint changes. Large number of studies have explored the deep learning techniques for vehicle detection but classification of vehicle images and re-identifying them is still a field of exploration. In this paper we have analyzed the issue of fine grained vehicle classification and thereby re-identifying them using different CNN models. Vehicle images are pre-processed by different data augmentation techniques and then transfer learning is applied for testing the Stanford cars dataset on three models namely ResNet152, ResNet50 and DenseNet201. The accuracy is obtained for all the models and ResNet152 produces a highest accuracy of 89% among the three models.

In the age of technology, the need for advanced security solutions has become paramount. This project explores the design and implementation of a spherical surveillance robot, aimed at enhancing monitoring capabilities across diverse environments. Its unique spherical shape enables omnidirectional movement, facilitating navigation in confined spaces and complex terrains. Equipped with high-definition cameras and advanced sensors, the robot captures realtime video and detects obstacles, utilizing autonomous navigation algorithms for efficient operation. The modular design allows for easy upgrades and maintenance, while energyefficient components ensure a prolonged operational lifespan. Initial testing demonstrates the robot's effectiveness in both indoor and outdoor settings, highlighting its potential applications in security, facility management, and emergency response. This report outlines the design methodologies, construction process, and testing results, discussing challenges encountered and the implications for future advancements in surveillance technology. Ultimately, the spherical surveillance robot represents a significant innovation in automated security solutions, addressing contemporary security needs in an increasingly technological world.

The recent emergence of integrated PicoRadio technology, the rise of low power, low cost, System-On-Chip (SOC) CMOS imagers, coupled with the fast evolution of networking protocols and digital signal processing (DSP), created a unique opportunity to achieve the goal of deploying large-scale, low cost, intelligent, ultra-low power distributed wireless sensor networks for the visualization of the environment. Of all sensors, vision is the most desired, but its applications in distributed sensor networks have been elusive so far. Not any more. The practicality and viability of ultra-low power vision networking has been proven and its applications are countless, from security, and chemical analysis to industrial monitoring, asset tracking and visual recognition, vision networking represents a truly disruptive technology applicable to many industries. The presentation discusses some of the critical components and technologies necessary to make these networks and products affordable and ubiquitous - specifically PicoRadios, CMOS imagers, imaging DSP, networking and overall wireless sensor network (WSN) system concepts. The paradigm shift, from large, centralized and expensive sensor platforms, to small, low cost, distributed, sensor networks, is possible due to the emergence and convergence of a few innovative technologies. Avaak has developed a vision network that is aided by other sensors such as motion, acoustic and magnetic, and plans to deploy it for use in military and commercial applications. In comparison to other sensors, imagers produce large data files that require pre-processing and a certain level of compression before these are transmitted to a network server, in order to minimize the load on the network. Some of the most innovative chemical detectors currently in development are based on sensors that change color or pattern in the presence of the desired analytes. These changes are easily recorded and analyzed by a CMOS imager and an on-board DSP processor. Image processing at the sensor node level may also be required for applications in security, asset management and process control. Due to the data bandwidth requirements posed on the network by video sensors, new networking protocols or video extensions to existing standards (e.g. Zigbee) are required. To this end, Avaak has designed and implemented an ultra-low power networking protocol designed to carry large volumes of data through the network. The low power wireless sensor nodes that will be discussed include a chemical sensor integrated with a CMOS digital camera, a controller, a DSP processor and a radio communication transceiver, which enables relaying of an alarm or image message, to a central station. In addition to the communications, identification is very desirable; hence location awareness will be later incorporated to the system in the form of Time-Of-Arrival triangulation, via wide band signaling. While the wireless imaging kernel already exists specific applications for surveillance and chemical detection are under development by Avaak, as part of a co-founded program from ONR and DARPA. Avaak is also designing vision networks for commercial applications - some of which are undergoing initial field tests.

Entrance and exit event detection in dynamic environments has a lot of real-world applications in security, crowd management, and retail analytics. Traditional methods used for this problem, namely Line Partition and Bounding Box Diameter methods often struggle in complex scenarios that contain less predictable movement patterns of individuals. This paper proposes a model that integrates deep learning-based object detection and tracking techniques with linear regression to enhance the overall performance of enter and exit detection in static and dynamic environments. This approach captures the movement patterns using advanced object detection and tracking algorithms, enabling the extraction of y-coordinate variations from bounding box centers which are used to calculate the tangent of the linear regression equation and determine if the event is entrance or exit. Experimentations were conducted on 132 video sequences and show the superiority of our approach over the traditional methods achieving an overall accuracy of 86.36% and an F1-score of 0.86. These results demonstrate the high efficiency of this approach to accurately detect entrance and exit events, making it highly reliable and applicable to this problem. This research contributes to computer vision by integrating object detection and tracking algorithms with linear regression offering a solution for enhancing entrance and exit events detection in dynamic environments.

Occupancy estimation has a broad range of applications in security, surveillance, traffic and resource management in smart building environments. Low-resolution thermal imaging sensors can be used for real-time non-intrusive occupancy estimation. Such sensors have a resolution that is too low to identify occupants, but it may provide sufficient data for real-time occupancy estimation. In this paper, we present a systematic study of three thermal imaging sensors with different resolutions, with a focus on sensor characterization, estimation algorithms, and comparative analysis of occupancy estimation performance. A unified processing algorithms pipeline for occupancy estimation is presented and the performance of three sensors are compared side-by-side. A number of specific algorithms are proposed for pre-processing of sensor data, feature extraction, and fine-tuning of the occupancy estimation algorithms. Our results show that it is possible to achieve about 99% accuracy for occupancy estimation with our proposed approach, which might be sufficient for many practical smart building applications.

Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.

Palm recognition, a subset of biometrics, has received a lot of attention due of its potential utility in many different domains, including security systems and human-computer interface. In this study, convolutional neural networks (CNNs) are being utilised to investigate the development of a palm detection system. Deep learning will be used to detect and categorise palm orientation in digital photographs. Data collection, which entails gathering a broad dataset of precisely labelled palm pictures for supervised learning, is an important component of the project. We manage data preprocessing and prepare datasets for model training by employing techniques such as picture scaling, pixel normalisation, and data augmentation. Our technology is built around a CNN model architecture, which generates a neural network capable of automatically collecting information from palm images and conducting smart classification. This design employs convolutional layers for feature extraction and fully linked layers for classification. During the training phase, we investigate the technique, optimizer selection, loss function selection, and hyperparameter optimisation. We pay close attention to two things: monitoring the model's performance on the validation set and putting countermeasures in place to avoid overfitting. The evaluation section provides information about the model's precision and generalizability. We review the results of testing on the validation and test datasets while keeping the problems and limits in mind. Our efforts to fine-tune the model involve adjusting hyperparameters and researching data augmentation approaches, all with the goal of improving model performance. During the inference phase, the trained model's potential in real-world situations is highlighted, demonstrating how it might be applied in practise. Our palm recognition technique paves the path for future biometric authentication use, with potential applications in security, access management, and human-computer interface.

This study develops a hybrid artificial intelligence model for fraud detection in fiber optic networks under the user classification strategy, combining various machine learning approaches to improve the accuracy in the classification as fraud, anomaly or normality. Individual models such as Random Forest, Gradient Boosting and Support Vector Machine were tested. The data being worked with is provided by a telecommunications company in Norte de Santander with approximately 10 thousand records and with the following variables: Anonymized personal data (age, geographic location, user type), consumption history and network usage patterns, transactional and financial data related to billing, incident reports and service anomalies. Preprocessing is performed, the data is cleaned by eliminating null values, duplicates and outliers; then, the variables are normalized and standardized, finally, the data is divided into training sets (70%) and validation sets (30%). The results demonstrate that hybrid approaches enable more accurate analysis of user behavior in telecommunications, improving the identification of suspicious patterns in data consumption, transactions, and anomaly reporting. Compared to previous studies, which use hybrid approaches to combat telecom fraud through network analysis and predictive models, this study confirms that the combination of multiple models improves detection and reduces errors; the proposed hybrid model optimizes fraud detection in fiber optic networks, offering a good alternative for telecommunications companies that can be combined with applications in security, risk management, and revenue protection.

This paper presents the design and implementation of the intelligent presence system using AI- powered facial recognition and cloud integration. The system leverages advanced images processing techniques to accurately recognize and identify a person’s face through a real time camera feed. Increases security and user interaction in addition to facial recognition the proposed system also has voice recognition capabilities. Enabling AI robots to understand and respond to command such as welcoming guests is based on, which is mask and equipped with an OLED display for better visual feedback. Our approach uses multiple algorithms optimized to accommodate the variability in human facial feature. This ensures robust performance across different shapes and sizes. This comprehensive review discusses the method used in face recognition. Integrating cloud technology for data management and possible application in security and services sectors. It emphasizes the importance of intelligent system in modern state management.

This paper presents experimental results of a series of performance tests for an indoor position tracking system. It tracks the position of a radio-frequency (RF) transmitter and is based on a novel estimation technique known as Partial Pulse Positioning (P/sup 3/). This technique reduces the negative impact of multipath and thereby leads to a high positional accuracy. Such high accuracy will have applications in inventory management, automated guided vehicle (AGV) navigation, security applications in airports, real time tracking of sports players, surveying and motion capture.