A.E. Guerrero-Sanchez

Bio: A.E. Guerrero-Sanchez is an academic researcher from Autonomous University of Queretaro. The author has contributed to research in topics: Automatic identification and data capture & Biometrics. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Face detection for efficient video-surveillance IoT based embedded system

Abstract: Video Surveillance systems are widely used in indoor and outdoor environments for prevention and security monitoring. Most of conventional video surveillance systems are designed to store huge amount of data which difficult efficient access to the data from remote locations due to bandwidth requirements. A smart surveillance system allows efficient data storage and flexible data access. In this document the design and development of an embedded system for intelligent video surveillance with IoT capabilities is presented. For this project, an OMRON biometric sensor with specific features for face, body and hand detection was used. Face detection provides a criterion for event detection and efficient data capture of the data. The information of interest can be retrieved from a smartphone through Telegram X app. The system was tested under different face conditions including variations of pose, partial occlusion and expression. The system was developed with specific and smart devices providing new and different designs, easily to connect and control for users, without forgetting the importance of security.

Blockchain Mechanism and Symmetric Encryption in A Wireless Sensor Network.

Abstract: The Internet of Things (IoT) paradigm allows the connection and exchange of information between millions of smart devices. This paradigm grows and develops exponentially as do the risks and attacks on IoT infrastructures. Security, privacy, reliability, and autonomy are the most important requirements in IoT Systems. If these issues are not guaranteed, the IoT system could be susceptible to malicious users and malicious use. In centralized IoT systems, attacks and risks are greater, especially when data is transmitted between devices and shared with other organizations. To avoid these types of situations, this work presents a decentralized system that guarantees the autonomy and security of an IoT system. The proposed methodology helps to protect data integrity and availability based on the security advantages provided by blockchain and the use of cryptographic tools. The accuracy of the proposed methodology was measured on a temperature and humidity sensing IoT-based Wireless Sensor Network (WSN). The obtained results prove that the proposal fulfils the main requirements of an IoT system. It is autonomous, secure to share and send information between devices and users, has privacy, it is reliable, and the information is available in the infrastructure. Furthermore, this research demonstrates that the proposal is less susceptible to the most frequent attacks against IoT systems, such as linking attack, man in the middle, and Distributed Denial of Service (DDoS) attack.

Reliable abnormal event detection from IoT surveillance systems

Abstract: Surveillance systems are widely used in airports, streets, banks, military areas, borders, hospitals, and schools. There are two types of surveillance systems which are real-time systems and offline surveillance systems. Usually, security people track videos on time in monitoring rooms to find out abnormal human activities. Real-time human tracking from videos is very expensive especially in airports, borders, and streets due to the huge number of surveillance cameras. There are a lot of research works have been done for automated surveillance systems. In this paper, we presented a new surveillance system to recognize human activities from several cameras using machine learning algorithms. Sequences of images are collected from cameras using the internet of things technology from indoor or outdoor areas. A feature vector is created for each recognized moving object, then machine learning algorithms are applied to extract moving object activities. The proposed abnormal event detection system gives very promising results which are more than 96% accuracy in Multilayer Perceptron, Iterative Classifier Optimizer, and Random Forest algorithms.

Chaotic Cryptosystem for Selective Encryption of Faces in Photographs

Abstract: In this article, a safe communication system is proposed that implements one or more portable devices denominated SBC (single-board computers), with which photographs are taken and that later utilizes the OpenCV Library for the detection and identification of the faces that appear in them. Subsequently, it consults the information in a stored database, whether locally in SBC or in a remote server, to verify that the faces should be coded, and it encrypts these, implementing a new cryptosystem that executes mathematical models to generate chaotic orbits, one of which is used for application on two occasions the technique of diffusion with the purpose of carrying out a small change in one of the pixels of the image, generating very different cryptograms. In addition, in order to make a safer system, it implements other chaotic orbits during the technique of confusion. With the purpose of verifying the robustness of the encryption algorithm, a statistical analysis is performed employing histograms, horizontal, vertical, and diagonal correlation diagrams, entropy, number of pixel change rate (NPCR), unified average change intensity (UACI), sensitivity of the key, encryption quality analysis, and the avalanche effect. The cryptosystem is very robust in that it generates highly disordered cryptograms, supports differential attacks, and in addition is highly sensitive to changes in the pixels as well as in the encrypted keys.

Smart Doorbell with Telegram Notification for Multifamily Dwellings

Abstract: Currently, Peru's population has a great sense of insecurity. According to INEI surveys, 85% of the population has this perception of insecurity, being burglary in homes one of the criminal acts that affect this perception, also according to INEI data, due to the lack of information that the victims have about the perpetrator, they do not report the case. That is why the present work aims to design and implement a smart doorbell for multifamily dwellings, in order to be able to register and identify people who visit the home through the Telegram messaging application. This ensures that vulnerable people in the home cannot let in unknown or dangerous people by being able to identify the visitor at the door of the home, when the person in charge is not present. This system is achieved by using a Raspberry Pi to connect all the devices used and send all the data collected to the respective people, according to a list of identifiers for each group of people living in the building.

Automatic Recognition for IoT Supervision Images Based on Modal Decomposition

Abstract: The automatic recognition for Internet of Things (IoT) supervision images is a prerequisite for the detection of abnormalities in monitoring images. This technology is a developmental trend in video surveillance. Various video image detection and recognition methods have certain weaknesses, such as poor generalization ability and poor anti-interference ability. In response, this paper conducts a study on automatic recognition for IoT supervision images based on modal decomposition. The paper presents an overall framework of the IoT supervision system. For the problems of poor real-time performance and few samples that commonly exist in video stream target recognition, the paper proposes a dynamic modal decomposition-based feature extraction algorithm for IoT supervision video stream to build a suitable platform for IoT supervision image foreground segmentation. The paper selects a dictionary with rich elements and exchanges a high computation time for minimizing the reconstruction error generated by applying the dynamic modal decomposition method. Experimental results validate the effectiveness of the proposed algorithm.