IoT-Based Access Management Supported by AI and Blockchains

Internet-of-Things (IoT), Artificial Intelligence (AI), and Blockchains (BCs) are essential techniques that are heavily researched and investigated today. This work here specifies, implements, and evaluates an IoT architecture with integrated BC and AI functionality to manage access control based on facial detection and recognition by incorporating the most recent state-of-the-art techniques. The system developed uses IoT devices for video surveillance, AI for face recognition, and BCs for immutable permanent storage to provide excellent properties in terms of image quality, end-to-end delay, and energy efficiency

Segmentation-guided privacy preservation in visual surveillance monitoring

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Sergio Escalera Guerrero, Zenjie Li i Kamal Nasrollahi[en] Video surveillance has become a necessity to ensure safety and security. Today, with the advancement of technology, video surveillance has become more accessible and widely available. Furthermore, it can be useful in an enormous amount of applications and situations. For instance, it can be useful in ensuring public safety by preventing vandalism, robbery, and shoplifting. The same applies to more intimate situations, like home monitoring to detect unusual behavior of residents or in similar situations like hospitals and assisted living facilities. Thus, cameras are installed in public places like malls, metro stations, and on-roads for traffic control, as well as in sensitive settings like hospitals, embassies, and private homes. Video surveillance has always been as- sociated with the loss of privacy. Therefore, we developed a real-time visualization of privacy-protected video surveillance data by applying a segmentation mask to protect privacy while still being able to identify existing risk behaviors. This replaces existing privacy safeguards such as blanking, masking, pixelation, blurring, and scrambling. As we want to protect human personal data that are visual such as appearance, physical information, clothing, skin, eye and hair color, and facial gestures. Our main aim of this work is to analyze and compare the most successful deep-learning-based state-of-the-art approaches for semantic segmentation. In this study, we perform an efficiency-accuracy comparison to determine which segmentation methods yield accurate segmentation results while performing at the speed and execution required for real-life application scenarios. Furthermore, we also provide a modified dataset made from a combination of three existing datasets, COCO_stuff164K, PASCAL VOC 2012, and ADE20K, to make our comparison fair and generate privacyprotecting human segmentation masks

Edge Artificial Intelligence for Real-Time Target Monitoring

The key enabling technology for the exponentially growing cellular communications sector is location-based services. The need for location-aware services has increased along with the number of wireless and mobile devices. Estimation problems, and particularly parameter estimation, have drawn a lot of interest because of its relevance and engineers' ongoing need for higher performance. As applications expanded, a lot of interest was generated in the accurate assessment of temporal and spatial properties. In the thesis, two different approaches to subject monitoring are thoroughly addressed. For military applications, medical tracking, industrial workers, and providing location-based services to the mobile user community, which is always growing, this kind of activity is crucial. In-depth consideration is given to the viability of applying the Angle of Arrival (AoA) and Receiver Signal Strength Indication (RSSI) localization algorithms in real-world situations. We presented two prospective systems, discussed them, and presented specific assessments and tests. These systems were put to the test in diverse contexts (e.g., indoor, outdoor, in water...). The findings showed the localization capability, but because of the low-cost antenna we employed, this method is only practical up to a distance of roughly 150 meters. Consequently, depending on the use-case, this method may or may not be advantageous. An estimation algorithm that enhances the performance of the AoA technique was implemented on an edge device. Another approach was also considered. Radar sensors have shown to be durable in inclement weather and bad lighting conditions. Frequency Modulated Continuous Wave (FMCW) radars are the most frequently employed among the several sorts of radar technologies for these kinds of applications. Actually, this is because they are low-cost and can simultaneously provide range and Doppler data. In comparison to pulse and Ultra Wide Band (UWB) radar sensors, they also need a lower sample rate and a lower peak to average ratio. The system employs a cutting-edge surveillance method based on widely available FMCW radar technology. The data processing approach is built on an ad hoc-chain of different blocks that transforms data, extract features, and make a classification decision before cancelling clutters and leakage using a frame subtraction technique, applying DL algorithms to Range-Doppler (RD) maps, and adding a peak to cluster assignment step before tracking targets. In conclusion, the FMCW radar and DL technique for the RD maps performed well together for indoor use-cases. The aforementioned tests used an edge device and Infineon Technologies' Position2Go FMCW radar tool-set

Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators

This study explores the suitability of neural networks with a convolutional component as an alternative to traditional multilayer perceptrons in the domain of trend classification of cryptocurrency exchange rates using technical analysis in high frequencies. The experimental work compares the performance of four different network architectures -convolutional neural network, hybrid CNN-LSTM network, multilayer perceptron and radial basis function neural network- to predict whether six popular cryptocurrencies -Bitcoin, Dash, Ether, Litecoin, Monero and Ripple- will increase their value vs. USD in the next minute. The results, based on 18 technical indicators derived from the exchange rates at a one-minute resolution over one year, suggest that all series were predictable to a certain extent using the technical indicators. Convolutional LSTM neural networks outperformed all the rest significantly, while CNN neural networks were also able to provide good results specially in the Bitcoin, Ether and Litecoin cryptocurrencies.We would also like to acknowledge the financial support of the Spanish Ministry of Science, Innovation and Universities under grant PGC2018-096849-B-I00 (MCFin

Towards Human-Centered AI-Powered Assistants for the Visually Impaired

Artificial intelligence has become ubiquitous in today's society, aiding us in many everyday tasks. Given particular prowess of today's AI technologies in visual perception and speech recognition, an area where AI can have tremendous societal impact is in assistive technologies for the visually impaired. Although assisting the visually impaired for tasks such as environment navigation and item localization improves independence and autonomy, concerns over privacy arise. Taking privacy of personal data into consideration, we present the design of a human-centered AI-powered assistant for object localization for impaired vision (OLIV). OLIV integrates multi-modal perception (custom-designed visual scene understanding and speech recognition and synthesis) for the purpose of assisting the visually impaired in locating misplaced items in indoor environments. OLIV is comprised of three main components: speech recognition, custom-designed visual scene understanding, and synthesis. Speech recognition allows these individuals to independently query and interact with the system, increasing their level of independence. Visual scene understanding performs on-device object detection and depth estimation to build up a representation of the surrounding 3D scene. Synthesis then combines the detected objects along with their locations and depths with the user’s intent to construct a verbal semantic description that is verbally conveyed via speech synthesis. An important component of OLIV is scene understanding. Current state-of-the-art deep neural networks for the two tasks have been shown to achieve superior performance, but requires high computation and memory, making them cost prohibitive for on-device operation. On-device operation is necessary to address privacy concerns related to misuse of personal data. By performing on-device scene understanding, data captured by the camera will remain on the device. To address the challenge of high computation and memory requirements, two different architecture design exploration approaches, micro-architecture exploration and human-machine collaborative design strategy, are taken to design efficient neural networks with an optimal trade-off between accuracy, speed and size. Micro-architecture exploration approach resulted in a highly compact single shot network architecture for object detection. Human-machine collaborative design strategy resulted in a highly compact densely-connected encoder-decoder network architecture for monocular depth estimation. Through experiments on two indoor datasets to simulate environments OLIV operates in, the object detection network and depth estimation network were able to achieve CPU speeds of 17 FPS and 9.35 FPS, sizes of 6.99 and 3.46 million parameters, respectively, while maintaining comparable accuracy performance. Size and speed are important for on-device scene understanding on OLIV to provide a more private assistance for the visually impaired

Implementation of Deep Learning models for Information Extraction on Identification Documents

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe development of object detection models has revolutionized the analysis of personal information on identification cards, leading to a decrease in external human labor. Although previous strategies have been employed to address this issue without using machine learning models, they all present certain limitations, which artificial intelligence aims to overcome. This report delves into the development of a deep learning-based object detection capable of recognizing relevant information from Portuguese identification cards. All the decisions made during the project will be accompanied by a detailed background theory. Additionally, we provide an in-depth analysis of Optical Character Recognition (OCR) technology, which was utilized throughout the project to generate text from images. As the newest member of the Machine learning Team of Biometrid, I had the privilege of being involved in this project that led to the improvement of the current approach that does not leverage machine learning in the detection of relevant sections from ID cards. The findings of this project provide a foundation for further research into the use of AI in identification card analysis

Mobile application to identify recyclable materials

This dissertation proposes a system to help the consumer recycle efficiently. The system is composed by a mobile application that can capture images of waste and classify their category through the usage of a machine learning model. Furthermore, this application can communicate with a server to update the model with new improved versions and also upload the images to the server in order to contribute to the creation of more precise model versions. The system has been validated by a fully working prototype. Although the proof of concept has been achieved, with some types of waste items correctly categorized, the machine learning model produced is not precise enough to be used in real-life scenarios, that is, for any type of waste. The main contributions of this study are a compendium of information in the area of computer vision and machine learning to categorize waste, and a working prototype system that utilizes crowdsourcing and machine learning elements to help the consumer recycle more efficiently.Nesta dissertação é proposto um sistema para ajudar o consumidor a reciclar eficientemente. O sistema é composto por uma aplicação móvel que captura imagens de lixo e classifica a sua categoria usando um modelo de aprendizagem automática. Consegue também comunicar com um servidor para atualizar o modelo com versões melhoradas e enviar as imagens para o servidor para contribuir para a criação de modelos mais precisos. Foi demonstrado através de um protótipo totalmente funcional que o sistema proposto funciona. Algumas imagens de lixo foram categorizadas correctamente, mas o modelo de aprendizagem automática produzido durante este projeto não é preciso o suficiente, em qualquer categoria de lixo, para usar em cenários da vida real. As principais contribuições deste estudo são um compêndio de informação na área de visão de computador e aprendizagem automática para categorizar lixo, e um sistema protótipo funcional que utiliza elementos de contribuição colaborativa e aprendizagem automática para ajudar o consumidor a reciclar mais eficientemente

Privacy Preserving Face Recognition in Cloud Robotics : A Comparative Study

Abstract: Real-time robotic applications encounter the robot on board resources’ limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition task’s accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms