Ultra-low-voltage self-body biasing scheme and its application to basic arithmetic circuits

The gate level body biasing (GLBB) is assessed in the context of ultra-low-voltage logic designs. To this purpose, a GLBB mirror full adder is implemented by using a commercial 45 nm bulk CMOS triple-well technology and compared to equivalent conventional zero body-biased CMOS and dynamic threshold voltage MOSFET (DTMOS) circuits under different running conditions. Postlayout simulations demonstrate that, at the parity of leakage power consumption, the GLBB technique exhibits a significant concurrent reduction of the energy per operation and the delay in comparison to the conventional CMOS and DTMOS approaches. The silicon area required by the GLBB full adder is halved with respect to the equivalent DTMOS implementation, but it is higher in comparison to conventional CMOS design. Performed analysis also proves that the GLBB solution exhibits a high level of robustness against temperature fluctuations and process variations

Software-based methods for Operating system dependability

Guaranteeing correct system behaviour in modern computer systems has become essential, in particular for safety-critical computer-based systems. However all modern systems are susceptible to transient faults that can disrupt the intended operation and function of such systems. In order to evaluate the sensitivity of such systems, different methods have been developed, and among them Fault Injection is considered a valid approach widely adopted. This document presents a fault injection tool, called Kernel-based Fault-Injection Tool Open-source (KITO), to analyze the effects of faults in memory elements containing kernel data structures belonging to a Unix-based Operating System and, in particular, elements involved in resources synchronization. This tool was evaluated in different stages of its development with different experimental analyses by performing Faults Injections in the Operating System, while the system was subject to stress from benchmark programs that use different elements of the Linux kernel. The results showed that KITO was capable of generating faults in different elements of the operating systems with limited intrusiveness, and that the data structures belonging to synchronization aspects of the kernel are susceptible to an appreciable set of possible errors ranging from performance degradation to complete system failure, thus preventing benchmark applications to perform their task. Finally, aiming at overcoming the vulnerabilities discovered with KITO, a couple of solutions have been proposed consisting in the implementation of hardening techniques in the source code of the Linux kernel, such as Triple Modular Redundancy and Error Detection And Correction codes. An experimental fault injection analysis has been conducted to evaluate the effectiveness of the proposed solutions. Results have shown that it is possible to successfully detect and correct the noxious effects generated by single faults in the system with a limited performance overhead in kernel data structures of the Linux kernel

Vulnerable road users and connected autonomous vehicles interaction: a survey

There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.This work was partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under Grant: Supervision of drone fleet and optimization of commercial operations flight plans, PID2020-116377RB-C21.Peer ReviewedPostprint (published version

Conceptual design and realization of a dynamic partial reconfiguration extension of an existing soft-core processor

Viele aktuelle Field Programmable Gate Arrays (FPGAs) unterstützen die Technik der partiellen Rekonfiguration (PR), durch die dynamisch zur Laufzeit ein Hardware-Design auch nur teilweise ausgetauscht werden kann. Die vorliegende Arbeit integriert PR-Funktionalität in die an der Technischen Universität Ilmenau für harte Echtzeitaufgaben mit hochpräzisen Fließkommaberechnungen entwickelte VHDL Integrated Softcore Architecture for Reconfigurable Devices (ViSARD). Zu diesem Zweck wird die arithmetisch-logische Einheit angepasst, um das Auswechseln von Fließkomma-Ausführungseinheiten zu ermöglichen. Ziele der Entwicklung des PR-Systems sind hohe Geschwindigkeit, niedrige Latenz, niedrige Ressourcenkosten und harte Echtzeitfähigkeit. Erreicht werden diese durch die Umsetzung einer eigenen Steuereinheit (partial reconfiguration controller), die partielle Bitströme aus externem RAM über einen standardmäßigen AXI-Bus lädt sowie die entsprechende Erweiterung der ViSARD. In einem Testdesign, das zwischen drei verschiedenen Konfigurationen mit je zwischen einer und drei Ausführungseinheiten wechselt, hat das entwickelte PR-System den maximal spezifierten Bitstromdurchsatz auf dem Ziel-FPGA erreicht und den Verbrauch an Lookup-Tabellen um etwa 40 % verringert.Many modern field-programmable gate arrays (FPGAs) support partial reconfiguration, which allows to dynamically replace only a part of a design at run time. In this thesis, partial reconfiguration capability is integrated with the VHDL Integrated Softcore Architecture for Reconfigurable Devices (ViSARD) developed at Technische Universität Ilmenau and conceived for hard real-time tasks requiring floating-point calculations with high precision. Specifically, its arithmetic logic unit is modified to allow exchanging floating-point arithmetic execution units. Design goals of the partial reconfiguration system are high speed, low latency, low resource overhead, and hard real-time capability. They are reached by implementing a custom partial reconfiguration controller loading partial bitstreams from external RAM over a standard AXI bus and extending the ViSARD appropriately. In a test design that switched between 3 different configurations each containing between 1 and 3 execution units, the proposed partial reconfiguration system achieved the maximum specified bitstream throughput on the target FPGA and allowed for roughly 40 % reduced look-up table usage

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Actas de SABI2020

Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial

7° Jornadas ITEE 2023

En esta publicación se reúnen los trabajos y resúmenes extendidos presentados en las VII Jornadas de Investigación, Transferencia, Extensión y Enseñanza (ITEE), de la Facultad de Ingeniería de la Universidad Nacional de La Plata, organizadas por la Secretaría de Investigación y Transferencia de dicha facultad, que tuvieron lugar entre el 25 y el 27 de abril de 2023.Facultad de Ingenierí