Methoden und Beschreibungssprachen zur Modellierung und Verifikation vonSchaltungen und Systemen: MBMV 2015 - Tagungsband, Chemnitz, 03. - 04. März 2015

Der Workshop Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV 2015) findet nun schon zum 18. mal statt. Ausrichter sind in diesem Jahr die Professur Schaltkreis- und Systementwurf der Technischen Universität Chemnitz und das Steinbeis-Forschungszentrum Systementwurf und Test. Der Workshop hat es sich zum Ziel gesetzt, neueste Trends, Ergebnisse und aktuelle Probleme auf dem Gebiet der Methoden zur Modellierung und Verifikation sowie der Beschreibungssprachen digitaler, analoger und Mixed-Signal-Schaltungen zu diskutieren. Er soll somit ein Forum zum Ideenaustausch sein. Weiterhin bietet der Workshop eine Plattform für den Austausch zwischen Forschung und Industrie sowie zur Pflege bestehender und zur Knüpfung neuer Kontakte. Jungen Wissenschaftlern erlaubt er, ihre Ideen und Ansätze einem breiten Publikum aus Wissenschaft und Wirtschaft zu präsentieren und im Rahmen der Veranstaltung auch fundiert zu diskutieren. Sein langjähriges Bestehen hat ihn zu einer festen Größe in vielen Veranstaltungskalendern gemacht. Traditionell sind auch die Treffen der ITGFachgruppen an den Workshop angegliedert. In diesem Jahr nutzen zwei im Rahmen der InnoProfile-Transfer-Initiative durch das Bundesministerium für Bildung und Forschung geförderte Projekte den Workshop, um in zwei eigenen Tracks ihre Forschungsergebnisse einem breiten Publikum zu präsentieren. Vertreter der Projekte Generische Plattform für Systemzuverlässigkeit und Verifikation (GPZV) und GINKO - Generische Infrastruktur zur nahtlosen energetischen Kopplung von Elektrofahrzeugen stellen Teile ihrer gegenwärtigen Arbeiten vor. Dies bereichert denWorkshop durch zusätzliche Themenschwerpunkte und bietet eine wertvolle Ergänzung zu den Beiträgen der Autoren. [... aus dem Vorwort

Analysis and Design Methodologies for Switched-Capacitor Filter Circuits in Advanced CMOS Technologies

Analog filters are an extremely important block in several electronic systems, such as RF transceivers, data acquisition channels, or sigma-delta modulators. They allow the suppression of unwanted frequencies bands in a signal, improving the system’s performance. These blocks are typically implemented using active RC filters, gm-C filters, or switched-capacitor (SC) filters. In modern deep-submicron CMOS technologies, the transistors intrinsic gain is small and has a large variability, making the design of moderate and high-gain amplifiers, used in the implementation of filter blocks, extremely difficult. To avoid this difficulty, in the case of SC filters, the opamp can be replaced with a voltage buffer or a low-gain amplifier (< 2), simplifying the amplifier’s design and making it easier to achieve higher bandwidths, for the same power. However, due to the loss of the virtual ground node, the circuit becomes sensitive to the effects of parasitic capacitances, which effect needs to be compensated during the design process. This thesis addresses the task of optimizing SC filters (mainly focused on implementations using low-gain amplifiers), helping designers with the complex task of designing high performance SC filters in advanced CMOS technologies. An efficient optimization methodology is introduced, based on hybrid cost functions (equation-based/simulation-based) and using genetic algorithms. The optimization software starts by using equations in the cost function to estimate the filter’s frequency response reducing computation time, when compared with the electrical simulation of the circuit’s impulse response. Using equations, the frequency response can be quickly computed (< 1 s), allowing the use of larger populations in the genetic algorithm (GA) to cover the entire design space. Once the specifications are met, the population size is reduced and the equation-based design is fine-tuned using the more computationally intensive, but more accurate, simulation-based cost function, allowing to accurately compensate the parasitic capacitances, which are harder to estimate using equations. With this hybrid approach, it is possible to obtain the final optimized design within a reasonable amount of computation time. Two methods are described for the estimation of the filter’s frequency response. The first method is hierarchical in nature where, in the first step, the frequency response is optimized using the circuit’s ideal transfer function. The following steps are used to optimize circuits, at transistor level, to replace the ideal blocks (amplifier and switches) used in the first step, while compensating the effects of the circuit’s parasitic capacitances in the ideal design. The second method uses a novel efficient numerical methodology to obtain the frequency response of SC filters, based on the circuit’s first-order differential equations. The methodology uses a non-hierarchical approach, where the non-ideal effects of the transistors (in the amplifier and in the switches) are taken into consideration, allowing the accurate computation of the frequency response, even in the case of incomplete settling in the SC branches. Several design and optimization examples are given to demonstrate the performance of the proposed methods. The prototypes of a second order programmable bandpass SC filter and a 50 Hz notch SC filter have been designed in UMC 130 nm CMOS technology and optimized using the proposed optimization software with a supply voltage of 0.9 V. The bandpass SC filter has a total power consumption of 249 uW. The filter’s central frequency can be tuned between 3.9 kHz and 7.1 kHz, the gain between -6.4 dB and 12.6 dB, and the quality factor between 0.9 and 6.9. Depending on the bit configuration, the circuit’s THD is between -54.7 dB and -61.7 dB. The 50 Hz notch SC filter has a total power consumption of 273 uW. The transient simulation of the circuit’s extracted view (C+CC) shows an attenuation of 52.3 dB in the 50 Hz interference and that the desired 5 kHz signal has a THD of -92.3 dB

Enhancing Usability, Security, and Performance in Mobile Computing

We have witnessed the prevalence of smart devices in every aspect of human life. However, the ever-growing smart devices present significant challenges in terms of usability, security, and performance. First, we need to design new interfaces to improve the device usability which has been neglected during the rapid shift from hand-held mobile devices to wearables. Second, we need to protect smart devices with abundant private data against unauthorized users. Last, new applications with compute-intensive tasks demand the integration of emerging mobile backend infrastructure. This dissertation focuses on addressing these challenges. First, we present GlassGesture, a system that improves the usability of Google Glass through a head gesture user interface with gesture recognition and authentication. We accelerate the recognition by employing a novel similarity search scheme, and improve the authentication performance by applying new features of head movements in an ensemble learning method. as a result, GlassGesture achieves 96% gesture recognition accuracy. Furthermore, GlassGesture accepts authorized users in nearly 92% of trials, and rejects attackers in nearly 99% of trials. Next, we investigate the authentication between a smartphone and a paired smartwatch. We design and implement WearLock, a system that utilizes one\u27s smartwatch to unlock one\u27s smartphone via acoustic tones. We build an acoustic modem with sub-channel selection and adaptive modulation, which generates modulated acoustic signals to maximize the unlocking success rate against ambient noise. We leverage the motion similarities of the devices to eliminate unnecessary unlocking. We also offload heavy computation tasks from the smartwatch to the smartphone to shorten response time and save energy. The acoustic modem achieves a low bit error rate (BER) of 8%. Compared to traditional manual personal identification numbers (PINs) entry, WearLock not only automates the unlocking but also speeds it up by at least 18%. Last, we consider low-latency video analytics on mobile devices, leveraging emerging mobile backend infrastructure. We design and implement LAVEA, a system which offloads computation from mobile clients to edge nodes, to accomplish tasks with intensive computation at places closer to users in a timely manner. We formulate an optimization problem for offloading task selection and prioritize offloading requests received at the edge node to minimize the response time. We design and compare various task placement schemes for inter-edge collaboration to further improve the overall response time. Our results show that the client-edge configuration has a speedup ranging from 1.3x to 4x against running solely by the client and 1.2x to 1.7x against the client-cloud configuration

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma