Energy-Efficient and Reliable Computing in Dark Silicon Era

Dark silicon denotes the phenomenon that, due to thermal and power constraints, the fraction of transistors that can operate at full frequency is decreasing in each technology generation. Moore’s law and Dennard scaling had been backed and coupled appropriately for five decades to bring commensurate exponential performance via single core and later muti-core design. However, recalculating Dennard scaling for recent small technology sizes shows that current ongoing multi-core growth is demanding exponential thermal design power to achieve linear performance increase. This process hits a power wall where raises the amount of dark or dim silicon on future multi/many-core chips more and more. Furthermore, from another perspective, by increasing the number of transistors on the area of a single chip and susceptibility to internal defects alongside aging phenomena, which also is exacerbated by high chip thermal density, monitoring and managing the chip reliability before and after its activation is becoming a necessity. The proposed approaches and experimental investigations in this thesis focus on two main tracks: 1) power awareness and 2) reliability awareness in dark silicon era, where later these two tracks will combine together. In the first track, the main goal is to increase the level of returns in terms of main important features in chip design, such as performance and throughput, while maximum power limit is honored. In fact, we show that by managing the power while having dark silicon, all the traditional benefits that could be achieved by proceeding in Moore’s law can be also achieved in the dark silicon era, however, with a lower amount. Via the track of reliability awareness in dark silicon era, we show that dark silicon can be considered as an opportunity to be exploited for different instances of benefits, namely life-time increase and online testing. We discuss how dark silicon can be exploited to guarantee the system lifetime to be above a certain target value and, furthermore, how dark silicon can be exploited to apply low cost non-intrusive online testing on the cores. After the demonstration of power and reliability awareness while having dark silicon, two approaches will be discussed as the case study where the power and reliability awareness are combined together. The first approach demonstrates how chip reliability can be used as a supplementary metric for power-reliability management. While the second approach provides a trade-off between workload performance and system reliability by simultaneously honoring the given power budget and target reliability

On-line health monitoring of passive electronic components using digitally controlled power converter

This thesis presents System Identification based On-Line Health Monitoring to analyse the dynamic behaviour of the Switch-Mode Power Converter (SMPC), detect, and diagnose anomalies in passive electronic components. The anomaly detection in this research is determined by examining the change in passive component values due to degradation. Degradation, which is a long-term process, however, is characterised by inserting different component values in the power converter. The novel health-monitoring capability enables accurate detection of passive electronic components despite component variations and uncertainties and is valid for different topologies of the switch-mode power converter. The need for a novel on-line health-monitoring capability is driven by the need to improve unscheduled in-service, logistics, and engineering costs, including the requirement of Integrated Vehicle Health Management (IVHM) for electronic systems and components. The detection and diagnosis of degradations and failures within power converters is of great importance for aircraft electronic manufacturers, such as Thales, where component failures result in equipment downtime and large maintenance costs. The fact that existing techniques, including built-in-self test, use of dedicated sensors, physics-of-failure, and data-driven based health-monitoring, have yet to deliver extensive application in IVHM, provides the motivation for this research ... [cont.]

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Minimal Infrastructure Radio Frequency Home Localisation Systems

The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls. Such pervasive monitoring functionality offers the potential for elders to live at home for longer periods of their lives with minimal human supervision. The focus of this thesis is on the investigation and development of a home roomlevel localisation technique which can be readily deployed in a realistic home environment with minimal hardware requirements. A conveniently deployed Bluetooth ® localisation platform is designed and experimentally validated throughout the thesis. The platform adopts the convenience of a mobile phone and the processing power of a remote location calculation computer. The use of Bluetooth ® also ensures the extensibility of the platform to other home health supervision scenarios such as wireless body sensor monitoring. Central contributions of this work include the comparison of probabilistic and nonprobabilistic classifiers for location prediction accuracy and the extension of probabilistic classifiers to a Hidden Markov Model Bayesian filtering framework. New location prediction performance metrics are developed and signicant performance improvements are demonstrated with the novel extension of Hidden Markov Models to higher-order Markov movement models. With the simple probabilistic classifiers, location is correctly predicted 80% of the time. This increases to 86% with the application of the Hidden Markov Models and 88% when high-order Hidden Markov Models are employed. Further novelty is exhibited in the derivation of a real-time Hidden Markov Model Viterbi decoding algorithm which presents all the advantages of the original algorithm, while producing location estimates in real-time. Significant contributions are also made to the field of human gait-recognition by applying Bayesian filtering to the task of motion detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even enables a floor recognition rate of 68% using only accelerometers. The unique application of time-varying Hidden Markov Models demonstrates the effect of integrating these freely available motion predictions on long-term location predictions

Complementary 2D/3D Imaging of Functional Materials using X-ray & Electron Microscopy

Catalysts and other functional materials are generally hierarchically structured materials. Hence, the detailed characterization at different length scales, and especially under reaction conditions, are necessary to unravel their mechanisms and to improve their performance and catalytic activities. Besides, a combination of several techniques is required to acquire complementary information owing to the lack of a single technique able to cover all the length scales. With respect to length, the best way to investigate is by microscopy either in 2D or more preferably in 3D. The work began with an exploration of three different 3D imaging techniques, i.e. ptychographic X-ray computed tomography, electron tomography, and focused ion beam slice-and view. Using nanoporous gold as the model, this study aimed to exhibit the versatility of 3D microscopy as a method beyond imaging as well as to confirm the necessity of complementary nature between them, where electron offers better spatial resolution and X-ray provides larger field of view. The study then continued by utilizing ptychographic X-ray computed tomography for quasi in situ thermal treatment of the same materials under atmospheric pressure. This study highlighted its ease of use of implementing in situ studies, complemented by electron tomography to prove its powerful ability to resolve what ptychographic tomography cannot. The resulting 3D volumes were then used for air permeability and CO2 diffusion simulations, along with material’s electrical and thermal conductivity simulations in order to further expose another excellent benefit from 3D microscopy. Ultimately, the work proceeded into developing two cells in order to perform full in situ investigations under controlled temperatures and atmospheres, where one cell was built for 2D only (X-ray) ptychography experiments with simultaneous X-ray fluorescence mapping, and the other was constructed with an additional capability for 3D limited-angle ptychographic tomography experiments. The feasibility tests were conducted using several functional materials, i.e. nanoporous gold, zeolite, and cobalt-manganese-oxides hollow sphere, as the models for thermal annealing process under specific atmospheres. This work eventually attests the importance of in situ studies in precisely determining the onset annealing temperatures under particular environments, to visualize the morphology online either in 2D or 3D, and to simultaneously map elemental distributions live. Moreover, a complementary technique via transmission electron microscopy was also demonstrated on the same sample, adding up another advantage in using the cells. Despite the preliminary results from in situ limited-angle ptychographic tomography experiments for limitation in data reconstruction, a new tomographic reconstruction technique was recently developed as a solution to acquire 3D images with shortened acquisition times. In conclusions, the work here converges into the ideal case of performing all-around in situ 3D imaging of functional materials for quantitative analysis and simulation

Phenomenological models in biological physics : cell growth and pluripotency maintenance

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references.A persistent challenge in quantitatively modeling a biological system is that the system often involves many components and just as dizzying number of interactions among those components. To further complicate matters, the parameters that characterize those interactions and components, like the rates of chemical reactions and concentrations of molecules inside the cell, have evaded detection by the conventional experimental tools. How does one model a system whose crucial parameters are unknown? And even if we know all the parameters inside the cell, there is an increasing uneasiness among many researchers that just writing down an equation for every interaction and components of the system is not practical. Crucially, it is not clear that such an extensive many-parameter model would always enhance our understanding of the complex biological system. A phenomenological model that involves just a few essential, easily measurable parameters that capture the essence of the complex biological system may provide insights that a many-parameter, large scale model may not provide. In this thesis, we describe our attempts at obtaining such a model for two complex biological systems: 1.) Cell growth as a result of glucose metabolism, and 2.) in vitro maintenance of the embryonic stem cell's pluripotency by a complex transcriptional network.by Hyun Youk.Ph.D

Structural Health Monitoring Damage Detection Systems for Aerospace

This open access book presents established methods of structural health monitoring (SHM) and discusses their technological merit in the current aerospace environment. While the aerospace industry aims for weight reduction to improve fuel efficiency, reduce environmental impact, and to decrease maintenance time and operating costs, aircraft structures are often designed and built heavier than required in order to accommodate unpredictable failure. A way to overcome this approach is the use of SHM systems to detect the presence of defects. This book covers all major contemporary aerospace-relevant SHM methods, from the basics of each method to the various defect types that SHM is required to detect to discussion of signal processing developments alongside considerations of aerospace safety requirements. It will be of interest to professionals in industry and academic researchers alike, as well as engineering students. This article/publication is based upon work from COST Action CA18203 (ODIN - http://odin-cost.com/), supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation