BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CECS - ECPv6.18.4//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.cecs.uci.edu
X-WR-CALDESC:Events for CECS
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20180311T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20181104T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20190310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20191103T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20200308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20201101T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190806T150000
DTEND;TZID=America/Los_Angeles:20190806T160000
DTSTAMP:20231011T190852Z
CREATED:20231011T190846Z
LAST-MODIFIED:20231011T190852Z
UID:14558-1565103600-1565107200@www.cecs.uci.edu
SUMMARY:Low-Power Integrated Circuits For Biomedical Applications
DESCRIPTION:Title: “Low-Power Integrated Circuits For Biomedical Applications”\nName: Karimi Bidhendi\, Alireza \nChair: Professor Payam Heydari \nDate: August 6\, 2019 \nTime: 1 pm \nLocation: Calit2\,  Room 3008 \nAbstract: \nWith thousands new cases of spinal cord injury reported everyday\, many people suffer from paralysis and loss of sensation in both legs. Beside the healthcare costs\, such a state severely deteriorates a patients quality of life and may even lead to additional medical conditions. Therefore\, there is a growing need for cyber-physical systems to restore the walking ability through bypassing the damaged spinal cord. This goal can be achieved by monitoring and processing patient’s brain signals to enable brain-directed control of prosthetic legs. Among several existing methods to record brain signals\, electrocorticography (ECoG) has gained popularity due to being robust to motion artifacts\, having high spatial resolution and signal to noise ratio\, being moderately invasive and the possibility of chronic implantation of recording grids with no or minor scar tissue formation. The latest property is of particular importance for the whole system to be a viable fully implantable solution. Furthermore\, the implanted system has to operate independently with no or minimal need of external hardware (e.g. a bulky personal computer) to be individually and socially accepted. \nTo implement a fully implantable system\, low-power and miniaturized electronics are needed to reduce heat generation\, increase battery life-time and be minimally intrusive. These requirements indicate that many of the system’s components should be custom-designed to integrated as much functionality as possible in a given real estate. This thesis presents silicon tested prototypes of several building blocks for the envisioned system\, namely\, ultra low-power brain signal acquisition front-ends\, a low-power and inductorless MedRadio transceiver\, and a fast start-up crystal oscillator. Brain signal acquisition front-ends provide low noise amplification of weak ECoG biosignals. MedRadio transceiver enables communication between the implant and end effectors or base station (e.g. prosthetic legs or desktop computer). Crystal oscillators generate the reference signal for other system components such as analog to digital converters. Novel techniques to improve important performance parameters (power consumption\, low noise operation and interference resilience) have been introduced. Electrical\, in-vitro and in-vivo experimental measurements have verified the functionality and performance of each design.
URL:https://www.cecs.uci.edu/event/low-power-integrated-circuits-for-biomedical-applications/
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190812T150000
DTEND;TZID=America/Los_Angeles:20190812T160000
DTSTAMP:20231011T190852Z
CREATED:20231011T190846Z
LAST-MODIFIED:20231011T190852Z
UID:14559-1565622000-1565625600@www.cecs.uci.edu
SUMMARY:Cooperative Power and Resource Management for Heterogeneous Mobile Architectures
DESCRIPTION:Name: Chenying Hsieh\n\nDate: Wednesday Aug 7\, 2019 \n\n\n\n\n\n\nTime: 2:00 pm\n\nLocation: Donald Bren Hall 4011\n\nCommittee: Nikil Dutt (Chair)\, Tony Givargis\, and Ardalan Amiri Sani\n\nTitle: Cooperative Power and Resource Management for Heterogeneous Mobile Architectures\n \nAbstract:\n\n\n\n\n\n\nHeterogeneous architectures have been ubiquitous in mobile system-on-chips (SoCs). The demand from different application domains such as games\, computer vision and machine learning which requires massive parallelism of computation has driven the integration of more accelerators into mobile SoCs to provide satisfactory performance energy-efficiently.These on-chip computing resources typically have their individual runtime systems including: (1) a software governor: continuously monitors hardware utilization and makes decisions of trade-off between performance and power consumption. (2) software stack: allows application developers to program the hardware for general purpose computation and perform memory management and profiling. As computation of mobile applications may demand all sorts of combinations of computing resources\, we identify two problems: (1) individual runtime can often lead to poor performance-power trade-off or inefficient utilization of computing resources. (2) existing approaches fail to schedule subprograms among different computing resources and further lose the opportunity to avoid resource contention to gain better performance.\n\nTo address these issues\, we propose a holistic approach to coordinate different runtime regrading application performance and energy efficiency in this dissertation. We first present MEMCOP\, a memory-aware collaborative CPU-GPU governor that considers both the memory access footprint as well as the CPU/GPU frequency to improve energy efficiency of high-end mobile game workloads by performing dynamic voltage and frequency scaling (DVFS). Second\, We present a case study executing a mix of popular data-parallel workloads such as convolutional neural networks (CNNs)\, computer vision filters and graphics rendering kernels on mobile devices\, and show that both performance and energy consumption of mobile platforms can be improved by synergistically deploying these underutilized compute resources. Third\, we present SURF: a self-aware unified runtime framework for parallel programs on heterogeneous mobile architectures. SURF supports several heterogeneous parallel programming languages (including OpenMP and OpenCL)\, and enables dynamic task-mapping to heterogeneous resources based on runtime measurement and prediction.The measurement and monitoring loop enables self-aware adaptation of run-time mapping to exploit the best available resource dynamically.\n\nWe implemented all the software components on real-world mobile SoCs and evaluate our proposed approaches with mobile games and mix of parallel benchmarks and CNN applications accordingly.
URL:https://www.cecs.uci.edu/event/cooperative-power-and-resource-management-for-heterogeneous-mobile-architectures/
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190826T150000
DTEND;TZID=America/Los_Angeles:20190826T160000
DTSTAMP:20231011T190904Z
CREATED:20231011T190859Z
LAST-MODIFIED:20231011T190904Z
UID:14565-1566831600-1566835200@www.cecs.uci.edu
SUMMARY:Event Detection and Estimation Using Distributed Population Owned Sensors
DESCRIPTION:Name: Ahmed Mokhtar Nagy Ibrahim\nChair: Prof. Ahmed Eltawil \nDate: August 7th\, 2019 \nTime: 1:00 pm \nLocation:  EH 2210 – EECS Chair’s Conference Room \nCommittee: Ahmed Eltawil(Chair)\, Ender Ayanoglu\, and Lee Swindlehurst \nTitle:”Event Detection and Estimation Using Distributed Population Owned Sensors ” \nAbstract:\nSmart phones are an indispensable tool in modern day-to-day life. Their widespread use has spawned numerous applications targeting diverse domains such as bio-medical\, environment sensing and infrastructure monitoring. In such applications\, the accuracy of the sensors at the core of the system is still questionable\, since these devices are not originally designed for high accuracy sensing purposes. In this thesis\, we investigate the accuracy limits of one of the commonly used sensors\, namely\, a smart phone accelerometer. As a use case\, we focus on utilizing smart phone accelerometers in structural health monitoring (SHM). Using the already deployed network of distributed citizen-owned sensors is considered a cheap alternative to standalone sensors. These devices can capture floors vibration during disasters\, and consequently compute the instantaneous displacement of each floor. Hence\, damage indicators defined by government standards such as peak relative displacement can be estimated. In this work\, we study the displacement estimation accuracy and propose a zero-velocity update (ZUPT) method for noise cancellation. Theoretical derivation and experimental validation are presented\, and we discuss the impact of sensor error on the achieved building classification accuracy. Moreover\, in spite of the presence of sensor error\, SHM systems can be resilient by adopting machine learning. Several algorithms such as support vector machine (SVM)\, K-nearest neighbor (KNN) and convolutional neural network (CNN) are adopted and compared. Techniques for addressing noise levels are proposed and the results are compared to regular noise cancellation techniques such as filtering. \nFinally\, since most previous work focused on modelling the sensor chip error itself\, we study other sources of error such as sampling time uncertainty which is introduced by the device operating system (OS). That type of error can be considered a major contributor to the overall error\, specially for sufficiently large signals. Hence\, we propose a novel smart device accelerometer error model that includes the traditional additive noise as well as sampling time uncertainty errors. The model is validated experimentally using shake table experiments\, and maximum likely-hood estimation (MLE) is used to estimate the model parameters. Moreover\, we derive the Cramer-Rao lower bound (CRLB) of acceleration estimation based on the proposed model.
URL:https://www.cecs.uci.edu/event/event-detection-and-estimation-using-distributed-population-owned-sensors/
CATEGORIES:PhD Defenses
END:VEVENT
END:VCALENDAR