2,737 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Neuromorphic Event-based Facial Expression Recognition

    Full text link
    Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition. NEFER is composed of paired RGB and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks. We detail the data acquisition process as well as providing a baseline method for RGB and event data. The collected data captures subtle micro-expressions, which are hard to spot with RGB data, yet emerge in the event domain. We report a double recognition accuracy for the event-based approach, proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal

    Towards trustworthy computing on untrustworthy hardware

    Get PDF
    Historically, hardware was thought to be inherently secure and trusted due to its obscurity and the isolated nature of its design and manufacturing. In the last two decades, however, hardware trust and security have emerged as pressing issues. Modern day hardware is surrounded by threats manifested mainly in undesired modifications by untrusted parties in its supply chain, unauthorized and pirated selling, injected faults, and system and microarchitectural level attacks. These threats, if realized, are expected to push hardware to abnormal and unexpected behaviour causing real-life damage and significantly undermining our trust in the electronic and computing systems we use in our daily lives and in safety critical applications. A large number of detective and preventive countermeasures have been proposed in literature. It is a fact, however, that our knowledge of potential consequences to real-life threats to hardware trust is lacking given the limited number of real-life reports and the plethora of ways in which hardware trust could be undermined. With this in mind, run-time monitoring of hardware combined with active mitigation of attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed as the last line of defence. This last line of defence allows us to face the issue of live hardware mistrust rather than turning a blind eye to it or being helpless once it occurs. This thesis proposes three different frameworks towards trustworthy computing on untrustworthy hardware. The presented frameworks are adaptable to different applications, independent of the design of the monitored elements, based on autonomous security elements, and are computationally lightweight. The first framework is concerned with explicit violations and breaches of trust at run-time, with an untrustworthy on-chip communication interconnect presented as a potential offender. The framework is based on the guiding principles of component guarding, data tagging, and event verification. The second framework targets hardware elements with inherently variable and unpredictable operational latency and proposes a machine-learning based characterization of these latencies to infer undesired latency extensions or denial of service attacks. The framework is implemented on a DDR3 DRAM after showing its vulnerability to obscured latency extension attacks. The third framework studies the possibility of the deployment of untrustworthy hardware elements in the analog front end, and the consequent integrity issues that might arise at the analog-digital boundary of system on chips. The framework uses machine learning methods and the unique temporal and arithmetic features of signals at this boundary to monitor their integrity and assess their trust level

    University of Windsor Graduate Calendar 2023 Spring

    Get PDF
    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp

    Battery Energy Storage System Shedding for Power System Emergency Stability Control

    Full text link
    Recently, renewable energy sources (e.g, solar energy, and wind energy) have become an increasingly important part of the power system, and the reliability and stability of the power supply in the system are being challenged. However, increasing renewable energy in the power grid will meet uncertain factors, such as reduction of system inertia and alteration of system dynamic characteristics, severely affecting power system stability when a disturbance with significant severity occurs, such as generator tripping and disconnection of transmission lines. Thus, this thesis will focus on effective and efficient controlling of BESS to enhance power system stability in an emergency state. This thesis firstly proposes the BESS shedding as a new approach for power systems EEC. The BESS load shedding would be a more cost-effective method for the EEC compared to traditional load shedding, considering that the connection between the electrical system and the BESS is controlled by a relay in the battery management system, which has a negligible control cost examines the feasibility analysis of a BESS under the state of charge being shed in the emergency control of a power system, Therefore, the article analyses the feasibility of BESS shedding in the charging state in emergency control of the power system, which is implemented on the New England 10 machine 39 bus system, which compares the ability of the system with and without BESS to recover its stability at different fault locations. The sensitivity analysis method is proposed secondly, based on the trajectory obtained from the dynamic simulation of the power system for comparative analysis, the method of sensitivity trajectory analysis is used, which is easy to implement and simple to calculate. This thesis proposes a hierarchical event-driven emergency control method for event-driven emergency control (EEC) of power systems and a hierarchical control method that considers the control cost of the deviation between different types of control actions (including BESS charging and discharging control, load shedding). The proposed method is validated by testing on a benchmark power network with extensive renewable energy sources and BESS for event-driven emergency control. The proposed method has been successfully presented in comparison with conventional emergency control methods

    Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

    Get PDF
    Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation

    Fluid MPC: Secure Multiparty Computation with Dynamic Participants

    Get PDF
    Existing approaches to secure multiparty computation (MPC) require all the participants to commit to the entire duration of the protocol. As interest in MPC continues to grow, it is inevitable that there will be a desire to use it to evaluate increasingly complex functionalities on massive datasets, resulting in computations spanning several hours or days. Such scenarios call for a dynamic participation model for MPC where participants have the flexibility to go offline as needed and (re)join when they have available computational resources. Such a model would also democratize access to privacy-preserving computation by facilitating an ``MPC-as-a-service\u27\u27 paradigm --- the deployment of MPC in volunteer-operated networks that perform computation on behalf of clients. In this work, we initiate the study of ``fluid MPC\u27\u27, where parties can dynamically join and leave the computation. The minimum commitment required from each participant is referred to as ``fluidity\u27\u27, measured in the number of rounds of communication that it must stay online. Our contributions are threefold: 1) We provide a formal treatment of fluid MPC, exploring various possible modeling choices. 2) We construct information-theoretic fluid MPC protocols in the honest-majority setting. Our protocols achieve ``maximal fluidity\u27\u27, meaning that a party can exit the computation after receiving and sending messages in one round. 3) We implement our protocol and test it in multiple network settings

    Improving low latency applications for reconfigurable devices

    Get PDF
    This thesis seeks to improve low latency application performance via architectural improvements in reconfigurable devices. This is achieved by improving resource utilisation and access, and by exploiting the different environments within which reconfigurable devices are deployed. Our first contribution leverages devices deployed at the network level to enable the low latency processing of financial market data feeds. Financial exchanges transmit messages via two identical data feeds to reduce the chance of message loss. We present an approach to arbitrate these redundant feeds at the network level using a Field-Programmable Gate Array (FPGA). With support for any messaging protocol, we evaluate our design using the NASDAQ TotalView-ITCH, OPRA, and ARCA data feed protocols, and provide two simultaneous outputs: one prioritising low latency, and one prioritising high reliability with three dynamically configurable windowing methods. Our second contribution is a new ring-based architecture for low latency, parallel access to FPGA memory. Traditional FPGA memory is formed by grouping block memories (BRAMs) together and accessing them as a single device. Our architecture accesses these BRAMs independently and in parallel. Targeting memory-based computing, which stores pre-computed function results in memory, we benefit low latency applications that rely on: highly-complex functions; iterative computation; or many parallel accesses to a shared resource. We assess square root, power, trigonometric, and hyperbolic functions within the FPGA, and provide a tool to convert Python functions to our new architecture. Our third contribution extends the ring-based architecture to support any FPGA processing element. We unify E heterogeneous processing elements within compute pools, with each element implementing the same function, and the pool serving D parallel function calls. Our implementation-agnostic approach supports processing elements with different latencies, implementations, and pipeline lengths, as well as non-deterministic latencies. Compute pools evenly balance access to processing elements across the entire application, and are evaluated by implementing eight different neural network activation functions within an FPGA.Open Acces

    University of Windsor Graduate Calendar 2023 Winter

    Get PDF
    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1026/thumbnail.jp

    Examining the Relationships Between Distance Education Students’ Self-Efficacy and Their Achievement

    Get PDF
    This study aimed to examine the relationships between students’ self-efficacy (SSE) and students’ achievement (SA) in distance education. The instruments were administered to 100 undergraduate students in a distance university who work as migrant workers in Taiwan to gather data, while their SA scores were obtained from the university. The semi-structured interviews for 8 participants consisted of questions that showed the specific conditions of SSE and SA. The findings of this study were reported as follows: There was a significantly positive correlation between targeted SSE (overall scales and general self-efficacy) and SA. Targeted students' self-efficacy effectively predicted their achievement; besides, general self- efficacy had the most significant influence. In the qualitative findings, four themes were extracted for those students with lower self-efficacy but higher achievement—physical and emotional condition, teaching and learning strategy, positive social interaction, and intrinsic motivation. Moreover, three themes were extracted for those students with moderate or higher self-efficacy but lower achievement—more time for leisure (not hard-working), less social interaction, and external excuses. Providing effective learning environments, social interactions, and teaching and learning strategies are suggested in distance education
    • …
    corecore