Electrical and Electronic Engineering - Conference Items

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    Behavioral modeling of low-frequency noise in switched-capacitor circuits using Python
    (Institute of Electrical and Electronics Engineers (IEEE), 2022-08-05) Kalogiros, Spyridon; Salgado, Gerardo; McCarthy, Kevin; O'Connell, Ivan; Science Foundation Ireland; European Regional Development Fund
    In precision circuits validating the performance in the presence of low-frequency noise is particularly challenging especially at transistor level, as long simulations are required to observe the low frequency performance. However, running such system-level simulations is rarely practical at transistor level as these simulations can take days to weeks to complete. This work presents a high-level model in Python for generating low-frequency noise which can be used for validating the low-frequency performance of a design in a timely manner. Simulation times can be reduced from days to minutes, enabling designers to achieve a high-level simulation coverage. With Python and NumPy this can be achieved using open-source software tools at no cost.
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    Lightweight anomaly detection framework for IoT
    (Institute of Electrical and Electronics Engineers (IEEE), 2020-08-31) Tagliaro Beasley, Bianca; O'Mahony, George D.; Gómez Quintana, Sergi; Temko, Andriy; Popovici, Emanuel; Science Foundation Ireland
    Internet of Things (IoT) security is growing in importance in many applications ranging from biomedical to environmental to industrial applications. Access to data is the primary target for many of these applications. Often IoT devices are an essential part of critical control systems that could affect well-being, safety, or inflict severe financial damage. No current solution addresses all security aspects. This is mainly due to the resource-constrained nature of IoT, cost, and power consumption. In this paper, we propose and analyse a framework for detecting anomalies on a low power IoT platform. By monitoring power consumption and by using machine learning techniques, we show that we can detect a large number and types of anomalies during the execution phase of an application running on the IoT. The proposed methodology is generic in nature, hence allowing for deployment in a myriad of scenarios.
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    FPGA hardware acceleration framework for anomaly-based intrusion detection system in IoT
    (Institute of Electrical and Electronics Engineers (IEEE), 2021-10-12) Ngo, Duc-Minh; Temko, Andriy; Murphy, Colin C.; Popovici, Emanuel; Science Foundation Ireland; European Regional Development Fund
    This study proposes a versatile framework for realtime Internet of Things (IoT) network intrusion detection using Artificial Neural Network (ANN) on heterogeneous hardware. With the increase in the volume of exchanged data, IoT networks' security has become a crucial issue. Anomaly-based intrusion detection systems (IDS) using machine learning have recently gained increased popularity due to their generation ability to detect new attacks. However, the deployment of anomaly-based AI-assisted IDS for IoT devices is computationally expensive. In this paper, a hierarchical decision-making approach for IDS is proposed and evaluated on the new IoT-23 dataset, with improved accuracy over the software-based methods. The inference engine is implemented on the Xilinx FPGA System on a Chip (SoC) hardware platform for high performance, high accuracy attack detection (more than 99.43%). For the resulting implemented design, the processing time of the ANN model on FPGA with an xc7z020clg400 device is 6.6 times and 40.5 times faster than GPU Quadro M2000 and CPU E5-2640 2.60GHz, respectively.
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    Open source airway navigation: initial experiences with CustusX and Anser EMT
    (International Society for Medical Innovation and Technology (iSMIT), 2017) Jaeger, Herman A.; Trauzettel, Fabian; Hofstad, Erlend Fagertun; Kennedy, Marcus P.; Leira, Håkon; Langø, Thomas; Cantillon-Murphy, Pádraig
    Electromagnetic tracking (EMT) is a common navigation technology used in image guided applications. EMT is particularly useful in procedures where line-of-sight of the operating field is not feasible. We present a major update of the open source electromagnetic tracking platform Anser EMT [1] and present its results when performing bronchoscopy in a pre-clinical setting using the CustusX navigation suite [2]. The updated system design is open source and free to use and modify under the Berkeley Standard Distribution (BSD) license.
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    Mechanical catheter navigation with electromagnetic tracking to peripheral airway targets
    (International Society for Medical Innovation and Technology (iSMIT), 2017) Trauzettel, Fabian; Jaeger, Herman A.; Hofstad, Erlend Fagertun; Kennedy, Marcus P.; Leira, Håkon; Langø, Thomas; Cantillon-Murphy, Pádraig
    Lung cancer remains the single most deadly cancer in men and women due to low rates of early detection and treatment. Since non-small cell lung cancer usually starts in the outer airways, targeted minimally invasive biopsy which limits radiation exposure and avoids surgery is highly desirable. Current commercial solutions such as the superDimension (Medtronic Inc., Dublin, Ireland), and SpIN (Veran Medical, St. Louis, USA) systems rely on electromagnetic tracking for virtual navigation. However, clinical outcomes have been unconvincing due to poor accuracy, limitations in instrumentation and the lack of tracked catheters. This work proposes a novel mechanical catheter design with embedded electromagnetic tracking to facilitate tip-tracked navigation without the need for proprietary instruments or probe exchange. The catheter was used to reach peripheral airway targets by multiple users in pre-clinical studies.