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Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

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Improving Signal-to-Noise Ratio (SNR) for Readout Signals Us...

Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Hans Johnson Silvia Zorzetti Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A. Fermi National Accelerator Laboratory Superconducting Quantum Materials and Systems Center (SQMS) Batavia IL U.S.A.

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.

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Seven-level Cascade Multilevel Inverter based Minimum Switches for Photovoltaic applications

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Seven-level Cascade Multilevel Inverter based Minimum Switch...

IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Ali Abedaljabar Al-Samawi Hafedh Trabelsi Department of Electronic and Communications Engineering College of Engineering Al-Muthanna University Al Muthanna Iraq Department of Electrical Engineering National Engineering School of Sfax (ENIS) Computer and Embedded Systems Laboratory (CES-Lab) Sfax University Sfax Tunisia

To enhance power quality and obtain high power from photovoltaic(PV), and mitigate the impact of the PV system's partial shading condition(PSC) and electrical mismatch power problems. This paper presents a single-phase, simple seven-level multilevel inverter architecture With a lower number of power components connected with four individual group PV systems integration converter DC/DC with independent Maximum Power Point Tracking(MPPT)in each group. The proposed inverter provides a reduced total harmonic distortion (THD) of voltage and unity active power. This reduction in the number of switches generates a decrease in the losses of switching and the number of drive circuits, which in turn causes a decrease in the control complication and decreases the cost and size of the control circuit. We compared this cascaded multilevel topology architecture to evaluate the performance of the suggested inverter, which needed modern cascade multilevel topologies with the same seven levels of voltage. The achievement of the comparison is dependent on several elements, including a set of parameters(diodes and capacitors), the overall harmonic distortion, and the number of switches included inside the inverter. The program(Simulink/Matlab) was used to verify the critical evaluation of the presented topology through simulation results.

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Traffic Sign Detection With Event Cameras and DCNN

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arXiv 2022年

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland Department of Digital Systems Silesian University of Technology Gliwice Poland

In recent years, event cameras (DVS – Dynamic Vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions – parameters that are particularly important in the context of advanced driver assistance systems (ADAS) and self-driving cars. In this work, we test whether these rather novel sensors can be applied to the popular task of traffic sign detection. To this end, we analyse different representations of the event data: event frame, event frequency, and the exponentially decaying time surface, and apply video frame reconstruction using a deep neural network called FireNet. We use the deep convolutional neural network YOLOv4 as a detector. For particular representations, we obtain a detection accuracy in the range of 86.9-88.9% mAP@0.5. The use of a fusion of the considered representations allows us to obtain a detector with higher accuracy of 89.9% mAP@0.5. In comparison, the detector for the frames reconstructed with FireNet is characterised by an accuracy of 72.67% mAP@0.5. The results obtained illustrate the potential of event cameras in automotive applications, either as standalone sensors or in close cooperation with typical frame-based cameras. © 2022, CC BY.

关键词： Cameras

Cloud Telescope: A distributed architecture for capturing Internet Background Radiation

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Cloud Telescope: A distributed architecture for capturing In...

IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Fabricio Bortoluzzi Barry Irwin Lucas Silvero Beiler Carla Merkle Westphall Cyber Security Programme Noroff University College Kristiansand Norway Postgraduate Programme in Computer Science University of Santa Catarina Florianópolis Brazil Department of Computer Science Rhodes University Grahamstown South Africa Laboratory of Embedded & Distributed Systems University of Vale do Itajai Itajai Brazil

Internet Background Radiation (IBR) comprises a range of unsolicited traffic directed towards Internet hosts. In general, this type of traffic is characterised by high levels of port scanning activity, malware propagation, application exploits, system misconfiguration and denial-of-service attacks. IBR capture is typically undertaken by a system termed a network telescope. This records unfiltered incoming internet traffic for a specific CIDR block in the form of a packet capture (PCAP) file for analysis. This work proposes a novel, cloud-native approach to capturing IBR by the deployment of an ephemeral and reproducible architecture, described as code, distributed across all regions of a cloud service provider. In this paper we discuss the technical and financial viability of using a fleet of small-sized compute instances, in a spot price auction model, to maximise platform collection, capillarity and duration. We also present an overview analysis of the primary characteristics of IBR as collected during a month long proof-of-concept experiment across 26 regions of a cloud service provider in May 2023. Our analysis discusses the aspects of the dataset in quantitative terms: traffic aggregation per protocol, top TCP and UDP ports, top radiation sources and radiation distribution per cloud region. We also provide an overview of the most relevant threats detected. Our results include a formalisation and validation of the cloud telescope, with the corresponding supporting architecture described in Terraform and Ansible. The aggregate dataset amounted to 2.2 GB, and 21.8 million packets. Composition by protocol was 78% TCP, 14% ICMP and 8% UDP.

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AutoToolVCC: Variable consistency check tool for automotive software projects

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AutoToolVCC: Variable consistency check tool for automotive ...

作者： Xie, Pingfu Wang, Yunlong He, Bo Xie, Guoqi College of Computer Science and Electronic Engineering Hunan University Changsha China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China United Automotive Electronic Systems Co. Ltd. Shanghai China

Automotive software systems have become increasingly complex and huge, leading to long development cycle and increased development difficulty. When a complete automotive software project has reached a scale of several gigabytes, the complicated development process is prone to produce the following two irregular problems: (1) variable inconsistency problem and (2) code coexistence problem. These irregular problems may eventually lead to serious consequences, such as car crashes. In response to the above irregular problems, we develop a variable consistency check tool called AutoToolVCC for automotive software projects. AutoToolVCC divides the variable consistency check process into two stages: (1) extracting all variable information from the automotive software project and (2) checking the consistency of the variables through the variable preprocessing technique and the variable attribute check technique. After an actual check of multiple automotive software projects (approximately 500–1000 MB in size) provided by an automotive manufacturer, the accuracy of variable consistency check is as high as 100%, and the check time only takes about 10–30 s, whereas the pervious plain method needs 100–600 s. © 2020 John Wiley & Sons, Ltd.

关键词： Accidents

Energy-Efficient Adaptive Machine Learning on IoT End-Nodes With Class-Dependent Confidence

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arXiv 2022年

作者： Daghero, Francesco A., Burrello D.J., Pagliari L., Benini E., Macii M., Poncino Department of Control and Computer Engineering Politecnico di Torino Italy Interuniversity Department of Regional and Urban Studies and Planning Politecnico di Torino Italy Energy-Efficient Embedded Systems Laboratory University of Bologna Italy Integrated Systems Laboratory ETH Zurich Switzerland

Energy-efficient machine learning models that can run directly on edge devices are of great interest in IoT applications, as they can reduce network pressure and response latency, and improve privacy. An effective way to obtain energy-efficiency with small accuracy drops is to sequentially execute a set of increasingly complex models, early-stopping the procedure for "easy" inputs that can be confidently classified by the smallest models. As a stopping criterion, current methods employ a single threshold on the output probabilities produced by each model. In this work, we show that such a criterion is sub-optimal for datasets that include classes of different complexity, and we demonstrate a more general approach based on per-classes thresholds. With experiments on a low-power end-node, we show that our method can significantly reduce the energy consumption compared to the single-threshold approach. © 2022, CC BY.

关键词： Energy efficiency

PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection

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TechRxiv

TechRxiv 2022年

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars backbone. © 2022, CC BY.

关键词： Object detection

Fast-moving object counting with an event camera

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arXiv 2022年

作者： Bialik, Kamil Kowalczyk, Marcin Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects – in this case, falling corn grains. These type of cameras transmit information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, correct operation in different lighting conditions, as well as very low power consumption. The proposed counting algorithm processes events in real time. The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of grains falling to be adjusted. The objective of the control system with a PID controller was to maintain a constant average number of falling objects. The proposed solution was subjected to a series of tests to determine the correctness of the developed method operation. On their basis, the validity of using an event camera to count small, fast-moving objects and the associated wide range of potential industrial applications can be confirmed. © 2022, CC BY.

关键词： Cameras

PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection

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arXiv 2022年

关键词： Object detection