Distributed Gesture Controlled Systems for Human-Machine Interface

This paper presents the design flow of an IoT human machine touchless interface. The device uses embedded computing in conjunction with the Leap Motion Controller to provide an accurate and intuitive touchless interface. Its main function is to augment current touchscreen devices in public spaces through a combination of computer vision technology, event driven programming, and machine learning. Especially following the COVID 19 pandemic, this technology is important for hygiene and sanitation purposes for public devices such as airports, food, and ATM kiosks where hundreds or even thousands of people may touch these devices in a single day. A prototype of the touchless interface was designed with a Leap Motion Controller housed on a Windows PC exchanging information with a Raspberry Pi microcontroller via internet connection.Comment: 5 Pages, Accepted for Publication in 2022 IEEE International Conference on Electro Information Technology (eIT

Reconfigurable Distributed FPGA Cluster Design for Deep Learning Accelerators

We propose a distributed system based on lowpower embedded FPGAs designed for edge computing applications focused on exploring distributing scheduling optimizations for Deep Learning (DL) workloads to obtain the best performance regarding latency and power efficiency. Our cluster was modular throughout the experiment, and we have implementations that consist of up to 12 Zynq-7020 chip-based boards as well as 5 UltraScale+ MPSoC FPGA boards connected through an ethernet switch, and the cluster will evaluate configurable Deep Learning Accelerator (DLA) Versatile Tensor Accelerator (VTA). This adaptable distributed architecture is distinguished by its capacity to evaluate and manage neural network workloads in numerous configurations which enables users to conduct multiple experiments tailored to their specific application needs. The proposed system can simultaneously execute diverse Neural Network (NN) models, arrange the computation graph in a pipeline structure, and manually allocate greater resources to the most computationally intensive layers of the NN graph.Comment: 4 pages of content, 1 page for references. 4 Figures, 1 table. Conference Paper (IEEE International Conference on Electro Information Technology (eit2023) at Lewis University in Romeoville, IL

ULTRASONIC SIGNAL PROCESSING: SYSTEM IDENTIFICATION AND PARAMETER ESTIMATION OF REVERBERANT AND INHOMOGENEOUS TARGETS

The nondestructive testing of multi-layered targets and targets with inhomogeneous and randomly distributed scatterers as in large grained materials have many important applications. Ultrasonic examination of such targets results in interfering multiple ecohes (reverberation) which complicates their evaluation by conventional techniques. This research consists of the analytical evaluation of backscattered echoes from sample targets coupled with the development of suitable digital signal processing techniques for their characterization. By decoupling the components of the backscattered echoes, an appropriate identification and classification technique is introduced which allows the characterization of the layered structure using detected echoes of significant intensities. Computer simulation was developed to verify the significance of the classification technique. The classification procedure allows the application of signal processing techniques such as subtraction, correlation, spectral analysis, and cepstral analysis. The subtraction technique is applied in order to separate various classes of echoes. This technique necessitates interpolation and synchronization of the digitized data. In this study an appropriate method of interpolation is presented based on the time-shift property of Fourier transforms. Correlation techniques are applied to the backscattered signal in order to improve the visibility of various classes of echoes. The correlation techniques improves the signal-to-noise ratio at the expense of resolution. The presence of the periodicity in the power spectrum can be related to layer thickness which is experimentally verified. Cepstral analysis is also appropriate for the processing of reverberant echoes in order to extract desired features. In this study, cepstral processing is used for separation of echoes, and extraction of the averaged echo waveshape for use in deconvolution. Results demonstrate that the power cepstrum provides good resolution. Various signal processing techniques, in both the time and frequency domains, have been applied to backscattered signals for grain size evaluation. Time domain analysis consists of time averaging, autocorrelation functions, and determination of the probability density function of the backscattered signals. Time averaging demonstrates significant sensitivity to grain size variation and also provides good reproducibility. Autocorrelation functions of the data were not informative since no periodicity exists in solids. It is shown that relative changes in statistical parameters (e.g., mean and standard deviation) of the probability density functions are also feasible for grain size evaluation. Quantitative evaluation of the magnitude spectra of backscattered signals were assessed by moment analysis. Moment values show inadequate sensitivity to grain sizes due to the presence of random peaks and valleys in the spectra. Furthermore, the magnitude spectrum were cepstrally smoothed in order to obtain an estimate of attenuation in the backscattered signals as a function of frequency. Results demonstrate a moderate performance of this technique for grain size evaluation

On the Ultrasonic Imaging of Tube/Support Structure of Power Plant Steam Generators

The corrosion and erosion of steam generator tubing in nuclear power plants can present problems of both safety and economics. In steam generators, the inconel tubes are fit loosely through holes drilled in carbon steel support plates. Corrosion is of particular concern with such tube/support plate structures. Non-protective magnetite can build up on the inner surface of the support plate holes, and allowed to continue unchecked, will fill the gap, eventually denting and fracturing the tube walls. Therefore, periodic nondestructive inspection can be valuable in characterizing corrosion and can be used in evaluating the effectiveness of chemical treatments used to control or reduce corrosion. Presently, we are investigating the feasibility and practicality of using ultrasound in routing testing for gap measurement, for evaluating the corrosion and assessing the degree of denting. The tube/support structure can be modeled as a multilayer, reverberant target, which when tested with ultrasound results in two sets of reverberating echoes [1]. One set corresponds to the tube wall and the other to the support plate. These echoes must be decomposed and identified in order to evaluate the tube/support structure. This report presents experimental results along with a discussion of various measurements and processing techniques for decomposing and interpreting tube/support echoes at different stages of corrosion.</p

Optimal Ultrasonic Flaw Detection Using a Frequency Diversity Technique

The major problem in an ultrasonic flaw detection system is the presence of microstructure noise (clutter) resulting from scattering at grain boundaries. Ultrasonic grain echoes are random in amplitude and arrival time and often interfere and mask the flaw echo. Grain echoes are stationary and correlated from scan to scan in the same propagation path. An effective method of decorrelating grain echoes can be achieved by changing the frequency from scan to scan, a method known as frequency diversity. In practice frequency diverse grain echoes can be obtained by transmitting a broadband echo through the materials and bandpass filtering the received echoes over many bands of frequencies. At any given time the outputs of bandpass filters are the features representing information related to flaw or grain echoes. Although these outputs are random, the statistics of flaws and grains echoes are different. This situation permits application of statistical pattern recognition using a Bayes classifier. Experimental data and computer simulation have confirmed that flaw and clutter echoes over different frequency bands have a Gaussian distribution with different covariance matrices. For this situation the Bayes classifier is quadratic and provides optimal flaw detection performance. Presented here is the design of an optimal classifier with experimental and simulated results.</p

Performance analysis of system-on-chip architectures for ultrasonic data compression

Ultrasonic NDE and imaging applications utilize a large amount of information. Most of these applications demand real-time data processing with low power consumption. Compression of the collected ultrasonic data helps to reduce the storage, as well as rapid data transmission to remote locations for expert analysis. The objective of this study is to develop embedded system-on-chip architectures for ultrasonic data compression, and analyze the performance of different design methods according to the application requirements. The system is implemented using Xilinx Zynq System-on-Chip (SoC), which combines both ARM processor and field-programmable gate array (FPGA) on the same chip. The major parameters analyzed in this study are signal fidelity, hardware resource utilization and computational processing speed. The hardware and software co-design implementation is about five times faster compared to software only implementation using Zynq SoC

Exploration of Optimizing FPGA-based Qubit Controller for Experiments on Superconducting Quantum Computing Hardware

This work explores avenues and target areas for optimizing FPGA-based control hardware for experiments conducted on superconducting quantum computing systems and serves as an introduction to some of the current research at the intersection of classical and quantum computing hardware. With the promise of building larger-scale error-corrected quantum computers based on superconducting qubit architecture, innovations to room-temperature control electronics are needed to bring these quantum realizations to fruition. The QICK (Quantum Instrumentation Control Kit) is one leading experimental FPGA-based implementations. However, its integration into other experimental quantum computing architectures, especially those using superconducting radiofrequency (SRF) cavities, is largely unexplored. We identify some key target areas for optimizing control electronics for superconducting qubit architectures and provide some preliminary results to the resolution of a control pulse waveform. With optimizations targeted at 3D superconducting qubit setups, we hope to bring to light some of the requirements in classical computational methodologies to bring out the full potential of this quantum computing architecture, and to convey the excitement of progress in this research

A Design Flow for Robust License Plate Localization and Recognition in Complex Scenes

ABSTRACT In this paper, we present a new design flow for robust license plate localization and recognition. The algorithm consists of three stages: 1) license plate localization; 2) character segmentation; and 3) feature extraction and character recognition. The algorithm uses Mexican hat operator for edge detection and Euler number of a binary image for identifying the license plate region. A pre-processing step using median filter and contrast enhancement is employed to improve the character segmentation performance in case of low resolution and blur images. A unique feature vector comprised of region properties, projection data and reflection symmetry coefficient has been proposed. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. A thorough testing of algorithm is performed on a database with varying test cases in terms of illumination and different plate conditions. Practical considerations like existence of another text block in an image, presence of dirt or shadow on or near license plate region, license plate with rows of characters and sensitivity to license plate dimensions have been addressed. The results are encouraging with success rate of 98.10% for license plate localization and 97.05% for character recognition

Design flow for robust license plate localization

In this paper, we present a new design flow for robust license plate localization and recognition. The algorithm consists of three stages: 1) license plate localization; 2) character segmentation; and 3) feature extraction and character recogni-tion. The algorithm uses Mexican hat operator for edge detection and Euler number of a binary image for identifying the license plate region. A pre-processing step using median filter and contrast enhancement is employed to improve the character segmentation performance in case of low resolution and blur images. A unique feature vector comprised of region properties, projection data and reflection symmetry coefficient has been proposed. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. A thorough testing of algorithm is performed on a database with varying test cases in terms of illumination and different plate con-ditions. Practical considerations like existence of another text block in an image, presence of dirt or shadow on or near license plate region, license plate with rows of characters and sensitivity to license plate dimensions have been ad-dressed. The results are encouraging with success rate of 98.10 % for license plate localization and 97.05 % for character recognition