Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Intra-SM Parallelism in GPUs via Persistent and Elastic Blocks

Emerging GPUs have multiple Streaming Multiprocessors (SM), while each SM is comprised of CUDA Cores and Tensor Cores. While CUDA Cores do the general computation, Tensor Cores are designed to speed up matrix multiplication for deep learning applications. However, a GPU kernel often either uses CUDA Cores or Tensor Cores, leaving the other processing units idle. Although many prior research works have been proposed to co-locate kernels to improve GPU utilization, they cannot leverage the Intra-SM CUDA Core-Tensor Core Parallelism. We therefore propose Plasticine to exploit the intra-SM parallelism for maximizing the GPU throughput. Plasticine involves compilation and runtime schedule to achieve the above purpose. Experimental results on an Nvidia 2080Ti GPU show that Plasticine improves the system-wide throughput by 15.3% compared with prior co-location work.
Design and Simulation Analysis of Modified Single Switch SEPIC Converter for BLDC Motor

In this work, a SEPIC converter with quasi resonant converter for high boost DC/DC operation in water pumping application with BLDC motor drive is implemented. Due to the quasi resonant topology, switching loss is reduced and it has coupled inductor with capacitors. Simulation result for proposed converter is analysed with the help of MATLAB and 97.72 % converter efficiency and 7.3 voltage gain is obtained with the 0.6 duty cycle.
Computer Vision Based Vehicle Intension Finding by Understanding Driver Hand Signal

Over a decade the increase in usage of vehicles and need for safety of people has greatly influenced the development of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD). The main purpose behind these systems is to enhance the ability of vehicle and its driver to react to various conditions on road. The ADAS provides high safety to vehicle drivers by warning about the external environment and possible threats. Similar to Vulnerable Road Users (VRUs) like pedestrian, cyclist, motorbike riders etc. if it is possible to anticipate the hand signals of the vehicle driver then the number of accidents can be controlled and its severity can be reduced drastically. This paper introduces a Computer Vision (CV) based solution to address this issue. A Convolutional Neural Network (CNN) architecture based automatic system is introduced that can help the ego-vehicle to recognize the hand signals of a vehicle driver and take the necessary actions in advance to prevent the road accidents. The proposed algorithm can recognize left, right and stop signals indicated by the vehicle driver that has never been addressed before. This method uses Regional Multi-Person Pose estimation (RMPE) - Alphapose [1] framework for human pose estimation and vehicle arm signal recognition algorithm is built on this framework. The testing of proposed work is done on self-captured dataset. The proposed work provides accurate results to recognize hand signals of the vehicle driver and the accuracy is 92.87% at 10 fps.
Optimal Transistor Placement Combined with Global In-cell Routing in Standard Cell Layout Synthesis

The synthesis of standard cell layouts is largely divided into two tasks namely transistor placement and in-cell routing. Since the result of transistor placement highly affects the quality of in-cell routing, it is crucial to accurately and efficiently predict in-cell routability during transistor placement. In this work, we address the problem of an optimal transistor placement combined with global in-cell routing with the primary objective of minimizing cell size and the secondary objective of minimizing wirelength for global in-cell routing. To this end, unlike the conventional indirect and complex SMT (satisfiability modulo theory) formulation, we propose a method of direct and efficient formulation of the original problem based on SMT. Through experiments, it is confirmed that our proposed method is able to produce minimal-area cell layouts with minimal wirelength for global in-cell routing while spending much less running time over the conventional optimal layout generator.
Attracting Girls to Civil Engineering through Hands-On Activities That Reveal the Communal Goals and Values of the Profession

In this paper we report on our work to develop hands-on activities for middle school classrooms that clearly reveal how civil engineers make a substantial societal impact. These activities and their accompanying messaging vividly show how, through their work, teams of civil engineers help people around the world have better quality of life in a sustainable way. All activities are easily reproducible with low-cost materials and represent a number of specialization areas within civil engineering—namely, structural, urban and community planning, and water resources. The messaging that is an integral part of the activities uses the latest available research on why girls are underrepresented in civil engineering in particular, and the science, technology, engineering, and mathematics (STEM) fields in general, to highlight the communal goals and values that civil engineers strive for. The results presented in this paper should allow civil engineers to more clearly show how their work enacts communal and altruistic goals that are known to be generally more highly endorsed by women than by men. Preliminary results on the implementation of these strategies in the context of an all-girls middle school show promise in enabling a better connection between the civil engineering profession and female students.
Effect of Chest X-Ray Contrast Image Enhancement on Pneumonia Detection using Convolutional Neural Networks

The problem in pneumonia detection using a chest X-ray is the differences in the image quality due to the difference in the image acquisition procedure. To overcome the issue, the image enhancement technique is used in the preprocessing step. The purpose is to examine the effect of three image enhancement techniques to detect pneumonia, i.e. histogram equalization (HE), contrast limited adaptive HE and exposure fusion framework. These enhanced images are used as input images in pneumonia detection using VGG16 convolutional neural network architecture. In total, 3,151 chest X-ray images are used. The best performance is achieved by the exposure fusion framework image enhancement technique. The combination of exposure fusion framework and VGG16 give the training loss and accuracy of 0.2113 and 0.9451, and validation and accuracy loss of 0.6034 and 0.8670. Deeper analysis shows that the exposure fusion framework not only stretches the image intensity but also keeps the shape of the histogram remains. This technique will minimize the information loss in the enhanced image during the enhancement process.
Tables for Cascaded Homogeneous Quarter-Wave Transformers

Quarter-wave transformers are frequently required in microwave and UHF systems. An exact design procedure is known but involves lengthy calculations. Faced with the design of many such transformers, the calculations were programmed on an IBM 704 digital computer. The speed of computation is such that several hundred designs for 2, 3, and 4 section transformers were systematically computed in a few minutes. The results are reproduced here in tables, which should permit the calculation of most cases of practical interest by interpolation.
Energy-Efficient CNNs Accelerator Implementation on FPGA with Optimized Storage and Dataflow

Convolutional neural networks (CNNs) has been widely used in computer vision and speech processing, and has achieved great success. However, the deployment of large-scale CNN model is limited by computing and memory in the smart embedded system. Through the current high parallel computing paradigm of CNNs accelerator, the computing requirements can be effectively met to achieve high throughput. However, because the communication cost may be higher than the computing cost for small smart platform, the energy consumption is still very high. In order to solve this problem, a new CNNs accelerator based on storage and data flow is proposed, which realizes energy-saving CNNs inference acceleration by minimizing data access and maximizing data reuse. This paper implements the accelerator on the Zynq UltraScale+ MPSoC ZCU102 evaluation board, and evaluates the throughput and energy efficiency of the accelerator for typical vgg16 and tiny Yolo benchmark networks. Compared with other accelerators, the our accelerator improves the system energy efficiency by 6.3×, the system throughput by 41×, and the throughput of a single DSP by 7.63×.
Noise and sensitivity analysis of periodically switched linear circuits in frequency domain

This paper presents new theories and efficient computational methods for noise and sensitivity analysis of multiphase periodically switched linear (PSL) circuits in frequency domain. Tellegen's theorem for PSL circuits in the phasor domain, frequency reversal theorem, and transfer function theorem, are introduced. The adjoint network of PSL circuits is developed using a frequency-domain approach. An adjoint network-based noise analysis algorithm for PSL circuits is proposed. It is shown that the computational overhead associated with multiple noise sources is eliminated by using the transfer function theorem. It is also shown that the excessive cost of computation due to aliasing effects is significantly reduced when the frequency reversal theorem is employed. In sensitivity analysis, the incremental form of Tellegen's theorem for PSL circuits in the phasor domain is introduced and frequency-domain sensitivity of PSL circuits Is obtained. It is shown that frequency-domain sensitivity of PSL circuits is a series summation of the network variables. Both the baseband and sideband frequency components of the network variables contribute to baseband sensitivity. The method yields sensitivities of one output with respect to all circuit elements in one frequency analysis. Sensitivity networks of PSL circuits are introduced. It is demonstrated that both the adjoint and sensitivity network approaches give the same sensitivity. Numerical results computed using the proposed methods are compared with measurement data and those from other CAD tools.
Hybrid Model–Based Fault Detection of Wind Turbine Sensors

Abstract In order to improve reliability of wind turbines, it is important to detect faults in their very early occurrence, and to handle them in an optimal way. This paper focuses on the pitch sensors of the turbine blade system, as they are mainly used for wind turbine control, in order to maximise the power production, and the efficiency of the whole process. On the other hand, as the input-output behaviour of the system under diagnosis is nonlinear, this work suggests a modelling scheme relying on piecewise affine models, whose parameters are identified through the acquired input–output measurements affected by measurement uncertainty. Therefore, these hybrid prototypes are exploited for generating suitable residual signals, which allow the detection and the isolation of the considered sensor faults. This noise rejection scheme is used since the wind turbine measurements are not very reliable, due to the uncertainty of wind speed acting on the wind turbine, and to the turbulence around the rotor plane. A detailed benchmark model simulating the wind turbine where realistic fault conditions can be considered shows the effectiveness of both the identification and fault diagnosis techniques.
A fully integrated 40‐GB/S CDR with eight‐phase VCO for optical fiber communication

This article presents a fully integrated 40‐Gb/s phase locked clock and data recovery (CDR) circuit with 1:4 demultiplexer (DEMUX) in International Business Machines (IBM) 90‐nm CMOS technology.The CDR circuit incorporates a novel eight‐phase CL ladder filtering voltage‐controlled oscillator and a quarter rate bang‐bang phase detector. The 40‐Gb/s input data are sampled with eight parallel differential master‐salve flip‐flops every 12.5 ps and the 40‐Gb/s data are demultiplexed into four 10‐Gb/s outputs when the CDR circuit is phase locked. The recovered and frequency divided 10‐GHz clock has a phase noise of −101.01 dBc/Hz at 1 MHz offset and a peak to peak jitter of 3.4 ps. The CDR and 1:4 DEMUX consumes 72 mW from a 1.2 V supply excluding out buffers. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:170–173, 2013; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27248
An Ultra-efficient Look-up Table based Programmable Processing in Memory Architecture for Data Encryption

Processing in Memory (PIM), a non-von Neumann computing paradigm, has emerged as a faster and more efficient alternative to the traditional computing devices for data-centric applications such as Data Encryption. In this work, we present a novel PIM architecture implemented using programmable Lookup Tables (LUT) inside a DRAM chip to facilitate massively parallel and ultra-efficient data encryption with the Advanced Encryption Standard (AES) algorithm. Its LUT-based architecture replaces logic-based computations with LUT ‘look-ups’ to minimize power consumption and operational latency. The proposed PIM architecture is organized as clusters of homogeneous, interconnected LUTs that can be dynamically programmed to execute operations required for performing AES encryption. Our simulations show that the proposed PIM architecture can offer up to 14.6× and 1.8× higher performance compared to CUDA-based implementation of AES Encryption on a high-end commodity GPU and a state-of-the-art GPU Computing Processor, respectively. At the same time, it also achieves 217× and 31.2× higher energy efficiency, respectively, than the aforementioned devices while performing AES Encryption.
Research on Remote Virtual Assembly Technology of Plug Valve Based on VRML

In this paper, three-dimensional plug valve was created. The function of virtual assembly was achieved using virtual reality modeling language. Network virtual assembly can operate, browse and assemble plug valve remotely by embedding virtual assembly in web pages. The virtual assembly method presented in this paper can be easily extended to deal with many other problems in complex engineering system if control parameters are modified properly.
Air Quality Monitoring Device for Smart Health Solution during Covid-19 Pandemic

The work from home, learning from home regulation and other outside activity restriction made the people should stay at home longer than usual due to the covid-19 outbreak. The environmental conditions at home, especially in urban communities that are mostly densely residential areas affected the air quality of the living environment. The modern home’s structure in urban has a less indoor air circulation. The bad indoor air quality may cause other health problem, especially when people should stay at home during a longer period. This paper, designed and implemented a monitoring box that may measure several air quality parameters, i.e. temperature, humidity, carbon dioxide (CO2) and carbon monoxide (CO) level. The air quality information in the monitoring box may become a smart health solution that informs the people about the air quality in their room especially during covid-19. The results show that the air quality monitoring device for smart health solution work properly. The average error rate for temperature is 0.90%, humidity 2.37%, CO is 14.21% and CO2 is 12.07%. Therefore, the accuracy of the temperature is 99.1%, humidity is 97.63%, CO is 85.79% and the CO2 is 87.93%.
Deep Q-Learning for Aggregator Price Design

This paper develops a reinforcement learning (RL) aggregator agent that intelligently determines real-time electricity prices to achieve grid-level goals. A diverse community of home energy management (HEMS) systems, equipped with model predictive control (MPC) capabilities, respond to the prices without revealing their state, resources, constraints, or objectives to the aggregator. Only the aggregate response of the community is visible to the RL agent, preserving the privacy of each homeowner. The results indicate that the RL agent is successful in improving the flattening the aggregate community demand while adhering to individual user constraints without requiring two-way negotiations, transactive frameworks, or any knowledge about individual homes.
Driving Teaching and Learning in Online mode: A Case Study

This Innovative Practice Work in Progress paper addresses the challenges faced in Online teaching. The issues faced in Online teaching are multidirectional. Both the teacher and the learner have their set of challenges. These need to be addressed effectively. This paper presents the scenario of an institution implementing innovative strategies in online teaching resolving the challenges faced by both the teachers and the learners resulting in in effective training of the teachers in delivering their content in the online mode of instruction, as well as improved student participation. These implementation strategies, and their implications are presented in this paper as a case study.
WAS-Deletion: Workload-Aware Secure Deletion Scheme for Solid-State Drives

Due to the intrinsic properties of Solid-State Drives (SSDs), invalid data remain in SSDs before erased by a garbage collection process, which increases the risk of being attacked by adversaries. Previous studies use erase and cryptography based schemes to purposely delete target data but face extremely large overhead. In this paper, we propose a Workload-Aware Secure Deletion scheme, called WAS-Deletion, to reduce the overhead of secure deletion by three major components. First, the WAS-Deletion scheme efficiently splits invalid and valid data into different blocks based on workload characteristics. Second, the WAS-Deletion scheme uses a new encryption allocation scheme, making the encryption follow the same direction as the write on multiple blocks and vertically encrypts pages with the same key in one block. Finally, a new adaptive scheduling scheme can dynamically change the configurations of different regions to further reduce secure deletion overhead based on the current workload. The experimental results indicate that the newly proposed WAS-Deletion scheme can reduce the secure deletion cost by about 1.2x to 12.9x compared to previous studies.
Comparative Analysis of Integral-Based Indices for On-line Assessment of Power System Transient Stability

This paper assesses the ability of four integral-based indices calculated using the post fault rotor angles, speed deviations, and accelerations to evaluate power system's transient stability. First, the impact of the integration time for the calculation of each of the indices to provide an adequate assessment of the system stability status is assessed. Then, a more detailed evaluation and calculation of the accuracies achieved by the indices is done by looking into the direct relationship between the index values and the stability status of test simulations. Results show that the proposed indices are able to represent correctly the instability degree of the system to different extents, while at the same time identifying the limitations in their use.
Context, Competency and Authenticity in STEM Education

This Research Full Paper presents work which links a consideration of the concept of context with that of learning competency and that of educational authenticity in STEM subjects. The word “context” is very familiar in both everyday language and in educational settings. However, because of its ubiquity, it is often taken for granted that both educators and students know what it means, and how it is being used. This paper reviews the concept of context, using the typology developed by Dohn et al. We draw attention to the way in which this can be applied to the notion of competence, specifically, the competence framework developed by Frezza et al, and consider how the concept underlies the idea of authentic learning. We argue that a clear understanding of authenticity depends critically on both students and academics being able to discriminate between the different types of contexts that occur in authentic assessment processes, such as projects based on real-world scenarios. We also consider contextual categories when describing the different ways in which learning can be transferred.
Special Session: Approximate TinyML Systems: Full System Approximations for Extreme Energy-Efficiency in Intelligent Edge Devices

Approximate computing (AxC) has advanced from being an emerging design paradigm to becoming one of the most popular and effective methods of energy optimization for applications in the domains of computer vision, image/video processing, data mining, analytics, and search. The simultaneous rise of artificial intelligence (AI) has provided an additional thrust to the adoption of various AxC techniques in intelligent edge platforms where energy-efficiency is not only desirable but necessary. In spite of the big rise in interest for AxC, the adoption of approximate hardware has mostly been limited to only one component of the system (usually the processing subsystem) which often contributes only a fraction of the overall system-level power. A full system approach to AxC enables us to extend approximations to other subsystems, such as the memory, sensor, and communications subsystems. This paper presents the foundational concepts of an approximate TinyML system that applies approximations synergistically to multiple subsystems in an edge inference device. These approximations are applied intelligently to significantly reduce energy while incurring a negligible loss in application-level quality. We demonstrate multiple versions of an approximate smart camera system that can execute state-of-the-art deep neural networks (DNNs) while consuming only a fraction of the total energy in a typical system.
Highlighting the Barren Landscape of Postdoctoral Resources: A Content Analysis of University Websites

This research paper serves as a benchmarking study to investigate the types and availability of resources available to postdoctoral scholars on university websites. Postdoctoral education in engineering and computer science disciplines is a forgotten stage of the academic pipeline, with very few scholars investigating the learning and development that occurs through the transient postdoctoral years. The few studies that have been done report postdocs feeling “forgotten” and on a “postdoctoral treadmill,” often without formal mentorship or guidance in developing the skills required to land academic careers. While most postdoctoral scholars do have supervisors to whom they report, most literature indicates that postdocs in engineering and computer science are still lacking mentorship in the peripheral skillsets essential for career success, and these effects are amplified for women and postdocs of color. Given a lack of interpersonal mentorship, it is plausible that postdocs turn to institutional resources for guidance and directions for professional development. To date, literature has not benchmarked the type or extent of resources available that are aimed at postdoctoral scholars. To this end, the purpose of this paper is to characterize university webpages using content analysis methods in order to understand the presence or absence of various types of support for postdocs at universities.
THE EFFECT OF REACHING TO AN OVERHEAD GOAL WHILE PERFORMING THE COUNTERMOVEMENT JUMP

One potentially simple way to maximize jumping effort and thus intensity is to have athletes jump to and attempt to touch challenging overhead goals during training. The purpose of this study was to compare the effect of jumping with and without the use of an overhead goal. Subjects performed 3 countermovement jumps in conditions with and without an overhead goal. Jump performance was evaluated using a force platform to determine peak ground reaction force, time to takeoff, power, and jump height. Data were evaluated with a two way ANOVA with results demonstrating no significant (p > 0.05) difference between goal conditions for any of the variables assessed and no interaction between goal condition and gender (p > 0.05).