Computer Engineering Commons^™

A Quantization Training Algorithm Of Adaptive Learning Quantization Scale Fators, Hui Nie, Kangshun Li, Yang Su

Journal of System Simulation

Abstract: Deep neural network model is difficult to effectively deploy in embedded terminals due to its excessive number of components, andone of the solutions is model miniaturization (such as model quantization, knowledge distillation, etc.). To address this problem, a quantization training algorithm (referred to as LSQ-BN algorithm) based on adaptive learning of quantizationscale factors with BN folding is proposed.A single CNN (convolutional neural) is usedtoconstruct BN folding and achieve BN and CNN fusion. During the process of quantitative training,the quantization scale factors are set as model parameters. An adaptive quantizationscale factor initialization scheme is proposed to solve the problem …

Joint Shift Scheduling Method For Call Center With Mechanism Of Delay Information, Miao Yu, Manru Li, Yu Zhao

Journal of System Simulation

Abstract: A joint shift scheduling method is studied for call center with delay information. According to the queue model of call center with delay information, the influence rule of the customer's patience and abandonment behavior is addressed, and a mechanism of delay information is proposed to estimate the waiting time of customers. Considering the influence of non-stationary arrival and other factors, the scheduling model of the call centers is established by the discrete Event-Scheduling approach. Based on the proposed evaluation method of delay information, the joint shift scheduling method by simulation optimization is designed to solve the scheduling problem …

Modeling And Simulation Of Ultra Supercritical Unit Using A Composite Weighted Human Learning Network, Chuanliang Cheng, Chen Peng, Deliang Zeng, Tengfei Zhang

Journal of System Simulation

Abstract: Intermediate point temperature is an important parameter in ultra supercritical (USC) unit. However, due to strong nonlinearity, it is difficult to determine the form and coefficients of the corresponding model by using traditional methods. In order to get a better control effect, a novel composite weighted human learning optimization network (CWHLON) is proposed to tackle the above-mentioned problems. Though the real-time dynamic linear model, the characteristics of the object are accurately simulated. In the simulation experiment, CWHLON is compared with the traditional recursive least squares and other three meta heuristic methods. The data show that the proposed method improves …

Research On The Number Of Passengers On The Platform Of Rail Transit Station Considering Congestion Propagation, Wei Chen, Zongping Li, Can Liu, Yanni Ju

Journal of System Simulation

Abstract: It is the basis of improving the safety guarantee ability of urban rail transit system to study and master the change law of the number of passengers in the urban rail transit station under the condition of Congestion Propagation. From the point of view of multi subsystem of passenger, station and train, combined with the multi-attribute characteristics of passenger flow, platform and train, the calculation model of the number of passengers in urban rail transit station is established based on system dynamics. A multi group sensitivity simulation experiment is designed to analyze the influence factors of the number of …

Multi-Stage Multi-Agv Path Planning With Walk Under Shelves For Robotic Mobile Fulfillment Systems, Teng Li, Peipei Ding, Jinfang Liu

Journal of System Simulation

Abstract: Aiming at the problem of increasing travel time due to turning and obstacle avoidance in robotic mobile fulfillment systems(RMFS) with large-scale multi-AGV path planning, a path planning model with the shortest task completion time is established. A path planning model considering no-load AGV that can pass through the shelf is proposed, and the model is solved by an improving A* algorithm. The AGV operation stage is divided, an turning penalty value is introduced into the A* algorithm to reduce the turning times, and the obstacle avoidance priority with the obstacle avoidance waiting time is set. The simulation results show …

Design And Simulation Of Ts Fuzzy Based Cooperative Control Of Missile Formation, Yexin Zhang, Yu Cheng, Hongyan Yan, Xuwei Fan, Xu Zhang, Yi Tian

Journal of System Simulation

Abstract: Aiming at the requirement of cooperative operation of multi-missile formation, a cooperative control algorithm of multi-missile formation based on Takagi-Sugeno(TS) fuzzy control theory is proposed.The flight speed, trajectory angle and trajectory deflection angle of the missile are taken as parameters in the leader-follower mode missile formation flying system.The local asymptotically stable controller is designed by using the systemlocal linearization of multiple groups of equilibrium pointsduring the whole flight process.Through the expert experience method,the membership function and fuzzy rules for the system are designedwith TS fuzzy theory, and the whole multi-missile cooperative control system is completed and the stability …

Towards Emulation Of Intelligent Iot Networks On Eu-Us Testbeds, Sachin Sharma, Saish Urumkar, Gianluca Fontanesi, Venkat Sai Suman Lamba Karanam, Boyang Hu, Byrav Ramamurthy, Avishek Nag

Conference papers

This paper introduces our project on experimental validation of intelligent Internet of Things (IoT) networks. The project is a part of the NGIAtlantic H2020 third open call to perform experiments on EU and US wireless testbeds. The project proposes five different experiments to be performed on EU/US testbeds: (1) automatic configuration/discovery of Software Defined Networking (SDN) in wireless IoT sensor networks, (2) Machine Learning (ML) assisted control and data traffic path discovery experiments, (3) GPU and Hadoop cluster assisted experiments for ML algorithms, (4) Inter-testbed experiments, and (5) Failure recovery intercity experiments. Further, initial experimentation on EU/US testbeds is explored …

Combining Solution Reuse And Bound Tightening For Efficient Analysis Of Evolving Systems, Clay Stevens, Hamid Bagheri

School of Computing: Conference and Workshop Papers

Software engineers have long employed formal verification to ensure the safety and validity of their system designs. As the system changes—often via predictable, domain-specific operations—their models must also change, requiring system designers to repeatedly execute the same formal verification on similar system models. State-of-the-art formal verification techniques can be expensive at scale, the cost of which is multiplied by repeated analysis. This paper presents a novel analysis technique—implemented in a tool called SoRBoT—which can automatically determine domain-specific optimizations that can dramatically reduce the cost of repeatedly analyzing evolving systems. Different from all prior approaches, which focus on either tightening the …

Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors And Machine Learning Algorithms, Rahul Soangra, Yuxin Wen, Hualin Yang, Marybeth Grant-Beuttler

Physical Therapy Faculty Articles and Research

Idiopathic toe walking (ITW) is a gait abnormality in which children’s toes touch at initial contact and demonstrate limited or no heel contact throughout the gait cycle. Toe walking results in poor balance, increased risk of falling, and developmental delays among children. Identifying toe walking steps during walking can facilitate targeted intervention among children diagnosed with ITW. With recent advances in wearable sensing, communication technologies, and machine learning, new avenues of managing toe walking behavior among children are feasible. In this study, we investigate the capabilities of Machine Learning (ML) algorithms in identifying initial foot contact (heel strike versus toe …

A Privacy-Preserving Strategy For The Trust Layer Of The Energy Grid Of Things Distributed Energy Resource Management System, Mohammed Abdullah Alsaid

Dissertations and Theses

Emergent from the shadows of the traditional grid flaws, the Smart Grid (SG) idea was born and led by government mandates toward cleaner energy production. The SG represents the next generation of electricity distribution systems that subsume recent technological innovations. It uses digital communication between its components and entities to attain more automation, self-sufficiency, and reliability. Unfortunately, this relatively new concept is not flawless; the intrinsic reliance on increased digital communication spreads open attack paths for adversaries. Therefore, finding solutions that address information exchange vulnerabilities has become imperative.

The Energy Grid of Things (EGoT) is Portland State University's implementation of …

Finding Approximate Pythagorean Triples (And Applications To Lego Robot Building), Ronald I. Greenberg, Matthew Fahrenbacher, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

This assignment combines programming and data analysis to determine good combinations of side lengths that approximately satisfy the Pythagorean Theorem for right triangles. This can be a standalone exercise using a wide variety of programming languages, but the results are useful for determining good ways to assemble LEGO pieces in robot construction, so the exercise can serve to integrate three different units of the Exploring Computer Science high school curriculum: "Programming", "Computing and Data Analysis", and "Robotics". Sample assignment handouts are provided for both Scratch and Java programmers. Ideas for several variants of the assignment are also provided.

A Distributed Trust Model Simulator For Energy Grid Of Things Distributed Energy Resource Management System, Abdullah Barghouti

Dissertations and Theses

The evolution of networks into more distributed, self-reliant nodes has mitigated single-point failures that plagued traditional centralized networks. Applied to power grids, distributed systems can increase the integrity and availability of grid services while also offering a power management solution. However, while distributed networks provide scalability, security, and sustainability compared to centralized networks, their distributed nature makes them harder for anomaly detection and prevention. Incorporating a Distributed Trust Model (DTM) System into an Energy Grid of Things Distributed Energy Resource Management System (EGOT DERMS) allows grid participants to be characterized and their communication to be analyzed for possible attacks. A …

Demonstrating Configuration Of Software Defined Networking In Real Wireless Testbeds, Saish Urumkar, Gianluca Fontanesi, Avishek Nag, Sachin Sharma

Conference papers

Currently, several wireless testbeds are available to test networking solutions including Fed4Fire testbeds such as w-ilab. t and CityLab in the EU, and POWDER and COSMOS in the US. In this demonstration, we use the w-ilab.t testbed to set up a wireless ad-hoc Software-Defined Network (SDN). OpenFlow is used as an SDN protocol and is deployed using a grid wireless ad-hoc topology in w-ilab.t. In this paper, we demonstrate: (1) the configuration of a wireless ad-hoc network based on w-ilab.t and (2) the automatic deployment of OpenFlow in an ad-hoc wireless network where some wireless nodes are not directly connected …

Experimenting An Edge-Cloud Computing Model On The Gpulab Fed4fire Testbed, Vikas Tomer, Sachin Sharma

Conference papers

There are various open testbeds available for testing algorithms and prototypes, including the Fed4Fire testbeds. This demo paper illustrates how the GPULAB Fed4Fire testbed can be used to test an edge-cloud model that employs an ensemble machine learning algorithm for detecting attacks on the Internet of Things (IoT). We compare experimentation times and other performance metrics of our model based on different characteristics of the testbed, such as GPU model, CPU speed, and memory. Our goal is to demonstrate how an edge-computing model can be run on the GPULab testbed. Results indicate that this use case can be deployed seamlessly …

Influence Of Aluminum Addition On The Laser Powder Bed Fusion Of Copper-Aluminum Mixtures, Nada Kraiem, Loic Constantin, A. Mao, Fei Wang, Bai Cui, Jean-François Silvain, Yongfeng Lu

Department of Electrical and Computer Engineering: Faculty Publications

The high optical reflectivity of copper (Cu) in the near infrared (NIR) domain and its elevated heat dissipation make Cu a challenging metal for laser powder bed fusion (LPBF), even with high energy densities (EDs). In this study, we demonstrated that adding aluminum (Al) powder by as little as 0.75, 1.5, and 3 wt.% substantially enhances Cu processability, leading to denser (up to 98%) and smoother (Ra = 3.3 𝜇m) Cu-Al parts as compared to 95% and 18 𝜇m, respectively, for the parts printed using pure Cu. In addition, this method reduces the ED required by a factor of two …

The Message Design Of Raiders Of The Lost Ark On The Atari 2600 & A Fan’S Map, Quick Start, And Strategy Guide, Miguel Ramlatchan, William I. Ramlatchan

Distance Learning Faculty & Staff Books

The message design and human performance technology in video games, especially early video games have always been fascinating to me. From an instructional design perspective, the capabilities of the technology of the classic game consoles required a careful balance of achievable objectives, cognitive task analysis, guided problem solving, and message design. Raiders on the Atari is an excellent example of this balance. It is an epic adventure game, spanning 13+ distinct areas, with an inventory of items, where those hard to find items had to be used by the player to solve problems during their quest (and who would have …

Consemblex: A Consensus-Based Transcriptome Assembly Approach That Extends Consemble And Improves Transcriptome Assembly, Richard Mwaba

School of Computing: Dissertations, Theses, and Student Research

An accurate transcriptome is essential to understanding biological systems enabling omics analyses such as gene expression, gene discovery, and gene-regulatory network construction. However, assembling an accurate transcriptome is challenging, especially for organisms without adequate reference genomes or transcriptomes. While several methods for transcriptome assembly with different approaches exist, it is still difficult to establish the most accurate methods. This thesis explores the different transcriptome assembly methods and compares their performances using simulated benchmark transcriptomes with varying complexity. We also introduce ConSemblEX to improve a consensus-based ensemble transcriptome assembler, ConSemble, in three main areas: we provide the ability to use any …

Addressing The "Leaky Pipeline": A Review And Categorisation Of Actions To Recruit And Retain Women In Computing Education, Alina Berry, Susan Mckeever, Brenda Murphy, Sarah Jane Delany

Conference papers

Gender imbalance in computing education is a well-known issue around the world. For example, in the UK and Ireland, less than 20% of the student population in computer science, ICT and related disciplines are women. Similar figures are seen in the labour force in the field across the EU. The term "leaky pipeline"; is often used to describe the lack of retention of women before they progress to senior roles. Numerous initiatives have targeted the problem of the leaky pipeline in recent decades. This paper provides a comprehensive review of initiatives related to techniques used to boost recruitment and improve …

Credit Card Fraud Detection Using Machine Learning Techniques, Nermin Samy Elhusseny, Shimaa Mohamed Ouf, Amira M. Idrees Ami

Future Computing and Informatics Journal

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today's banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary …

Cobol Cripples The Mind!: Academia And The Alienation Of Data Processing, Neel Shah

Swarthmore Undergraduate History Journal

This paper writes a social history of the programming language COBOL that focuses on its reception in academia. Through this focus, the paper seeks to understand the contentious relationship between data processing and the academy. In historicizing COBOL, the paper also illuminates the changing nature of the academy-industry-military triangle that was a mainstay of early computing.

A Monte Carlo Framework For Incremental Improvement Of Simulation Fidelity, Damian M. Lyons, James Finocchiaro, Misha Novitzky, Chris Korpela

Faculty Publications

Robot software developed in simulation often does not be- have as expected when deployed because the simulation does not sufficiently represent reality - this is sometimes called the `reality gap' problem. We propose a novel algorithm to address the reality gap by injecting real-world experience into the simulation. It is assumed that the robot program (control policy) is developed using simulation, but subsequently deployed on a real system, and that the program includes a performance objective monitor procedure with scalar output. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate …

A Monte Carlo Framework For Incremental Improvement Of Simulation Fidelity, Damian Lyons, James Finocchiaro, Misha Novitsky, Chris Korpela

Faculty Publications

Robot software developed in simulation often does not be- have as expected when deployed because the simulation does not sufficiently represent reality - this is sometimes called the `reality gap' problem. We propose a novel algorithm to address the reality gap by injecting real-world experience into the simulation. It is assumed that the robot program (control policy) is developed using simulation, but subsequently deployed on a real system, and that the program includes a performance objective monitor procedure with scalar output. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate …

A Half- Yearly E-Newsletter Of The Department Of Computer Science And Engineering, Manipal Institute Of Technology - Jul 2022, Ashalatha Nayak Dr.

Faculty work

No abstract provided.

Spiking Neural Networks And Their Applications: A Review, Kashu Yamazaki, Viet-Khao Vo-Ho, Darshan Bulsara, Ngan Le

Computer Science and Computer Engineering Faculty Publications and Presentations

The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given …

Asymmetric Control Of Light At The Nanoscale, Christos Argyropoulos

Department of Electrical and Computer Engineering: Faculty Publications

Breaking reciprocity at the nanoscale can produce directional formation of images due to the asymmetric nonlinear optical response of subwavelength anisotropic resonators. The self-induced passive non-reciprocity has advantages compared to magnet or time modulation approaches and may impact both classical and quantum photonics.

Learning Term Weights By Overfitting Pairwise Ranking Loss, Ömer Şahi̇n, İlyas Çi̇çekli̇, Gönenç Ercan

Turkish Journal of Electrical Engineering and Computer Sciences

A search engine strikes a balance between effectiveness and efficiency to retrieve the best documents in a scalable way. Recent deep learning-based ranker methods are proving to be effective and improving the state-of-the-art in relevancy metrics. However, as opposed to index-based retrieval methods, neural rankers like bidirectional encoder representations from transformers (BERT) do not scale to large datasets. In this article, we propose a query term weighting method that can be used with a standard inverted index without modifying it. Query term weights are learned using relevant and irrelevant document pairs for each query, using a pairwise ranking loss. The …

Automated Question Generation And Question Answering From Turkish Texts, Fati̇h Çağatay Akyön, Ali̇ Devri̇m Eki̇n Çavuşoğlu, Cemi̇l Cengi̇z, Si̇nan Onur Altinuç, Alpteki̇n Temi̇zel

Turkish Journal of Electrical Engineering and Computer Sciences

While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience and resources. Automatic question generation (QG) techniques can be utilized to satisfy the need for a continuous supply of new questions by streamlining their generation. However, compared to automatic question answering (QA), QG is a more challenging task. In this work, we fine-tune a multilingual T5 (mT5) transformer in a multitask setting for QA, QG and answer extraction tasks using Turkish QA datasets. To the best of our knowledge, this is the first academic work …

Design And Implementation Of A Bioinspired Leaf Shaped Hybrid Rectenna As A Green Energy Manufacturing Concept, Kayhan Çeli̇k, Erol Kurt

Turkish Journal of Electrical Engineering and Computer Sciences

In this communication, the novel low cost hybrid energy harvester combining rectifying antenna with the solar cell for feeding the low power energy systems are reported. The bioinspired leaf shaped monopole antenna is designed to work in the most used communication frequency bands such as GSM-1800, UMTS-2100, WIFI-2.45 and LTE-2.65 GHz for the energy harvesting purposes and microstrip low pass filter is also added on the feeding line for the second harmonic rejection for increasing the efficiency of the harvester. The solar cell is placed on the ground plane of the designed leaf shaped antenna for using volumetric space efficiently …

Preserving Users’ Privacy In Iot Systems Through Network-Based Access Control, Ahmed Khalid A Alhazmi

Theses and Dissertations

Privacy issues have plagued the rapid proliferation of the Internet of Things (IoT) devices. Resource-constrained IoT devices often obscure transparency for end-users. A lack of transparency and control complicates user trust in IoT. Additionally, a growing history of misuse and abuse exists in IoT. Notably, a smart TV has periodically scanned and collected users’ private information without consent, while power companies have adjusted the temperature of smart thermostats during heat waves. Due to a hybrid of distributed ecosystems within IoT, users cannot easily implement traditional access control over their devices as data flows within different nodes for storage and processing. …

Breast Cancer-Caps: A Breast Cancer Screening System Based On Capsule Network Utilizing The Multiview Breast Thermal Infrared Images, Devanshu Tiwari, Manish Dixit, Kamlesh Gupta

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposed an accurate and fully automated breast cancer early screening system called the "Breast Cancer-Caps". The capsule network is used in this approach for the cancer detection in breast utilizing the thermal infrared images for the first time. This capsule network is trained with the help of Dynamic as well as Static breast thermal images dataset consisting of left, right, frontal views along with a new multiview thermal images. These multiview breast thermal images are fabricated by concatenating the conventional left, frontal and right view breast thermal images. The other current and popular deep transfer learning models such …