Computer Engineering Commons^™

Side Channel Attack Counter Measure Using A Moving Target Architecture, Jithin Joseph

Electrical and Computer Engineering ETDs

A novel countermeasure to side-channel power analysis attacks called Side-channel Power analysis Resistance for Encryption Algorithms using DPR or SPREAD is investigated in this thesis. The countermeasure leverages a strategy that is best characterized as a moving target architecture. Modern field programmable gate arrays (FPGA) architectures provide support for dynamic partial reconfiguration (DPR), a feature that allows real-time reconfiguration of the programmable logic (PL). The moving target architecture proposed in this work leverages DPR to implement a power analysis countermeasure to side-channel attacks, the most common of which are referred to as differential power analysis (DPA) and correlation power analysis …

Visible-To-Thermal Transfer Learning For Facial Landmark Detection, Domenick D. Poster, Shuowen Hu, Nathan J. Short, Benjamin S. Riggan, Nasser M. Nasrabadi

Department of Electrical and Computer Engineering: Faculty Publications

There has been increasing interest in face recognition in the thermal infrared spectrum. A critical step in this process is face landmark detection. However, landmark detection in the thermal spectrum presents a unique set of challenges compared to in the visible spectrum: inherently lower spatial resolution due to longer wavelength, differences in phenomenology, and limited availability of labeled thermal face imagery for algorithm development and training. Thermal infrared imaging does have the advantage of being able to passively acquire facial heat signatures without the need for active or ambient illumination in low light and nighttime environments. In such scenarios, thermal …

Machine Learning Meets Internet Of Things: From Theory To Practice, Bharath Sudharsan, Pankesh Patel

Publications

Standalone execution of problem-solving Artificial Intelligence (AI) on IoT devices produces a higher level of autonomy and privacy. This is because the sensitive user data collected by the devices need not be transmitted to the cloud for inference. The chipsets used to design IoT devices are resource-constrained due to their limited memory footprint, fewer computation cores, and low clock speeds. These limitations constrain one from deploying and executing complex problem-solving AI (usually an ML model) on IoT devices. Since there is a high potential for building intelligent IoT devices, in this tutorial, we teach researchers and developers; (i) How to …

Analysis Of Denial Of Service Attacks In Emerging Software Defined Network Infrastructures, Andrea P. Wright

USF Tampa Graduate Theses and Dissertations

Software defined networking (SDN) improves upon traditional networking protocol technologies by decoupling the data and control planes and moving all control provisioning decisions to a centralized SDN controller entity. This concept has matured over the last decade, having gained strong industry traction, and is now being widely deployed within enterprise and carrier networks to streamline network services provisioning and reduce costs. Overall, centralized control delivers much more cost-effective and flexible networking setups that can support a wide range of customized user-driven network management applications, e.g., traffic engineering, security, survivability, admission control, policy control, etc.

However, the separation of the data …

Guest Editorial: Edge Intelligence For Beyond 5g Networks, Yan Zhang, Zhiyong Feng, Hassnaa Moustafa, Feng Ye, Usman Javaid, Chunfen Cui

Electrical and Computer Engineering Faculty Publications

Beyond fifth-generation (B5G) networks, or so-called "6G", is the next-generation wireless communications systems that will radically change how Society evolves. Edge intelligence is emerging as a new concept and has extremely high potential in addressing the new challenges in B5G networks by providing mobile edge computing and edge caching capabilities together with Artificial Intelligence (AI) to the proximity of end users. In edge intelligence empowered B5G networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with …

Learning Discriminative And Efficient Attention For Person Re-Identification Using Agglomerative Clustering Frameworks, Kshitij Nikhal

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Recent advancements like multiple contextual analysis, attention mechanisms, distance-aware optimization, and multi-task guidance have been widely used for supervised person re-identification (ReID), but the implementation and effects of such methods in unsupervised person ReID frameworks are non-trivial and unclear, respectively. Moreover, with increasing size and complexity of image- and video-based ReID datasets, manual or semi-automated annotation procedures for supervised ReID are becoming labor intensive and cost prohibitive, which is undesirable especially considering the likelihood of annotation errors increase with scale/complexity of data collections. Therefore, this thesis proposes a new iterative clustering framework that incorporates (a) two attention architectures that learn …

Data-Efficient Machine Learning With Focus On Transfer Learning, Shuteng Niu

Doctoral Dissertations and Master's Theses

Machine learning (ML) has attracted a significant amount of attention from the artificial intelligence community. ML has shown state-of-art performance in various fields, such as signal processing, healthcare system, and natural language processing (NLP). However, most conventional ML algorithms suffer from three significant difficulties: 1) insufficient high-quality training data, 2) costly training process, and 3) domain discrepancy. Therefore, it is important to develop solutions for these problems, so the future of ML will be more sustainable. Recently, a new concept, data-efficient ma- chine learning (DEML), has been proposed to deal with the current bottlenecks of ML. Moreover, transfer learning (TL) …

Classification Of Primary Versus Metastatic Pancreatic Tumor Cells Using Multiple Biomarkers And Whole Slide Imaging, Poupack Pooshang Baghery

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Pancreatic cancer is a challenging cancer with a high mortality rate and a 5-year survival rate between 2% to 9%. The role of biomarkers is crucial in cancer prognosis, diagnosis, and predicting the possible responses to a specific therapy. The Discovery and development of various types of biomarkers have been studied intensively in the hope of determining the best treatment approaches, better management, and possibly cure of this deadly cancer. However, metastasis, responsible for about 90% of the deaths from cancer, is still poorly understood. A few research that have investigated the expression of a particular biomarker or a panel …

Experimental Study Of Hollow-Core Slab Containing Waste Pet Bottles, Mulia Orientilize, Josia Irwan Rastandi, R. M. Dimas Aries C., Marsha Niken P., Krisna Adi S.S., Abimantrana Abimantrana

Makara Journal of Technology

This study investigated the utilization of plastic-waste concrete as an effort to reduce urban waste problems. The waste plastic bottles were utilized to form the hollows of the hollow-core slabs (HCSs). The bottles were made of polyethylene terephthalate (PET). As a part of green research to reuse waste material, shredded PET was also added to the concrete mixture to improve the HCS strength. The cast-in-site HCS could be constructed without any difficulties. Three parameters were investigated: the effects of void content, shredded PET content, and steel-fiber (SF) content on the HCS ultimate bending capacity (Mu). Fifteen specimens were tested under …

Improving The Intellectual And Social Development Of Children With Autism: Design Of A Training Center For Autism, Hasti Jalalian

Makara Journal of Technology

Research on the relationship between the role of architecture and the psychology of children with autism, which indicates that appropriate environments for these children can have a significant effect on their healthcare process, has been recently conducted. However, statistics show that autism spectrum disorders are pervasive, and its extent in Iran has been increasing. Moreover, centers that are specifically designed for children with autism are limited, and paradigms are made without considering the mood of these children. Given the role of architecture in designing a suitable space for these children, an investigation that can identify the specific requirements of these …

Numerical Study Of A Buckling Restrained Brace (Brb) In Steel Structures And Comparison With A Convergent Ordinary Brace (Ocb) Under Static And Dynamic Loading, Fatemeh Habibpour

Makara Journal of Technology

Bulk metallic glass (BMG) has good mechanical strength, high hardness, wear resistance, and corrosion resistance with promising application in various industries. However, for the industrial production of BMG, the main issue is how to overcome limitations of joining with other materials. The present study focuses on solder processing at low operating temperature to avoid exceeding the recrystallization temperature. A feasible joining process for BMG was developed using lead-free solders. The BMG surface is pre-plated with copper, nickel, or titanium as a wetting layer. The reaction temperature is set between the glass transition temperature of BMG and the melting point of …

Analysis Of The Interfacial Reaction Between Bulk Metallic Glass Coated Copper, Nickel, And Titanium With Lead-Free Solders, Andromeda Dwi Laksono, Yee-Wen Yen, Rifqi Aulia Tanjung, Teodoro A. Amatosa, Ruki Harwahyu

Makara Journal of Technology

Bulk metallic glass (BMG) has good mechanical strength, high hardness, wear resistance, and corrosion resistance with promising application in various industries. However, for the industrial production of BMG, the main issue is how to overcome limitations of joining with other materials. The present study focuses on solder processing at low operating temperature to avoid exceeding the recrystallization temperature. A feasible joining process for BMG was developed using lead-free solders. The BMG surface is pre-plated with copper, nickel, or titanium as a wetting layer. The reaction temperature is set between the glass transition temperature of BMG and the melting point of …

Design Of Ac Microgrid Topology With Photovoltaic Uncertainties In A Rural Village, Vannak Vai

Makara Journal of Technology

Energy needs are increasing day by day, especially for developing countries, due to population growth and changing lifestyles. A suitable microgrid topology with renewable energy integration is considered to fulfill the people and society’s needs. This paper focuses on the design of AC microgrid topology for a nonelectrified village with the integration of PV uncertainties in both sitting and sizing. The development of an optimal algorithm based on the conductor use minimization and unbalanced load improvement is proposed. The shortest path is proposed to search for the minimum conductor use. Then, the algorithm for improving the unbalanced load with two …

Mean-Shift Object Tracking Algorithm With Systematic Sampling Technique, Yoanes Bandung, Aris Ardiansyah

Makara Journal of Technology

Mean shift is a fast object tracking algorithm that only considers pixels in an object area, hence its relatively small computational load. This algorithm is suitable for use in real-time conditions in terms of execution time. The use of histograms causes this algorithm to be relatively resistant to rotation and changes in object size. However, its resistance to lighting changes is not optimal. This study aims to improve the performance of the algorithm under lighting changes and reduce its processing time. The proposed technique involves the use of sampling techniques to reduce the number of iterations, optimization of candidate search …

Analysis Of Correlation And Mapping Of Chlorophyll-A Concentrations And Sea Surface Temperatures In Coastal Areas Based On Terra Modis Satellite Image Data, Hendrata Wibisana, Bangun Muljo Sukotjo, Umboro Lasminto

Makara Journal of Technology

Ecosystems in aquatic environments are distinct from ecosystems on land. Changes that occur in ecosystems in aquatic environments affect the lives of biota in these waters, including the fish used as a food source in fishing communities in coastal areas. This study aims to determine the role of remote sensing in mapping and analyzing the relationship between the parameters of sea surface temperature and chlorophyll-a concentrations on the coast. The correlation of sea surface temperature with chlorophyll-a concentrations is modeled via linear regression. An analysis of variance test is performed to establish the suitability of the temperature data for the …

Electrostatic Design And Characterization Of A 200 Kev Photogun And Wien Spin Rotator, Gabriel G. Palacios Serrano

Electrical & Computer Engineering Theses & Dissertations

High-energy nuclear physics experiments at the Jefferson Lab Continuous Electron Beam Accelerator Facility (CEBAF) require high spin-polarization electron beams produced from strained super-lattice GaAs photocathodes activated to negative electron affinity in a high voltage photogun operating at 130 kV dc. A pair of Wien filter spin rotators in the injector provides precise control of the electron beam polarization at the end station target. An upgrade of the CEBAF injector to better support the upcoming Moller experiment requires increasing the electron beam energy to 200 keV, resulting in better transmission through injector apertures and improved photocathode lifetime. In addition, the energy …

The Design Of Dynamic Probabilistic Caching With Time-Varying Content Popularity, Jie Gao, Shan Zhang, Lian Zhao, Xuemin Shen

Electrical and Computer Engineering Faculty Research and Publications

In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying …

Measurements And Analysis Of Propagation Channels In Vehicle-To-Infrastructure Scenarios, Wei Li, Xiaoya Hu, Jie Gao, Lian Zhao, Xuemin Shen

Electrical and Computer Engineering Faculty Research and Publications

In this paper, we present measurements and analysis of propagation channels in vehicle-to-infrastructure (V2I) scenarios, which are the basis of designing vehicular communication systems. Firstly, we propose a deterministic geometry-based method to classify V2I links into three types, i.e., line-of-sight beneath (LOS-B), non-LOS (NLOS), and line-of-sight above (LOS-A), based on the environmental features, where roadside row of trees constitute the main obstacles. Secondly, for each link, we investigate the large-scale fading effect on V2I channels, including the path loss exponent and shadowing components. Subsequently, we validate the empirical path loss model using extensive measurements and two classical channel models. The …

Distance-Based Formation Control Using Decentralized Sensing With Infrared Photodiodes, Steven Williams

LSU Master's Theses

This study presents an onboard sensor system for determining the relative positions of mobile robots, which is used in decentralized distance-based formation controllers for multi-agent systems. This sensor system uses infrared photodiodes and LEDs; its effective use requires coordination between the emitting and detecting robots. A technique is introduced for calculating the relative positions based on photodiode readings, and an automated calibration system is designed for future maintenance. By measuring the relative positions of their neighbors, each robot is capable of running an onboard formation controller, which is independent of both a centralized controller and a global positioning-like system (e.g., …

"When They Say Weed Causes Depression, But It's Your Fav Antidepressant": Knowledge-Aware Attention Framework For Relationship Extraction, Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth

Publications

With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provide deeper insights about these associations to public health analysts. In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression. We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that leverages the pre-trained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology (DAO)) to jointly extract …

Strategies In Botnet Detection And Privacy Preserving Machine Learning, Di Zhuang

USF Tampa Graduate Theses and Dissertations

Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few existing work claimed to detect traditional botnets effectively, the problem of detecting P2P botnets involves more challenges. In this dissertation, we present two P2P botnet detection systems, PeerHunter and Enhanced PeerHunter. PeerHunter starts from a P2P hosts detection component. Then, it uses mutual contacts as the main feature to cluster bots into communities. Finally, it uses community behavior analysis to detect potential botnet communities and further identify bot candidates. Enhanced PeerHunter is an …

Recent Advances In Terahertz Photonic Technologies Based On Graphene And Their Applications, Tianjing Guo, Christos Argyropoulos

Department of Electrical and Computer Engineering: Faculty Publications

Graphene is a unique 2D material that has been extensively investigated due to its extraordinary photonic, electronic, thermal, and mechanical properties. Excited plasmons along its surface and other unique features are expected to play an important role in many emerging photonic technologies with drastically improved and tunable functionalities. This review is focused on presenting several recently introduced photonic phenomena based on graphene, beyond its usual linear response, such as nonlinear, active, topological, and nonreciprocal effects. The physical mechanisms and various envisioned photonic applications corresponding to these novel intriguing functionalities are also reported. The presented graphene-based technologies promise to revolutionize the …

On The Impact Of Gravity Compensation On Reinforcement Learning In Goal-Reaching Tasks For Robotic Manipulators, Jonathan Fugal, Hasan A. Poonawala, Jihye Bae

Electrical and Computer Engineering Faculty Publications

Advances in machine learning technologies in recent years have facilitated developments in autonomous robotic systems. Designing these autonomous systems typically requires manually specified models of the robotic system and world when using classical control-based strategies, or time consuming and computationally expensive data-driven training when using learning-based strategies. Combination of classical control and learning-based strategies may mitigate both requirements. However, the performance of the combined control system is not obvious given that there are two separate controllers. This paper focuses on one such combination, which uses gravity-compensation together with reinforcement learning (RL). We present a study of the effects of gravity …

Kegg2net: Deducing Gene Interaction Networks And Acyclic Graphs From Kegg Pathways, Sree K. Chanumolu, Mustafa Albahrani, Handan Can, Hasan H. Otu

Department of Electrical and Computer Engineering: Faculty Publications

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database provides a manual curation of biological pathways that involve genes (or gene products), metabolites, chemical compounds, maps, and other entries. However, most applications and datasets involved in omics are gene or protein-centric requiring pathway representations that include direct and indirect interactions only between genes. Furthermore, special methodologies, such as Bayesian networks, require acyclic representations of graphs. We developed KEGG2Net, a web resource that generates a network involving only the genes represented on a KEGG pathway with all of the direct and indirect gene-gene interactions deduced from the pathway. KEGG2Net offers …

Color-Compressive Bilateral Filter And Nonlocal Means For High-Dimensional Images, Christina Karam, Kenjiro Sugimoto, Keigo Hirakawa

Electrical and Computer Engineering Faculty Publications

We propose accelerated implementations of bilateral filter (BF) and nonlocal means (NLM) called color-compressive bilateral filter (CCBF) and color-compressive nonlocal means (CCNLM). CCBF and CCNLM are random filters, whose Monte-Carlo averaged output images are identical to the output images of conventional BF and NLM, respectively. However, CCBF and CCNLM are considerably faster because the spatial processing of multiple color channels are combined into a single random filtering process. This implies that the complexity of CCBF and CCNLM is less sensitive to color dimension (e.g., hyperspectral images) relatively to other BF and NLM methods. We experimentally verified that the execution time …

Deep Learning For Anisoplanatic Optical Turbulence Mitigation In Long-Range Imaging, Matthew A. Hoffmire, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Barry K. Karch

Electrical and Computer Engineering Faculty Publications

We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as …

Simulating A Mobile Wireless Sensor Network Monitoring The Air Force Marathon, Matthew D. Eilertson

Theses and Dissertations

This thesis explores the feasibility of deploying a mobile Wireless Sensor Networks (WSN) to the Air Force (AF) Marathon in support of Air Force Research Laboratory (AFRL) research of sensor and networking infrastructure in denied or degraded environments. A simulation called MarathonSim is developed in the Objective Modular Network Testbed in C++ (OMNeT++) Discrete Event Simulator to test the performance of a mobile WSN. A full factorial design using numbers of runners, transmission powers, and routing protocols is executed to measure Packet Delivery Ratio (PDR) to a central database, average end-to-end delay of application packets, and average power consumed per …

"Is Depression Related To Cannabis?": A Knowledge-Infused Model For Entity And Relation Extraction With Limited Supervision, Kaushik Roy, Usha Lokala, Vedant Khandelwal, Amit P. Sheth

Publications

With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators. However, preliminary scientific research does not conclusively associate cannabis with improved mental health. In this study, we explore the relationship between depression and consumption of cannabis in a targeted social media corpus involving personal use of cannabis with the intent to derive its potential mental health benefit. We use tweets that contain an association among three categories annotated by domain experts - Reason, Effect, and Addiction. The state-of-the-art Natural Langauge Processing techniques fall short in extracting these relationships between …

Commuting Compositions For Quantum Circuit Reduction, Brenna R. Cole

Theses and Dissertations

Quantum circuit simplification improves program execution on quantum hardware by reducing error from prolonged environmental interaction and noisy gate operations. One simplification technique is template matching, which repeatedly conducts local optimization by replacing small sequences of gates within a circuit by optimized versions. Underlying this method is the problem of identifying sequences matching templates. This is challenging because some, but not all, gates can commute within a circuit. This means there may not be a subcircuit that matches a template in the original circuit specification, but a match may exist in an equivalent rearrangement of gates. In such cases, certain …

Knowledge Infused Policy Gradients For Adaptive Pandemic Control, Kaushik Roy, Qi Zhang, Manas Gaur, Amit P. Sheth

Publications

COVID-19 has impacted nations differently based on their policy implementations. The effective policy requires taking into account public information and adaptability to new knowledge. Epidemiological models built to understand COVID-19 seldom provide the policymaker with the capability for adaptive pandemic control (APC). Among the core challenges to be overcome include (a) inability to handle a high degree of non-homogeneity in different contributing features across the pandemic timeline, (b) lack of an approach that enables adaptive incorporation of public health expert knowledge, and (c) transparent models that enable understanding of the decision-making process in suggesting policy. In this work, we take …