Defense Notices

Emerging applications in next-generation wireless networks are driving the need for innovative communication and computation systems. Notable examples include augmented and virtual reality (AR/VR), autonomous vehicles, and mobile edge computing, all of which demand significant computational and communication resources at the network edge. These demands place a strain on edge devices, which are often resource-constrained. In order to incorporate available communication and computation resources, while enhancing user experience, this PhD research is dedicated to developing joint communication and computation solutions for next generation wireless applications that could potentially operate in high frequencies such as millimeter wave (mmWave) bands.

In the first thrust of this study, we examine the problem of energy-constrained computation offloading to edge servers in a multi-user multi-channel wireless network. To develop a decentralized offloading policy for each user, we model the problem as a partially observable Markov decision problem (POMDP). Leveraging bandit learning methods, we introduce a decentralized task offloading solution, where edge users offload their computation tasks to a nearby edge server using a selected communication channel. The proposed framework aims to meet user's requirements, such as task completion deadline and computation throughput (i.e., the rate at which computational results are produced).

The second thrust of the study emphasizes user-driven requirements for these resource-intensive applications, specifically the Quality of Experience (QoE) in 2D and 3D video streaming. Given the unique characteristics of mmWave networks, we develop a beam alignment and buffer predictive multi-user scheduling algorithm for 2D video streaming applications. This scheduling algorithm balances the trade-off between beam alignment overhead and playback buffer levels for optimal resource allocation across users. Next, we extend our investigation and develop a joint rate adaptation and computation distribution algorithm for 3D video streaming in mmWave-based VR systems. Our proposed framework balances the trade-off between communication and computation resource allocation to enhance the users’ QoE. Our numerical results using real-world mmWave traces and 3D video dataset, show promising improvements in terms of video quality, rebuffering time, and quality variation perceived by users. 

Smart grids face challenges in maintaining the balance between generation and consumption at the residential and grid scales with the integration of renewable energy resources. Decentralized, dynamic, and distributed control algorithms are necessary for smart grids to function effectively. The inherent variability and uncertainty of renewables, especially wind and solar energy, complicate the deployment of distributed control algorithms in smart grids. In addition, smart grid systems must handle real-time data collected from interconnected devices and sensors while maintaining reliable and secure communication regardless of network failures. To address these challenges, our research models the integration of renewable energy resources into the smart grid and evaluates how predictive analytics can improve distributed control and energy management, while recognizing the limitations of communication channels and networks.

In the first thrust of this research, we develop a model of a smart grid with renewable energy integration and evaluate how forecasting affects distributed control and energy management. In particular, we investigate how contextual weather information and renewable energy time-series forecasting affect smart grid energy management. In addition to modeling the smart grid system and integrating renewable energy resources, we further explore the use of deep learning methods, such as the Long Short-Term Memory (LSTM) and Transformer models, for time-series forecasting. Time-series forecasting techniques are applied within Reinforcement Learning (RL) frameworks to enhance decision-making processes.

In the second thrust, we note that data collection and sharing across the smart grids require considering the impact of network and communication channel limitations in our forecasting models. As renewable energy sources and advanced sensors are integrated into smart grids, communication channels on wireless networks are overflowed with data, requiring a shift from transmitting raw data to processing only useful information to maximize efficiency and reliability. To this end, we develop a task-oriented communication model that integrates data compression and the effects of data packet queuing with considering limitation of communication channels, within a remote time-series forecasting framework. Furthermore, we jointly integrate data compression technique with age of information metric to enhance both relevance and timeliness of data used in time-series forecasting.

DRAM constitutes over 50% of server cost and 75% of the embodied carbon footprint of a server. To mitigate DRAM cost, far memory architectures have emerged. They can be separated into two broad categories: software-defined far memory (SFM) and disaggregated far memory (DFM). In this work, we compare the cost of SFM and DFM in terms of their required capital investment, operational expense, and carbon footprint. We show that, for applications whose data sets are compressible and have predictable memory access patterns, it takes several years for a DFM to break even with an equivalent capacity SFM in terms of cost and sustainability. We then introduce XFM, a near-memory accelerated SFM architecture, which exploits the coldness of data during SFM-initiated swap ins and outs. XFM leverages refresh cycles to seamlessly switch the access control of DRAM between the CPU and near-memory accelerator. XFM parallelizes near-memory accelerator accesses with row refreshes and removes the memory interference caused by SFM swap ins and outs. We modify an open source far memory implementation to implement a full-stack, user-level XFM. Our experimental results use a combination of an FPGA implementation, simulation, and analytical modeling to show that XFM eliminates memory bandwidth utilization when performing compression and decompression operations with SFMs of capacities up to 1TB. The memory and cache utilization reductions translate to 5∼27% improvement in the combined performance of co-running applications.

This PhD research addresses two challenges in future wireless systems: hybrid precoder design for sub-Terahertz (sub-THz) massive multiple-input multiple-output (MIMO) communications and private federated learning (FL) over wireless channels. The first part of the research introduces a novel hybrid precoding framework that combines true-time delay (TTD) and phase shifters (PS) precoders to counteract the beam squint effect - a significant challenge in sub-THz massive MIMO systems that leads to considerable loss in array gain. Our research presents a novel joint optimization framework for the TTD and PS precoder design, incorporating realistic time delay constraints for each TTD device. We first derive a lower bound on the achievable rate of the system and show that, in the asymptotic regime, the optimal analog precoder that fully compensates for the beam squint is equivalent to the one that maximizes this lower bound. Unlike previous methods, our framework does not rely on the unbounded time delay assumption and optimizes the TTD and PS values jointly to cope with the practical limitations. Furthermore, we determine the minimum number of TTD devices needed to reach a target array gain using our proposed approach. Simulations validate that the proposed approach demonstrates performance enhancement, ensures array gain, and achieves computational efficiency. In the second part, the research devises a differentially private FL algorithm that employs time-varying noise perturbation and optimizes transmit power to counteract privacy risks, particularly those stemming from engineering-inversion attacks. This method harnesses inherent wireless channel noise to strike a balance between privacy protection and learning utility. By strategically designing noise perturbation and power control, our approach not only safeguards user privacy but also upholds the quality of the learned FL model. Additionally, the number of FL iterations is optimized by minimizing the upper bound on the learning error. We conduct simulations to showcase the effectiveness of our approach in terms of DP guarantee and learning utility.

A coalition of companies collaborates collectively and shares information to improve their operations together. Distributed trust and transparency cannot be obtained with centralized file-sharing platforms. File sharing may be done transparently and securely with blockchain technology. This project suggests an inter-organizational secure file-sharing system based on blockchain technology. The group can use it to securely share files in a distributed manner. The creation of smart contracts and the configuration of blockchain networks are carried out by Hyperledger Fabric, an enterprise blockchain platform. Distributed file storage is accomplished through the usage of the Inter Planetary File System (IPFS). The workflow for file-sharing and identity management procedures is provided in the paper. Using blockchain technology, the recommended approach enables a group of businesses to share files with availability, integrity, and confidentiality. The suggested method uses blockchain to enable safe file exchange amongst a group of enterprises. It offers shared file availability, confidentiality, and integrity. It guarantees complete file encryption. The blockchain provides tamper-resistant storage for the shared file's content ID. On the distributed storage and blockchain ledger, respectively, the encrypted file and file metadata are stored.

This project offers a hands-on investigation of object identification utilizing the YOLO method, Python, and OpenCV. It begins by explaining the YOLO architecture, focusing on the single-stage detection process for bounding box prediction and class probability calculation. The setup phase includes library installation and model configuration, resulting in a smooth implementation procedure. Using OpenCV, the project includes preparatory processes required for object detection in images. The YOLO model is seamlessly integrated into the OpenCV framework, enabling object detection. Post-processing techniques, such as non-maximum suppression, are used to modify detection results and improve accuracy. Visualizations, such as bounding boxes and labels, are used to help interpret the discovered items. The project finishes by investigating potential expansions and optimizations, such as custom dataset training and deployment on edge devices, opening up new paths for further investigation and development. This project provides developers with the tools and knowledge they need to build effective object detection systems for a wide range of applications, from surveillance and security to autonomous vehicles and augmented reality.

This project explores the adaptation of the U-Net convolutional neural network, renowned for its medical image segmentation prowess, to the analysis of Printed Circuit Boards (PCBs). By utilizing the Fine-Printed Circuit Board Image Collection (FPIC) dataset, we address key challenges in PCB inspection, such as the precise segmentation of complex components, handling class imbalances, and capturing minute details. The U-Net model has been finely tuned with an encoding-decoding architecture, enhanced by convolutional layers, batch normalization, and dropout techniques to extract and reconstruct high-quality features from PCB images effectively. The Dice coefficient, used as the loss function, significantly improves boundary accuracy, and manages class diversity. Throughout extensive training and validation phases, the model has demonstrated superior performance metrics compared to traditional methods, making substantial advancements in automated PCB inspection. During the rigorous training and validation stages, the U-Net model demonstrated excellent performance metrics, eclipsing traditional inspection methods. For capacitors, the model achieved a training accuracy of 95.03% and a validation accuracy of 95.92%. For resistors, training using transfer learning techniques resulted in even more remarkable performance, with training accuracy reaching 98% and validation accuracy hitting 98.23%. These metrics highlight the model's robustness and accuracy, marking a significant advancement in automated PCB inspection and suggesting the model's potential for wider industrial applications in multiclass component segmentation within complex PCB.

In this dissertation our goal is to evaluate how the loss of information at binary-level affects the performance of existing compiler-level techniques in terms of both efficiency and effectiveness. Binary analysis is difficult, as most of semantic and syntactic information available at source-level gets lost during the compilation process. If the binary is stripped and/ or optimized, then it negatively affects the efficacy of binary analysis frameworks. Moreover, handwritten assembly, obfuscation, excessive indirect calls or jumps, etc. further degrade the accuracy of binary analysis. Challenges to precise binary analysis have implications on the effectiveness, accuracy, and performance, of security and program hardening techniques implemented at the binary level. While these challenges are well-known, their respective impacts on the effectiveness and performance of program hardening techniques are less well-studied.

In this dissertation, we employ classes of defense mechanisms to protect software from the most common software attacks, like buffer overflows and control flow attacks, to determine how this loss of program information at the binary-level affects the effectiveness and performance of defense mechanisms. Additionally, we aim to tackle an important problem of type recovery from binary executables that in turn help bolster the software protection mechanisms.

Advancements in web technologies have significantly expanded access to diverse cultural narratives, yet black literature remains underrepresented in digital domains. The BlackLitNetwork addresses this oversight by harnessing Elasticsearch, MongoDB, React, Python, CSS, HTML, and Node.js, to enhance the discoverability and engagement with black novels. A major component of the platform is a novel generator built with Elasticsearch, which employs powerful full-text search capabilities, essential for users to navigate an extensive literary database effectively.

MongoDB supports the archives platform with a flexible data schema for managing varied literary content efficiently, while Python facilitates robust data cleaning and preprocessing to ensure data integrity and usability. The user interface, created using React, transforms Figma designs from our design team into a dynamic web presence, integrating HTML and CSS to ensure both aesthetic appeal and accessibility.

To further enhance security and manageability, we've implemented a Node.js backend. This layer acts as a middleware, managing and processing requests between our frontend and Elasticsearch. This not only secures our data interactions but also allows for request handling before querying Elasticsearch. This architecture ensures that BlackLitNetwork remains scalable and maintainable.

BlackLitNetwork also features specialized pages for podcasts, briefs, and interactive data visualizations, each designed to highlight historical, and contextual elements of black literature. These components aid in fostering a deeper understanding, establishing BlackLitNetwork as a tool for scholars. This project not only enriches the field of humanities but also promotes a broader understanding of the black literary heritage, making it a resource for researchers, educators, and readers keen on exploring the richness of black literature.

This project aims to enhance predictive models for forecasting healthcare resource demand, particularly focusing on hospital bed occupancy and emergency room visits while considering external factors such as disease outbreaks and weather conditions. Utilizing a range of machine learning techniques, the research seeks to improve the accuracy and reliability of these forecasts, essential for optimizing healthcare resource management. The project involves multiple phases, starting with the collection and preparation of historical data from public health databases and hospital records, enriched with external variables such as weather patterns and epidemiological data. Advanced feature engineering is key, transforming raw data into a machine learning-friendly format, including temporal and lag features to identify patterns and trends. The study explores various machine learning methods, from traditional models like ARIMA to advanced techniques such as LSTM networks and GRU models, incorporating rigorous training and validation protocols to ensure robust performance. Model effectiveness is evaluated using metrics like MAE, RMSE, and MAPE, with a strong focus on model interpretability and explainability through techniques like SHAP and LIME. The project also addresses practical implementation challenges and ethical considerations, aiming to bridge academic research with practical healthcare applications. Findings are intended for dissemination through academic papers and conferences, ensuring that the models developed meet both the ethical standards and practical needs of the healthcare industry.