Defense Notices

In high dynamic-range environments, matched-filter radar performance is often sidelobe-limited with correlation error being fundamentally constrained by the TB of the collective emission. To contend with the regulatory necessity of spectral containment, the gradient-based complementary-FM framework was developed to produce complementary sidelobe cancellation (CSC) after coherently combining responses from distinct pulses from within a pulse-agile emission. In contrast to most complementary subsets, which were discovered via brute force under the notion of phase-coding, these comp-FM waveform subsets achieve CSC while preserving hardware compatibility since they are FM. Although comp-FM addressed a primary limitation of complementary signals (i.e., hardware distortion), CSC hinges on the exact reconstruction of autocorrelation terms to suppress sidelobes, from which optimality is broken for Doppler shifted signals. This work introduces a Doppler-generalized comp-FM (DG-comp-FM) framework that extends the cancellation condition to account for the anticipated unambiguous Doppler span after post-summing. While this framework is developed for use within a combine-before-Doppler processing manner, it can likewise be employed to design an entire coherent processing interval (CPI) to minimize range-sidelobe modulation (RSM) within the radar point-spread-function (PSF), thereby introducing the potential for cognitive operation if sufficient scattering knowledge is available a-priori. 

Some radar systems operate with multiple emitters, as in the case of Multiple-input-multiple-output (MIMO) radar. Whereas a single emitter must contend with the self-inflicted autocorrelation sidelobes, MIMO systems must likewise contend with the cross-correlation with coincident (in time and spectrum) emissions from other emitters. As such, the determination of "orthogonal waveforms" comprises a large portion of research within the MIMO space, with a small majority now recognizing that true orthogonality is not possible for band-limited signals (albeit, with the exclusion of TDMA). The notion of complementary-FM is proposed for exploration within a MIMO context, whereby coherently combining responses can achieve CSC as well as cross-correlation cancellation for a wide Doppler space. By effectively minimizing cross-correlation terms, this enables improved channel separation on receive as well as improved estimation capability due to reduced correlation error. Proposal items include further exploration/characterization of the space, incorporating an explicit spectral 

Recent advancements in explainable artificial intelligence (XAI) have brought significant transparency to machine learning by providing interpretable explanations alongside model predictions. However, this transparency has also introduced vulnerabilities, enhancing adversaries’ ability for the model decision processes through explanation-guided attacks. In this paper, we propose a robust, model-agnostic defense framework to mitigate these vulnerabilities by explanations while preserving the utility of XAI. Our framework employs a multinomial sampling approach that perturbs explanation values generated by techniques such as SHAP and LIME. These perturbations ensure differential privacy (DP) bounds, disrupting adversarial attempts to embed malicious triggers while maintaining explanation quality for legitimate users. To validate our defense, we introduce a threat model tailored to image classification tasks. By applying our defense framework, we train models with pixel-sampling strategies that integrate DP guarantees, enhancing robustness against backdoor poisoning attacks with XAI. Extensive experiments on widely used datasets, such as CIFAR-10, MNIST, CIFAR-100 and Imagenette, and models, including ConvMixer and ResNet-50, show that our approach effectively mitigates explanation-guided attacks without compromising the accuracy of the model. We also test our defense performance against other backdoor attacks, which shows our defense framework can detect other type backdoor triggers very well. This work highlights the potential of DP in securing XAI systems and ensures safer deployment of machine learning models in real-world applications.

Cold emailing remains an effective strategy for software service companies to improve organizational reach by acquiring clients. Generic emails often fail to get a response.
This project leverages Generative AI to automate the cold email generation. This project is built with the Llama-3.1 model and a Chroma vector database that supports the semantic search of keywords in the job description that matches the project portfolio links of software service companies. The application automatically extracts the technology related job openings for Fortune 500 companies. Users can either select from these extracted job postings or manually enter URL of a job posting, after which the system generates email and sends email upon approval. Advanced techniques like Chain-of-Thought Prompting and Few-Shot Learning were applied to improve the relevance making the email more responsive. This AI driven approach improves engagement and simplifies the business development process for software service companies.

Predictive maintenance plays a central role in reducing vehicle downtime and improving operational efficiency by using data-driven methods to classify the condition of automotive engines. Rather than relying on fixed service schedules or reacting to unexpected breakdowns, this approach leverages machine learning to distinguish between healthy and failed engines based on operational data.

In this project, engine telemetry data capturing key parameters such as engine speed, fuel pressure, and coolant temperature was used to train and evaluate several machine learning models, including logistic regression, random forest, k-nearest neighbors, and a neural network. To further enhance predictive performance, ensemble strategies such as soft voting and stacking were applied. The stacking ensemble, which combines the strengths of multiple classifiers through a meta-learning approach, demonstrated particularly effective results.

This classification-based framework demonstrates how data-driven fault detection can enhance automotive maintenance operations. By identifying engine failures more reliably, machine learning enables safer transportation, reduces maintenance costs, and enhances overall vehicle dependability. Beyond individual vehicles, such approaches have broader applications in fleet management, where proactive decision-making can improve service continuity, reduce operational risks, and increase customer satisfaction.

The Doppler tolerance of a waveform refers to its behavior when subjected to a fast-time Doppler shift imposed by scattering that involves nonnegligible radial velocity. While previous efforts have established decision-based criteria that lead to a binary judgment of Doppler tolerant or intolerant, it is also useful to establish a measure of the degree of Doppler tolerance. The purpose in doing so is to establish a consistent standard, thereby permitting assessment across different parameterizations, as well as introducing a Doppler “quasi-tolerant” trade-space that can ultimately inform automated/cognitive waveform design in increasingly complex and dynamic radio frequency (RF) environments. 

Separately, the application of slow-time coding (STC) to the Doppler-tolerant linear FM (LFM) waveform has been examined for disambiguation of multiple range ambiguities. However, using STC with non-adaptive Doppler processing often results in high Doppler “cross-ambiguity” side lobes that can hinder range disambiguation despite the degree of separability imparted by STC. To enhance this separability, a gradient-based optimization of STC sequences is developed, and a “multi-range” (MR) modification to the reiterative super-resolution (RISR) approach that accounts for the distinct range interval structures from STC is examined. The efficacy of these approaches is demonstrated using open-air measurements. 

The proposed work to appear in the final dissertation focuses on the connection between Doppler tolerance and STC. The first proposal includes the development of a gradient-based optimization procedure to generate Doppler quasi-tolerant random FM (RFM) waveforms. Other proposals consider limitations of STC, particularly when processed with MR-RISR. The final proposal introduces an “intrapulse” modification of the STC/LFM structure to achieve enhanced sup pression of range-folded scattering in certain delay/Doppler regions while retaining a degree of Doppler tolerance.

Scheduling a graph of moldable tasks, where each task can be executed by a varying number of

processors with execution time depending on the processor allocation, represents a fundamental

problem in high-performance computing (HPC). The online version of the scheduling problem

introduces an additional constraint: each task is only discovered when all its predecessors have

been completed. A key challenge for this online problem lies in making processor allocation

decisions without complete knowledge of the future tasks or dependencies. This uncertainty can

lead to inefficient resource utilization and increased overall completion time, or makespan. Recent

studies have provided theoretical analysis (i.e., derived competitive ratios) for certain processor

allocation algorithms. However, the algorithms’ practical performance remains under-explored,

and their reliance on fixed parameter settings may not consistently yield optimal performance

across varying workloads. In this thesis, we conduct a comprehensive evaluation of three processor

allocation strategies by empirically assessing their performance under widely used speedup models

and diverse graph structures. These algorithms are integrated into a List scheduling framework that

greedily schedules ready tasks based on the current processor availability. We perform systematic

tuning of the algorithms’ parameters and report the best observed makespan together with the

corresponding parameter settings. Our findings highlight the critical role of parameter tuning in

obtaining optimal makespan performance, regardless of the differences in allocation strategies.

The insights gained in this study can guide the deployment of these algorithms in practical runtime

systems.

Genetic Algorithms (GAs), a subclass of evolutionary algorithms, seek to apply the concept of natural selection to promote the optimization and furtherance of “something” designated by the user. GAs generate a population of chromosomes represented as value strings, score each chromosome with a “fitness function” on a defined set of criteria, and mutate future generations depending on the scores ascribed to each chromosome. In this project, each chromosome is a bitstring representing one canvased color artwork. Artworks are scored with a variety of design fundamentals and user preference. The artworks are then evolved through thousands of generations and the final piece is computationally drawn for analysis. While the rise of gradient-based optimization has resulted in more limited use-cases of GAs, genetic algorithms still have applications in various settings such as hyperparameter tuning, mathematical optimization, reinforcement learning, and black box scenarios. Neural networks are favored presently in image generation due to their pattern recognition and ability to produce new content; however, in cases where a user is seeking to implement their own vision through careful algorithmic refinement, genetic algorithms still find a place in visual computing.

Wireless edge systems typically need to complete timely computation and inference tasks under strict power, bandwidth, latency, and processing constraints. As AI models and datasets grow in size and complexity, the traditional model of sending all data to a remote cloud or running full inference on edge device becomes impractical. This creates a need for communication-computation co-design to enable efficient AI task processing at the wireless edge. To address this problem, we investigate task-aware communication-computation optimization for two specific problem settings.

First, we explore semantic communication that transmits only the information essential for the receiver’s computation tasks. We propose a semantic-aware and goal-oriented communication method for object detection. Our proposed approach is built upon the auto-encoders, with the encoder and the decoder are respectively implemented at the transmitter and receiver to extract semantic information for the specific computation goal (e.g., object detection). Numerical results show that transmitting only the necessary semantic features significantly improves the overall system efficiency.

Second, we study collaborative inference in wireless edge networks, where energy-constrained devices aim to complete delay-sensitive inference tasks. The inference computation is split between the device and an edge server, thereby achieving collaborative inference. We formulate a utility maximization problem under energy and delay constraints and propose Bayes-Split-Edge, which uses Bayesian optimization to determine the optimal transmission power and neural network split point. The proposed framework introduces a hybrid acquisition function that balances inference task utility, sample efficiency, and constraint violation penalties. We evaluate our approach using the VGG19 model, the ImageNet-Mini dataset, and real-world mMobile wireless channel datasets.

Overall, this research is aimed at developing efficient edge AI systems by incorporating the underlying wireless communications limitations and challenges into AI tasks processing.

Web browsers are notorious for consuming large amounts of memory. Yet, they have become the dominant framework for writing GUIs because the web languages are ergonomic for programmers and have a cross-platform reach. These benefits are so enticing that even a large portion of mobile apps, which have to run on resource-constrained devices, are running a web browser under the hood. Therefore, it is important to keep the memory consumption of web browsers as low as practicable.

In this thesis, we investigate the memory consumption of web browsers, in particular, compared to applications written in native GUI frameworks. We introduce smaps-profiler, a tool to profile the overall memory consumption of Linux applications that can report memory usage other profilers simply do not measure. Using this tool, we conduct experiments which suggest that most of the extra memory usage compared to native applications could be due the size of the web browser program itself. We discuss our experiments and findings, and conclude that even more rigorous studies are needed to profile GUI applications.

Open-source package managers (e.g., npm for Node.js) have become an established component of modern software development. Rather than creating applications from scratch, developers may employ modular software dependencies and frameworks--called packages--to serve as building blocks for writing larger applications. Package managers make this process easy. With a simple command line directive, developers are able to quickly fetch and install packages across vast open-source repositories. npm--the largest of such repositories--alone hosts millions of unique packages and serves billions of package downloads each week. 

However, the widespread code sharing resulting from open-source package managers also presents novel security implications. Vulnerable or malicious code hiding deep within package dependency trees can be leveraged downstream to attack both software developers and the end-users of their applications. This downstream flow of software dependencies--dubbed the software supply chain--is critical to secure.

This research provides a deep dive into the npm-centric software supply chain, exploring distinctive phenomena that impact its overall security and usability. Such factors include (i) hidden code clones--which may stealthily propagate known vulnerabilities, (ii) install-time attacks enabled by unmediated installation scripts, (iii) hard-coded URLs residing in package code, (iv) the impacts of open-source development practices, (v) package compromise via malicious updates, (vi) spammers disseminating phishing links within package metadata, and (vii) abuse of cryptocurrency protocols designed to reward the creators of high-impact packages. For each facet, tooling is presented to identify and/or mitigate potential security impacts. Ultimately, it is our hope that this research fosters greater awareness, deeper understanding, and further efforts to forge a new frontier for the security of modern software supply chains.