Northeastern University College of Engineering

NSF Graduate Research Fellowship Program Writing Group
2023-07-11 15:00 - 16:00 (America/New_York)
The CommLab and the Khoury College Graduate Program are running a 7-week summer writing group for graduate students applying for the NSF Graduate Research Fellowship Program (GRFP). The virtual Zoom meetings begin Tuesday, July 11 at 3pm ET and conclude on Tuesday, August 22. Find out more and RSVP here: https://forms.gle/WLc4ZKBaGSEJLxah6

Gordon Institute Virtual Information Session
2023-07-12 17:30 - 18:30
Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty plus Master of Science degrees offered through Northeastern’s College of Engineering, College of Science, or Khoury College of Computer Sciences.
The National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL) for its innovative curriculum that combines technical education, leadership capabilities, and the “Challenge Project”: an opportunity for students to receive master’s level credit while working in industry.
By aligning technical proficiency with leadership capabilities, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon completing the program, more than 88% of the 2021 class reported increased leadership responsibility, while more than 50% of the 2021 class reported being promoted within one year of graduation.
Our Director of Admissions will answer your application questions for Fall 2023.
You will have the opportunity to hear from Alumni on how The Gordon Institute propelled their engineering careers. Program professors will also be present to answer curriculum questions.

Poster Design and Editing: A CommLab Workshop Series
2023-07-17 15:00 - 17:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
COE Graduate students are invited to learn techniques, develop, edit and practice delivering your poster. Join the CommLab’s weekly meetings to improve your poster design and communication skills. Drop in for some or all of the time. This is a hybrid opportunity. Join us on Zoom or in the Curry Student Center. We will be in room 435 on Monday, July 17 and in room 335 for the rest of July and August.

Small Talk for Networking: A CommLab Workshop Series
2023-07-19 13:00 - 14:00
Looking to improve your conversational skills and effortlessly connect with others? This workshop series will equip you with the essential tools, techniques, and practice to connect with others, whether you are attending networking events, social gatherings, or simply looking to strike up conversations with new acquaintances. Join us for these fun-filled sessions where we’ll explore icebreakers, conversation starters, and strategies to keep the dialogue flowing naturally. Don’t miss out on this opportunity to boost your confidence and unlock new connections. This is a virtual workshop series, please register and join us Wednesdays on Zoom from 1-2pm ET.

Qing Jin's PhD Dissertation Defense
2023-07-20 13:00 - 14:00
Title:Decoupling Efficiency-Performance Optimization for Modern Neural Networks
Date:
7/20/2023
Committee Members:
Yanzhi Wang (Advisor); Prof. David Kaeli; Prof. Sunil Mittal; Prof. Jennifer Dy
Abstract:
Deep learning has achieved remarkable success in a variety of modern applications, but this success is often accompanied by inefficiency in terms of storage and inference speed, which can hinder their practical use on resource-constrained hardware. Developing highly efficient neural networks that maintain high prediction accuracy is crucial and challenging. This dissertation explores the potential for simultaneously achieving high efficiency and high prediction accuracy in neural networks, and can be broadly divided into three sections. (1) In Section One, we explore the implementation of highly efficient generative adversarial networks (GANs) capable of generating high-quality images within a predefined computational budget. The key challenge lies in identifying the optimal architecture for the generative model while simultaneously preserving the quality of the generated images from the compressed model, despite its reduced computational cost. To achieve this, we propose a novel neural architecture search (NAS) algorithm and a new knowledge distillation technique. (2) In Section Two, we explore the challenge of quantizing discriminative models without relying on high-precision multiplications. To address this issue, we present an innovative approach to determine the optimal fixed-point formats for both weights and activations based on their statistical properties. Our results demonstrate that high accuracy in quantized neural networks can be achieved without the need for high-precision multiplications. (3) In Section Three, we delve into the challenge of training neural networks for innovative computing platforms, specifically processing-in-memory (PIM) systems. Through a detailed mathematical derivation of the backward propagation algorithm, we facilitate the training of quantized models on these platforms. Additionally, through a thorough theoretical analysis of training dynamics, we ensure convergence and propose a systematic solution for quantizing neural networks on PIM systems.

Daniel Uvaydov's PhD Dissertation Defense
2023-07-21 14:00 - 15:30 | 432 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
Title: Real-Time Spectrum Sensing for Inference and Control
Committee Members:
Prof. Tommaso Melodia (Advisor)
Prof. Kaushik Choudhury
Prof. Francesco Restuccia
Abstract:
Through growing cellular innovations, the usage and congestion of the wireless spectrum is increasing at incredible speeds. High demand and limited supply pose a resource issue known as the “spectrum crunch”. With the high diversity of users sharing a large portion of the spectrum to request and receive diverse services, spectrum coordination becomes very difficult. Large scale device synchronization for spectrum coordination requires high overhead and more wireless transmissions further reducing spectrum resources. However, by monitoring the spectrum, otherwise known as spectrum sensing, we can develop mechanisms where users can opportunistically take action based on the current state of the spectrum, without need for direct coordination between devices. Spectrum sensing can enable the next generation of wireless applications ranging from opportunistic spectrum access to cognitive radio networks. The key unaddressed challenges of spectrum sensing are that (i) it requires very extensive and diverse datasets; (ii) it has to be performed with extremely low latency over varying bandwidths and must guarantee strict real-time processing constraints; (iii) its underlying algorithms need to be extremely accurate, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. This dissertation focuses on addressing these challenges in multiple wireless applications by utilizing Deep Learning (DL) techniques as the main vehicle of spectrum sensing for both inference and control. Algorithmic spectrum sensing has generally been model-based which limits its performance in diverse settings and environments, for this reason we explore data-driven spectrum sensing algorithms. Mainly, this work takes a holistic approach to address spectrum sensing problems from multiple directions with the overarching goal of developing the core building blocks for the next generation of intelligent, AI-driven, efficient spectrum sharing systems. By leveraging mechanisms such as data augmentation, channel attention, voting, and segmentation we are able to push beyond the capabilities of existing DL techniques and create generalizable spectrum sensing algorithms. Furthermore we deploy different spectrum sensing solutions in real testbeds for over the air evaluations and applicable proof-of-concepts. The contributions of this work includes (i) multiple datasets and implementations for DL enabled spectrum sensing with applications in radio frequency and underwater; (ii) a method for tackling the core issue of dataset generation in supervised learning algorithms for spectrum sensing via a novel data augmentation technique; (iii) a study into one of the first ever semi-unsupervised approaches for wideband multi-class spectrum sensing.

Batool Salehihikouei Phd Proposal Review
2023-07-25 13:00 - 14:00 | 532 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
Title:
Leveraging Deep Learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins
Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Hanumant Singh
Prof. Josep Jornet
Dr. Mark Eisen
Abstract:
With the widespread Internet of Things (IoT) devices, a wide variety of sensors are now present in different environments. For example, self-driving vehicles and automated warehouses depend on sensor information for navigation and management of the robots, respectively. In this dissertation, we present a paradigm, where these sensors are re-purposed to assist network management in wireless communication, especially when classic approaches fall short to provide the required quality of service (QoS). This thesis presents data-driven and AI-based methods, where the multimodal sensor information is used for beamforming at the mmWave band, and envisions a systematic framework for joint optimization of the navigation and network management in factory floor environments. In particular, the contributions in this dissertation are as follows. First, we present deep learning fusion algorithms, where the inputs from a multitude of sensor modalities such as GPS (Global Positioning System), camera, and LiDAR (Light Detection and Ranging) are combined towards predicting the optimum beam at the mmWave band. We prove that fusing the multimodal sensor data improves the prediction accuracy compared to using single modalities. Second, we study the trade-off between the accuracy and cost of different learning strategies for multimodal beamforming. In this regard, we make a case for using federated learning for beamforming at the mmWave band and demonstrate that it is the most successful learning strategy, with respect to the communication overhead. Finally, we take measures to further optimize the computation and communication overhead, by incorporating a pruning strategy tailored to the disturbed nature of the federated learning systems. In the proposed research work, we suggest using digital twins to overcome the challenges of scarcity of data and close-world assumption in deep learning algorithms. A digital twin is a replica of a real world entity, which is typically used for studying the impact of any configuration settings in a safe, digital environment. In this dissertation, we propose using digital twins for generating training data for multimodal beamforming, in unseen scenarios. Moreover, we study a robotic industrial setting, where the path planning policy is continuously updated by monitoring the dynamics of the real world, constructing the digital twin, and updating the policy.

LinkedIn, CV, Resume: A CommLab Workshop Series
2023-07-26 17:00 - 18:00 | 206 Egan, 360 Huntington Ave, Boston, MA, 02115, United States
Join our empowering LinkedIn, CV, Resume Workshop any Wednesday from 5 pm to 6 pm ET, starting the 26th July. This collaborative space offers valuable tips and peer feedback to enhance your online profile and professional presence. Whether you’re a student or seasoned professional, our community will help you optimize your LinkedIn profile, CV, and resume to stand out in today’s job market. Don’t miss this chance to learn, grow, and build a strong network together! This is a hybrid workshop, join virtually on Zoom or in person in room 206 Egan.

Huan Wang's PhD Proposal Review
2023-08-01 10:00 - 11:30
Title:
Towards Efficient Deep Learning in Computer Vision via Sparsity and Distillation
Committee Members:
Prof. Yun Fu (Advisor)
Prof. Octavia Camps
Prof. Zhiqiang Tao
Abstract:
AI, empowered by deep learning, has been profoundly transforming the world. However, the excessive size of these models remains a central obstacle that limits their broader utility. Modern neural networks commonly consist of millions of parameters, with foundation models extending to billions. The rapid expansion in model size introduces many challenges including training cost, sluggish inference speed, excessive energy consumption, and negative environmental implications such as increased CO2 emissions.
Addressing these challenges necessitates the adoption of efficient deep learning. This thesis focuses on two overarching approaches, network sparsity and knowledge distillation, to enhance the efficiency of deep learning models in the context of computer vision. Network sparsity focuses on eliminating redundant parameters in a model while preserving the performance. Knowledge distillation aims to enhance the performance of the target model, referred to as the “student,” by leveraging guidance from a stronger model, known as the “teacher”. This approach leads to performance improvements in the target model without reducing its size. In the proposal, I will start with the background and major challenges of leveraging these techniques towards efficient deep learning. Then, I shall present the potential solutions in various tasks (e.g., image classification, image super-resolution, neural rendering, and text-to-image generation), with preliminary results to justify the efficacy of the proposed approaches. Finally, a comprehensive outlook of the future work will conclude this proposal.

Gordon Institute Virtual Information Session
2023-08-02 17:30 - 18:30
Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty-three Master of Science degrees offered through Northeastern’s College of Engineering, College of Science, or Khoury College of Computer Sciences.
The National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL) for its innovative curriculum that combines technical education, leadership capabilities, and the “Challenge Project”: an opportunity for students to receive master’s level credit while working in industry.
By aligning technical proficiency with leadership capabilities, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon completing the program, more than 88% of the 2021 class reported increased leadership responsibility, while more than 50% of the 2021 class reported being promoted within one year of graduation.
Our Director of Admissions will answer your application questions for Fall 2023.
You will have the opportunity to hear from Alumni on how The Gordon Institute propelled their engineering careers. Program professors will also be present to answer curriculum questions.

Yu Yin's PhD Dissertation Defense
2023-08-03 10:00 - 11:00
“Synthetic Data Generator: Understanding Human Face & Body via Image Synthesis”
Committee Members:
Prof. Yun Fu (Advisor)
Prof. Sarah Ostadabbas
Prof. Ming Shao
Abstract:
The community has long enjoyed the benefits of synthesizing data, as it provides a reliable and controllable source for training machine learning models while reducing the need for data collection from the real world. Human face and body synthesis are especially appealing to research communities, where model fairness and ethical deployment are critical concerns. However, generating digit humans that are convincing, realistic-looking, identity-preserving, and high-quality are still challenging in 2D and 3D image synthesis.
This dissertation investigates the potential for understanding human behavior by recreating it, and can be broadly divided into three sections. (1) In Section one, we explore the 2D image generation models and their interaction with face applications (i.e., landmark localization and face recognition tasks). Specifically, super-resolution (SR) and landmark localization of tiny faces are highly correlated tasks. To this end, we propose joint frameworks that enable face alignment and SR to benefit from one another, hence enhancing the performance of both tasks. Moreover, we demonstrate that face frontalization provides an effective and efficient way for face data augmentation and further improves face recognition performance in extreme pose scenarios. (2) In Section two, we explore the 3D parametric generation models and how they support human body pose and shape estimation. Advancing technology to monitor our bodies and behavior while sleeping and resting is essential for healthcare. However, keen challenges arise from our tendency to rest under blankets. To mitigate the negative effects of blanket occlusion, we use an attention-based restoration module to explicitly reduce the uncertainty of occluded parts by generating uncovered modalities, which further update the current estimation via a cyclic fashion. (3) In Section three, we explore the 3D Nerf-based Generative models in generating high-quality images with consistent 3D geometry. We propose a universal method to surgically fine-tune these NeRF-GAN models in order to achieve high-fidelity animation of real subjects only by a single image.

Yukui Luo's PhD Dissertation Defense
2023-08-08 10:00 - 11:00
Title:
Securing FPGA as a Shared Cloud-Computing Resource: Threats and Mitigations
Committee Members:
Prof. Xiaolin Xu (Advisor)
Prof. Yunsi Fei
Prof. Xue Lin
Abstract:
With the widespread adoption of cloud computing, the demand for programmable hardware acceleration devices, such as field-programmable gate arrays (FPGA), has increased. These devices benefit the growth of efficient hardware accelerators, making cloud computing possible for a wide range of research and commercial projects, including genetic engineering, intensive online secure trading, the Artificial Intelligence (AI) interface, etc. To further improve the performance of FPGA-enabled cloud computing, one promising technology is to virtualize the hardware resources of an FPGA device, which allows multiple users to share the same FPGA. This solution can provide on-demand FPGA instances, significantly improving the hardware utilization and energy efficiency of the cloud FPGA. However, due to the hardware reconfigurability of FPGA, current virtualization technologies used for multi-tenant CPU and GPU instances are incompatible with multi-tenant FPGA.
We aim to enhance the security of multi-tenant FPGA by defining the threat model and evaluating security concerns from the perspectives of confidentiality, data integrity, and availability. As part of this goal, we constructed multi-tenant FPGA prototypes and demonstrated potential attacks. These attacks serve as preliminary steps toward developing a secure multi-tenant FPGA virtualization system. This system involves hardware and software co-design, which extends the multi-tenant isolation from software to hardware, ultimately resulting in a secure FPGA shared cloud computing service.

Dinesh Murugan MS Thesis Defense
2023-08-08 10:30 - 11:30
Title: Advances in Modelling, Control, and Perception for Soft Robotics and Autonomous Vehicle Systems
Location on Campus: Snell Room
Committee Members:
Advisor: Prof. Milad Siami
Prof. Bahram Shafai
Prof. Rozhin Hajian – University of Massachusetts, Lowell
Abstract:
In this research project, we investigate the distributed consensus and vehicle platoons control problem. We first investigate the performance deterioration of commensurate fractional-order consensus networks under exogenous stochastic disturbances. We formulate fractional-order differential equations for the network dynamics using Caputo derivatives and the Laplace transform, and employ the H_2 norm of the dynamical system as a performance measure. By developing a graph-theoretic methodology, we relate the structural specifications of the underlying graphs to the performance measure and explicitly quantify fundamental limits on the best achievable levels of performance in fractional-order consensus networks. We also establish new connections between the sparsity of the network and the performance measure, characterizing fundamental tradeoffs that reveal the interplay between the two. Finally, we provide numerical illustrations to verify our theoretical results, which could help in the design of robust fractional-order control systems in the presence of disturbances.
Additionally, the study examines the real-time application of the theoretical advancements on Quanser’s Qcars, a scaled model vehicle used for academic purposes. The findings are highly relevant to the design and implementation of large-scale consensus networks and autonomous vehicle platoons, as they emphasize the importance of balancing network density and update cycle speed for optimal performance.
To extend the research’s findings to viscoelastic based networks, the interaction between agents is modeled as a fractional-order system.

Nasim Soltani PhD Proposal
2023-08-09 11:00 - 12:00 | 532 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
Title: Deep Learning for the Physical Layer: From Signal Classification to Decoding
Location: ISEC 532
Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Stratis Ioannidis
Prof. Robert Nowak
Abstract:
The growth in wireless spectrum usage has created new physical layer applications and intensified the importance of the existing ones. Physical layer applications ranging from device authentication to signal decoding and interpretation are traditionally handled by deterministic signal processing algorithms. Such algorithms, while effective, often require long sequences of data for decision making, or need approximations of the environmental conditions, such as noise models, which may not be always correct in practical conditions. For these reasons, traditional algorithms are not suitable for making quick decisions on the high rate wireless data with higher noise and interference that is a result of crowded spectrum. To this end, deep learning-based methods have been explored extensively by the researchers to substitute for the traditional signal processing algorithms for the physical layer. This thesis explores novel methods in this area in the following parts:
Part I – Signal classification: In this part, we look at two distinct problems of waveform classification and Radio Frequency (RF) fingerprinting. In the first problem, we study two use cases of modulation classification on edge devices, followed by waveform classification and spectrum localization in the Citizen Broadband Radio Service (CBRS) band. In the second problem, we look at RF fingerprinting that is classifying received signals in terms of subtle impairments that each transmitter leaves in its emitted waveform, due to its hardware manufacturing imperfections. We propose methods to overcome the wireless channel effect for RF fingerprinting in both stationary transmitters on a large scale dataset (i.e., 5k WiFi devices), and identical hovering Unmanned Aerial Vehicles (UAVs) that transmit proprietary signals.
Part II – Signal decoding: In this part, we introduce our design of a modular machine learning (ML)-aided Orthogonal Frequency Division Multiplexing (OFDM) receiver that improves the bit error rate (BER) of the traditional receiver. We show how a neural network-based demapper block can be used for secure data transmission. Furthermore, we show how an ML-aided receiver can provide the possibility of reducing communication overhead by obviating the need for the first field of preamble in WiFi signals. We show that reducing the preamble length contributes to higher throughput in WiFi networks, without BER degradation.
Part III – As the proposed work, we will explore the use of active learning for smart sampling of training sets in wireless communications tasks. Active learning reduces the labeling overhead that is often performed using the compute-intensive traditional signal processing algorithms, by intelligently selecting the most informative training samples to be labeled instead of labeling the whole set. We will also design an ML-life cycle control scheme to monitor and update the performance of an ML-aided 5G receiver, when deployed in the field with varying environmental conditions.

Yuanyuan Li PhD Dissertation Defense
2023-08-09 15:00 - 16:30
Title: Sub-modularity in Cache Networks
Committee Members:
Prof. Stratis Ioannidis
Prof. Lili Su
Prof. Edmund Yeh
Abstract:
As information-based demand surges, distributed network services, e.g., cache networks, play an important role to mitigate network traffic. Cache networks are a natural abstraction for many applications, including information-centric networks, content delivery networks, cloud computing, and edge/wireless IoT. How to allocate resources (routing, placing items in caches, flow control, etc.) in cache networks is a crucial problem, as resources (storage space, and bandwidths) are usually limited. Resource allocation in networks has been traditionally approached through classic convex optimization. However, simple problems becomes combinotorial in cache networks, which leads to NP-hardness. Enlightened by several works studying cache networks, we identify a useful property, submodularity, which is the key to approximation algorithms solving those NP hard resource allocation problem in cache networks.
Leveraging submodularity, we study a cache network, in which intermediate nodes equipped with caches can serve content requests, from different angles.
First, we model this network as a universally stable queuing system, in which packets carrying identical responses are consolidated before being forwarded downstream. We refer to resulting queues as $info$ or counting queues, as consolidated packets carry a counter indicating the packet’s multiplicity. Cache networks comprising such queues are hard to analyze; we propose two approximations: one via $mminf$ queues, and one based on $info$ queues under the assumption of Poisson arrivals. We show that, in both cases, the problem of jointly determining (a) content placements and (b) service rates admits a poly-time, $1-1/e$ approximation algorithm. We also show that our analysis, with respect to both algorithms and associated guarantees, extends to (a) counting queues over items, rather than responses, as well as to (b) queuing at nodes and edges, as opposed to just edges.
Second, we refer to the cost reduction enabled by caching as the caching gain, and the product of the caching gain of a content request and its request rate as emph{caching gain rate}. We aim to study emph{fair} content allocation strategies through a utility-driven framework, where each request achieves a utility of its caching gain rate, and consider a family of $alpha$-fair utility functions to capture different degrees of fairness. The resulting problem is an NP-hard problem with a non-decreasing submodular objective function. Submodularity allows us to devise a deterministic allocation strategy with an optimality guarantee factor arbitrarily close to $1-1/e$. When $0 < alpha leq 1$, we further propose a randomized strategy that attains an improved optimality guarantee, $(1-1/e)^{1-alpha}$, in expectation.
Third, we study a cache network, and model the problem of jointly optimizing caching and routing decisions with link capacity constraints over an arbitrary network topology. This problem can be formulated as a continuous diminishing-returns(DR) submodular maximization problem under multiple continuous DR-supermodular constraints, and is NP-hard. We propose a poly-time alternating primal-dual heuristic algorithm, in which primal steps produce solutions within $1-frac{1}{e}$ approximation factor from the optimal. Through extensive experiments, we demonstrate that our proposed algorithm significantly outperforms competitors.
Forth, we study a cache network under arbitrary adversarial request arrivals. We propose a distributed online policy based on the online tabular greedy algorithm. Our distributed policy achieves sublinear $(1-frac{1}{e})$-regret, also in the case when update costs cannot be neglected.
Finally, we propose an {em experimental design network} paradigm, wherein learner nodes train possibly different Bayesian linear regression models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners, and the decision variables are the data transmission strategies subject to network constraints. We first show that, assuming Poisson data streams, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a $1-1/e$ factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling.

Jinkun Zhang PhD Proposal Review
2023-08-11 14:00 - 15:00
Location: ISEC305
Title: Low-latency forwarding, caching and computation placement in data-centric networks
Committee Members:
Prof. Edmund Yeh (Advisor)
Prof. Stratis Ioannidis
Prof. Kaushik Chowdhury
Abstract:
With the exponential growth of data- and computation-intensive network applications, such as real-time augmented reality/virtual reality rendering and large-scale language model training, traditional cloud computing frameworks exhibit inherent limitations. These limitations include significant round-trip delays caused by backhaul network capacity bottlenecks and exorbitant costs associated with centralized computing power, e.g., training GPT-4 requires over 16,000 A100 GPUs.
To address these challenges, dispersed computing has emerged as a promising next-generation networking paradigm. By enabling geographically distributed nodes with heterogeneous computation capabilities to collaborate, dispersed computing overcomes the bottlenecks of traditional cloud computing and facilitates in-network computation tasks, including the training of large models.
Furthermore, in data-centric networking, communication and computation are resolved around data names instead of host addresses.
The deployment of network caches, by enabling data reuse, offers substantial benefits for data-centric networks.
For instance, consider a scenario where multiple machine learning applications seek to train different models simultaneously. These application could (partially) share data samples and intermediate results, and carefully designed data-reusing mechanisms become necessary. Optimal caching of data or intermediate results can significantly reduce the overall training cost, compared to each application independently gathering and transmitting data.
To efficiently manage computation and storage resources in heterogeneous data-centric networks, several frameworks have been proposed with different design objectives, such as optimizing throughput or incorporating multicast flows. However, previous approaches have failed to minimize average user delay despite the latency sensitivity of numerous real-world applications.
This proposal aims to address this gap by introducing a low-latency framework that jointly optimizes packet forwarding, storage deployment, and computation placement. The proposed framework effectively supports data-intensive and latency-sensitive computation applications in data-centric networks with heterogeneous communication, storage, and computation capabilities.
Specifically, to minimize user latency in congestible networks, we model delays caused by link transmissions and CPU computations using
traffic-dependent nonlinear functions. We formulate the joint forwarding, caching, and computation problem as an NP-hard mixed-integer non-submodular optimization, for which no constant-factor approximation algorithms are currently known. To make progress, we approach the joint problem by dividing it into two subproblems: the joint forwarding/computation problem and the joint forwarding/caching problem. Despite the non-convexity of the former subproblem, we provide a set of sufficient optimality conditions that lead to a distributed algorithm with polynomial-time convergence to the global optimum. For the latter subproblem, we demonstrate its NP-hardness and non-submodularity, even after continuous relaxation. We show that the objective function is a sum of a convex function and a geodesic convex function, and propose a set of conditions that provide a finite bound from the optimum. To the best of our knowledge, our method represents the first analytical progress in addressing the joint caching and forwarding problem with arbitrary topology and non-linear costs. Furthermore, our theoretical bound leads to a constant-factor approximation under additional assumptions.
As future work, we propose to develop a novel in-network large model training framework, building upon the aforementioned method.
Due to the substantial model size and extensive data samples required for training, centralized model storing and training are nearly infeasible for small and intermediate service providers.
Consequently, we will adopt horizontal model partitioning and distribute different model layers across the network nodes through caching.
Data samples or batches are input into the network and undergo the forward-backward procedure for training. Our objective is to jointly optimize data forwarding and model/computation placement, thereby minimizing the total cost of transmission, computation, and storage.
Furthermore, we introduce several network resource allocation optimization problems related to data-centric networking, thereby expanding the scope of our proposal.

Sumegha Singhania MS Thesis Defense
2023-08-15 15:30 - 16:30
Title: Exploring Log of RGB Space as a Better Input for Computer Vision Tasks
Committee Members:
Prof. Bruce Maxwell (Advisor)
Prof. Hanumant Singh
Prof. David Rosen
Prof. Mahdi Imani
Abstract:
There are specific, physics-based rules that govern the interaction of light and matter. Though studied extensively in the greater computer vision community, these rules are largely broken by common image processing techniques like JPEG compression and sRGB conversion. While the reliability and usability of color and intensity found in RAW images might better train networks to successfully complete vision-based tasks, these smaller, more heavily-processed formats have become the standard input for training sets. As a result, many of the images used to train neural networks do not retain the inherent structure that would enable neural networks to learn more general rules that exist in the natural world.
We hypothesize that using linear RGB or log RGB images, which preserve the physics of reflection, can simplify the learning process for certain vision tasks, enhance overall robustness and performance, and provide invariance to visual variations that exist in real-world vision applications. Our research demonstrates that employing linear and log RGB images to train deep networks for the task of object detection improves their performance when using the same network architecture and the same set of training images. Additionally, we also show that the networks trained on linear and log RGB show greater resilience to variations in intensity and color balance. Specifically, the network trained on linear and log RGB inputs shows invariance to intensity and color balance variations that were not encountered during training, while the network trained on the same images in sRGB JPEG format experiences significant performance degradation. To understand the reasons behind this disparity, we analyze and visualize low-level features in log RGB, linear RGB, and JPEG data. Our findings reveal that the log space preserves certain relevant features across variations in intensity and color balance.

Jagatpreet Nir PhD Proposal
2023-08-17 09:00 - 11:00
Title: Low Contrast Visual Sensing and Inertial Navigation in GPS Denied Environments
Committee Members:
Prof. Hanumant Singh
Prof. Martin Ludvigsen
Prof. Pau Closas
Prof. Michael Everrett
Abstract:
Visual inertial navigation has shown remarkable performance in publicly available datasets, assuming certain ideal conditions such as textured scenes, uniform illumination, and static environments. However, real-world scenarios often violate these assumptions, resulting in significant visual degradation. Consequently, the classical visual navigation pipelines fail and produce erroneous results, rendering these systems ineffective for demanding field robotic missions.
This research aims to enhance the robustness of visual-inertial systems in visually degraded situations, taking a comprehensive approach from both systems and algorithm perspectives. The work encompasses two primary objectives. Firstly, it focuses on refining the characterization of MEMS-based inertial sensors and their error propagation in position, while proposing improved dead-reckoning algorithms. Secondly, it explores the performance limits of visual navigation under moderate to extreme visual degradation and investigates novel algorithms that leverage deep learning methods to bolster the visual navigation engine. To validate the efficacy of these advancements, new datasets comprising drone and underwater robot scenarios are utilized, demonstrating the applicability of this work in field robotic applications.
By addressing the limitations of existing visual-inertial navigation systems and developing robust algorithms, this research aims to significantly enhance the reliability and performance of such systems in visually degraded environments, thus expanding their potential for real-world applications in demanding field robotic missions.

Rohit Rajput MS Thesis Defense
2023-08-17 10:30 - 11:00
Title:Towards Autonomous Multi-Modal Mobility Morphobot (M4) Robot: Traversability Estimation and 3D Path Planning
Location: ISEC 632 & Zoom
Committee Members:
Prof. Rifat Siphai
Prof. Hanumant Singh
Prof. Alireza Ramezani (advisor)
Abstract:
This thesis enhances the autonomy of the M4 (Multi-Modal Mobility Morphobot) robot, designed for Mars and rescue missions. The research enables the robot to autonomously select its locomotion mode and path in complex terrains. Focusing on walking and flying modes, a Gazebo simulation and custom perception-navigations pipelines are developed. Leveraging deep learning, the robot determines optimal mode transitions based on a 2.5D map. Additionally, an energy-efficient path planner is implemented and validated in simulations. The contributions demonstrate scalability for future mode integrations. The M4 robot showcases intelligent mode switching, efficient navigation, and reduced energy consumption, bringing us closer to fully autonomous multi-modal robots for exploration and rescue missions. This work paves the way for future advancements in autonomous robotics, with the ultimate vision of deploying the M4 robot for exploration and rescue tasks, making a significant impact in the quest for intelligent and versatile robotic systems.

Zhiyong Zhang PhD Proposal
2023-08-17 13:00 - 14:00
Title: Towards Indoor Mapping and Navigation with Perceptual Aliasing using Visual Semantic SLAM
Committee Members:
Prof. Hanumant Singh (Advisor)
Prof. Huaizu Jiang
Prof. David Rosen
Abstract:
Modern SLAM (Simultaneous Localization And Mapping) techniques allow us to create accurate 3D maps of the environment primarily using visual sensors in GPS-denied regions. In this context, numerous deep learning-based approaches have emerged, enabling the extraction of rich semantic information from images, including shapes, objects, and text.
Leveraging these technologies, our aim is to construct comprehensive 3D maps of indoor environments, which could be utilized by robots for path planning and navigation. Additionally, the solution can be integrated with a large language model, enabling the robot to interact intuitively with people.
This research comprises four main components: Semantic Feature Extraction and Tracking with SLAM: Given that the same semantic features can appear in multiple frames, some of which may not be conducive to feature detection and recognition (such as blurry images or distant views), we are developing a pipeline to ensure the optimal detection and recognition of semantic features within the most suitable frame. The pipeline also involves tracking the same feature across frames while maintaining its 3D location in the global map.
Resolving Perceptual Aliasing: Many indoor places can exhibit high visual similarity, which confuses the robot when powered up with a prior map in its memory. Semantic features can be used to localize the robot in the map, determining its specific floor or room. This capability can also aid SLAM in performing loop closure with high-level information.
Cross-Floor Constraints for SLAM Optimization: Most buildings contain a symmetric layout across floors, which can be exploited to establish constraints between them. For instance, vertically aligned rooms like 425 and 525, as well as elevators, offer opportunities for vertical constraint. Such constraints can enhance SLAM optimization, resulting in improved map accuracy.
Indoor Path Planning and Navigation: Once we have a comprehensive 3D map of the indoor environment, path planning becomes an intuitive way to utilize this map. With semantic features integrated into the map, the robot can associate 3D point clouds with high-level information, such as door numbers or office names. Large language models are available to provide a more human-like way to interact with the robot. For example, a command like “Navigate to Professor Hanumant’s office and locate the book ‘The Hitchhiker’s Guide to the Galaxy'” can be executed by the robot.

Jaehyeon Ryu PhD Dissertation Defense
2023-08-18 10:00 - 11:00
Title: Materials Strategies for Scaling Soft Neuroelectrode Arrays
Location: Snell 012/Teams
Committee Members:
Prof. Hui Fang (Advisor)
Prof. Yongmin Liu
Prof. Ryan Koppes
Abstract:
The evolution of electronics to seamlessly interface with biological tissue hinges on addressing multifaceted material constraints spanning electrochemical, electrical, and mechanical domains. Conventional bioelectronic interfaces, while endowed with established electrochemical functionality, remain hampered by rigidity that contradicts the pliability of surrounding tissue. While conductive materials exhibiting tissue-like softness and stretchability have been realized, their potential for electrochemical probing of tissue is impeded by strain-induced performance degradation and an ill-suited integration with the irregular tissue interface. Nevertheless, a significant challenge in ultrasoft bioelectronics pertains to scalability for achieving cellular resolution, primarily due to mechanical disparities between conventional microelectronic materials and soft elastomer substrates. In this thesis, by using a novel approach involving a multifunctional nanomesh, composed of distinct purposefully designed layers including polymer for mechanical buffering, metal for electrical conduction, and low impedance coating for electrochemical interfacing in the same nanomeshed structure, the resultant microelectrodes, scalable down to 20μm at cellular resolution, exhibit comparable performance to rigid devices alongside a stretchablity of approximately 50%, with potential for future enhancement through in-plane structural optimizations. In addition, we introduce a high-density neuroelectronic array featuring 256 filamentary neuroelectrodes on a flexible substrate. These electrodes are integrated with a single-transistor multiplexing acquisition circuit, effectively reducing noise and footprint while potentially extending device lifetime. Remarkably, the array’s rollable contact pad design allows for minimally invasive delivery through a syringe. Experimental validation demonstrates the array’s capability to record neural signals with high tone decoding accuracy. Utilizing high-density arrays of these microelectrode arrays, this unique frame works holds significant promise for advancing the field of neural interfacing, enabling a wide range of application from fundamental neuroscience studies to various biomedical applications.

Enabling Engineering Summer Showcase
2023-08-18 11:00 - 14:00 | 002 Ell Hall, 360 Huntington Ave, Boston, MA, 02115, United States
We invite you to the Enabling Engineering Summer Showcase on Friday – August 18th, 11:00am – 2:00pm EST in 002 Ell Hall (360 Huntingtin Ave, 002 Ell Hall, Boston Massachusetts 02115) where students will present their final design projects. We will have our clients present including the director of nurses from Mass Brigham and the Mass Brigham Press.
NU Enabling Engineering works to build devices for individuals with disabilities in Massachusetts and around the world. We maintain relationships with special education schools, community organizations, and individuals, bring projects ideas to campus, and then give students all the resources they need including funding, lab space, equipment, and management and technical support. There’s also a technical elective that students can take for credit to complete projects. Over the past 10 years, more than 1,000 students from across the university have worked on >75 projects.
The overall goal is to train the next generation of engineers, clinicians, and all students to be more knowledgeable about, and aware of, the needs of individuals with disabilities.

Haoling Li MS Thesis Defense
2023-08-18 14:00 - 15:00
Title: Ultracompact and Conformal Magnetodielectric Antennas
Committee Members:
Prof. Nian-Xiang Sun (Advisor)
Prof. Xufeng Zhang
Prof. Marvin Onabajo
Abstract:
Novel approaches are needed for improving antenna performance, enhancing efficiency, and reducing the size, profile, number, and signature of antennas. Efficient conformal antennas are increasingly replacing traditional antennas across platforms such as ships, aircraft, and human interfaces. Magnetodielectric antennas made with high-hesistivity magnetic materials are getting more and more attention. Defined as the maximum magnetic conductivity, hesitivity is directly related to the radiation efficiency of magnetodielectric antennas, with a higher hesitivity corresponding to higher attainable efficiency. In this study, new ultra-compact conformal magnetodielectric antennas are demonstrated, employing commercially available ferrite ceramic substrates. Through rigorous simulation and fabrication, a comprehensive comparison of our magnetodielectric antennas with reference monopole antennas demonstrated superior efficiency, enhanced gain, bandwidth, and a substantial reduction in antenna size compared to monopole antennas. State-of-the-art hesitivity as high as 6×10^6 Ω/m has been reported in CoZrNb alloy films, with an expectation of further 10× improvement in thin carrier substrates. This study forecasts the potential development of new magnetic materials with higher hesitivity, leading to further advancements in magnetodielectric antennas with enhanced radiation efficiency and ground plane immunity.

Xu Yizhe MS Thesis Defense
2023-08-18 14:30 - 15:30 | 532 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
Title:
Integration of Polyimide Flexible PCB Wings in Northeastern’s Aerobat
Location:
Room: ISEC 532, Teams link
Committee Members:
Prof. Alireza Ramezani(Advisor)
Prof. Rifat Sipahi
Abstract:
The principal aim of this Master’s thesis is to propel the optimization of the membrane wing structure of the Northeastern Aerobat through origami techniques and enhancing its capacity for secure hovering within confined spaces. Bio-inspired drones offer distinctive capabilities that pave the way for innovative applications, encompassing wildlife monitoring, precision agriculture, search and rescue operations, as well as the augmentation of residential safety. The evolved noise-reduction mechanisms of birds and insects prove advantageous for drones utilized in tasks like surveillance and wildlife observation, ensuring operation devoid of disturbances. Traditional flying drones equipped with rotary or fixed wings encounter notable constraints when navigating narrow pathways. While rotary and fixed-wing systems are conventionally harnessed for surveillance and reconnaissance, the integration of onboard sensor suites within micro aerial vehicles (MAVs) has garnered interest in vigilantly monitoring hazardous scenarios in residential settings. Notwithstanding the agility and commendable fault tolerance exhibited by systems such as quadrotors in demanding conditions, their inflexible body structures impede collision tolerance, necessitating operational spaces free of collisions. Recent years have witnessed an upsurge in integrating soft and pliable materials into the design of such systems; however, the pursuit of aerodynamic efficiency curtails the utilization of excessively flexible materials for rotor blades or propellers. This thesis introduces a guard design incorporating feedback-driven stabilizers, enabling stable hovering flights within Northeastern’s Robotics-Inspired Study and Experimentation (RISE) cage.

Julian Gutierrez PhD Proposal Review
2023-08-25 10:00 - 11:00
Title: Towards Real-Time Safe Flight Paths for Urban Air Mobility
Committee Members:
Prof. David Kaeli, (Advisor)
Prof. Pau Closas
Dr. Evan Dill (NASA)
Dr. Natasha Neogi (NASA)
Abstract:
The emergence and development of advanced technologies and vehicle types have created a growing demand for introducing new forms of flight operations. These new and increasingly complex operational paradigms, such as Advanced and Urban Air Mobility (AAM/UAM), present regulatory authorities and the aviation community with the challenge of finding methods to integrate these emerging operations without significant additional risk to pedestrians and infrastructure. Predictive and autonomous risk mitigation capabilities become critical to meet this challenge. However, urban environments experience effects that are computationally expensive to model, limiting conventional aviation concepts, policy, and risk prediction tools from being effectively translated into this space. With the emergence of High-Performance Computing (HPC) ecosystems in the last two decades, we can use these software and hardware capabilities to help bridge the gap between real-time predictive responses and modeling accuracy.
In this dissertation we first present a simulation framework to estimate the quality of Global Navigation Satellite System (GNSS) performance for autonomous aircraft in urban environments. We propose a new algorithm designed for HPC to accelerate modeling the characteristic effects of dense urban canyons on GNSS, allowing the extension of established GNSS integrity techniques into urban navigation. Additionally, we provide a thorough validation of the simulator, which proves high-accuracy modeling when compared to sensors in the real world. Second, we use this simulation framework as the input into a new 4D path-planning algorithm based on an adaptation of the Bellman-Ford algorithm. HPC techniques are employed to accelerate the algorithm to produce flight paths that minimize exposure to GNSS risks. We evaluate the computational cost of satellite availability fluctuations by prioritizing events when satellite availability changes as triggers for these updates.

Mostafa Abedi PhD Proposal Review
2023-09-01 13:00 - 14:00 | 532 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
Title: Power-Efficient and Security-Enhancing Techniques for Ultra-low Power IoT Devices
Committee Members:
Prof. Aatmesh Shrivastava (Advisor)
Prof. Marvin Onabajo
Prof. Nian X. Sun
Abstract:
Internet-of-things (IoT) devices often rely on ambient energy sources such as photovoltaic (PV) cells and thermoelectric generators (TEGs) for their operation. Minimizing power loss through ambient energy harvesting optimization can significantly extend the battery life or support battery-free sensor nodes in IoT devices. A maximum power point tracking (MPPT) circuit is often used for impedance matching to maximize energy transfer efficiency. This research proposes an ultra-low power, high-tracking efficiency MPPT circuit based on Hill-Climbing (HC) algorithm suitable for micro-power DC harvesters. The proposed system employs a modified version of the hill-climbing algorithm. In case of input power changes and consequent deviation of the harvester from the MPP, an integrated Power Change Detector (PCD) is proposed to reactivate the MPPT circuit. The PCD detects changes in input power and activates the MPPT circuit, enabling automatic activation and resulting in substantial power savings. Furthermore, due to the proposed power estimation technique, the MPPT is not dependent on the internal structure of the energy source, and its tracking efficiency is unrelated to the conversion ratio of the converter. This approach enables us to achieve a peak tracking efficiency of over 99.9%. To adjust the input power of the harvester to track the maximum power point, we propose a new, efficient Pulse Width Modulation (PWM) circuit. This circuit exhibits a wide duty cycle range, low power consumption, linearity, and robustness against variations.
This research also focuses on increasing the security of IoT devices. In the past, chip fabrication was mostly done internally by semiconductor firms. Now, it is more collaborative, pulling in designs from various sources and having a few factories produce them. This new way of working means that companies that only handle design might face more challenges like the threat of hardware Trojans (HT) being added either during the design phase or production. With that in mind, we introduce a different circuit design approach. We aim to find these Trojans, particularly the newer analog Trojans. The idea is to boost the security of IoT devices by detecting these issues early. In addition, to improve the security of IoT systems, we propose an ultra-low power energy monitoring system (EMS) to detect and mitigate denial-of-sleep (DoSL) attacks. In this project, we explore a new method of defense against DoSL attacks by monitoring energy consumption. We will implement a low-power system to monitor the lifetime of the IoT node by continuously evaluating the harvested, stored, and consumed energy in the node.

NOBCChE 50th Annual Conference
2023-09-11 - 2023-09-14
Join the College of Engineering’s Chemical Engineering department at the NOBCChE’s Annual Conference! Graduate Admissions will be in attendance to answer all your questions about our graduate Chemical Engineering programs during the Exhibit Fair on September 14th.

CommLab Writing Group
2023-09-11 14:00 - 16:00
Setting and sticking to a consistent writing schedule is key to improving skills and accomplishing your writing tasks (don’t just take our word for it).
COE graduate students are invited to join the CommLab writing group to share best practices, get feedback from your peers, and work on any writing project (dissertation, thesis, manuscript, fellowship, poster, presentation, or other forms of technical communications). We will meet weekly in 306 Egan for 2 hours; you do not have to attend all the sessions or participate for the full 2 hours. (Note we will be in 336 CSC on 9/18 and we will not meet on Monday, Nov 6).

Poster Design and Editing: A CommLab Workshop Series
2023-09-11 15:00 - 16:30 | 344 CSC, 360 Huntington Ave, Boston, MA, 02115, United States
COE Graduate students are invited to learn techniques, develop, edit and practice delivering your poster. Join the CommLab’s weekly workshop to improve your poster design and communication skills. Drop in for as many workshops as you want to get your poster ready. You can also join any time between 3:00-4:30pm ET. This is a hybrid opportunity. Join us on Zoom or in 344 Curry Student Center. We will be in room 435 on Monday 9/11 and 9/30. We will meet virtually only on Monday, 11/6.

College of Engineering Global Co-op Info Session
2023-09-12 17:30 - 18:30 | 168 SN, 360 Huntington Ave, Boston, MA, 02115, United States
Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Spring/ Summer I 2024! Topics discussed will include:

Search techniques and global positions in your field
Locations that students have co-oped in the past
What to consider when interested in a global co-op
Information on logistics, including visas and housing
Tips and resources for self-developing global positions

Attendance to one of these information sessions is required if you plan to do a global co-op in Spring/ Summer I 2024. Please reach out to Sally Conant, Global Co-op Coordinator, s.conant@northeastern.edu or Kristina Kutsukos, Global Co-op Coordinator, k.kutsukos@northeastern.edu for additional information.
