Northeastern University College of Engineering

ECE PhD Dissertation Defense: Maher Kachmar
2021-07-13 10:00 - 11:00 (America/New_York)
PhD Dissertation Defense: Active Resource Partitioning and Planning for Storage Systems using Time Series Forecasting and Machine Learning Techniques
Maher Kachmar
Location: Zoom
Abstract: In today’s enterprise storage systems, supported data services such as snapshot delete or drive rebuild can result in tremendous performance overhead if executed inline along with heavy foreground IO, often leading to missing Service Level Objectives (SLOs). Moreover, new classes of data services, such as thin provisioning, instant volume snapshots, and data reduction features make capacity planning and drive wear-out prediction quiet challenging. Having enough free storage pool capacity available ensures that the storage system operates in favorable conditions during heavy foreground IO cycles. This enables the storage system to defer background work to a future idle cycle. Static partitioning of storage systems resources such as CPU cores or memory caches may lead to missing data reduction rate (DRR) guarantees. However, typical storage system applications such as Virtual Desktop Infrastructure (VDI) or web services follow a repetitive workload pattern that can be learned and/or forecasted. Learning these workload pattern allows us to address several storage system resource partitioning and planning challenges that may not be overcome with traditional manual tuning and primitive feedback mechanism.
First, we propose a priority-based background scheduler that learns this pattern and allows storage systems to maintain peak performance and meet service level objectives (SLOs) while supporting a number of data services. When foreground IO demand intensifies, system resources are dedicated to service foreground IO requests. Any background processing that can be deferred is recorded to be processed in future idle cycles, as long as our forecaster predicts that the storage pool has remaining capacity. A smart background scheduler can adopt a resource partitioning model that allows both foreground and background IO to execute together, as long as foreground IOs are not impacted, harnessing any free cycles to clear background debt. Using traces from VDI and web services applications, we show how our technique can out-perform a static policy that sets fixed limits on the deferred background debt and reduces SLO violations from 54.6% (when using a fixed background debt watermark), to only 6.2% when employing our dynamic smart background scheduler.
Second, we propose a smart capacity planning and recommendation tool that ensures the right number of drives are available in the storage pool in order to meet both capacity and performance constraints, without over-provisioning storage. Equipped with forecasting models that characterize workload patterns, we can predict future storage pool utilization and drive wear-outs. Similarly, to meet SLOs, the tool recommends expanding pool space in order to defer more background work through larger debt bins. Overall, our capacity planning tool provides a day/hour countdown for the next Data Unavailability/Data Loss (DU/DL) event, accurately predicting DU/DL events to cover a future 12-hour time window.
Moreover, supported services such as data deduplication are becoming a common feature adopted in the data center, especially as new storage technologies mature. Static partitioning of storage system resources, memory caches, may lead to missing SLOs, such as the Data Reduction Rate (DRR) or IO latency. Lastly, we propose a Content-Aware Learning Cache (CALC) that uses online reinforcement learning models (Q-Learning, SARSA and Actor-Critic) to actively partition the storage system cache between a deduplicated data digest cache, content cache, and address-based data cache to improve cache hit performance, while maximizing data reduction rates. Using traces from popular storage applications, we show how our machine learning approach is robust and can out-perform an iterative search method for various data-sets and cache sizes. Our content-aware learning cache improves hit rates by 7.1% when compared to iterative search methods, and 18.2% when compared to traditional LRU-based data cache implementation.

ECE PhD Dissertation Defense: Berkan Kadioglu
2021-07-19 11:30 - 12:30
PhD Dissertation Defense: An Analysis of Algorithms with Discrete Choice Models
Berkan Kadioglu
Location: Zoom Link
Abstract: In the first half of our work, we consider a rank regression setting, in which a dataset of $N$ samples with features in $mathbb{R}^d$ is ranked by an oracle via $M$ pairwise comparisons.
Specifically, there exists a latent total ordering of the samples; when presented with a pair of samples, a noisy oracle identifies the one ranked higher w.r.t. the underlying total ordering.
A learner observes a dataset of such comparisons, and wishes to regress sample ranks from their features.
We show that to learn the model parameters with $epsilon > 0$ accuracy, it suffices to conduct $M in Omega(dNlog^3 N/epsilon^2)$ comparisons uniformly at random when $N$ is $Omega(d/epsilon^2)$.
Compared to learning from class labels, learning from comparison labels has two advantages: First, comparison labels reveal both inter and intra-class information, where class labels only contain the former.
Second, comparison labels also exhibit lower variability across different labelers.
This has been observed experimentally in multiple domains, including medicine citep{campbell2016plus,kalpathy2016plus, stewart2005absolute} and recommendation systems citep{schultz2004learning,zheng2009mining,brun2010towards, koren2011ordrec}, and is due to the fact that humans often find it easier to make relative, rather than absolute, judgements.
Many works focusing on empirically learning comparison labels show excellent performance in practice citep{tian2019severity,yildiz2019classification}.
Our work provides a theoretical foundation for analyzing and understanding this empirical performance.
Moreover, we extend the problem we initially study to a harder setting.
We do this by moving from pairwise comparisons to multi-way comparisons.
Furthermore, we study an online variant of the previous problem where the goal is to maintain high user engagement throughout the learning period.
This of course, indirectly leads to the goal of learning parameters of the discrete choice model as accurately as possible, fast.
This new problem is directly related to a setting in which a retailer recommends products to customers.
A common problem in many recommendation tasks is to simultaneously learn the utilities of items to be recommended and maintain high user engagement.
We are generally constrained by a limit on the total number of items to be recommended at a time for an unknown time horizon.
Recently, bandit algorithms have been proposed for this setting where the multinomial logit model is assumed.
Bounds on error metrics are provided for upper confidence and Thompson sampling based algorithms.
In our paper, we propose a variational inference based Thompson sampling algorithm and identify the required properties to achieve $tilde O(D^{3/2}sqrt T)$ worst-case regret.
Through extensive experiments we show that our method performs much better than the recently proposed emph{TSMNL} algorithm in many error metrics.
We further accelerate our algorithm to be used in practical settings.

COE CommLab/Khoury College Writing Retreat
2021-07-20 08:00 - 2021-07-22 13:00
College of Engineering PhD students are invited to join us for a writing retreat July 20 – 22. The aim of this retreat is to create sustained writing time for researchers to work in a calm, supportive environment on a longer project. Studies have shown that an academic writing retreat supports productivity and progress while also encouraging helpful guidance from peers.
Our virtual retreat is organized around alternating periods of quiet work on individual projects with collective sessions on topics related to research writing. Each of the three days begins with a welcome message and group gathering. On the last day, we’ll wrap up the retreat with a virtual lunch to share concluding thoughts.
Register here for this event by June 24.

ECE PhD Dissertation Defense: Mahmoud Ibrahim
2021-07-23 10:00 - 11:00
PhD Dissertation Defense: Low-Power Integrated Circuit Design for Wireless Devices in the Internet of Things
Mahmoud Ibrahim
Location: Zoom
Abstract: Numerous integrated sensing devices are under development for wireless medical diagnostic and monitoring applications. However, the data rates of wireless devices connected to the Internet of Things are limited and strongly depend on the available power. This research addresses the need for circuit-level design methods to enable higher data rates with lower power consumption in order to facilitate the proliferation of wireless devices that can overcome the speed-power conundrum. The potential applications include continuous-time monitoring of physiological signals, where increased data rates imply the ability to exchange more information during the same time, more accurate data, and/or data from a greater number of sites associated with each wireless node.
An energy-efficient binary frequency shift keying (BFSK) transmitter architecture for biomedical applications is introduced as the first part of this dissertation research. To achieve low power consumption with higher data rates, the novel transmitter architecture leverages image rejection techniques to generate each of the two tones of the transmitted BFSK signal while keeping the phase-locked loop (PLL) oscillator frequency unchanged, and thus maintaining low PLL power and overall transmitter power. A fabricated prototype chip in 130nm complementary metal-oxide-semiconductor (CMOS) technology achieves data rates up to 10 Mbps while consuming 180 µW with up to -20 dBm output power according to Medical Implant Communication System (MICS) band requirements. The measurement results confirm state-of-the-art energy-efficient performance with 18 pJ/bit.
As a natural continuation of the first part of this research, a complementary receiver architecture is described in the second part of this dissertation to provide full transceiver capabilities. The new receiver design approach takes advantage of the transmitted signal characteristics by using both the frequency information and phase information to demodulate the received digital bits. This design method results in improved sensitivity with reduced power consumption through relaxed receiver block specification requirements. The custom-designed receiver circuits include a new low-noise amplifier (LNA) topology for energy-efficient antenna impedance matching, and a single mixer circuit that realizes the signal down-conversion with differential in-phase and quadrature-phase baseband output signals to circumvent the complexity associated with two mixers and to save power. Measurement results of the fabricated receiver in 65nm CMOS technology show a sensitivity of -82 dBm with an input signal at 10 Mbps centered around 416 MHz. With a power consumption of 610 µW and an energy efficiency of 61 pJ/bit, this receiver architecture displays state-of-the-art performance with respect to data rate, power and sensitivity compared to other receivers in the same frequency range.
In addition to the new transmitter and receiver architectures, a large-signal transconductance linearization technique is presented as part of this dissertation research to extend the dynamic range of analog baseband filters. Furthermore, a low-power sinusoidal signal generation technique is introduced and analyzed, which is a versatile and essential component of the transmitter design approach.

ECE PhD Proposal Review: Bahare Azari
2021-07-28 14:00 - 15:00
PhD Proposal Review: Circular-Symmetric Correlation Layer based on FFT
Bahare Azari
Location: Zoom Link
Abstract: Planar convolutional neural networks, widely known as CNNs, have been exceptionally successful in many computer vision and machine learning tasks, such as object detection, tracking, and classification. The convolutional layers in CNN are characterized by pattern-matching filters that can identify motifs in the signal residing on a 2D plane. However, there exists various applications in which we have signals lying on a curved manifold or an arbitrary collection of coordinates, e.g., temperature and climate data on the surface of the (spherical) earth, and 360-panoramic images acquired from LiDAR. In these applications, we usually need our network to be equivariant/invariant to various transformations of the input, i.e., as we transform the input according to a certain action of a group, the output is respectively transformed (equivariance), or remains unchanged (invariance). The convolution layers are empirically known to be invariant to small translations of their input image, but they are not completely immune to relatively large translations Hence, they may fail on the tasks that requires invariance to a specific transformation, and and on the data that includes a wide range of that transformation.
In this work we consider equivariant/invariant tasks on 360-panoramic data. For a systematic treatment of analyzing the 360-panoramic data, we propose a circular-symmetric correlation Layer (CCL) based on the formalism of roto-translation equivariant correlation on the continuous group constructed of the unit circle and the real line. We implement this layer efficiently using the well-known Fast Fourier Transform (FFT) and discrete cosine transform (DCT) algorithm. We discuss how the FFT yields the exact calculation of the correlation along the panoramic direction due to the circular symmetry and guarantees the invariance with respect to circular shift. The DCT provides an improved approximation with respect to transnational symmetry compared to what we observe in CNNs. We demonstrated the invariance analysis of networks built with CCL on two benchmark datasets comparing the equivariance of neural networks adopting CCL layers and regular CNN. Then, we showcase the performance analysis of a general network equipped with CCL on recognition and classification tasks, such as panoramic scene change detection, 3D object classification, LIDAR Semantic Segmentation.

COE CommLab Research Dissemination Series: Preprints Workshop
2021-07-28 16:00 - 17:00
Do you have a fully drafted journal article ready to share with other researchers that you want to share before the traditional peer review process is done?
Preprinting is one way to disseminate your research almost immediately. Join the Northeastern COE Communication Lab for a workshop discussing the importance of preprints and open science. We will cover the basics of preprints and address common concerns about preprinting.
Register via Zoom

ECE PhD Proposal Review: Weite Zhang
2021-07-29 15:00 - 16:00
PhD Proposal Review: High Sensing-capacity Multi-dimensional-coded Millimeter-wave MIMO Imaging System
Weite Zhang
Location: Microsoft Teams Link
Abstract: Millimeter-wave (mm-wave) MIMO imaging systems have been explored to use more and more complicated radar waveforms to achieve advanced multiplexing and high-performance imaging. As the complexity of the radar waveform increases, conventional systems inevitably suffer from higher design difficulty and cost. In spite of the radar waveform design, existing mm-wave imaging systems are still suboptimal due to the fact that the sensing matrix is not tailored properly to achieve its maximum capacity, which often results in large mutual information between successive measurements, and limited imaging performance.
As the first contribution of this proposal, high sensing-capacity mm-wave MIMO imaging systems with multi-dimensional-coding are built. In the first prototype, a 70-77 GHz frequency-modulated continuous wave (FMCW) MIMO imaging system with massive channels is studied. To enhance the sensing-capacity, a compressive reflector antenna (CRA) is added to perform randomized spatial wavefront coding to increase the measurement diversity. Both static and on-the-move experiments are carried out to show the functionality of the imaging system. In the second prototype, an 81-86 GHz software-defined mm-wave MIMO imaging system is designed, which makes use of cost-effective software-defined radios (SDRs) with mm-wave mixers. Due to the baseband flexibility of SDRs, efficient orthogonal frequency-division multiplexing (OFDM) with binary phase coding is designed as the radar waveform to achieve simultaneous MIMO transmission, where high receiving signal-to-noise ratio and spectrum efficiency are achieved. Again, a CRA is designed and applied to increase the measurement diversity. Primary simulation and experimental results show good imaging performance with reduced side lobe effect.
As the second contribution of this proposal, a material characterization method is developed, which is vital in some important mm-wave imaging applications, such as security screening, where both object profile and material information are required for potential threats prediction. Specifically, a Geometrical Optics (GO) forward model based on a reflectarray imaging system is developed. The GO forward model can be adapted to any other imaging systems as long as their geometrical configurations are known. Both simulations and experiments are performed to show the effectiveness and efficiency of the proposed material characterization method, where the complex relative permittivity as well as a more accurate shape of the object is retrieved.

ECE PhD Dissertation Defense: Yulun Zhang
2021-08-02 14:00 - 15:00
PhD Dissertation Defense: Deep Convolutional Neural Network for Image Restoration and Synthesis
Yulun Zhang
Location: Zoom Link
Abstract: Image restoration and synthesis with deep learning play a fundamental role in the computer vision community. They are widely used on mobile devices (e.g., smartphones) or lead to billion-dollar startups. However, how to design efficient deep convolutional neural networks (CNNs) to extract higher-quality deep CNN features for better image restoration and synthesis is still challenging. In this dissertation talk, I will describe my recent works to enhance CNN features in the channel dimension or/and the spatial dimensions. First, for image restoration, I will briefly introduce our proposed residual dense network. Then, I will introduce the residual in residual (RIR) structure to train very deep super-resolution networks. Such an RIR structure could also make the network learn more high-frequency information, being critical for high-resolution output. Attention mechanism (e.g., channel attention and spatial attention) is further explored to highlight the features. Second, for image synthesis, I will introduce multimodal style transfer via graph cuts. I visualize the deep features and find the multimodal style representation. I then formulate the style matching problem as an energy minimization one, which could be solved via graph cuts. As a result, the transferred features contain spatially semantic information, providing more visually pleasing stylized results. Besides, we investigate image synthesis about texture hallucination with large scaling factors. We propose an efficient high-resolution hallucination network for very large scaling factors.

Creative Expression Workshops Series: The Power of 'Yes, and...'
2021-08-02 14:00 - 16:00
MONDAY, AUGUST 2, 2021
2:00 – 4:00pm
Venue: Boston Campus, details to follow soon
Sign up via this FORM.
NU College of Engineering and the Department of Theatre (CAMD) are excited to host in-person creative expression workshops that help foster confidence building, reflexivity, collaboration and more.
Theatre-based improvisation provides a playful environment in which to discover, to understand and to maximize the ways in which we interact, communicate and collaborate with others. Above all, Improvisation helps us embrace failure as the only possible way towards real success.
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Participants will be presented with a variety of exercises that explore community and team building, spatial awareness, mental agility, status, adaptability, risk taking, intuition, physical presence and relaxation.
The ultimate goal is for participants to experience more confidence in their ability to command the attention of an audience, large or small, and to be able to deepen their ability to collaborate effectively.
After participating in the series, team members may be able to:
· Recognize and quickly adapt how they present themselves to others;
· Read situations accurately, practicing critical observation skills;
· React to unexpected situations with confidence and agility;
· Listen more deeply to others;
· Find creative resolutions to improvised conflicts.
INSTRUCTOR: Jesse Hinson, Associate Teaching Professor of Theatre

Creative Expression Workshop Series: The Eloquent Bilingual Speaker
2021-08-03 14:00 - 16:00
TUESDAY, AUGUST 3, 2021
2:00 – 4:00pm
Venue: Boston Campus, details to follow soon
Sign up via this FORM.
NU College of Engineering and the Department of Theatre (CAMD) are excited to host in-person creative expression workshops that help foster confidence building, reflexivity, collaboration and more.
This workshop is designed by and for non-native speakers of English to improve our eloquence in personal and professional interactions. Participants will learn how to free the habitual tensions, holding patterns and inefficient uses that block the clear communication of thoughts and ideas. The playful yet demanding exercises are designed to help enhance the quality of the spoken voice, as well as the clarity and resolve with which we express ourselves. The basic progression of exercises include:
· Discover the skeleton & spine;
· Stimulate breath;
· Awaken and encourage resonance;
· Invigorate articulation;
· Learning the International Phonetic Alphabet as a tool to recognize clear English speech sounds and to swiftly adjust pronunciation;
After participating in the series, team members may be able to:
· Practice relaxation, resonance and expressiveness in the use of their voice;
· Experience more confidence in their ability to command the attention of an audience.
INSTRUCTOR: Antonio Ocampo-Guzman, Associate Professor & Chair of the Department of Theatre

ECE PhD Dissertation Defense: Amirreza Farnoosh
2021-08-04 10:00 - 11:00
PhD Dissertation Defense: Unsupervised Learning of Low-Dimensional Dynamical Representations from Spatiotemporal Data
Amirreza Farnoosh
Location: Zoom Link
Abstract: Ever-improving sensing technologies offer a fast and accurate collection of large-scale spatiotemporal data, recorded from multimodal sensors of heterogeneous natures, in various application domains, ranging from medicine and biology to robotics and traffic control. In this dissertation, we propose frameworks for learning the underlying representation of these data in an unsupervised manner, tailored towards several emerging applications, namely indoor navigation and mapping, neuroscience hypothesis testing, time series forecasting, 3D motion segmentation, and human action recognition.
As such, (1) we developed an unsupervised framework for real-time depth and view-angle estimation from an inertially augmented video recorded from an indoor scene by employing geometric-based machine learning and deep learning models. (2) We introduced a hierarchical deep generative factor analysis framework for temporal modeling of neuroimaging datasets. Our model approximates high dimensional data by a product between time-dependent weights and spatially-dependent factors which are in turn represented in terms of lower dimensional latents. This framework can be extended to perform clustering in the low dimensional temporal latent or perform factor analysis in the presence of a control signal. (3) We developed a deep switching dynamical system for dynamical modeling of multidimensional time-series data. Specifically, we employ a deep vector auto-regressive latent model switched by a chain of discrete latents to capture higher-order multimodal latent dependencies. This results in a flexible model that (i) provides a collection of potentially interpretable states abstracted from the process dynamics, and (ii) performs short- and long-term vector time series prediction in a complex multi-relational setting. (4) We developed a dynamical deep generative latent model for segmentation of 3D pose data over time that parses the meaningful intrinsic states in the dynamics of these data and enables a low-level dynamical generation and segmentation of skeletal movements. Our model encodes highly correlated skeletal data into a set of few spatial basis of switching temporal processes in a low-dimensional latent framework. We extended this model for human action recognition by decoding from these low-dimensional latents to the motion data and their associated action labels.

ECE MS Thesis Defense: Owen McElhinney
2021-08-04 10:00 - 11:00
MS Thesis Defense: On the Application of Spline Functions to Problems in Hyperspectral Imaging
Owen McElhinney
Location: Zoom Link
Abstract: Hyperspectral Imaging (HSI) is a rapidly growing topic in the field of remote sensing. Hyperspectral cameras trade off a reduction in the spatial resolution of modern imaging for a higher spectral resolution. This allows for the detection of surface materials using the principles of spectroscopy.
This work will investigate the application of a class of functions called splines to three different problems in the field of HSI. Splines are a special class of data-fitting functions that guarantee continuity. Common data-fitting techniques like polynomial and piecewise-polynomial fitting are unable to match the complexity of HSI data. Splines provide a robust fitting procedure that matches the physical reality of material spectra. They can additionally be used to smooth data, calculate derivatives, interpolate between points, and more.
The first problem will look at the smoothing of noisy data for detecting small materials. When objects are smaller than the pixel, the observed spectrum will mix the target with background materials. The problem of unmixing removes the influence of these background materials from observed data. If the object is too small, the estimates from unmixing will be dominated by error. In this scenario, splines will be used to smooth out these random variations.
The second problem will use splines to introduce a new solution to the problem of Temperature Emissivity Separation (TES). The physical quantity captured by the camera is radiance. For the detection of materials, ground reflectance or emissivity are desired. TES is the process by which ground radiance is converted to material emissivity. Splines will be used to replace estimated roughness in this problem with an analytical solution.
The third and final application looks at using splines to detect gases without relying on image statistics. Gas features are sharp and only impact a narrow window of the spectrum. This application attempted to use splines to detect these sharp features by looking at the difference between the collected data and an interpolation across the feature.
The first two applications yielded interesting and useful results. The third application yielded some interesting conclusions about the general problem and improved methods for using splines in this space.

ECE MS Thesis Defense: Hooman Barati Sedeh
2021-08-04 13:00 - 14:00
MS Thesis Defense: Space-Time Graphene Metasurfaces
Hooman Barati Sedeh
Location: Zoom Link
Abstract: The unprecedented growth of the data exchanged between wireless devices and the rapid emergence of high-quality wireless services have raised the demand for communication bandwidth and data transmission rates. This has motivated the migration of wireless networks toward the utilization of carrier waves with higher frequencies beyond the millimeter-wave band. Terahertz (THz) band is envisioned as one of the enabling technologies for future generations of wireless communication mobile networks (such as 6G) to address the needs of high-speed and bandwidth-intensive applications. THz communications are expected to provide broadband services to several wireless devices in the internet of things applications. The recent migration from internet-of-things to the internet of nano-things imposes certain constraints in terms of size, weight, and power (SWaP) on the THz antennas responsible for communications, calling for the development of smart antennas with adaptive response capable of establishing multiple active data links through multi-beam scanning in a multiple-input, multiple-output (MIMO) network while meeting the demands on the capacity and SWaP. Moreover, provisioning a reliable communication channel that ensures the security of its users’ information remains vitally essential for the next generation of communication networks.
Metasurfaces, consisting of subwavelength elements, are poised to enable improved free-space optical communications with low SWaP thanks to their small form factor and capability to provide unprecedented control over the wavefront of electromagnetic waves at the subwavelength scale. The recent investigations of active metasurfaces have aimed toward overcoming the fixed response of conventional metasurfaces and developing smart antenna systems with adaptive beamforming capabilities that can point the beam toward the desired users in real-time through pixelated control over the phase of the scattered wave. Moreover, the adaptive communication by such quasi-static tunable metasurfaces can be secured by encrypting the transmitted data via holography to impose restrictions on the data access from an adversary. Despite the fruitful progress in this area, quasi-static active metasurface face several challenges to meet the high demands on the capacity of communication due to their reliance on resonant phase shift accumulations which limit the operation bandwidth and hinders the scalability in terms of the number of channels in the account of non-trivial coupling effects between resonant unit cells. Furthermore, these metasurfaces cannot be used for covert communication as they do not allow for engineering the spectral content of scattered light.
This thesis explores the roles of space and time in active metasurfaces for establishing adaptive and secure multichannel communication at low-THz frequency regime. As the primary goal of this work is twofold, we will tackle each problem separately. At first, we propose a technique for adaptive multichannel communication through simultaneous and independent multifrequency multibeam scanning via a single time-modulated metasurface consisting of graphene micro-patch antennas whose Fermi energy levels are modulated by radio-frequency biasing signals. To this aim, we divide the metasurface aperture into interleaved orthogonally modulated sub-array antennas with distinct modulation frequencies, rendering a shared aperture in space-time. The higher-order frequency harmonics generated by the sub-arrays in such a space-time shared-aperture metasurface are mutually orthogonal in the sense that they do not yield an observable interference pattern and can be separated by spectral filtering. A distinct constant progressive modulation phase delay is then adopted in each sub-array to independently scan its corresponding higher-order frequency harmonics via dispersionless modulation-induced phase gradient with minimal sidelobe level and full angle-of-view over a wide bandwidth. In the second part of this work, we will propose another technique for establishing active secure communication links over single and multiple orthogonal frequency channels via a metasurface that consists of graphene micro-ribbons. To this aim, the Fermi energy level of each graphene micro-ribbons is modulated via pseudo-random radio-frequency biasing signals whose DC offsets are adjusted to tilt the reflected beam toward the predefined direction by imposing a spatial phase gradient profile across the surface, while their waveforms are engineered to expand the incident wave spectrum into a noise-like spectrum with a near-zero power spectral density via random modulation of the reflection phase of each element with respect to its offset phase. This permits for addressing a legitimate mobile user in real-time who can retrieve the incident signal via synchronous demodulation with the pseudo-random key of the metasurface while camouflaging the signal from the adversary by lowering the probability of detection and spectral encryption. The approach is then extended to enable multi-channel secure communication by dividing the metasurface into interleaved sub-arrays modulated with orthogonal pseudo-random keys, which provides simultaneous and independent control over multiple beams with non-overlapping spread spectra which can be retrieved by independent legitimate users while rejecting unwarranted access by eavesdroppers as well as other users.

ECE PhD Dissertation Defense: Mohammadreza Sharif
2021-08-06 14:00 - 15:00
PhD Dissertation Defense: Human-in-the-loop Prosthetic Robot Hand Control through Contextual Adaptation
Mohammadreza Sharif
Location: ISEC136 or Zoom Link
Abstract: Despite fifty years of research on prosthetic robot hands, this technology is yet to be fully acknowledged by amputees as well as manufacturers, due to lack of robustness and intuitive control. Our goal is to enhance the control robustness and intuitiveness through integrating the context, i.e. knowledge of environment and task, with human bio-signals, e.g. hand trajectory. Although this solution has already been studied in the literature, no unified framework is proposed for multi-modal information fusion. This research is aimed at introducing a novel framework for human-in-the-loop prosthetic robot hand control. We propose our solution in two parts, (1) grasp inference and (2) end-to-end actuator control. For (1), we propose a model-based and a model-free grasp inference framework. Our model-based method is based on particle filters method. With knowledge of context hard-coded into the solution per se, our particle-filter-based framework can incorporate any input signal using a proper weight function. In our model-free method we use hidden Markov mode to learn the grasp-inference task directly from human hand transport trajectories. For (2), we propose a reinforcement learning (RL) framework which learns to control robot actuator directly from the context with less information hard-coded into the solution. We leverage imitation learning (IL) besides RL to overcome challenging exploration in the problem. To provide invariance to the human hand transport trajectories, we first provide a solution based on synthesized trajectories based on a human motion model. Later, we adopt an over-sampling technique for real human hand transport trajectories, to serve as a means of data augmentation. This research provides a step forward in more rigorous frameworks for multi-modal information fusion for prosthetic robot hand control and grasp inference through model- /data-based methods.

ECE PhD Dissertation Defense: Pu Zhao
2021-08-10 10:00 - 11:00
PhD Dissertation Defense: Towards Robust Image Classification with Deep learning and Real-Time DNN Inference on Mobile
Pu Zhao
Location: Zoom Link
Abstract: As the rapidly increasing popularity of deep learning, deep neural networks (DNN) have become the fundamental and essential building blocks in various applications such as image classification and object detection. However, there are two main issues which potentially limit the wide application of DNNs: 1) the robustness of DNN models raises security concerns, and 2) the large computation and storage requirements of DNN models lead to difficulties for its wide deployment on popular yet resource-constrained devices such as mobile phones.
To investigate the DNN robustness, we explore the DNN attack, robustness evaluation and defense. More specifically, for DNN attack, we achieve various attack goals (e.g. adversarial examples and fault sneaking attacks) with different algorithms (e.g. alternating direction method of multipliers (ADMM) and natural gradient descent (NGD) attacks) under various conditions (white-box and black-box attacks). For robustness evaluation, we propose a fast evaluation method to obtain the model perturbation bound such that any model perturbation within the bound does not alter the model classification outputs or incur model mis-behaviors. For the DNN defense, we investigate the defense performance with model connection techniques and successfully mitigate the fault sneaking and backdoor attacks. With a deeper understanding of the DNN robustness, we further explore the deployment problem of DNN models on edge devices with limited resources.
To satisfy the storage and computation limitation on edge devices, we adopt model pruning to remove the redundancy in models, thus reducing the storage and computation during inference. Besides, as some applications have real-time requirements with high inference speed sensitivities such as object detection on autonomous cars, we further try to implement real-time DNN inference for various DNN applications on mobile devices with pruning and compiler optimization. To summary, we mainly investigate the DNN robustness and implement real-time DNN inference on the mobile.

Virtual Webinar with Northeastern University and University of Southern California
2021-08-11 09:00 - 10:00
Topic: Engineering Studies in the United States. – Explore the newly emerging fields
This session will be jointly presented by EducationUSA Kolkata and two private research which are pioneer in the fields of Engineering and Applied Sciences and deal with experimental learning and focuses on newly emerging fields. This is a very pertinent session in the present context and suitable to all graduate applicants in Engineering for pursuing MS/PhD degrees
(Note: presenters details may be added later)
Audience: Prospective graduate students, and parents

ECE Teaching Presentation: Mohammad Fanaei
2021-08-12 11:30 - 12:30
Title: Introduction to Python Programming with Applications in Computational Problem Solving.
Mohammad Fanaei
Location: Zoom Link
Abstract: Python is a high-level, interpreted programming language, which supports multiple programming paradigms, including functional and object-oriented programming. In this introductory learning conversation, we will introduce the conditional execution of code, loops, and function definitions in Python. We will then apply the newly learned concepts to implement several numerical analysis methods and a Monte-Carlo simulation. The lecture will involve peer learning and assessment and assumes no background knowledge of Python programming.
Bio: Dr. Mohammad Fanaei received his Ph.D. degree in Electrical Engineering from West Virginia University, Morgantown, WV, in 2016, and his M.Sc. and B.Sc. degrees, both in Electrical Engineering, from Isfahan University of Technology, Iran, in 2008 and 2005, respectively. Over the last six years, Dr. Fanaei has worked at three different universities, as an assistant professor of Electrical and Robotics & Mechatronic Systems Engineering at the University of Detroit Mercy (from 2016 to 2017 and from 2018 to present), as an assistant professor of Electrical Engineering at Bucknell University (from 2017 to 2018) and in the Iron Range Engineering Program in the Department of Integrated Engineering at Minnesota State University Mankato (from 2015 to 2016). Dr. Fanaei’s research interests are in the broad areas of the design, analysis, and evaluation of machine learning and deep learning technologies enabling connected, automated, and autonomous driving systems, as well as the applications of stochastic signal processing in wireless communication systems and sensor networks. His teaching interests include embedded systems, digital design, wireless networks, cryptology and network security, communication systems, stochastic signal processing, and digital signal processing.

ECE MS Thesis Defense: Rahul Bathini
2021-08-13 13:00 - 14:00
MS Thesis Defense: Distributed Containerized Wireless Systems
Rahul Bathini
Location: Zoom Link
Abstract: Future wireless communication paradigms that will form the basis of 5G and beyond architectures will rely on the availability of network infrastructure as a service. In this approach, the same radio and compute resources are partitioned to serve many users, each of which may prioritize different performance metrics. Thus, with the diversity of wireless services and applications, the current ”one size fits all” network architecture is no longer an option. Indeed, virtualization incorporates flexibility into the network architecture design to help meet the diverse application/user needs.
In this thesis we consider a shared compute resource that must support a number of wireless configuration options. We study the effects of CPU allocation alongside baseband (PHY Layer) configurations such as transmitter gain and Modulation and Coding Schemes (MCS), related to various performance metrics of containerized wireless communications systems. We analyze these results and quantify the trends to gain a better understanding of these multiple, interconnected effects. Finally we propose a controller to provide a user with required QoS from pre-defined Service Set identifiers (SSIDs) given the limited availability of CPU resources. We implement this controller system using open-source baseband software and USRP N310 platforms provided in the COSMOS testbed

ECE MS Thesis Defense: Hafiyya Malik
2021-08-13 13:00 - 14:00
MS Thesis Defense: Studies for Improving Multi-channel Confocal Fluorescence Imagining in a Multi-modal Microscope
Hafiyya Malik
Location: Zoom Link
Meeting ID: 988 8423 1362 Passcode: [REDACTED]
Abstract: The goal of this engineering thesis was to implement confocal fluorescence microscopy in an upgraded legacy electro-optical microscopy system for characterization of the viability of cells in biofilms. This application necessitated a 2-channel fluorescence measurement with the possible addition of confocal reflectance for context. Two major areas were addressed. First was the potential for using a silicon photomultiplier (SiPM) detector to replace the usual vacuum photomultiplier employed in confocal fluorescence microscope. A SiPM development board was purchased and tested to determine its performance at different light levels and in the presence of background light. The SiPM has the potential to be a more robust and reliable detector, with the one disadvantage of a smaller area than some vacuum photomultipliers. The second area was the alignment of a long optical path that must include optics for complementary modes of imaging and allow for automated measurement across two spectral channels without loss of alignment. Alignment procedures were developed to simplify the process of alignment and a sliding stage was used to maintain alignment of two dichroic filters when changing channels. Based on this work, multi-channel confocal fluorescence imaging in conjunction with confocal reflectance and bright-field microscope will be possible.

How can I avoid predatory publishers? A Bite-Sized Webinar
2021-08-23 12:00 - 12:30
Have you ever received an email from a journal asking you to submit your work? How can you tell whether the journal is predatory or legitimate? In this 20-minute workshop, you will learn how to recognize signs of deceptive publishing practices by examining solicitation emails and journal websites.
Click this link to register!
Presented by Philip Espinola Coombs and Luesoni Kuck as part of Snell Library’s popular Bite-Sized Webinars series:
Curious about a topic, but simply don’t have the time to attend a full length webinar? Our Bite-Sized Webinars are 20 minute webinars designed to give you a short introduction or overview of a topic. We have presenters from a variety of library departments that include research and instruction, data management, data scholarship, and data visualization. Each of the webinars will be recorded, captioned and sent to registrants.
You can view the list of upcoming Bite-Sized Webinars below or see the full listing here.

ECE PhD Dissertation Defense: Apoorve Mohan
2021-08-25 10:30 - 11:30
PhD Dissertation Defense: Rethinking the Choice between Static and Dynamic Provisioning for Centralized Bare-Metal Deployments
Apoorve Mohan
Location: Zoom Link
Abstract: Technological inertia leads many organizations to continue to employ static provisioning strategies for bare-metal deployments. This results in architectural and runtime inefficiencies. However, most performance and security-sensitive organizations currently employ static provisioning strategies to run and manage their workloads on physical servers (also known as bare-metal servers). This thesis demonstrates how recent technological advances enable dynamic provisioning strategies to improve aggregate bare-metal resource efficiency in centralized bare-metal deployments. Conceptually, this dissertation has four parts. First, it introduces a new system architecture for dynamic bare-metal provisioning. The proposed architecture reduces overhead and operational complexity and improves fault tolerance and performance. To achieve these improvements, it decouples the provisioned state from the bare-metal servers. Second, the thesis identifies the performance and operational issues due to intrusive software introspection strategies employed in existing bare-metal deployments. We present a dynamic bare-metal provisioning-based system to enable non-intrusive software introspection of bare-metal deployments to mitigate these issues. Many organizations have realized the cost benefits of consolidating their computing infrastructure over managing multiple, relatively smaller compute facilities.
Over the past decade, virtual infrastructure solutions have become the obvious choice in consolidated (i.e., single-tenant managed or centralized) deployments for many organizations due to the scalability, availability, and cost benefits it offers. However, most performance and security-sensitive organizations such as medical companies and hospitals, financial institutions, federal agencies run their workloads directly on physical (i.e., bare-metal) infrastructure. They are unwilling to tolerate the performance unpredictability and security risks due to co-location and performance overhead or vulnerabilities due to a large code base of the complex virtualization stack. In addition, even if an organization uses virtual infrastructure, bare-metal infrastructure is used to set up virtualization software stack and when their workloads require direct and exclusive access to hardware components (e.g., InfiniBand, RAID, FPGAs, GPUs) that are difficult to virtualize. Such organizations invest enormous sums of money to buy or rent bare-metal servers and set up and manage bare-metal clusters. Thus, it is imperative that these organizations efficiently operate and utilize the bare-metal clusters they set up to maximize their investment returns. However, despite the technological advances over the past decade, organizations continually employ static provisioning strategies in bare-metal deployments that contribute to poor aggregate bare-metal resource efficiency. Thesis Statement: This thesis demonstrates how dynamic provisioning can mitigate observed inefficiencies in centralized bare-metal deployments. The proposed dynamic provisioning strategies leverages existing storage disaggregation and fault-tolerance technologies to improve aggregate bare-metal resource efficiency. By applying it to four real-world scenarios, this thesis demonstrates the effectiveness of dynamic provisioning.

How do I get started with ArcGIS at Northeastern? A Bite-Sized Webinar
2021-08-25 12:00 - 12:30
In this session, I will introduce ArcGIS web maps and applications which are available through the ESRI license to Northeastern Community. Join me to have a glance at ArcGIS webmaps, storymaps, filed data collector, dashboard and business analyst.
Click this link to register!
Presented by Geospatial & GIS Specialist, Bahare Sanaie-Movahed, as part of Snell Library’s popular Bite-Sized Webinars series:
Curious about a topic, but simply don’t have the time to attend a full length webinar? Our Bite-Sized Webinars are 20 minute webinars designed to give you a short introduction or overview of a topic. We have presenters from a variety of library departments that include research and instruction, data management, data scholarship, and data visualization. Each of the webinars will be recorded, captioned and sent to registrants.
You can view the list of upcoming Bite-Sized Webinars below or see the full listing here.

How do I get free access to the standards I need? A Bite-Sized Webinar
2021-08-26 12:00 - 12:30
If you’re designing or testing a new product or process, you’ll probably need to use standards. But standards can be expensive, and tough to find. This workshop will cover some tips for getting the standards you need, including how to find them, how you can access (some) standards for free through the library, and what to do when the standard you need is behind a paywall.
Click this link to register!
Presented by Jodi Bolognese, Engineering Librarian as part of Snell Library’s popular Bite-Sized Webinars series:
Curious about a topic, but simply don’t have the time to attend a full length webinar? Our Bite-Sized Webinars are 20 minute webinars designed to give you a short introduction or overview of a topic. We have presenters from a variety of library departments that include research and instruction, data management, data scholarship, and data visualization. Each of the webinars will be recorded, captioned and sent to registrants.
You can view the list of upcoming Bite-Sized Webinars below or see the full listing here.

What are persistent identifiers and why do they matter? A Bite-Sized Webinar
2021-08-27 12:00 - 12:30
Persistent identifiers, or PIDs, are unique codes used to unambiguously identify people, places, and things. You should care about them because when you use PIDs, you can get credit for your hard-fought research outputs from papers to data and more. In this session, we’ll give a quick overview of some of the more common PIDs in academia like ORCIDs, DOIs, RORs and RAIDs (oh my). Join us and become a PID Person!
Click this link to register!
This webinar is part of Snell Library’s popular Bite-Sized Webinars series:
Curious about a topic, but simply don’t have the time to attend a full length webinar? Our Bite-Sized Webinars are 20 minute webinars designed to give you a short introduction or overview of a topic. We have presenters from a variety of library departments that include research and instruction, data management, data scholarship, and data visualization. Each of the webinars will be recorded, captioned and sent to registrants.
You can view the list of upcoming Bite-Sized Webinars below or see the full listing here.

ECE PhD Proposal Review: Keng Chen
2021-08-30 09:30 - 10:30
PhD Proposal Review: Design of Accurate and Responsive Power Management Integrated Circuits for Microprocessor Systems
Keng Chen
Location: Zoom Link
Abstract: As technology improves, the processors used in data centers are becoming increasingly powerful. Since the number of complementary metal-oxide semiconductor (CMOS) transistors integrated into these processors continues to rise, it becomes more challenging to design the power management integrated circuits to accurately regulate the supply voltages and ensure fast operational speeds for the processors. Furthermore, with multi-core technology being extensively used in processor design, there is more than just one voltage rail per chip that needs to be precisely regulated. In this research, circuit design methods will be developed to improve the regulation accuracy, both of the output voltage and converter switching frequency, which are proposed for an integrated buck regulator designed for point of load (POL) applications. In order to ensure both accuracy and fast speed of operation, a constant-on-time (COT) architecture was selected for this integrated buck regulator design approach. To provide accurate output voltage regulation, the on-chip feedback sensing network and the bandgap reference circuit need to exhibit high accuracy. For this reason, a highly accurate bandgap reference generation circuit with 5.8 ppm/°C – 13.5 ppm/°C over a wide operational region (-40 °C to 150 °C) has been designed. This bandgap reference circuit has a current-mode architecture and provides a 1.16V reference voltage with a 3.3V supply. A multi-section curvature compensation method is proposed to alleviate the nonlinear temperature-dependent error from the bipolar junction transistor’s base-emitter voltage. The two operational amplifiers utilized in this bandgap reference design to generate proportional-to-absolute-temperature (PTAT) and complementary-to-absolute-temperature (CTAT) current sources share one auxiliary auto-zero amplifier to ensure low input-referred offset voltage. Within many voltage regulators, the feedback sensing network consists of multiple operational amplifiers, and a major error source is the impact of the input-referred offset voltages of these amplifiers.
This research introduces the utilization of two different input-referred offset voltage correction methods for multiple amplifiers. The multi-amplifier system under investigation is used for feedback sensing during voltage regulation. It contains an instrumentation amplifier consisting of three folded cascode stages, and an additional amplifier configured as a unity-gain buffer for a reference voltage. The first method in this work alleviates voltage offsets in a 4-amplifier system based on a shared auxiliary amplifier correction circuit that switches between different target amplifiers. The second method applies a chopping-based auto-zero procedure to cancel the input-referred offset voltage of the same system. Since chopping causes voltage ripples, a correction circuit with a successive approximation register (SAR) analog-to-digital converter is used to reduce the output ripples. Both proposed methods can achieve less than 1mV feedback sensing error in voltage regulator applications.
For the constant-on-time buck regulator itself, a new frequency locking loop to regulate the on-time pulse is also proposed in this work. The system shows less than 1% frequency shift over a wide programmable operational frequency range (400 KHz to 2 MHz). This frequency accuracy will further benefit the constant-on-time circuit to meet electromagnetic interference requirements without harming the fast-transient performance.

Northeastern University Graduate Expo
2021-08-31
This day-long virtual experience will allow you to connect with staff, students, and admissions representatives to learn more about the university and how we can help your academic journey and career.

Click here to view the agenda for the day.

Join live panels and information sessions during the event and enjoy access to on-demand video content whenever you’d like.

Register Today

Inside the PhD Fellowship Experience: Grad Student Panelists Share Advice and Insights
2021-09-02 14:00 - 15:00
In a panel co-presented by the College of Engineering CommLab, Khoury College Graduate Program, and the Network Science Institute, this panel reveals the experiences and insights of doctoral students who have been awarded fellowships. The six panelists, responding to questions from the moderator and audience, will discuss such topics as the application or nomination process, tips for effective application statements and supporting materials, and the place of a fellowship award in one’s doctoral career.
Though the discussion may focus on opportunities for STEM doctoral students, we will also touch on general principles and suggestions that may be helpful to grad students in any discipline, so anyone from the NU grad community is welcome to attend.
Register to join us by Zoom

ECE PhD Proposal Review: Nikita Mirchandani
2021-09-03 11:00 - 12:00
PhD Proposal Review: Ultra-Low Power and Robust Analog Computing Circuits and System Design Framework for Machine Learning Applications
Nikita Mirchandani
Location: Zoom Link
Abstract: As the scaling of CMOS transistors has almost halted, performance gains of digital systems have also started to stagnate. There is a renewed interest in alternate computing techniques such as in-memory computing, hybrid computing, approximate computing, and analog computing. In particular, analog computing has reemerged as a promising alternative to save power and improve performance specifically for machine-learning (ML) applications. Analog computing has better area and power efficiency when compared to their digital counterpart. Power and chip area efficiency make analog computing highly appealing for implementing deep learning algorithms on-chip, computing circuits for the internet-of-things (IoT) devices, and implantable and wearable biomedical devices. However, compared to digital computing, analog computing methods have not nearly been utilized to their fullest potential due to longstanding challenges related to reliability, programmability, power consumption, and high susceptibility to variations.
The subject of this dissertation research is to develop robust ultra-low power analog hardware suitable for machine learning applications. First, a robust analog design methodology is presented to address issues of variability in analog circuits. A constant transconductance design technique using switched capacitor circuits is presented. The design approach is then applied to build circuits for ML applications. An analog vector matrix multiplier (VMM) is designed to be used in the convolutional layer in an ML analog computing vision hardware platform. Computing circuits are tested as part of an image classification DNN algorithm on the MNIST dataset and can achieve a classification accuracy of 96.1%.
The design approach is also used to design an analog computing system architecture for detection of seizures using EEG signals. A conventional EEG monitoring system includes an analog front-end (AFE), ADC, digital filtering stage, EEG feature extraction engine, and SVM classification. Such systems suffer from high power and chip area requirements. The corresponding analog architecture is composed of AFE amplifiers to provide gain for the incoming signal. The AFE is followed by an analog filtering stage, where spectral power from each of the bands is used as a feature for seizure classification. The output of each filter is applied to a corresponding feature extraction circuit to continuously monitor the onset of a seizure in an ultra-lower power mode with sub-threshold analog processing. The system level architecture is first modeled to obtain classification accuracy of seizures. Simulation times for the design of such complex analog systems can be prohibitively long, particularly when the impacts of nonidealities such as noise, nonlinearity, and device mismatches have to be considered at the system level. The simulation time is reduced by building accurate models of the analog blocks for faster simulations. The analog models help to define the required specifications for each block in order to achieve a specified system-level classification accuracy.
Infrastructure circuits like oscillators and voltage regulators for the proposed SoC are presented. A 254 nW 21 kHz on-chip RC oscillator with 21.5 ppm/oC temperature stability is presented to provide stable clock source for the proposed SoC.

Explore Global Engineering Co-ops
2021-09-09 12:00 - 13:00
The College of Engineering is offering advice on how to discover and pursue co-ops around the world, including search techniques, information on logistics, and tips for self-developing positions. For anyone planning to do an engineering co-op in Spring/Summer 2022, attendance is required at one of these sessions.
Learn More

ECE PhD Dissertation Defense: Wenqian Liu
2021-09-10 14:00 - 15:00
PhD Dissertation Defense: Explainable Efficient Models for Computer Vision Applications
Wenqian Liu
Location: Zoom Link
Abstract: State of the art deep learning based models, such as Convolutional Neural Networks (CNNs) and generative models, achieve impressive results, but with their great performance comes great complexity and opacity, huge parametric spaces and little explainability. The criticality of model explainability and output interpretability, manifests clearly in real-time critical decision making processes and human-centred applications, such as in healthcare, security and insurance. Explainability and interpretability are tackled in this thesis, as intrinsic qualities in the model architecture as well as post-hoc improvement on existing models. In the area of frame prediction in video sequences, we introduce DYAN, a novel network with very few parameters, that is easy to train and produces accurate high quality predictions. Another key aspect of DYAN is interpretability, as its encoder-decoder architecture is designed following concepts from systems identification theory and exploits the dynamics-based invariants of the data. We also introduce KW-DYAN, an extension of DYAN that tackles the issue of time lagging in video predictions, by implementing a novel way of quantifying prediction timeliness and proposing a new recurrent network for adaptive temporal sequence prediction. The experimental results show the reduced lagging across datasets, while also performing well in other metrics. In this thesis we also propose the first technique to visually explain VAEs by means of gradient-based attentions, with methods to generate visual attentions from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAEs. We show how these attention maps can be used to localize anomalies in images, conducting state-of-the-art performance on multiple datasets. We also apply our technique for skin image anomaly detection and diagnosis and achieve competitive quantitative and qualitative results.
