Department Publications

Indoor cycling is a concept that has emerged with technological advancements to serve multiple purposes such as fitness, virtual tourism, and leisure. Commercial solutions have been developed to serve different user groups, with emphasis on corresponding aspects of user experience. This paper proposes a novel approach to indoor cycling, combining key features including real-world 360-degree view, immersive environment, real-time interaction between the real and virtual worlds and dynamic resistance control to simulate changes in elevation. In the proposed solution, a mobile application provides an immersive cycling environment by playing 360-degree videos on a Virtual Reality headset. The environment responds to user movements such as head rotation and cycling speed changes, and in return replicates the actual resistance to the rider using automatic resistance control. These components are …

Penetration of Intelligent Transport Systems (ITS) applications is slow, particularly in developing countries, mainly due to the costs associated with the two state-of-the-art technologies, Dedicated Short-Range Communication (DSRC) and Cellular-V2x (c-V2x).As a solution, this paper presents a DSRC/Wi-Fi hybrid system and evaluates its performance experimentally. Communication within the Wi-Fi segment is via customized beacons serving a similar function as roadside alerts in DSRC, but with a configurable repetition interval. The results show that Wi-Fi beacons can be received over a 200m range with a packet delivery rate of more than 50%. Considering the effective range and the information update rate, we illustrate several applications that are feasible with the hybrid system in Vehicle-to-Vehicle (V2V) and Infrastructure-to-Vehicle (I2V) modes. With our novel hardware design, the expected functionality can …

Inspired by recent improvements in point cloud processing for autonomous navigation, we focus on using hierarchical graph neural networks for processing and feature learning over large-scale outdoor LiDAR point clouds. We observe that existing GNN based methods fail to overcome challenges of scale and irregularity of points in outdoor datasets. Addressing the need to preserve structural details while learning over a larger volume efficiently, we propose Hierarchical Point Graph Neural Network (HPGNN). It learns node features at various levels of graph coarseness to extract information. This enables to learn over a large point cloud while retaining fine details that existing pointlevel graph networks struggle to achieve. Connections between multiple levels enable a point to learn features in multiple scales, in a few iterations. We design HPGNN as a purely GNN-based approach, so that it offers modular …

We propose a novel multi-dimensional zero-phase infinite impulse response (IIR) notch filter having multiple independently-tunable notches. The proposed notch filter employs a cascade structure with M N-dimensional ( N -D) zero-phase IIR filters to realize an N -D notch filter with M notches. In the proposed filter, the frequency and the bandwidth of each notch can be independently tuned. Furthermore, the proposed filter can have odd number of notches, which may be required for complex-valued signals. Experimental results confirm that the proposed filter significantly attenuates multiple narrowband interference and provides significant improvement in the signal-to-interference ratio compared to a previously proposed three-dimensional IIR filter.

Mostly-online teaching experiences in circuits and systems (CAS) during 2020-2021 COVID-19 pandemic are presented. Three case studies are shared summarizing course details, tools and platforms, best practices and limitations across three universities representing different continents. The presented approaches attempt to address several limitations of passive online delivery of CAS courses via interactive simulations, formative assessments via interactive web content and slide-embedded polls, at-home labs with student acquired hardware and instructor-lead and student-centered activity based learning.

Predicting the possible future trajectories of the surrounding dynamic agents is an essential requirement in autonomous driving. These trajectories mainly depend on the surrounding static environment, as well as the past movements of those dynamic agents. Furthermore, the multimodal nature of agent intentions makes the trajectory prediction problem more challenging. All of the existing models consider the target agent as well as the surrounding agents similarly, without considering the variation of physical properties. In this paper, we present a novel deep-learning based framework for multimodal trajectory prediction in autonomous driving, which considers the physical properties of the target and surrounding vehicles such as the object class and their physical dimensions through a weighted attention module, that improves the accuracy of the predictions. Our model has achieved the highest results in the nuScenes trajectory prediction benchmark, out of the models which use rasterized maps to input environment information. Furthermore, our model is able to run in real-time, achieving a high inference rate of over 300 FPS.

We address the compensation of distortions in terahertz time-domain spectroscopy (THz-TDS) systems. In this context, the main sources of possible distortions are misalignment of the hardware and the sample during the scan, vibration of the measurement system and time delay variations due to the unevenness of the material surface. This heavily affects the achievable accuracy of material characterization by the THz-TDS system. Therefore, it is important to develop approaches to mitigate these effects. In this work, we profit from the low-rankness of the distortion profile. To this end, we formulate an iterative algorithm based on rank minimization to estimate the material parameters in the presence of the aforementioned impairments. In contrast to the standard convex relaxation of the rank, we propose an iteratively reweighted nuclear norm for improved estimation performance. Further, we compare our approach with …

We propose a weighted least-square (WLS) method to design autoregressive moving average (ARMA) graph filters. We first express the WLS design problem as a numerically-stable optimization problem using Chebyshev polynomial bases. We then formulate the optimization problem with a non-convex objective function and linear constraints for stability. We employ a relaxation technique and convert the non-convex optimization problem into an iterative second-order cone programming problem. Experimental results confirm that ARMA graph filters designed using the proposed WLS method have significantly improved frequency responses compared to those designed using previously proposed WLS design methods.

Designing new optical systems from the ground up for microscopy imaging tasks such as phase retrieval, requires substantial scientific expertise and creativity. To augment the traditional design process, we propose differentiable microscopy ( ), which introduces a top-down design approach. Using all optical phase retrieval as an illustrative example, we demonstrate the effectiveness of data-driven microscopy design through . Furthermore, we conduct comprehensive comparisons with existing all-optical phase retrieval methods, showcasing the consistent superiority of our learned designs across multiple datasets, including biological samples. To substantiate our ideas, we experimentally validate the functionality of one of the learned designs, providing a proof of concept. The proposed differentiable microscopy framework supplements the creative process of designing new phase microscopy systems and may be extended to other similar applications in optical design.

The hybrid dc–dc converter family has desirable features to fulfil the performance matrix requirements of point-of-load converters. However, there are no well-established methods for synthesizing single and dual-path converters addressing design specifications. The existing connection matrix-based approaches are not helpful because they give rise to nonrealizable, redundant, and degenerative solutions. Therefore, a design-oriented and low-entropy equation-based single-path hybrid converter synthesis method is proposed in this article. To this end, general structures for the single- and multi-inductor converters have been proposed. They are based on the switched-capacitor cells. Therefore, a set of guidelines have been provided in this article to decompose a voltage gain polynomial for identifying the switched-capacitor cells. Moreover, a structured method to derive and approximate a converter gain polynomial …

Many of the existing Person Re-identification (Re-ID) approaches depend on feature maps which are either partitioned to localize parts of a person or reduced to create a global representation. While part localization has shown significant success, it uses either na{\i}ve position-based partitions or static feature templates. These, however, hypothesize the pre-existence of the parts in a given image or their positions, ignoring the input image-specific information which limits their usability in challenging scenarios such as Re-ID with partial occlusions and partial probe images. In this paper, we introduce a spatial attention-based Dynamic Part Template Initialization module that dynamically generates part-templates using mid-level semantic features at the earlier layers of the backbone. Following a self-attention layer, human part-level features of the backbone are used to extract the templates of diverse human body parts using a simplified cross-attention scheme which will then be used to identify and collate representations of various human parts from semantically rich features, increasing the discriminative ability of the entire model. We further explore adaptive weighting of part descriptors to quantify the absence or occlusion of local attributes and suppress the contribution of the corresponding part descriptors to the matching criteria. Extensive experiments on holistic, occluded, and partial Re-ID task benchmarks demonstrate that our proposed architecture is able to achieve competitive performance. Codes will be included in the supplementary material and will be made publicly available.

This paper compares the two take-off methods; vertical take-off and bird take-off for quad-plane unmanned aerial vehicles. Energy consumption and flying quality of the two methods are assessed. Often, the quad-plane aerial vehicles take off vertically using the quad mode, and when the reference altitude is reached the plane mode kicks in. The rarely used bird take-off mode, uses both quad and plane modes kick in together at the take-off so that the UAV gains the altitude and airspeed symultaneously lower energy. The experiments were carried out with a 1.35kg quad-plane on a two short flights conducted back-to-back under similar environmental conditions. It has been found from the flight data that the bird take-off helps quad-plane to fly 10.8\% more distance for a unit of energy than with the vertical take-off. It was also observed that the VTO method shows sudden altitude drop during the quad to plane control …

We present an end-to-end virtual try-on pipeline, that can fit different clothes on a personalized 3-D human model, reconstructed using a single RGB image. Our main idea is to construct an animatable 3-D human model and try-on different clothes in a 3-D virtual environment. The existing frame by frame volumetric reconstruction of 3-D human models are highly resource-demanding and do not allow clothes switching. Moreover, existing virtual fit-on systems also lack realism due to predominantly being 2-D or not using user's features in the reconstruction. These shortcomings are due to either the human body or clothing model being 2-D or not having the user's facial features in the dressed model. We solve these problems by manipulating a parametric representation of the 3-D human body model and stitching a head model reconstructed from the actual image. Fitting the 3-D clothing models on the parameterized human model is also adjustable to the body shape of the input image. Our reconstruction results, in comparison with recent existing work, are more visually-pleasing.

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the speed of electronic hardware. Complementarily, to improve throughput further, here we propose to acquire images in a compressed form such that more information can be transferred beyond the existing electronic hardware bottleneck. To this end, we present a learnable optical compression-decompression framework that learns content-specific features. The proposed differentiable quantitative phase microscopy ( ) first uses learnable optical feature extractors as image compressors. The intensity representation produced by these networks is then captured by the imaging sensor. Finally, a reconstruction network running on electronic hardware decompresses the QPM images. In numerical experiments, the proposed system achieves compression of 64 while maintaining the SSIM of and PSNR of dB on cells. The results demonstrated by our experiments open up a new pathway for achieving end-to-end optimized (i.e., optics and electronic) compact QPM systems that may provide unprecedented throughput improvements.

Mobile users may experience frequent service outages or poor quality of service in rural areas due to the scarcity of base stations in the proximity. Therefore, a cost effective and flexible solution is introduced in this paper with unmanned aerial vehicles (UAVs) and terrestrial base stations (TBSs), coexisting under a stochastic network with the combination of millimeter waves and non-orthogonal access (NOMA) techniques. This is achieved by deploying both the UAVs (millimeter wave frequencies) and TBSs (sub-6 GHz frequencies) in the same system, and selecting the transmitter based on a user association scheme. The maximum signal-to-interference-plus-noise ratio (SINR) and the closest transmitter user association schemes are compared using extensive simulations to identify the most suitable scheme in terms of the outage probability and the achievable downlink rate. The results show that the closest …

3D generative models have been recently successful in generating realistic 3D objects in the form of point clouds. However, most models do not offer controllability to manipulate the shape semantics of component object parts without extensive semantic attribute labels or other reference point clouds. Moreover, beyond the ability to perform simple latent vector arithmetic or interpolations, there is a lack of understanding of how part-level semantics of 3D shapes are encoded in their corresponding generative latent spaces. In this paper, we propose 3DLatNav; a novel approach to navigating pretrained generative latent spaces to enable controlled part-level semantic manipulation of 3D objects. First, we propose a part-level weakly-supervised shape semantics identification mechanism using latent representations of 3D shapes. Then, we transfer that knowledge to a pretrained 3D object generative latent space to unravel disentangled embeddings to represent different shape semantics of component parts of an object in the form of linear subspaces, despite the unavailability of part-level labels during the training. Finally, we utilize those identified subspaces to show that controllable 3D object part manipulation can be achieved by applying the proposed framework to any pretrained 3D generative model. With two novel quantitative metrics to evaluate the consistency and localization accuracy of part-level manipulations, we show that 3DLatNav outperforms existing unsupervised latent disentanglement methods in identifying latent directions that encode part-level shape semantics of 3D objects. With multiple ablation studies and testing on state-of-the …

To improve spatial resolution in the PET-laparoscope system for intraoperative metastatic lymph nodes detection, design of a flat panel detector was optimized using Monte Carlo simulation tools. The performance of the new fixed detector was evaluated in point radio-source and phantom experiments. The experiment result shows that the system with the new fixed detector design has a spatial resolution better than 10 mm in three dimensions, and the system can distinguish two radio-sources clearly in a 2-minute scan.

The Kalman filter is often used with plants without zeros. However, when the plant has zeros, the Kalman filter tends to neglect the effects of zeros, leading to errors in output estimation. Without the effects of zeros, the Kalman filter estimation would lag behind the true output, or it would lead to true output if the zeros are on the RHP. The parallel Kalman filter bank, instead of a single filter is proposed to remedy this problem. Each Kalman filter in the bank operates on a plant submodel that is obtained by decomposing the plant model. The output estimations of all of the Kalman filters in the bank are added together to synthesize the plant output estimation. The method was simulated in a PID-controlled system where better results have been obtained.

The quadrotor is an under-actuated nonlinear system that poses challenges in modeling and controller design. This work uses the translational (Newtonian) and rotational (Eulerian) dynamics of the quadrotor to build a decoupled control system. A linear quadratic Gaussian (LQG) regulator is used as the controller, and two extended Kalman filters are used for state estimation in the two dynamic models. The entire design process from modeling to flight simulation is presented. Model linearization while preserving controllability, state-space modeling under gravity disturbance, feedforward compensation, LQR gain adjustment, actuator dynamics, sensor system for observability, adaptation of extended Kalman filter, and its limitations are presented.

Wireless signals-based non-destructive material characterization and object identification is a vital research area that has many potential day-to-day and industrial applications. Here, one of the main challenges is suppression of the unwanted reflections coming from the surroundings that are not in our interest, ie, clutter. The clutter echoes are much stronger than the reflection that comes from the desired object. Also, in many scenarios, the clutter source and the desired objects are in close proximity. Therefore, clutter may mask the reflections coming from the desired objects. This dissertation intensively discusses clutter suppression for the identification of objects/defects. In this regard, we utilize advances in supervised learning and nonconvex optimization for clutter suppression. In this context, we profit from the low-rankness and the sparseness of the responses of the clutter and the objects. Instead of the convex relaxation of rank and sparsity commonly applied in state-ofthe-art, we propose a non-convex iteratively reweighted nuclear and ℓ1− norm-based iterative algorithm to obtain higher accuracy. Moreover, in contrast to the standard parameter tuning of the iterative algorithm, we propose supervised deep learning to tune the parameters, which is referred to as algorithm unrolling. Our numerical results show that deep learning-based parameter tuning significantly improves the accuracies of the recovered low-rank and sparse components and the speed of the convergence of the iterative algorithm. Additionally, we investigated the robustness of our approach by introducing artificial errors into the measurement matrices. Our findings show that …

Oceans cover about 70% of Earth's surface and it is difficult to keep them under continuous surveillance with ever increasing criminal activities. Conventional surveillance methods utilize manned vessels to patrol large sea areas, and due to repetitive-monotonous nature, the personnel involved can often make inevitable human errors due to fatigue. As an alternative, solar-charged battery-powered surface vessel that can autonomously navigate in maritime waters is proposed. Autonomous surveillance vessels are required to navigate in oceans for an extended time duration, requiring them to use much power in propulsion. To properly manage propulsion power, the vessel design must minimize the drag acting upon the vessel. In this work, computational fluid dynamics (CFD) techniques are used to identify the most suitable type of vessel shape that can minimize the drag and maximize the lift and stability. According …

Stroke patients with hemiparesis display decreased beta band (13–25Hz) rolandic activity, correlating to impaired motor function. However, clinically, patients without significant weakness, with small lesions far from sensorimotor cortex, exhibit bilateral decreased motor dexterity and slowed reaction times. We investigate whether these minor stroke patients also display abnormal beta band activity. Magnetoencephalographic (MEG) data were collected from nine minor stroke patients (NIHSS< 4) without significant hemiparesis, at~ 1 and~ 6 months postinfarct, and eight age-similar controls. Rolandic relative beta power during matching tasks and resting state, and Beta Event Related (De) Synchronization (ERD/ERS) during button press responses were analyzed. Regardless of lesion location, patients had significantly reduced relative beta power and ERS compared to controls. abnormalities persisted over visits, and were present in both ipsi-and contra-lesional hemispheres, consistent with bilateral impairments in motor dexterity and speed. Minor stroke patients without severe weakness display reduced rolandic beta band activity in both hemispheres, which may be linked to bilaterally impaired dexterity and processing speed, implicating global connectivity dysfunction affecting sensorimotor cortex independent of lesion location. Findings not only illustrate global network disruption after minor stroke, but suggest rolandic beta band activity may be a potential biomarker and treatment target, even for minor stroke patients with small lesions far from sensorimotor areas.

Two-dimensional (2-D) infinite-extent impulse response (IIR) filter design is a challenging problem due to the difficulty in verifying stability. optimization methods proposed so far predominantly consider the design of 2-D IIR filters having quadrantally-symmetric frequency responses, where the transfer functions have separable denominators. In this paper, we propose a weighted least-squares (WLS) design method for 2-D IIR filters having arbitrary frequency response and stable in the practical bounded-input bounded-output (P-BIBO) sense. Our design considers transfer functions with nonseparable numerators and denominators having complex-and real-valued coefficients, respectively. We formulate the WLS design as an iterative second-order cone programming problem, which includes constraints to guarantee the P-BIBO stability. Design examples confirm that the proposed WLS method leads to P-BIBO stable …

This letter investigates the exploitation of an intelligent reflecting surface (IRS) to communicate securely in a two-way network consisting of an untrusted user. In particular, the transmit powers and the phase shift at each element of the IRS are optimized to maximize the sum-secrecy rate, such that the IRS-reflected and non-IRS-reflected signals are added destructively at the untrusted user. The proposed iterative algorithm converges rapidly to a feasible solution of high accuracy with a few iterations. Numerical results demonstrate sum-secrecy rate gains up to 120% compared to naive or partially optimized schemes.