Department Publications

With the recent emergence of virtual reality, augmented reality, and mixed-reality technologies, considerable attention has been received to integrate spatial audio recording capabilities into these devices. Extracting higher order spherical harmonics from the recordings is not always possible due to complex scattering effects from the arbitrary-shaped devices, which motivates the need for modeling the acoustic properties beyond free-field convention. In this paper, we propose modeling the wearable device-related transfer function (WDRTF) that incorporates scattering, and diffraction from the device and wearer’s body to an incident soundfield. We adopt the spherical wave model to translate the measurements into microphone alignment, by exploiting both magnitude and phase corrections. We use a discrete set of measurements by multiple circular arrays for efficiently estimating the WDRTF coefficients. We illustrate …

The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML based algorithms for sensing and perception of the waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to high chip area and power consumption. We address this issue by exploiting recently proposed approximate discrete Fourier transforms (ADFT) which has its own sparse factorization for real-time implementation at low complexity and power consumption. The ADFT is used to create a wideband multi-beam RF digital beamformer and temporal spectrum-based attention unit which monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm having low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high …

A low-complexity multibeam wideband transmit beamformer using a 2D sparse FIR filter design capable of multiple beams is proposed as a digital building block for fully digital beamformers. The 2D sparse FIR filter has multiple trapezoid-shaped passbands pertaining to wideband beams aimed at particular directions. The proposed multibeam digital beamformer drives a uniform linear array of wideband antenna elements to achieve the wideband multibeam transmit-mode signals desired by the communication system. The 2D sparse FIR filter is designed to be optimal in the minimax sense using convex optimization techniques. Full-wave electromagnetic simulations using real antenna models confirm that the proposed wideband transmit beamformer can achieve multibeam transmission in the 1.3–2.8 GHz frequency range, with more than 70% fractional bandwidth. Furthermore, the adoption of the wideband transmit multibeam beamformer leads to a significant reduction in digital arithmetic (computational) complexity compared with previously reported wideband transmit beamformers of similar transfer function type, without deteriorating beam directionality and causing increases in the side-lobe level. The proposed sparse 2D FIR multibeam beamformer architecture is well-suited for both sub-6 GHz (legacy) band transmit beamforming, frequency range three (FR3) beamforming up to 28 GHz, and mmWave operation for emerging 5G/6G applications.

Frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) long-range radars currently employed for autonomous robotics have limited maximum range. By employing transmit beamforming and beam scanning, the range can be increased. However, the beam scanning time reduces the achievable velocity resolution. In this paper, we propose an FMCW MIMO radar, operating at 77 GHz, with transmit (TX) beamforming with subarrays to increase the range. We employ four TX subarrays, each having three antennas, with analog beamforming. Compared to TX beamforming as one TX array, our approach provides sufficiently wide beams alleviating beam scanning, further, without substantially reducing the virtual elements of the MIMO radar. We implement the radar signal processing pipeline on a ZYNQ Ultrascale+ ZCU106 FPGA to achieve real-time processing. Furthermore, we employ a …

Cycling is a popular leisure and fitness activity, making it a prime candidate for emerging virtual and augmented reality applications. This paper details the design, development, and testing of a competitive virtual cycling platform that allows two players to compete regardless of their geographical location on customized stationary bicycles. They wear head-mounted Virtual Reality (VR) devices to immerse themselves in a 360° video experience. Video playback responds to their cycling speed and head orientation. Competitors' locations are displayed on a map within their VR dashboard. The platform's functionality is tested on 4G and 5G networks and an experimental 4G network implemented on software-defined radios. To evaluate the system's usability, the critical metric of end-to-end delay is measured for the different communication architectures. Delays of under 100ms, crucial for preventing motion sickness, are …

Accurate and accessible cognitive load monitoring is essential for improving personalized learning and facilitating better mental health monitoring. Existing methods for measuring cognitive load can be intrusive, costly, and less effective for individuals with high melanin concentrations, emphasizing the need for an improved approach. This study presents a novel approach to monitoring the cognitive load using functional near-infrared spectroscopy (fNIRS) technology. A lightweight, cost-effective fNIRS device with a single optode was developed to record prefrontal cortex data, specially designed to suit the skin with high melanin concentration. Data from participants performing n-back tasks revealed significant differences between hemoglobin concentrations among 1-back, 2-back, and 3-back tasks via ANOVA analysis. To further explore cognitive load assessment, various machine learning models were …

This chapter presents how optimal control is used in the form of feedback so that optimal control can be implemented on real systems. We consider linear plants and quadratic cost functions and develop the optimal regulator, which is known as the linear quadratic regulator (LQR). The nature of the optimality under LQR is analyzed, and the finite-time and infinite-time regulation processes are presented. Finally, the performance of LQR and PID control systems is compared.

Multichannel Wiener filter (MWF), commonly used as a spatial filter for noise reduction, exhibits promising performance due to its robustness in multi-channel setups. Additionally, the preservation of spatial cues at the MWF is usually a favorable property that contributes to sound source localization. Previous works have shown that the MWF can be used in drone noise suppression for acoustic sensing tasks in drone audition. In this paper, we analyze the preservation of spatial cues at the MWF from on-board microphone recordings on a drone at very low signal-to-drone noise ratio (SDNR) levels. The analysis builds upon the relative transfer function (ReTF), and explores the spatial information of the desired sound sources such as human speakers and birds through experimental recordings from simulated and real-life environments. We also demonstrate the effectiveness of the preservation of spatial cues at the MWF in an acoustic localization application

This study aims to check the behaviour of various controllers that can successfully control a catamaran vessel on the ocean surface for trajectory tracking. Unlike on the ground, the ocean surface has disturbances from water currents, wind forces and ocean waves. Therefore, trajectory tracking is challenging work. A 3D model of a catamaran vessel was simulated to check the vessel’s behaviour with the controllers: Polynomial recurrent neural network-based adaptive PID controller, first and second-order sliding mode controllers, and first-order adaptive controller are among them. The control system is a combination of two controllers. The primary controller controls the vessel’s surge and yaw according to the velocity error. The secondary controller controls the velocity errors required by the primary controller, according to the displacement error of the catamaran vessel from the trajectory and the required forward …

The real-time adaptive nulling of a digital beamformer is crucial for receiving weak signals in a high RFI or RF denied environment. The Howells-Applebaum and MVDR adaptive beamformers use the Weiner optimum solution to ensure that RFI sources are blanked out in an array processor. In this paper, we create a custom 32-channel fully-digital array operating at 5.7-5.8 GHz, with real-time FPGA processing for both adaptation and subsequent beamforming with RFI suppression. Real-time measurements with up to three RFI sources show correct real-time digital beamforming in the FPGA processor. The developed microwave and digital systems along with hardware accelerated Howells-Applebaum array-null adaptation architecture will be used as a building block to create RF identification, location, spectrum sensing, and wireless communications that are resilient to jammers and RFI sources in the environment.

We present SimuSIL, a software-in-the-loop (SIL) simulation platform using open-source tools to capture the behavior of connected vehicles in intelligent transportation system (ITS) applications. Connected cooperative vehicles use vehicle-to-everything (V2X) communication to enable ITS. Real-world testing of ITS applications involves significant risks. Simulating true vehicle dynamics and real-time behavior is challenging in discrete time step environments. We extend the Veins platform, integrating SUMO and OMNet++, with Webots, a simulator for robots and vehicles. Webots implements engine control unit (ECU) models in software to mimic physical vehicle behavior. We validate SimuSIL with a car-following application using Webots and demonstrate its ability to capture communication anomalies like packet losses. Additionally, we demonstrate fuel consumption and CO 2 emissions in autonomous eco-driving …

The ability to accurately localize vehicles is essential for driver assistance applications. Existing modern localization systems, while sophisticated, often come with considerable costs and limitations in accessibility. Recognizing the need for a more accessible solution, there is a growing demand for user-friendly and cost-effective alternatives. One such alternative lies in leveraging mobile devices for localization. However, relying solely on Global Positioning System (GPS), Wi-Fi, or cellular networks, the most commonly used technologies in mobile device localization, often suffer from issues with accuracy. Addressing these drawbacks is crucial to ensuring its effectiveness and widespread adoption. Map matching is a commonly pursued and well-researched approach taken to rectify these erroneous location data. This paper presents the implementation of a hybrid of two map-matching implementations on a mobile …

The radio spectrum is a scarce and vital resource. Every country in the world has sovereign control over its spectrum. Demand for the spectrum in terms of mobile communication-related applications is increasing continuously. As a result, Mobile network operators (MNO) are keen to acquire additional spectrum bands to deploy in mobile networks. 5G technology, as the latest generation of mobile telephony with many advanced features has opened many doors to other industries such as virtual reality, the Internet of Things(IoT), smart cities, smart health care, etc. and demand for them is trending. As a result of its technological advancements and the economic benefits of 5th generation technology (5G), network operators are keen to migrate to 5G. Due to the excessive demand for the spectrum, the true value of the spectrum has gone up drastically. Therefore, understanding the true value of spectrum has become a …

Few-shot learning (FSL) aims to enable models to recognize novel objects or classes with limited labeled data. Feature generators, which synthesize new data points to augment limited datasets, have emerged as a promising solution to this challenge. This paper investigates the effectiveness of feature generators in enhancing the embedding process for FSL tasks. To address the issue of inaccurate embeddings due to the scarcity of images per class, we introduce a feature generator that creates visual features from class-level textual descriptions. By training the generator with a combination of classifier loss, discriminator loss, and distance loss between the generated features and true class embeddings, we ensure the generation of accurate same-class features and enhance the overall feature representation. Our results show a significant improvement in accuracy over baseline methods, with our approach …

Minimally invasive liver cancer treatments are increasingly favoured for their reduced risks and improved patient outcomes. However, real-time guidance during liver ablation remains challenging due to limited resources and the reliance on ultrasound (US) imaging. This project introduces a deep learning-based approach for 2D US to 3D Computed Tomography (CT) image registration, eliminating the need for 3D tracking systems. By integrating self-supervised feature embeddings and matching point estimation methods, our approach enhances real-time visualization of the liver, improving surgical precision and cost-effectiveness. With around 0.99 Normalized Cross-Correlation (NCC) value and 25.48-pixel distance in US and CT image registration, this research promises significant advancements in liver tumor ablation procedures and holds potential for broader applications in other ablation therapies.

This chapter presents how to combine the equality constraints with the objective function in the optimization process. How to deal with inequality constraints using KKT conditions is presented next. Then, the implementation of quadratic optimization with equality constraints and boundary limits is demonstrated using the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathtt quadprog$$\end{document} command in Matlab. In the end, the optimization of an arbitrary function subject to nonlinear constraints, linear constraints, and boundary limits is demonstrated using the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage …

Some control systems need to maximize a certain outcome while minimizing the cost. For example, a rocket propulsion system intends to reach the maximum altitude with the least fuel. In such systems, the cost minimization concept in optimal control is changed to outcome maximization, and the particular optimal control design process can be implemented using Pontryagin’s maximum principle. This chapter presents the maximum principle based on the Hamiltonian and demonstrates its application through several exercises. The bang–bang control and control switching, which are outcomes of the maximum principle, are also presented.

Modern control systems are mostly implemented in discrete time. Hence, the optimal control process also needs discrete-time implementation. This chapter presents how discrete-time optimal control can be developed for discrete state-space systems with quadratic cost functions. Several worked exercises are presented to show how discrete-time optimal control is implemented.

This chapter presents the optimal control design for linear, continuous-time systems. The two main formulations of functional optimization—Lagrange–Euler and Pontryagin–Hamiltonian—are presented, and the conditions for optimality in each formulation are derived. The nature of optimality is also explored. The process of introducing end-point conditions of final time and final state is presented. Several exercises are provided to emphasize the application of the two formulations in optimal control system design.

Optimization aims at finding a unique point, a unique state, or a unique function (a sequence of points or states) that is associated with the minimum cost or maximum benefit with respect to a certain criterion, subject to constraints. Function optimization is largely based on calculus developed by Sir Isaac Newton and Gottfried Wilhelm Leibniz. The most comprehensible optimization process is finding the minima or maxima of a single variable function by setting the first derivative , which can be extended to multi-variable functions by setting all partial derivatives to zero . More often, the functions to be optimized are costs that can be modeled in the quadratic form , which allows wider applicability of mathematical tools in the optimization process, hence quadratic optimization.

This chapter presents how to use the calculus of variations to find the optimum function within a group of functions without considering the constraints. A few examples of functional optimization, including the shortest path and the minimum-time path, are presented. The Matlab implementation of functional optimization is also demonstrated. Figure  shows possible paths f ( x ) of a single variable functional between two fixed points and . It also shows the optimal function .

The optimal control sequence generated offline does not help realize optimal control in actual systems due to state deviations that are not captured in the state-space model. Such deviations are caused by unmodeled dynamics and noise, leaving only the first few control signals in the optimal control sequence valid and practically useful. In this context, the model predictive control (MPC) is presented in which an optimal control sequence is generated for a finite time into the future (the prediction horizon), and only the first few control signals (the control horizon) are practically used. After each control horizon, the process recurs in its actual state at that time.

Making drones talk is the next step in Human-Unmanned Aerial Vehicle interactions by installing an on-board loudspeaker array. However, due to the drone being loud, it creates an adverse noisy environment of very low signal-to-drone noise ratio (SDNR) levels (less than -10 dB) making it difficult to project a perceptual soundfield to a desired location on the ground. In this paper, we present a preliminary study on developing a sparsity-based interior response model of a commercial loudspeaker that can be used to modify the speech signals before playback through the loudspeaker. We incorporate the ground reflections into the incident response model of the real loudspeaker to simulate the outdoor environment. We validate the proposed model using soundfield recording over a larger region for low frequencies up to 114 Hz.

This thesis explores the acoustic signal processing on drones which can enhance the drone audition functions for search and rescue missions, wildlife monitoring, and video recording for media and filming industries. With the recent emergence of drone-based applications and services, microphone arrays are beginning to be mounted to drones. However, audio capturing using drone-mounted microphone arrays leads to very low signal-to-drone noise ratio (SDNR) recordings (below negative 10 dB) due to the drone-generated motor and propeller noise signals. Hence, this causes a highly adverse noisy environment and degrades the quality and intelligibility of the recorded audio signals. Therefore, there is a growing interest in deriving new signal processing algorithms for the future deployment of the drone audition. To this end, we develop novel methods for spatial soundfield modelling, and spatial audio processing using a microphone array embedded into the body of drones for sound source localisation and audio signal enhancement. To a lesser extent, drone noise characterisation is also studied. We propose modelling of the drone noise harmonics as current to noise transfer function by taking the significant harmonics of the drone noise signature. The model is derived from the experimental measurements of a single motor propeller combination, and validated using extensive error analysis. A cross-correlation-based DOA estimation method with drone noise angular spectrum subtraction is derived for acoustic source localisation. Our approach is to exploit the underlying spatial characteristics of drone noise to mitigate the noise emission by …

With the recent emergence of virtual reality, augmented reality, and mixed-reality technologies, considerable attention has been received to integrate spatial audio recording capabilities into these devices. Extracting higher order spherical harmonics from the recordings is not always possible due to complex scattering effects from the arbitrary-shaped devices, which motivates the need for modeling the acoustic properties beyond free-field convention. In this paper, we propose modeling the wearable device-related transfer function (WDRTF) that incorporates scattering, and diffraction from the device and wearer's body to an incident soundfield. We adopt the spherical wave model to translate the measurements into microphone alignment, by exploiting both magnitude and phase corrections. We use a discrete set of measurements by multiple circular arrays for efficiently estimating the WDRTF coefficients. We illustrate the WDRTF modeling procedure from a head-worn microphone array and obtain accurate reconstruction performance over frequency and 3D directions.