Department Publications

The radio frequency (RF) cochlea, a biomimetic LCR transmission-line filter bank that performs O(N) broadband spectrum analysis, has been demonstrated in active CMOS and as passive microwave channelizers but has remained a one-dimensional (1-D) structure since its introduction two decades ago. We argue that the natural next step is to add a second dimension, and we explore three complementary aspects of this extension. First, we review existing 1-D RF cochlea architectures and present a 20-channel, 1 GHz constant-bandwidth passive channelizer as a representative implementation. Second, we analyze the two-dimensional (2-D) cochlear-duct geometry of Watts and Neely using finite-difference and Liouville–Green simulations: the second dimension introduces a cut-off mode whose interference with the traveling-wave mode produces an upper-skirt roll-off 1.6–1.7× steeper than the 1-D long-wave limit …

The optimal selection of approximate computing units towards low-complexity FPGA-based hardware architectures for approximate discrete cosine transform (ADCT) and approximate discrete Fourier transform (ADFT) are explored. The proposed methodology employs an existing library of FPGA based approximate adders to select Pareto-optimal 1 approximate adders for cascaded signal flow architectures found in ADCT and ADFT operations. Benefits are presented with examples from 8-point ADCT based image compression and 16-point ADFT based digital multi-beamforming applications. Examples demonstrate a nearly 25% LUT reduction in FPGA utilization compared to error-free computing, with a corresponding degradation of nearly 1 dB in terms of PSNR in image compression and 0.3 dB loss in SNR improvement in beamforming.

Low-complexity digital multi-beamforming via multiplier-less approximations for discrete spatial Fourier transform (DSFT) is explored. The proposed approach employs an approximate-DSFT (ADSFT) which - for a uniform linear array with N antennas producing N simultaneous beams, reduces the hardware multiplier requirement from 3N 2 to 0 with marginal error in the beam profiles. Importantly, the proposed approach extends the previously reported approximate discrete Fourier transform (ADFT) multi-beamforming systems having fixed beam profiles to enable configurable beam profiles, where the user can obtain N simultaneous beams within a given spatial angular region. Simulations demonstrate that the multi-beam profiles are largely accurate, and approximate selection results in deviations for 8- and 16-element ULAs, with worst-case beam angle errors of 0.3° and 0.2°, and worst-case sidelobe deviations of …

Substantial data losses occur during transmission between the sensor nodes of a wireless sensor network due to various causes such as the instability of wireless communication links and network congestion. Most of the data recovery methods proposed are computationally expensive or rely on additional data from neighboring sensors or other types of correlated data which may not always be available. In this paper, we instead propose a lightweight real-time method of missing data recovery based on chromatic derivatives (CDs), a noise robust alternative to standard derivatives that enables high-fidelity local signal approximation. Using the bandlimited nature of the measured signals, our method interpolates the missing data using CDs-based non-uniform sample reconstruction. In contrast to many state-of-the-art methods, our approach relies solely on the received data stream from the transmitting sensor for …

A four-dimensional light field (LF) captures both textural and geometrical information of a scene in contrast to a two-dimensional image that captures only the textural information of a scene. Post-capture refocusing is an exciting application of LFs enabled by the geometric information captured. Previously proposed LF refocusing methods are mostly limited to the refocusing of single planar or volumetric region of a scene corresponding to a depth range and cannot simultaneously generate in-focus and out-of-focus regions having the same depth range. In this paper, we propose an end-to-end pipeline to simultaneously refocus multiple arbitrary planar or volumetric regions of a dense or a sparse LF. We employ pixel-dependent shifts with the typical shift-and-sum method to refocus an LF. The pixel-dependent shifts enables to refocus each pixel of an LF independently. For sparse LFs, the shift-and-sum method introduces ghosting artifacts due to the spatial undersampling. We employ a deep learning model based on U-Net architecture to almost completely eliminate the ghosting artifacts. The experimental results obtained with several LF datasets confirm the effectiveness of the proposed method. In particular, sparse LFs refocused with the proposed method archive structural similarity index higher than 0.9 despite having only 20% of data compared to dense LFs.

The combined use of algorithms for approximate discrete cosine transform (DCT) with approximate computing is examined toward area-efficient field programmable gate array (FPGA) hardware architectures. The performance of previously reported, multiplierless approximate DCT transforms are explored in the presence of non-exact hardware adders which provide better utilisation of FPGA resources in terms of look-up-tables (LUTs). In the context of 2D image compression, provided examples are demonstrating a 20% LUT reduction in FPGA utilization compared to accurate adders, with a corresponding image quality degradation of 0.6 dB in terms of PSNR. The potential benefit of such area-efficient FPGA hardware designs is explored on 3D (video compression), 4D (static light field compression), and 5D (light field video compression) cases where significant FPGA resource savings are obtained at the cost of …

Discrete Fourier Transform (DFT) based beam-formers provide N beams with a uniform linear array having N antennas. Despite low-complexity, DFT beamformers cannot generate deep nulls at arbitrary directions of arrivals (DOAs) in the beam pattern to attenuate interferences. On the other hand, data-dependent adaptive beamformers can achieve deep nulls at arbitrary DOAs, however, at higher complexity due to an inversion of a matrix in deriving optimum weights. In this paper, we propose a combined approximate DFT (ADFT) and adaptive beamformer for a uniform linear array consisting of P subarrays, each consisting of M antennas. Here, we first employ P ADFT beamformers to process signals received by subarrays, and the P outputs of these subarrays are then processed by an adaptive beamformer. Simulation results confirm that the proposed beamformer provides the benefits of both DFT and adaptive …

Five-dimensional (5-D) light field videos (LFVs) capture spatial, angular, and temporal variations in light rays emanating from scenes. This leads to a significantly large amount of data compared to conventional three-dimensional videos, which capture only spatial and temporal variations in light rays. In this paper, we propose an LFV compression technique using low-complexity 5-D approximate discrete cosine transform (ADCT). To further reduce the computational complexity, our algorithm exploits the partial separability of LFV representations. It applies two-dimensional (2-D) ADCT for sub-aperture images of LFV frames with intra-view and inter-view configurations. Furthermore, we apply one-dimensional ADCT to the temporal dimension. We evaluate the performance of the proposed LFV compression technique using several 5-D ADCT algorithms, and the exact 5-D discrete cosine transform (DCT). The experimental results obtained with LFVs confirm that the proposed LFV compression technique provides a more than 250 times reduction in the data size with near-lossless fidelity with a peak-signal-to-noise ratio greater than 40 dB and structural similarity index greater than 0.9. Furthermore, compared to the exact DCT, our algorithms requires approximately 10 times less computational complexity.

Real-time pedestrian sensing at crossings is essential for Intelligent Transportation Systems (ITS). Yet traditional vision-based solutions and wearable devices face privacy, cost, and deployment challenges. While Wi-Fi channel state information (CSI) has shown promise in indoor environments, its potential for dynamic outdoor environments remains largely unexplored. This paper presents a device-free, CSI-based system for detecting human movement and walking direction at crossings and alert oncoming vehicles to enhance pedestrian safety. A twostage classification framework is adopted with noise suppression and Doppler-sensitive features. Classical and deep learning models are evaluated across four outdoor locations and seven subjects, achieving 96.9% accuracy for movement detection and up to 62% accuracy for classification of movement direction. These findings demonstrate the feasibility of …

Cardiac auscultation is an essential clinical skill, requiring excellent hearing to distinguish subtle differences in timing and pitch of heart sounds. However, diagnosing solely from these sounds is often challenging due to interference from surrounding noise, and the information may be limited. Most of the existing solutions that adaptively cancel external noise are either non-real-time or computationally intensive, making them unsuitable for implementation in a portable system. This work proposes an end-to-end system with a real-time adaptive noise cancellation pipeline integrated into a device that simultaneously acquires electrocardiogram (ECG) and phonocardiogram (PCG) signals. We employ a burst adaptive normalized least mean square algorithm that adjusts its adaptation in response to high-energy, non-stationary hospital noise. The algorithm's performance was initially assessed using datasets with artificially induced noise. Subsequently, the complete end-to-end system was validated using real-world hospital recordings captured with the dual-modality device. For PCG and ECG signals recorded from the device in noisy hospital settings, the proposed system achieved signal-to-noise ratio improvements of 37.01 dB and 30.32 dB, respectively. Furthermore, complexity analysis confirms the pipeline's suitability for embedded implementation. These results demonstrate the system's effectiveness in enabling reliable and accessible cardiac screening in noisy hospital environments typical of resource-constrained settings.

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 the sensing and perception of 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 a high chip area and power consumption. We address this issue by exploiting the recently proposed approximate discrete Fourier transform (ADFT), which has its own sparse factorization for real-time implementation at a low complexity and power consumption. The ADFT is used to create a wideband multibeam RF digital beamformer and temporal spectrum-based attention unit that monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm with 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 …

This paper presents several machine learning methods and highlights the most effective one for detecting elephant rumbles in infrasonic seismic signals. The design and implementation of electronic circuitry to amplify, filter, and digitize the seismic signals captured through geophones are presented. The process converts seismic rumbles to a spectrogram and the existing methods of spectrogram feature extraction and appropriate machine learning algorithms are compared on their merit for automatic seismic rumble identification. A novel method of denoising the spectrum that leads to enhanced accuracy in identifying seismic rumbles is presented. It is experimentally found that the combination of the Mel-frequency cepstral coefficient (MFCC) feature extraction method and the ridge classifier machine learning algorithm give the highest accuracy of 97% in detecting infrasonic elephant rumbles hidden in seismic signals. The trained machine learning algorithm can run quite efficiently on general-purpose embedded hardware such as a Raspberry Pi, hence the method provides a cost-effective and scalable platform to develop a tool to remotely localize elephants, which would help mitigate the human–elephant conflict.

Signal propagation in wireless communication undergoes an effect called shadowing (or shadow fading) depending on the built and natural surroundings that impede propagation. Thus, the fixed surroundings contribute to a level of correlation in shadow fading over a given distance in space, which is referred to as spatial correlation in shadow fading. This paper propose a method to simulate homogeneous spatial-correlation in shadow fading in the 2D space with a roughly specified decorrelation distance, using a 2D convolutional process having a 2D linear time invariant system. Such methods are commonly used in image processing. Simulation results are presented to verify the gener ated correlated spatial process for the shadowing component, and further insights are drawn through 2D spatial autocorrelation values. The proposed method for a single transmitter can be repeated for multiple transmitters that …

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.

Wi-Fi has become a cost-effective sensing mechanism due to its widespread availability. Channel State Information (CSI) from the OFDM-based physical layer of Wi-Fi contains rich information for sensing static and dynamic channel conditions, and hence for sensing activities within the channel. Numerous applications which combine CSI-based sensing with machine learning (ML) available for both indoor and outdoor environments. We extend the use of CSI- based Wi-Fi sensing to a unique setting: a confined environment. An industrial beverage cooler is specifically considered. We address the challenges of sensing device placement in such confined environments, where line-of-sight obstruction and near-field effects play a key role in CSI stability. We compensate for these effects by introducing novel combinations of data preprocessing techniques and input feature definitions. We demonstrate an accuracy of 89 …

Denoising of four-dimensional (4-D) light fields (LFs) is a fundamental preprocessing step before employing LFs for downstream tasks. Most of LF denoising methods have higher computational complexities despite excellent performance. Denoising of LFs using 4-D linear filters is a low-complexity approach that can achieve near real-time or real-time processing. We propose an ultra low-complexity LF denoising methods using 4-D infinite-extent impulse response (IIR) hyperfan filters in this paper. Our approach exploits the sparsity of an LF in the 4-D frequency domain, and employ a set of one-dimensional and two-dimensional first-order IIR filters to approximate the 4-D hyperfan-shaped passband. On average, the proposed 4-D IIR hyperfan filter needs only 2.56 multiplications and 21.06 additions to process a LF sample leading to more than 92% and 64% savings in the number of multiplications and additions to …

A spatially-interpolated (SI) beamformer is proposed in this paper to enhance narrowband plane waves received by a sparse linear antenna array. Similar to a conventional SI filter, the proposed beamformer employs two filters: an SI prototype filter and a masking filter. However, the proposed SI beamformer employs a parallel structure compared to a cascade structure of conventional SI filters leading to a sparse antenna array. Both the prototype filter and the masking filter are designed as optimal filters in the minimax sense. Furthermore, we employ a bell-shaped passband for the ideal magnitude response for the design compared to a conventional ideal frequency response that employs 1 and 0 as the passband and the stopband gains with a finite transition band. Simulation results confirm that the proposed SI beamformer provides approximately 37% reduction in computational complexity and a similar reduction …

We propose a minimax design method for multi-dimensional (M-D) interpolated finite-support impulse response (IFIR) filters. As a result of the cascade structure of an MD IFIR filter, the optimization problem of the minimax design is nonconvex. In order to solve this nonconvex optimization problem, we employ a convex-concave procedure, where each iteration solves a convex constrained optimization problem. We present experimental results to confirm the effectiveness and the superior performance of the proposed method compared to previously proposed minimax designs of M-D IFIR filters. Furthermore, we present an implementation of M-D IFIR filter on the Xilinx Kintex UltraScale KCU105 field-programmable gate array, where a quantization error of −60.59 dB is achieved with the coefficient word length of 24 bits.

Chromatic derivatives (CDs) and associated chromatic approximations (CAs) provide a numerically robust and powerful framework for digital processing of continuous-time/space signals, using not only the discrete signal amplitudes, but also higher order derivatives. Acquisition of CDs has so far been limited to software implementations. This paper presents field-programmable gate array (FPGA)-based digital hardware architectures suitable for the real-time acquisition of CDs, thus enabling CDs to be used for practical signal processing algorithm development in embedded systems. With 24 bits of fixed-point precision, the proposed hardware architectures provide approximate 116 dB accuracy in the frequency responses of FIR filters capturing the CDs, and 76.85 dB and 75.09 dB accuracy in signal reconstruction with synthetic and real audio signals using CDs up to order 28 degrees. The designs have been …

This paper presents an effective method of identifying elephant rumbles in infrasonic seismic signals. The design and implementation of electronic circuitry to amplify, filter, and digitize the seismic signals captured through geophones are presented. A collection of seismic infrasonic elephant rumbles was collected at a free-ranging area of an elephant orphanage in Sri Lanka. The seismic rumbles were converted to spectrograms, and several methods were used for spectral feature extraction. Using LasyPredict, the features extracted using different methods were fed into their corresponding machine-learning algorithms to train them for automatic seismic rumble identification. It was found that the Mel frequency cepstral coefficient (MFCC) together with the Ridge classifier machine learning algorithm produced the best performance in identifying seismic elephant rumbles. A novel method for denoising the spectrum that leads to enhanced accuracy in identifying seismic rumbles is also presented.

A low-complexity multibeamforming scheme for uniform linear arrays is proposed based on warped and approximated discrete Fourier transform (DFT), albeit in the spatial domain. Conventional DFT- and approximated-DFT-based multibeamformers provide a low-complexity alternatives to produce multiple simultaneous beams with fixed beam profiles. The proposed approach aims to produce configurable (i.e., steerable) multibeam profiles using the warped DFT. Design examples are presented for an 8-element uniform linear array verifying, 81% and 18 % reduction in multiplier and adder complexities, respectively, when compared to a conventional narrowband multibeamformer having dedicated steering weights for each beam. Three system configurations are proposed using approximations for warped transform and the DFT. Hardware designs implemented on Xilinx Ultrascale KCU105 FPGA device verify 5.2 …

The optimal designs of multi-dimensional (M-D) complex-coefficient finite-extent impulse response (FIR) filters with low group delays predominantly consider only the minimization of the frequency response error. This leads to substantial deviation of the group delay from the desired constant group delay of such a filter, especially near the passband edges. In this paper, we propose a minimax design method for M-D complex-coefficient FIR filters with reduced group delay error. We incorporate constraints on phase response error while minimizing the frequency response error. We formulate the proposed minimax design method as a second-order cone programming problem. Design examples confirm that the proposed minimax design method significantly reduces the group delay error compared to previously proposed methods.

Broadband beamformers designed as two-dimensional (2-D) spatially-interpolated finite-extent impulse responses (FIR) filters, using a cascade structure, cannot employ sparse linear arrays despite significantly reducing the computational complexity. This happens due to the fact that the 2-D masking filter requires spatial samples equal to the spatial order at the input. Therefore, the output of the 2-D spatially-interpolated prototype filter should be computed for more than one spatial index. In order to address this limitation, we propose a 2-D spatially-interpolated FIR filter using a parallel structure. With the proposed structure, both 2-D spatially-interpolated prototype filter and the 2-D masking filter need to compute the output only for one spatial index, therefore allowing to employ sparse linear arrays. In order to support broadband beamforming, we design the 2-D FIR filter to have a parallelogram passband …

The use of approximate transforms in conjunction with approximate adder hardware is explored towards implementing low-complexity multi-beam antenna arrays. The use of approximate adders is motivated by the efficient look up table (LUT) usage on field programmable gate arrays (FPGA) resulting in better area and time complexities on hardware. 8-beam and 16-beam hardware architectures for multi-beamforming are designed using previously reported 8-point and 16-point spatial approximate-DFT (ADFT) algorithms, respectively, albeit with approximate adders. Designs have been implemented on Xilinx Kintex UltraScale KCU105 FPGA device verifying 31.5% reduction in (FPGA) LUT count and 58.08% improvement in critical path delay with a maximum beam error of 0.6 degrees and maximum peak side lobe level deviation 0.21 dB for the 8-beam case, when compared to ADFT with accurate adders with the …