Department Publications

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.

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 …

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 …