Department Publications

The number of coefficients of multi-dimensional (M-D) finite-extent impulse response (FIR) filters increases exponentially with the number of dimensions leading to significantly high computational complexities. In this brief, we propose a minimax design method for M-D FIR filters having sparse coefficients, therefore, having low computational complexities. We consider the design of M-D FIR filters with arbitrary frequency responses and low group delays of which the coefficients are complex valued. We formulate the minimax design as a second-order cone programming problem. Design examples confirm that M-D sparse FIR filters designed using the proposed method provide more than 60% reduction in the computational complexity for a similar error in the frequency response approximation compared to M-D FIR nonsparse filters designed using previously proposed minimax methods.

Transformers combined with convolutional encoders have been recently used for hand gesture recognition (HGR) using micro-Doppler signatures. In this letter, we propose a vision-transformer-based architecture for HGR with multiantenna continuous-wave Doppler radar receivers. The proposed architecture consists of three modules: 1) a convolutional encoder–decoder, 2) an attention module with three transformer layers, and 3) a multilayer perceptron. The novel convolutional decoder helps to feed patches with larger sizes to the attention module for improved feature extraction. Experimental results obtained with a dataset corresponding to a two-antenna continuous-wave Doppler radar receiver operating at 24 GHz (published by Skaria et al.) confirm that the proposed architecture achieves an accuracy of 98.3% which substantially surpasses the state-of-the-art on the used dataset.

We propose a minimax design method for finite impulse response (FIR) graph filters in this brief. We first convert the graph filter design problem into a numerically stable equivalent problem using a shifted monomial basis. Then, we convert the minimax graph filter design problem into a semidefinite programming problem. Furthermore, we propose a computationally-efficient structure to implement the FIR graph filters designed using the proposed method. Design examples confirm that the proposed method can be used to design FIR graph filters having reasonably high orders, and the proposed implementation structure substantially saves additions required to process a graph signal compared to graph filters designed using a previously proposed Chebyshev polynomial approximation method.

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.

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.

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 …

Dynamic adjustment of signal timing based on the number of pedestrians waiting at a crossing results in safer and more efficient intersections. Traditionally, such information is obtained through push-buttons or vision-based techniques. Though counting is possible with the latter, the techniques are associated with privacy concerns, are affected by poor lighting, and equipment is costly and bulky. We present a novel alternative that uses Wi-Fi Channel State Information (CSI) processed through a Deep Neural Network (DNN) model. The amplitude and phase of 51 Orthogonal Frequency Division Multiplexing sub carriers in the IEEE802.11n standard are captured with two ESP32 devices. Of the candidate DNNs trained with the CSI data, a ID-Convolutional Neural Network is experimentally demonstrated to count up to 12 pedestrians with an accuracy of 79 % at the current level of training. This can be generalized to …

Light fields (LFs) and light field videos (LFVs) capture both angular and spatial variation of light rays emanating from scenes. This richness of information leads to novel applications such as post-capture refocusing, depth estimation and depth-velocity filtering which are not possible with images and videos. These capabilities come, however, with a significant increase in data to be processed. In order to fully exploit opportunities provided by LFs and LFVs, low-complexity signal processing algorithms that process LF and LFV data in real-time are required. In this brief, we survey such state-of-the-art algorithms, in particular for depth filtering, refocusing and denoising of LFs and depth-velcoty filtering for LFVs, and future directions for these real-time LF an LFV processing algorithms.

In four-dimensional (4-D) light field (LF) processing, 4-D linear shift-invariant filters having hyperplanar passbands are used for depth-based scene enhancement. In this paper, two low-sensitivity and low-complexity field programmable gate array (FPGA)-based digital hardware architectures for 4-D hyperplanar filters are proposed for on-chip real-time processing of LFs. Both 4-D filters are designed exploiting resonant properties of multi-dimensional passive prototype networks, and are realized as wave digital filters (WDFs). The two 4-D WDF realizations are implemented as raster-scanned processing architectures on a Xilinx Virtex 6 Sx35 FPGA with a real-time clock speed of up to 33 MHz. This corresponds to a real-time throughout of 16.8 LFs/s for an LF of size .

We propose a weighted least-squares (WLS) design method for multi-dimensional (M-D) complex-coefficient finite- extent impulse response (FIR) filters. We consider the general form of M-D FIR filters having arbitrary frequency responses and low group delays. We formulate the proposed WLS design as a second-order cone programming problem. Design examples confirm that the proposed method provides the state-of-the-art M-D FIR filter designs with almost constant group delay.

Three-dimensional space-time velocity filters may be used to enhance dynamic passband objects of interest in videos while attenuating moving interfering objects based on their velocities. In this paper, we show that the attenuation of interfering stopband objects may be significantly improved using recently proposed shifted-velocity filters. It is shown that an improvement of approximately 20 dB in signal-to-interference ratio may be achieved for stopband to passband velocity differences of only 1 pixels/frame. More importantly, this improvement is achieved without increasing the computational complexity.

Geometric information of scenes available with four-dimensional (4-D) light fields (LFs) paves the way for post-capture refocusing. Light field refocusing methods proposed so far have been limited to a single planar or a volumetric region of a scene. In this letter, we demonstrate simultaneous refocusing of multiple volumetric regions in LFs. To this end, we employ a 4-D sparse finite-extent impulse response (FIR) filter consisting of multiple hyperfan-shaped passbands. We design the 4-D sparse FIR filter as an optimal filter in the least-squares sense. Experimental results confirm that the proposed filter provides 63% average reduction in computational complexity with negligible degradation in the fidelity of multi-volumetric refocused LFs compared to a 4-D nonsparse FIR filter.

Low-complexity signal processing algorithms and reconfigurable digital hardware architectures are proposed for multi-depth filtering and occlusion suppression in 4-D light fields (LFs). The proposed multi-depth-pass (MDP) and multi-depth-reject (MDR) 4-D filters allow simultaneous enhancement of planar objects at multiple desired depths and attenuation of planar objects at multiple undesired depths, thereby achieving electronically tunable multi-depth focusing. Partial-separability and recursive nature of the proposed MDP and MDR transfer functions ensure ultra-low hardware complexity suitable for real-time processing of large volume of input samples encountered in 4-D LFs. Filter synthesis details and examples with synthetic and real LFs of size 15  ×  15 × 434  ×  625 are presented. A generic framework encapsulating a multitude of possible digital hardware realizations is described, employing J …

Four-dimensional (4-D) light fields (LFs) enable novel imaging technologies, which are traditionally based on two-dimensional images. In most of these applications, denoising of LFs is required as a preprocessing technique before any subsequent processing. We propose a real-time LF denoising method using a novel 4-D linear and shift-invariant hyperfan filter. The proposed method exploits sparsity of the spectrum of a LF and the 4-D hyperfan filter is implemented in the 4-D mixed-domain (i.e.,two-dimensional space and two-dimensional frequency) leading to significant reductions in computational and memory complexities. A software implementation of the proposed method provides better or comparable denoising performance for grayscale and color LFs with respect to the metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to previously reported linear LF denoising methods, while …

Growing concerns over privacy invasion due to video camera based monitoring systems have made way to non-invasive Wi-Fi signal sensing based alternatives. This paper introduces a novel end-to-end deep learning framework that utilizes the changes in orthogonal frequency division multiplexing (OFDM) sub-carrier amplitude information to simultaneously predict the identity, activity and the trajectory of a user and create a user profile that is of similar utility to a one made through a video camera based approach. The novelty of the proposed solution is that the system is fully autonomous and requires zero user intervention unlike systems that require user originated initialization, or a user held transmitting device to facilitate the prediction. Experimental results demonstrate over 95% accuracy for user identification and activity recognition, while the user localization results exhibit a ±12cm error, which is a significant …

A low-complexity codec and a hardware architecture are proposed for achieving real-time compression of four-dimensional (4-D) light field (LF) signals captured from camera/lenslet arrays. The proposed system employs the 4-D extension of the two-dimensional (2-D) 8×8 approximate discrete cosine transform (ADCT) that has recently appeared in the literature. Motivated by the partial separability of the multidimensional spectrum of LFs, the proposed 4-D ADCT is obtained by cascading 2-D inter-view and 2-D intra-view transform stages. Software simulations are provided to confirm the performance of the 4-D ADCT based compression and comparisons are made with respect to 2-D inter-view only and 2-D intra-view only ADCT-based compression. Proposed digital architectures are validated using stepped hardware co-simulation on a Xilinx Virtex-7 VC-707 FPGA platform verifying 597 MHz maximum possible …

The potential of drone based services is enormous with applications ranging from consumer product delivery to health services. When meeting this demand, one of the major challenges we face is the noise produced by drones, which not only contributes to listener discomfort, but also hinders the device's ability to effectively communicate via audio. Thus, there exists a pressing need for understanding the characteristics of drone related noise, which can then be suppressed using suitable methods. This paper presents a preliminary study on modeling the relationship between input motor current and acoustic noise produced by a drone. An experimental study is conducted indoors for a drone under hovering manoeuvre with a single active motor and propeller. The drone noise was measured by a single on-board microphone. We identify multiple tones or harmonics in the drone noise spectrum that vary proportionally …

A combined approach of low-complexity light field depth filtering and deep learning is proposed for object classification in the presence of partial occlusions. The proposed approach exploits depth information embedded in multi-perspective four-dimensional (4-D) light fields via low-complexity 4-D sparse depth filtering and deep-learning. The proposed 4-D depth filter, designed using numerical optimization techniques by formulating as an ℓ 1 - ℓ ∞ minimization problem, is shown to outperform typical light field refocusing based on 4-D shift-sum averaging filters. Experiments conducted using a light field dataset acquired by a Lytro camera verify 45% and 27% better performance in terms of object classification accuracy compared to the cases when no depth filtering is employed and standard shift-sum refocusing is employed, respectively.

A minimax design for 2-D complex-coefficient FIR filters having asymmetric frequency responses is proposed in this paper. We consider the general form of 2-D FIR filters with low group delay and formulate the minimax design as a semidefinite programming problem. The 2-D linear-phase FIR filters with conjugate-symmetric coefficients are a special case of the proposed design. Example filter designs having near-equiripple magnitude responses are presented to verify the effectiveness of the proposed design method.

A moving object in a five-dimensional (5-D) light field video (LFV) can be selectively enhanced using the depth and the velocity of the object. In this paper, a 5-D depth-velocity (DV) filter is proposed to enhance multiple moving objects at different depths and with different velocities in an LFV. The 5-D DV filter is designed as a cascade of an infinite-extent impulse response multi-depth filter and a finite-extent impulse response multi-velocity filter. Experimental results obtained with numerically generated and real LFVs indicate that more than 15 dB improvement in signal-to-interference ratio can be achieved with the proposed 5-D multi DV filter compared to previously proposed multi depth-only filters.

Accurately estimating the remaining useful life (RUL) of industrial machinery is beneficial in many real-world applications. Estimation techniques have mainly utilized linear models or neural network based approaches with a focus on short term time dependencies. This paper, introduces a system model that incorporates temporal convolutions with both long term and short term time dependencies. The proposed network learns salient features and complex temporal variations in sensor values, and predicts the RUL. A data augmentation method is used for increased accuracy. The proposed method is compared with several state-of-the-art algorithms on publicly available datasets. It demonstrates promising results, with superior results for datasets obtained from complex environments.

This paper presents the design, implementation, and the experimental verifications of a 2.4-GHz multibeam array receiver based on two-dimensional (2-D) spatially bandpass (SBP) digital plane wave (PW) filters. The digital array receiver consists of 2.4-GHz 16-element uniform linear array, and quadrature sampling is employed to process the broadband PWs at the baseband. Both 2-D finite impulse response (FIR) and infinite impulse response (IIR) digital filters having multiple trapezoidal-shaped passbands are employed as the 2-D SBP digital PW filters. Both 2-D FIR and IIR trapezoidal filters are implemented on a Xilinx Virtex 6 sx475t field programmable gate array chip that comes with the reconfigurable open architecture computing hardware version-2 (ROACH-2) platform. The 16 in-phase and quadrature-phase downconverted signals are sampled using the ROACH-2 platform and are processed using the …

Application of depth-selective filtering to modulated-sparse light fields towards reducing the DSP and memory complexities in real-time light field processing is investigated. A modulated-sparse light field is obtained by spatially windowing an original 4-D light-field signal, which is subsequently processed by 4-D depth-selective filters to achieve planar focus and occlusion suppression. Multidimensional spectral properties of modulated-sparse light fields are explored and demonstrative examples with real light fields are provided to show that almost similar depth filtering and occlusion suppression performance can be achieved by processing such modulated-sparse light fields, leading to significant reduction in DSP hardware and memory complexities.

A low-complexity wideband transmit beamformer is proposed using a digitally-fed uniform linear array of broadband Vivaldi antennas operating in the S-band. The proposed transmit beamformer employs a novel DSP feeding network based on a transmit-mode 2-D sparse finite-extent impulse response (FIR) filter having a planar passband in the 2-D frequency-wavenumber space. Electronic beam steering is achieved by changing the filter coefficients defined in closed-form and hard-thresholding (HT) is employed to obtain a sparse 2-D impulse responses of the filter. Full-wave electromagnetic simulations are used to obtain the far-field beam patterns produced by the 2-D sparse FIR filter in the frequency range 2-2.8 GHz for wideband signals with 33% fractional bandwidth. Computational complexity, beam directionality and side-lobe performance are investigated with varying HT along with quantitative comparisons …

A low-complexity 4-D sparse FIR hyperfan filter is proposed for volumetric refocusing of light fields. By exploiting the partial separability of the spectral region of support of a light field, the proposed filter is designed as a cascade of two 4-D hyperfan filters. The sparsity of the filter coefficients is achieved by hard thresholding the nonsparse filter coefficients. The experimental results confirm that the proposed 4-D sparse FIR hyperfan filter provides 72% mean reduction of computational complexity compared to a 4-D nonsparse FIR hyperfan filter withoudeteriorating the fidelity of volumetric refocused light fields. In particular, the mean structure similarity (SSIM) index between the volumetric refocused light fields by the proposed sparse filter and the nonsparse filter is 0.989.