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1. Learning-Based Optimization of Hyperspectral Band Selection for Classification

   https://doi.org/10.3390/rs15184460  

   Ayna, Cemre Omer; Mdrafi, Robiulhossain; Du, Qian; Gurbuz, Ali Cafer (September 2023, Remote Sensing)

   Hyperspectral sensors acquire spectral responses from objects with a large number of narrow spectral bands. The large volume of data may be costly in terms of storage and computational requirements. In addition, hyperspectral data are often information-wise redundant. Band selection intends to overcome these limitations by selecting a small subset of spectral bands that provide more information or better performance for particular tasks. However, existing band selection techniques do not directly maximize the task-specific performance, but rather utilize hand-crafted metrics as a proxy to the final goal of performance improvement. In this paper, we propose a deep learning (DL) architecture composed of a constrained measurement learning network for band selection, followed by a classification network. The proposed joint DL architecture is trained in a data-driven manner to optimize the classification loss along band selection. In this way, the proposed network directly learns to select bands that enhance the classification performance. Our evaluation results with Indian Pines (IP) and the University of Pavia (UP) datasets show that the proposed constrained measurement learning-based band selection approach provides higher classification accuracy compared to the state-of-the-art supervised band selection methods for the same number of bands selected. The proposed method shows 89.08% and 97.78% overall accuracy scores for IP and UP respectively, being 1.34% and 2.19% higher than the second-best method. 

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2. Sequential Classification of ASL Signs in the Context of Daily Living Using RF Sensing

   https://doi.org/10.1109/RadarConf2147009.2021.9455178  

   Kurtoglu, Emre; Gurbuz, Ali C.; Malaia, Evie; Griffin, Darrin; Crawford, Chris; Gurbuz, Sevgi Z. (May 2021, IEEE Radar Conference)

   null (Ed.)

   RF sensing based human activity and hand gesture recognition (HGR) methods have gained enormous popularity with the development of small package, high frequency radar systems and powerful machine learning tools. However, most HGR experiments in the literature have been conducted on individual gestures and in isolation from preceding and subsequent motions. This paper considers the problem of American sign language (ASL) recognition in the context of daily living, which involves sequential classification of a continuous stream of signing mixed with daily activities. In particular, this paper investigates the efficacy of different RF input representations and fusion techniques for ASL and trigger gesture recognition tasks in a daily living scenario, which can be potentially used for sign language sensitive human-computer interfaces (HCI). The proposed approach involves first detecting and segmenting periods of motion, followed by feature level fusion of the range-Doppler map, micro-Doppler spectrogram, and envelope for classification with a bi-directional long short-term memory (BiL-STM) recurrent neural network. Results show 93.3% accuracy in identification of 6 activities and 4 ASL signs, as well as a trigger sign detection rate of 0.93. 

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3. Boosting multi‐target recognition performance with multi‐input multi‐output radar‐based angular subspace projection and multi‐view deep neural network

   https://doi.org/10.1049/rsn2.12405  

   Kurtoğlu, Emre; Biswas, Sabyasachi; Gurbuz, Ali_C; Gurbuz, Sevgi_Zubeyde (May 2023, IET Radar, Sonar & Navigation)

   Abstract Current radio frequency (RF) classification techniques assume only one target in the field of view. Multi‐target recognition is challenging because conventional radar signal processing results in the superposition of target micro‐Doppler signatures, making it difficult to recognise multi‐target activity. This study proposes an angular subspace projection technique that generates multiple radar data cubes (RDC) conditioned on angle (RDC‐ω). This approach enables signal separation in the raw RDC, making possible the utilisation of deep neural networks taking the raw RF data as input or any other data representation in multi‐target scenarios. When targets are in closer proximity and cannot be separated by classical techniques, the proposed approach boosts the relative signal‐to‐noise ratio between targets, resulting in multi‐view spectrograms that boosts the classification accuracy when input to the proposed multi‐view DNN. Our results qualitatively and quantitatively characterise the similarity of multi‐view signatures to those acquired in a single‐target configuration. For a nine‐class activity recognition problem, 97.8% accuracy in a 3‐person scenario is achieved, while utilising DNN trained on single‐target data. We also present the results for two cases of close proximity (sign language recognition and side‐by‐side activities), where the proposed approach has boosted the performance. 

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4. Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning

   https://doi.org/10.1145/3219819.3219996  

   Vandal, Thomas; Kodra, Evan; Dy, Jennifer; Ganguly, Sangram; Nemani, Ramakrishna; Ganguly, Auroop R. (August 2018, KDD 2018)

   Deep Learning (DL) methods have been transforming computer vision with innovative adaptations to other domains including climate change. For DL to pervade Science and Engineering (S&EE) applications where risk management is a core component, well-characterized uncertainty estimates must accompany predictions. However, S&E observations and model-simulations often follow heavily skewed distributions and are not well modeled with DL approaches, since they usually optimize a Gaussian, or Euclidean, likelihood loss. Recent developments in Bayesian Deep Learning (BDL), which attempts to capture uncertainties from noisy observations, aleatoric, and from unknown model parameters, epistemic, provide us a foundation. Here we present a discrete-continuous BDL model with Gaussian and lognormal likelihoods for uncertainty quantification (UQ). We demonstrate the approach by developing UQ estimates on “DeepSD’‘, a super-resolution based DL model for Statistical Downscaling (SD) in climate applied to precipitation, which follows an extremely skewed distribution. We find that the discrete-continuous models outperform a basic Gaussian distribution in terms of predictive accuracy and uncertainty calibration. Furthermore, we find that the lognormal distribution, which can handle skewed distributions, produces quality uncertainty estimates at the extremes. Such results may be important across S&E, as well as other domains such as finance and economics, where extremes are often of significant interest. Furthermore, to our knowledge, this is the first UQ model in SD where both aleatoric and epistemic uncertainties are characterized. 

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5. Multi-Frequency RF Sensor Fusion for Word-Level Fluent ASL Recognition

   https://doi.org/10.1109/JSEN.2021.3078339  

   Gurbuz, Sevgi Z.; Mahbubur Rahman, M.; Kurtoglu, Emre; Malaia, Evie; Gurbuz, Ali C.; Griffin, Darrin J.; Crawford, Chris (May 2021, IEEE Sensors Journal)

   null (Ed.)

   Deaf spaces are unique indoor environments designed to optimize visual communication and Deaf cultural expression. However, much of the technological research geared towards the deaf involve use of video or wearables for American sign language (ASL) translation, with little consideration for Deaf perspective on privacy and usability of the technology. In contrast to video, RF sensors offer the avenue for ambient ASL recognition while also preserving privacy for Deaf signers. Methods: This paper investigates the RF transmit waveform parameters required for effective measurement of ASL signs and their effect on word-level classification accuracy attained with transfer learning and convolutional autoencoders (CAE). A multi-frequency fusion network is proposed to exploit data from all sensors in an RF sensor network and improve the recognition accuracy of fluent ASL signing. Results: For fluent signers, CAEs yield a 20-sign classification accuracy of %76 at 77 GHz and %73 at 24 GHz, while at X-band (10 Ghz) accuracy drops to 67%. For hearing imitation signers, signs are more separable, resulting in a 96% accuracy with CAEs. Further, fluent ASL recognition accuracy is significantly increased with use of the multi-frequency fusion network, which boosts the 20-sign fluent ASL recognition accuracy to 95%, surpassing conventional feature level fusion by 12%. Implications: Signing involves finer spatiotemporal dynamics than typical hand gestures, and thus requires interrogation with a transmit waveform that has a rapid succession of pulses and high bandwidth. Millimeter wave RF frequencies also yield greater accuracy due to the increased Doppler spread of the radar backscatter. Comparative analysis of articulation dynamics also shows that imitation signing is not representative of fluent signing, and not effective in pre-training networks for fluent ASL classification. Deep neural networks employing multi-frequency fusion capture both shared, as well as sensor-specific features and thus offer significant performance gains in comparison to using a single sensor or feature-level fusion. 

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