Miracle-dz/UniFAR-FTUs huggingface dataset:
Transmural Temperature Monitoring to Quantify Thermal Conduction And Lesion Formation During Gastric Ablation, an Emerging Therapy for Gastric Dysrhythmias

Gastric ablation is emerging as a potential therapy for electrical dysrhythmias associated with gastric disorders. Thermal conduction properties of gastric tissue during ablation have not yet been defined, but are necessary for optimizing the technique and translating ablation to clinical therapy. We developed custom needle-based transmural temperature probes to quantify the temperature of gastric tissue during ablation. These probes were applied in vivo in pigs (n=5), during gastric ablation (70 °C, 10 s duration), at distances of 2.5 – 20 mm from the ablation catheter tip. Thermal response of the tissue was non-linear; the maximum temperature increase from baseline (33.3 ± 1.0 °C) was observed at the closest temperature probe to the catheter tip (2.5 mm, 14.9 °C), and temperature change decreased with distance from the catheter tip. Probes positioned between 5 -20 mm from the catheter tip recorded temperature increases of less than 5.6 °C. This study provides methods for monitoring temperature during in vivo ablation, and demonstrates that functional temperature increases from ablation were restricted to within approximately 5 mm of the catheter. These methods can now be applied to optimize effective ablation parameters, and to inform models of gastric ablation.
Usability and acceptance of using a lower-limb exoskeleton controlled by a BMI in incomplete spinal cord injury patients: a case study

Spinal cord injury (SCI) limits life expectancy and causes a restriction of patient’s daily activities. In the last years, robotics exoskeletons have appeared as a promising rehabilitation and assistance tool for patients with motor limitations, as people that have suffered a SCI. The usability and clinical relevance of these robotics systems could be further enhanced by brain–machine interfaces (BMIs), as they can be used to foster patients’ neuroplasticity. However, there are not many studies showing the use of BMIs to control exoskeletons with patients. In this work we show a case study where one SCI patient has used a BMI based on motor imagery (MI) in order to control a lower limb exoskeleton that assists their gait.
Revising the biome concept for understanding and predicting global change impacts

Biomes are globally distributed, structurally and functionally similar vegetation units defined without reference to plant species composition. The boundaries between biomes are presumed to correspond with species turnover and changes in biogeochemical cycling. Determining the controls of biome distributions is thus critical for anticipating global change impacts. Historically, climate and soils have been understood to adequately explain the global distribution of biomes. Convergent evolution and environmental filtering are assumed to be pervasive, ultimately resulting in deterministic patterns of vegetation structure and function in relation to prevailing environmental conditions. Recent studies have highlighted significant problems with this view of biomes. Firstly, systematic structural and functional divergence within biomes has been identified when comparing environmentally similar, yet floristically distinct regions. Secondly, climatic determinism is being further undermined by evidence suggesting multiple stable biome states are possible for some combinations of climatic drivers. We argue that biomes remain useful and necessary constructs for organizing our knowledge of how ecosystems function and for predicting how they might respond to change. However, biome concepts should acknowledge the limits to predictability from environmental conditions alone and the influence of historical processes on modern vegetation patterns. We discuss how direct mapping of plant structure and function, the incorporation of insights into biome evolution and a new generation of vegetation models will lead to improvements in the concept of what biomes are, where they occur, and efforts to predict their distribution.
Robotic Grasp Detection By Learning Representation in a Vector Quantized Manifold

For a robot to perform complex manipulation tasks, it is necessary for it to have a good grasping ability. However, vision based robotic grasp detection is hindered by the unavailability of sufficient labelled data. Furthermore, the application of semi-supervised learning techniques to grasp detection is underexplored. In this paper, a semi-supervised learning based grasp detection approach has been presented, which models a discrete latent space using a Vector Quantized Variational AutoEncoder (VQ-VAE). To the best of our knowledge, this is the first time a Variational AutoEncoder (VAE) has been applied in the domain of robotic grasp detection. The VAE helps the model in generalizing beyond the Cornell Grasping Dataset (CGD) despite having a limited amount of labelled data by also utilizing the unlabelled data. This claim has been validated by testing the model on images, which are not available in the CGD. Along with this, we augment the Generative Grasping Convolutional Neural Network (GGCNN) architecture with the decoder structure used in the VQ-VAE model with the intuition that it should help to regress in the vector-quantized latent space. Subsequently, the model performs significantly better than the existing approaches which do not make use of unlabelled images to improve the grasp.
Video-Based Neonatal Motion Detection

Newborns admitted to the neonatal intensive care unit (NICU) require a high level of care due to their precarious condition. Nurses typically monitor their vital signs continuously using wearable sensors such as electrocardiogram (ECG) electrodes placed on their chest and a pulse oximeter on a limb. When the patient moves, this can cause motion artifacts on one or more physiologic signals, potentially resulting in a false alarm on the patient monitor. We therefore propose a motion detection algorithm to mitigate these alarms by providing context. Using a camera positioned above the crib or overhead warming bed, we recorded videos from six patients and annotated all patient movements. These data were used to train and evaluate two different approaches for non-contact motion detection. Results were stronger for the optical flow technique than for the long short-term memory network approach. This represents a challenging problem due to variable lighting, patient clothing and bed coverings, and the complex clinical environment in the NICU.
Recurrent Fusion of Time-Domain Descriptors Improves EMG-based Hand Movement Recognition

Controlling powered prostheses with myoelectric pattern recognition (PR) provides a natural human-robot interfacing scheme for amputees who lost their limbs. Research in this direction reveals that the challenges prohibiting reliable clinical translation of myoelectric interfaces are mainly driven by the quality of the extracted features. Hence, developing accurate and reliable feature extraction techniques is of vital importance for facilitating clinical implementation of Electromyogram (EMG) PR systems. To overcome this challenge, we proposed a combination of Range Spatial Filtering (RSF) and Recurrent Fusion of Time Domain Descriptors (RFTDD) in order to improve the classifier performance and make the prosthetic hand control more appropriate for clinical applications. RSF is used to increase the number of EMG signals available for feature extraction by focusing on the spatial information between all possible logical combinations of the physical EMG channels. RFTDD is then used to capture the temporal information by applying a recurrent data fusion process on the resulting orientation-based time-domain (TD) features, with a sigmoidal function to limit the features range and overcome the vanishing amplitudes problem. The main advantages of the proposed method include 1) its potential in capturing the temporal-spatial dependencies of the EMG signals, leading to reduced classification errors, and 2) the simplicity with which the features are extracted, as any kind of simple TD features can be adopted with this method. The performance of the proposed RFTDD is then benchmarked across many well-known TD features individually and as sets to prove the power of the RFTDD method on two EMG datasets with a total of 31 subjects. Testing results revealed an approximate reduction of 12% in classification errors across all subjects when using the proposed method against traditional feature extraction methods.Clinical Relevance—Establishing significance and importance of RFTDD, with simple time-domain features, for robust and low-cost clinical applications.
Lossless Compression of Intracortical Extracellular Neural Recordings using Non-Adaptive Huffman Encoding

This paper investigates the effectiveness of four Huffman-based compression schemes for different intracortical neural signals and sample resolutions. The motivation is to find effective lossless, low-complexity data compression schemes for Wireless Intracortical Brain-Machine Interfaces (WI-BMI). The considered schemes include pre-trained Lone 1st and 2nd order encoding [1], pre-trained Delta encoding, and pre-trained Linear Neural Network Time (LNNT) encoding [2]. Maximum codeword-length limited versions are also considered to protect against overfit to training data. The considered signals are the Extracellular Action Potential signal, the Entire Spiking Activity signal, and the Local Field Potential signal. Sample resolutions of 5 to 13 bits are considered. The result show that overfit-protection dramatically improves compression, especially at higher sample resolutions. Across signals, 2nd order encoding generally performed best at lower sample resolutions, and 1st order, Delta and LNNT encoding performed best at higher sample resolutions. The proposed methods should generalise to other remote sensing applications where the distribution of the sensed data can be estimated a priori.
A Wideband Circularly Polarized Microstrip Antenna With Multiple Modes

A new compact wideband circularly polarized (CP) antenna is proposed in this work. The antenna consists of a CP square-loop with sequential phase (SP) characteristics, four strip-lines as driven elements, four L-shaped patches with I-slots and four I-shaped patches as parasitic elements. The square-loop is utilized to feed the two pairs of parasitic patch arrays by a capacitively coupled way. The proposed antenna has a broad 3-dB axial ratio bandwidth (ARBW) of 18.9% (6.35 GHz, 5.75-6.95 GHz), and a wide 10-dB impedance bandwidth (IBW) of 23% (6.38 GHz, 5.65-7.12 GHz). Besides, the presented antenna features flat gain within the CP operation bandwidth and compact structure.
FDBSCAN-APT: A Fuzzy Density-based Clustering Algorithm with Automatic Parameter Tuning

Density-based clustering algorithms represent a convenient approach when the number of clusters is not known in advance and their shapes are arbitrary. Nevertheless, they are highly sensitive to the input parameter setting, especially when clusters’ borders are close to each other, or even overlap. In this paper we propose FDBSCAN-APT, a fuzzy extension of the DBSCAN algorithm. FDBSCAN-APT is able to discover clusters with fuzzy overlapping borders and relies on the automatic setting of input parameters thanks to the definition of a novel heuristic based on the statistical modelling of the density distribution of objects. An extensive experimental analysis carried out on synthetic datasets shows that FDBSCAN-APT always finds reasonable parameter configurations and produces good clustering results in a variety of challenging scenarios.
Cache-Aided Scalar Linear Function Retrieval

In the shared-link coded caching problem, formulated by Maddah-Ali and Niesen (MAN), each cache-aided user demands one file (i.e., single file retrieval). This paper generalizes the MAN problem so as to allow users to request scalar linear functions (aka, linear combinations with scalar coefficients) of the files. We propose a novel coded delivery scheme, based on MAN uncoded cache placement, that allows for the decoding of arbitrary scalar linear functions of the files on arbitrary finite fields. Surprisingly, it is shown that the load for cache-aided scalar linear function retrieval depends on the number of linearly independent functions that are demanded, akin to the cache-aided single-file retrieval problem where the load depends on the number of distinct file requests. The proposed scheme is proved to be optimal under the constraint of uncoded cache placement, in terms of worst-case load, and within a factor 2 otherwise.
Design Methodology and Implementation of Half-Bridge LLC Resonant Converter

DC-DC resonant converter topologies are widely used in the applications of battery charging and renewable energy systems because of their high-power density and high efficiency. LLC resonant converter is the simplest and the most popular type of the resonant converters. In this paper, a half-bridge LLC resonant converter with detailed transformer design is examined by using first harmonic approximation method. A 500W experimental prototype is built to verify the analysis and design methodology, and the maximum efficiency is measured about 97.2%.
EMG-Based Hand Gesture Classification with Long Short-Term Memory Deep Recurrent Neural Networks

Electromyogram (EMG) pattern recognition has been utilized with the traditional machine and deep learning architectures as a control strategy for upper-limb prostheses. However, most of these learning architectures, including those in convolutional neural networks, focus the spatial correlations only; but muscle contractions have a strong temporal dependency. Our primary aim in this paper is to investigate the effectiveness of recurrent deep learning networks in EMG classification as they can learn long-term and non-linear dynamics of time series. We used a Long Short-Term Memory (LSTM-based) neural network to perform multiclass classification with six grip gestures at three different force levels (low, medium, and high) generated by nine amputees. Four different feature sets were extracted from the raw signals and fed to LSTM. Moreover, to investigate a generalization of the proposed method, three different training approaches were tested including 1) training the network with feature extracted from one specific force level and testing it with the same force level, 2) training the network with one specific force level and testing it with two remained force levels, and 3) training the network with all of the force levels and testing it with a single force level. Our results show that LSTM-based neural network can provide reliable performance with average classification errors of around 9% across all nine amputees and force levels. We demonstrate the applicability of deep learning for upperlimb prosthesis control.
A Road Map for Optimizing Smeared-Spectrum Jamming Against Pulse Compression Radars

Jamming search radar has been conventionally addressed using the so-called brute force jamming, in which the jamming power is increased to be higher than that of the signal at the detector input. However, modern radars such as compression linear frequency modulation radars introduce a significant processing gain using matched filtering and wideband signals. This additional processing gain hinders the ability of jamming systems to deny the detection of these modern radars as the required power level using the conventional brute force techniques may be infeasible. In this paper, the smeared jamming is considered as a smart noise-free jamming technique. It can deprive modern radar detection at reasonable jamming power levels. In this regard, we optimize the parameters of the smeared jamming to maximize its effectiveness against high processing-gain radars, which has not been considered heretofore. To evaluate the efficacy of the proposed parameters optimization, a challenging radar model is employed, and the detection performance is investigated under two constant false alarm rate detectors. Monte Carlo simulations show that the proposed optimized jamming technique has a substantial effect on the radar detection with a relatively low jamming power.
Wildfire Prediction: Handling Uncertainties Using Integrated Bayesian Networks and Fuzzy Logic

Wildfire is one of the most frequent natural hazards across the globe, one which has cast a malevolent shroud over many communities in recent years, causing significant risk to human lives, infrastructure, and property. Wildfires are hydrogeological events which are bound to escalate, especially due to climate change. They are different from other natural hazards as they are mainly triggered by human interventions rather than natural triggers. The wildfire risk management is a complex process with many uncertainties in the assessment, fire behavior and spread modelling, and decision making. To predict wildfires, sophisticated temporal geospatial methods are required. This paper develops a wildfire probability prediction method considering the capabilities of Bayesian networks and fuzzy logic that can handle uncertainties and update probabilities in response to the availability of new data. The model takes into account the data from a geographic information system (GIS) for a specific area at micro level to estimate the wildfire probability and is able to update the probability due to any planned or unplanned changes in the area. Therefore, the proposed method can feed to future macro and micro risk-based decision-making situations in wildfire prone areas. The method is evaluated through a sensitivity analysis and its performance is investigated through a case study in New South Wales (NSW), Australia.
Predominant Instrument Recognition in Polyphonic Music Using GMM-DNN Framework

In this paper, the predominant instrument recognition in polyphonic music is addressed using timbral descriptors in three frameworks-Gaussian mixture model (GMM), deep neural network (DNN), and hybrid GMM-DNN. Three sets of features, namely, mel-frequency cepstral coefficient (MFCC) features, modified group delay features (MODGDF), and lowlevel timbral features are computed, and the experiments are conducted with individual set and its early integration. Performance is systematically evaluated using IRMAS dataset. The results obtained for GMM, DNN, and GMM-DNN are 65.60%, 85.60%, and 93.20%, respectively on timbral feature fusion. Architectural choice of DNN using GMM derived features on the feature fusion paradigm showed improvement in the system performance. Thus, the proposed experiments demonstrate the potential of timbral descriptors and DNN based systems in recognizing predominant instrument in polyphonic music.
Learning Salient Segments for Speech Emotion Recognition Using Attentive Temporal Pooling

In the temporal process of expressing the emotions, some intervals embed more salient emotion information than others. In this paper, by introducing an attentive temporal pooling module into the deep neural network (DNN) architecture, we present a simple but effective speech emotion recognition (SER) framework, which is able to automatically highlight the emotionally salient segments while suppressing the influence of less relevant ones. For an input speech utterance, the extracted feature sequence of hand-crafted low-level descriptors (LLDs) are evenly split into several overlapping temporal segments, and the segment-level features are computed by performing functionals on the LLDs of each segment. These segment-level features are then input into a DNN model outputting the emotion probabilities as well as the more condensed representation of each segment. An attentive temporal pooling module, consisting of an auxiliary DNN and a Gaussian Mixture Model (GMM), is proposed to learn the emotional saliency weights of different temporal segments from the condensed representations, which are then assigned to the segment-level emotion probabilities for the final utterance-level prediction. Notably, the attentive temporal pooling module and the DNN architecture for feature abstraction can be jointly trained using only the utterance-level labels, while without any frame-level or segment-level supervisory information. Experimental results on the three public released emotion datasets RML, EMO-DB, and IEMOCAP show that the proposed framework obtains state-of-the-art performance on SER.
A Hybrid BERT Model That Incorporates Label Semantics via Adjustive Attention for Multi-Label Text Classification

The multi-label text classification task aims to tag a document with a series of labels. Previous studies usually treated labels as symbols without semantics and ignored the relation among labels, which caused information loss. In this paper, we show that explicitly modeling label semantics can improve multi-label text classification. We propose a hybrid neural network model to simultaneously take advantage of both label semantics and fine-grained text information. Specifically, we utilize the pre-trained BERT model to compute context-aware representation of documents. Furthermore, we incorporate the label semantics in two stages. First, a novel label graph construction approach is proposed to capture the label structures and correlations. Second, we propose a neoteric attention mechanism—adjustive attention to establish the semantic connections between labels and words and to obtain the label-specific word representation. The hybrid representation that combines context-aware feature and label-special word feature is fed into a document encoder to classify. Experimental results on two publicly available datasets show that our model is superior to other state-of-the-art classification methods.
Spatial HRTF Interpolation using Spectral Phase Constraints

In this paper, a novel method for spatial Head Related Transfer Function (HRTF) interpolation is proposed. The property of linearity of HRTF spectral phase with frequency is first illustrated. An optimization problem is then formulated to compute the interpolated HRTF phase by imposing linearity constraints on the spectral phase of adjacent spatial angles. A weighted error minimization is carried out in an alternating optimization framework to obtain the solution to this problem. Phase-only reconstruction is used to obtain the HRIR for subsequent rendering of binaural audio. Interpolated HRTFs obtained by using the CIPIC and SYMARE database are evaluated using statistical analysis. Subjective evaluations are also performed to evaluate the quality of the binaural audio rendered using the proposed method.