Miracle-dz/UniFAR-FTUs huggingface dataset:
Limited-Angle Computed Tomography Reconstruction using Combined FDK-Based Neural Network and U-Net

The limited-angle cone-beam Computed Tomography (CT) is often used in C-arm for clinical diagnosis with the advantages of cheap cost and radiation dose reduction. However, due to incomplete projection data, the 3-dimensional CT images reconstructed by conventional methods, such as the Feldkamp, Davis and Kres (FDK) algorithm [1], suffer from heavy artifacts and missing features. In this paper, we propose a novel pipeline of neural networks jointly by a FDK-based neural network revisited from Würfl et al.’s work [2] and an image domain U-Net to enhance the 3-dimensional reconstruction quality for limited projection sinogram less than 180 degrees, i.e. 145 degrees in our work. Experimental results, on simulated projections of real-scan CTs, show that the proposed pipeline can reduce some of the major artifacts caused by the limited views while keep the key features, with a 16.60% improvement than Würfl et al.’s work on peak signal-to-noise ratio.
Estimating the Population of Large Animals in the Wild Using Satellite Imagery: A Case Study of Hippos in Zambia’s Luangwa River

Degradation of natural ecosystems as influenced by increasing human activity and climate change is threatening many animal populations in the wild. Zambia’s hippo population in Luangwa Valley is one example where declining forest cover from increased farming pressures has the potential of limiting hippo range and numbers by reducing water flow in this population’s critical habitat, the Luangwa River. COMACO applies economic incentives through a farmer-based business model to mitigate threats of watershed loss and has identified hippos as a key indicator species for assessing its work and the health of Luangwa’s watershed. The goal of this effort is to develop automated machine learning tools that can process fine resolution commercial satellite imagery to estimate the hippo population and associated characteristics of the habitat. The focus is the Luangwa River in Zambia, where the ideal time for imagery acquisition is the dry season of June through September. This study leverages historical commercial satellite imagery to identify selected areas with observable hippo groupings, develop an-image-based signature for hippo detection, and construct an initial image classifier to support larger-scale assessment of the hippo population over broad regions. We begin by characterizing the nature of the problem and the challenges inherent in applying remote sensing methods to the estimation of animal populations. To address these challenges, spectral signatures were constructed from analysis of historical imagery. The initial approach to classifier development relied on spectral angle to distinguish hippos from background, where background conditions included water, bare soil, low vegetation, trees, and mixtures of these materials. We present the approach and the initial classifier results. We conclude with a discussion of next steps to produce an imagebased estimate of the hippo populations and discuss lessons learned from this study.
Multi-criteria decision making using Fuzzy Logic and ATOVIC with application to manufacturing

In this paper multi-criteria decision making (MCDM) is investigated as a framework for classification of part quality in a manufacturing process. The importance of linguistic interpretability of decisions is highlighted, and a new framework relying on the integration of Fuzzy Logic and an existing MCDM method is proposed. ATOVIC, previously developed as a TOPSIS-VIKOR-based MCDM framework is enhanced with a Fuzzy Logic framework for decision making - Fuzzy-ATOVIC. This research work demonstrates how to add linguistic interpretability to decisions made by the MCDM framework. This contributes to explainable decisions, which can be crucial on numerous domains, for example on safety-critical manufacturing processes. The case study presented is the one of ultrasonic inspection of plastic pipes, where thermomechanical joining is a critical part of the manufacturing process. The proposed framework is used to classify (take decisions) on the quality of manufactured parts using ultrasonic images around the joint region of the pipes. For comparison, both the original and the Fuzzy Logic-enhanced MCDM methods are contrasted using data from manufacturing trials and subsequent ultrasonic testing. It is shown, that Fuzzy-ATOVIC provides a framework for linguistic interpretability while the performance is the same or better compared to the original MCDM framework.
Circularly Polarised Dual Band Antennas for Wireless Communication

A unique dual band circularly polarized antenna is presented. Antenna structure includes stubs and slots of different shapes and dimensions, embedded on both planes. It exhibits 10 dB Impedance bandwidth (IBW) of 4.72 GHz (2.63-7.35 GHz, 94.6%) with corresponding 3 dB Axial Ratio Bandwidth (ARBW) of 2.12GHz (3.62-5.74 GHz, 45.3%) in band I, and IBW of 0.98 GHz (7.86 - 8.84 GHz, 117.4%) with corresponding ARBW of 1.82 GHz (7.06 - 8.88 GHz, 22.84%) in band II. The average gain value was measured to be 3.09dBi in band I and 1.51 dBi in band II, with a peak gain value of 4.0dBi at 6.25 GHz and an efficiency of 95.94% at 3.4GHz. Antenna has an advantage of simple structure and reduced size.
Automated Segmentation of Nucleus, Cytoplasm and Background of Cervical Cells from Pap-smear Images using a Trainable Pixel Level Classifier

Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early detection provides the opportunity to help save life. Automated diagnosis of cervical cancer from pap-smear images enables accurate, reliable and timely analysis of the condition’s progress. Cell segmentation is a fundamental aspect of successful automated pap-smear analysis. In this paper, a potent approach for segmentation of cervical cells from a pap-smear image into the nucleus, cytoplasm and background using pixel level information is proposed. A number of pixels from the nuclei, cytoplasm and background are extracted from 100 images to form a feature vector which is trained using noise reduction, edge detection and texture filters to produce a pixel level classifier. Comparison of the segmented images’ nucleus and cytoplasm parameters (nucleus area, longest diameter, roundness, perimeter and cytoplasm area, longest diameter, roundness, perimeter) with the ground truth image features yielded average percentage errors of 0.14, 0.28, 0.03, 0.30, 0.15, 0.25, 0.05 and 0.39 respectively. Validation of the pixel classifier with 10fold cross-validation yielded pixel classification accuracy of 98.50%, 97.70% and 98.30% with Fast Random Forest, Naïve Bayes and J48 classification methods respectively. Comparison of the segmented nucleus and cytoplasm with the ground truth nucleus and cytoplasm segmentations resulted into a Zijdenbos similarity index greater than 0.9321 and 0.9639 for nucleus and cytoplasm segmentation respectively. The results indicated that the proposed pixel level segmentation classifier was able to extract the nucleus and cytoplasm regions accurately and worked well even though there was no significant contrast between the components in the image. The results from cross-validation and test set evaluation imply that the classifier can segment cells outside the training dataset with high precision. Choosing an appropriate feature vector for training the classifier was a great challenge and a novel task in the proposed approach. As a result, good segmentation of the nucleus and cytoplasm was attained. Given the accuracy of the classifier in segmenting the nucleus, which plays an important role in cervical cancer diagnosis, the classifier can be adopted in systems for automated diagnosis of cervical cancer from pap-smear images.
Subject Identification Using a Depth Camera for Patient Ambulation Monitoring

Our work identifies subjects based on their height and the distance between their joints. Using a depth sensing camera, we obtained the position of a person’s joints in 3D space relative to each other. The distances between adjacent joints and height of a subject’s head are used to create a vector of eight features for an individual to use for identification. Using modified KNN, full and partial feature sets were used to identify subjects. Additionally, our classifier can be utilized to assess ambulation (such as walking’s velocity and distance) of subject, when identified.
Expanding the Vocabulary of a Protein: Application of Subword Algorithms to Protein Sequence Modelling

Deep learning has proven to be a useful tool for modelling protein properties. However, given the variability in the length of proteins, it can be difficult to summarise the sequence of amino acids effectively. In many cases, as a result of using fixed-length representations, information about long proteins can be lost through truncation, or model training can be slow due to the use of excessive padding. In this work, we aim to overcome these problems by expanding upon the original vocabulary used to represent the protein sequence. To this end, we utilise two prominent subword algorithms that have been previously used to reach state-of-the-art results in various Natural Language Processing tasks. The algorithms are used to encode the original protein sequence into a set of subsequences before they are analysed by a Doc2Vec model. The pre-trained encodings produced by each algorithm are tested on a variety of downstream tasks: four protein property prediction tasks (plasma membrane localization, thermostability, peak absorption wavelength, enantioselectivity) as well as drug-target affinity prediction tasks over two datasets. Our results significantly improve on the state-of-the-art for these tasks, demonstrating the benefits of using subword compression algorithms for modelling proteins.
Potential pitfalls of widely used implementations of common spatial patterns

We have uncovered serious flaws in handling EEG signals with a decreased rank in implementations of the common spatial patterns (CSP). The CSP algorithm assumes covariance matrices of the signal to have full rank. However, preprocessing techniques, such as artifact removal using independent component analysis, may decrease the rank of the signal, leading to potential errors in the CSP decomposition. We inspect what could go wrong when CSP implementations do not take this into consideration on a binary motor imagery classification task. We review CSP implementations in open-source toolboxes for EEG signal analysis (FieldTrip, BBCI Toolbox, BioSig, EEGLAB, BCILAB, and MNE). We show that unprotected implementations decreased mean classification accuracy by up to 32%, with spatial filters resulting in complex numbers, for which corresponding spatial patterns do not have a clear interpretation. We encourage researchers to check their implementations and analysis pipelines.
Clone Chaotic Niche Evolutionary Algorithm for Duty Cycle Control Optimization in Wireless Multimedia Sensor Networks

One of the most interesting issue regarding to wireless multimedia sensor networks (WMSNs) is to maximizing the network lifetime. Because sensor nodes are constrained in energy, it is very important and necessary to exploit novel duty cycle design algorithms. Such a problem is important in improving network lifetime in WMSNs. The new contribution of our paper is that we propose a clone chaotic niche evolutionary algorithm (CCNEA) for duty cycle design problem in WMSNs. Novel clone operator and chaotic operator have been designed to develop solutions randomly. The strategy merges the merits of clone selection, chaotic generation, and niche operator. CCNEA is a style of swarm algorithm, which has strong global exploit ability. CCNEA utilizes chaotic generation approach which targets to avoid local optima. Then, simulations are performed to verify the robust and efficacy performance of CCNEA compared to methods according to particle swarm optimization (PSO) and quantum genetic algorithm (QGA) under an WMSNs conditions. Simulation experiments denote that the presented CCNEA outperforms PSO and QGA under different conditions, especially for WMSNs that has large number of sensors.
Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation

Automatic segmentation of prostatic zones on multi-parametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89± 0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (DeeplabV3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.
Physiological response to Vibro-Acoustic stimulation in healthy subjects: a preliminary study*

Over the last decade Vibro-Acoustic Therapy (VAT) was used for several clinical applications. This paper investigates the use of AcusticA®, an innovative VAT solution represented by a wooden chaise longue that follows the construction principles of a "musical instrument that stimulates the whole body" in relation to the sound frequencies produced by the music tracks. Ten healthy young subjects were enrolled for this study. Wearable sensors were used to monitor the human physiological response during the VAT session but also during a traditional acoustic therapy (AT) to highlight similarity and differences of those stimulations. Signals from heart activity, brain activity and electrodermal activity were analyzed to investigate the response during the non-stimulated and the stimulated phases. Additionally, two supervised classification algorithms were used to investigate whether the extracted instances could be grouped into two different groups. The results identify a trend of the attention and meditation features extracted from brain activity, which pointed out the relax efficacy of the VAT.Clinical Relevance - There are not significant differences (p<0.05) in the physiological response between the VAT and the AT stimulation, but during the VAT the alpha coefficients were significant different during the stimulated phase. Finally, the classification algorithms were able to classify the groups with an accuracy equal to 100% in the best case.
Freestanding Multi-Gate Amorphous Oxide-Based TFTs on Graphene Oxide Enhanced Electrolyte Membranes

Freestanding thin-film transistors (TFTs) with multiple in-plane gates could meet increasing demands of mechanical flexibility and multifunctional integration. In this letter, freestanding flexible indium-zinc-oxide based TFTs with multi-gate structure and high electrical performance were reported. No obviously electrical degradation is observed under various mechanical stimuli due to the mechanical reinforcement effect of graphene oxide. Multifunctional operations including AND, OR gates and a Schmitt trigger are successfully demonstrated. More importantly, OR logic to AND logic transformation is also realized by a third modulatory terminal. Such freestanding TFTs are promising candidates for next-generation flexible smart electronic systems.
Comparative Analysis between SAR Pulse Compression Techniques

Remote sensing is the backbone for several civilian and military applications. Synthetic Aperture Radar (SAR) is considered as one of the most important tools, which has a significant rule in remote sensing applications. For SAR signal processing, pulse compression techniques aim to obtain a fine map resolution, decrease the peak-transmitted power, and increase Signal to Noise Ratio (SNR) of the sensed target. In this paper, we introduce a performance assessment for two well-known Linear Frequency Modulation (LFM) pulse compression techniques, which are Matching Filtering and Stretch Processing. For matching filtering, it is known as Correlation processing technique. It is mainly used for narrow band and some medium band radar operations. While, stretch processing technique is usually used for high bandwidth LFM signal processing. Besides that, we discuss the properties of the LFM signal and the two compression techniques in both time and frequency domain. Also, the paper investigates the concept of the principle of stationary phase (POSP) and its use in deriving the frequency characteristics for the LFM signal and matched filter output. A mathematical model for each compression technique has been derived such that these models will be used for hardware implementation purpose. For simulation and performance assessment, the two techniques have been analyzed based on some quantitative indices like, Pulse Compression Ratio (PCR) and Peak Side-Lobe Ratio (PSLR).
40-Hz Rhythmic Visual Stimulation Facilitates Attention by Reshaping the Brain Functional Connectivity*

Recently, rhythmic visual stimulation (RVS) has been demonstrated to affect the brain function by entraining neural oscillations. However, less is known about how RVS influences the functional connectivity across the whole brain. Here, we applied a graph theoretical approach to analyze the electroencephalography (EEG) connections of 60 nodes when subjects deployed their attention on visual task with different background stimulation, i.e. no background flicker, jittered flicker, and RVS of 6, 10, 15 and 40 Hz, respectively. Thirty-three subjects participated in this study. As a result, the 40-Hz rhythm led to the significantly fastest reaction among all conditions. Furthermore, significantly higher clustering coefficient (C) and small worldness (σ) of θ-band brain network were observed for higher-frequency RVS, which were significantly negatively correlated with reaction time (RT) (C-RT: r =-0.917, p =0.010; σ-RT: r =-0.894, p =0.016). In addition, we found an increase in the connections between dorsolateral prefrontal and visual cortices under RVS compared to no flicker. Our results indicate that RVS can improve the efficiency of brain cortical functional network to facilitate attention.
Crowdsourced Classification with XOR Queries: An Algorithm with Optimal Sample Complexity

We consider the crowdsourced classification of m binary labels with XOR queries that ask whether the number of objects having a given attribute in the chosen subset of size d is even or odd. The subset size d, which we call query degree, can be varying over queries. Since a worker needs to make more efforts to answer a query of a higher degree, we consider a noise model where the accuracy of worker’s answer changes depending both on the worker reliability and query degree d. For this general model, we characterize the information-theoretic limit on the optimal number of queries to reliably recover m labels in terms of a given combination of degree-d queries and noise parameters. Further, we propose an efficient inference algorithm that achieves this limit even when the noise parameters are unknown.1
A female-specific cardiovascular lumped-parameter model

Historically, cardiovascular computational models have been developed considering the case of a 70 Kg male patient. However, hemodynamic quantities differ widely due to sex, age, and weight. In this study, we developed a female-specific model of the blood circulation of a young (18-40 y.o.) woman with BSA of 1.6 m2. The lumped-parameter (0D) model, which includes the uterus, has been calibrated with female-specific parameters and validated with sex-specific literature data.
Recovery Sets for Subspaces from a Vector Space

Recovery sets for vectors and subspaces are important in constructions of distributed storage system codes. These concepts are also interesting in their own right. In this paper we consider the following very basic recovery question: what is the maximum number of possible pairwise disjoint recovery sets if the recovered element is a d-dimensional subspace and the elements stored are the one-dimensional subspaces of an n-dimensional vector space over GF(q). Lower and upper bounds on the number of such recovery sets are provided. It is shown that generally these bounds are either tight or very close of being tight.
Probability distribution guided optic disc and cup segmentation from fundus images

In this paper, we proposed and validated a probability distribution guided network for segmenting optic disc (OD) and optic cup (OC) from fundus images. Uncertainty is inevitable in deep learning, as induced by different sensors, insufficient samples, and inaccurate labeling. Since the input data and the corresponding ground truth label may be inaccurate, they may actually follow some potential distribution. In this study, a variational autoencoder (VAE) based network was proposed to estimate the joint distribution of the input image and the corresponding segmentation (both the ground truth segmentation and the predicted segmentation), making the segmentation network learn not only pixel-wise information but also semantic probability distribution. Moreover, we designed a building block, namely the Dilated Inception Block (DIB), for a better generalization of the model and a more effective extraction of multi-scale features. The proposed method was compared to several existing state-of-the-art methods. Superior segmentation performance has been observed over two datasets (ORIGA and REFUGE), with the mean Dice overlap coefficients being 96.57% and 95.81% for OD and 88.46% and 88.91% for OC.