Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Power Spectrum-Based Sequential Tracker for Time-Variant Radio Propagation Channel

Cluster tracking is a mainstream approach for the study of time-variant channel characteristics. In the paper, we propose a power spectrum based sequential tracker (PSBST) to compensate for the disadvantages of existing cluster tracking algorithms. The proposed tracker identifies clusters via simple three-stage power spectrum processing. Furthermore, fuzzy c-means (FCM) algorithm is incorporated to separate clusters considering the overlapped clusters which may appear in the power spectrum. In terms of tracking, we implement Kalman filter to sequentially predict candidate ranges of tracked clusters in consecutive snapshots and simultaneously a novel gradient-based histogram of power (GBHOP) method is devised to determine the evolution of clusters. We also investigate the performance of the tracker by synthetic channel simulation. It demonstrates high accuracy and less computational time compared with the results derived from the existing cluster tracking algorithms. Besides, we verify applicability of the tracker by analyzing the field measurement conducted in vehicle-to-everything (V2X) scenario, and preliminary statistical characteristics for intra-cluster and inter-cluster parameters can be readily obtained in the sequel.
Achievability of nearly-exact alignment for correlated Gaussian databases

We study the conditions that allow for the alignment of correlated databases with multivariate Gaussian features. We present some analysis tools that allow us to go beyond the achievability result for exact alignment and derive the condition for nearly-exact alignment. Our main theorem gives an expression for the order of magnitude of the error in alignment as a function of mutual information between features.
Automatic Detection of Intestinal Bleeding using an Optical Sensor for Wireless Capsule Endoscopy

Wireless capsule endoscopy (WCE) has been an effective and safe way to diagnose gastrointestinal (GI) disorders, such as, colon cancers, polyps and bleeding. The detection of bleeding and other anomalies is currently determined through conventional visual inspection of the WCE images by the physicians. An on-chip bleeding sensor is thus required, that can perform an automatic prescreening of the bleeding areas in real-time using blood's optical properties to assist the diagnosis. In this study, a spectrophotometer was initially used to evaluate the chromatic properties of blood. It is found that the reflection ratio pairs of 700 nm to 630 nm and 480 nm to 530 nm provide important statistics to separate blood from non-blood samples. It has been implemented hardware using small LEDs and photodiodes to validate the results. Therefore, the proposed sensor system works as a good candidate to be integrated in a WCE device to detect GI bleeding quickly and in real-time.
Deep Convolutional Long Short-Term Memory Network for Fetal Heart Rate Extraction

Fetal electrocardiography is a valuable alternative to standard fetal monitoring. Suppression of the maternal electrocardiogram (ECG) in the abdominal measurements, results in fetal ECG signals, from which the fetal heart rate (HR) can be determined. This HR detection typically requires fetal R-peak detection, which is challenging, especially during low signal-to-noise ratio periods, caused for example by uterine activity. In this paper, we propose the combination of a convolutional neural network and a long short-term memory network that directly predicts the fetal HR from multichannel fetal ECG. The network is trained on a dataset, recorded during labor, while the performance of the method is evaluated both on a test dataset and on set-A of the 2013 Physionet /Computing in Cardiology Challenge. The algorithm achieved a positive percent agreement of 92.1% and 98.1% for the two datasets respectively, outperforming a top-performing state-of-the-art signal processing algorithm.
Modeling, Localization, and Segmentation of the Foveal Avascular Zone on Retinal OCT-Angiography Images

The Foveal Avascular Zone (FAZ) is a capillary-free area that is placed inside the macula and its morphology and size represent important biomarkers to detect different ocular pathologies such as diabetic retinopathy, impaired vision or retinal vein occlusion. Therefore, an adequate and precise segmentation of the FAZ presents a high clinical interest. About to this, Angiography by Optical Coherence Tomography (OCT-A) is a non-invasive imaging technique that allows the expert to visualize the vascular and avascular foveal zone. In this work, we present a robust methodology composed of three stages to model, localize, and segment the FAZ in OCT-A images. The first stage is addressed to generate two FAZ normality models: superficial and deep plexus. The second one uses the FAZ model as a template to localize the FAZ center. Finally, in the third stage, an adaptive binarization is proposed to segment the entire FAZ region. A method based on this methodology was implemented and validated in two OCT-A image subsets, presenting the second subset more challenging pathological conditions than the first. We obtained localization success rates of 100% and 96% in the first and second subsets, respectively, considering a success if the obtained FAZ center is inside the FAZ area segmented by an expert clinician. Complementary, the Dice score and other indexes (Jaccard index and Hausdorff distance) are used to measure the segmentation quality, obtaining competitive average values in the first subset: 0.84± 0.01 (expert 1) and 0.85± 0.01 (expert 2). The average Dice score obtained in the second subset was also acceptable (0.70± 0.17), even though the segmentation process is more complex in this case.
Populations of Bactrocera oleae (Diptera: Tephritidae) and Its Parasitoids in Himalayan Asia

Abstract Olive fruit fly, Bactrocera oleae Rossi, is a worldwide pest of olives. To discover new parasitoids for a biological control program in California, olives were collected from various locations in the Himalayan foothills (China, Nepal, India, Pakistan) as part of a comprehensive search for B. oleae throughout its range. Wild olives, Olea europaea ssp. cuspidata, were sparsely distributed and B. oleae-infested olives were scarce. Wild olives were most widespread in Pakistan where fly infestation reached 30%. Infested olives in southwestern China were rare, reaching only 5%. Flies were identified morphologically as B. oleae, the first record from China. No B. oleae were recovered from India or Nepal. Mitochondrial gene sequences from NADH dehydrogenase (ND1), cytochrome oxidase subunit 1 (COX1), and 16S rRNA were obtained from flies and compared with B. oleae sequences in GenBank. A single mitochondrial haplotype was found in Chinese flies. Chinese B. oleae represent a maternal lineage based on ND1 and COX1 that is highly divergent from other B. oleae. Phylogenetic analysis by maximum likelihood and Bayesian inference based on the concatenated dataset of B. oleae sequences with sequences of two close subcongeners, Bactrocera biguttula (Bezzi) and Bactrocera munroi White, and analysis of delineation of species boundaries using the genealogical sorting index, supported the idea that Chinese flies share recent common ancestry with B. oleae. Flies were parasitized by braconid wasps, Psyttalia ponerophaga (Silvestri) in Pakistan, and a Diachasmimorpha species in China. Our survey reinforces the possibility of finding new biocontrol agents of olive fruit fly in the Himalayan region.
Genetic Algorithm Application to Feature Selection in sEMG Movement Recognition with Regularized Extreme Learning Machine*

This paper presents a genetic algorithm (GA) feature selection strategy for sEMG hand-arm movement prediction. The proposed approach evaluates the best feature set for each channel independently. Regularized Extreme Learning Machine was used for the classification stage. The proposed procedure was tested and analyzed applying Ninapro database 2, exercise B. Eleven time domain and two frequency domain metrics were considered in the feature population, totalizing 156 combined feature/channel. As compared to previous studies, our results are promising – 87.7% accuracy was achieved with an average of 43 combined feature/channel selection.
Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations

Gait is an essential function for humans, and gait patterns in daily life provide meaningful information about a person’s cognitive and physical health conditions. Inertial measurement units (IMUs) have emerged as a promising tool for low-cost, unobtrusive gait analysis. However, large varieties of IMU gait analysis algorithms and the lack of consensus for their validation make it difficult for researchers to assess the reliability of the algorithms for specific use cases. In daily life, individuals adapt their gait patterns in response to changes in the environment, making it necessary for IMU gait analysis algorithms to provide accurate measurements despite these gait variations. In this paper, we reviewed common types of IMU gait analysis algorithms and appropriate analysis methods to evaluate the accuracy of gait parameters extracted from IMU measurements. We then evaluated stride lengths and stride times calculated from a comprehensive double integration based IMU gait analysis algorithm using an optoelectric walkway as gold standard. In total, 729 strides from five healthy subjects and three different walking patterns were analyzed. Correlation analyses and Bland-Altman plots showed that this method is accurate and robust against large variations in walking patterns (stride length: correlation coefficient (r) was 0.99, root mean square error (RMSE) was 3% and average limits of agreement (LoA) was 6%; stride time: r was 0.95, RMSE was 4% and average LoA was 7%), making it suitable for gait evaluation in daily life situations. Due to the small sample size, our preliminary findings should be verified in future studies.
Robustness of Muscle Synergies under Variant Muscle Contraction Force during Forearm Movements

The Electromyography-based Pattern-Recognition (EMG-PR) framework has been investigated for almost three decades towards developing an intuitive myoelectric prosthesis. To utilize the knowledge of the underlying neurophysiological processes of natural movements, the concept of muscle synergy has been applied in prosthesis control and proved to be of great potential recently. For a muscle-synergy-based myoelectric system, the variation of muscle contraction force is also a confounding factor. This study evaluates the robustness of muscle synergies under a variant force level for forearm movements. Six channels of forearm surface EMG were recorded from six healthy subjects when they performed 4 movements (hand open, hand close, wrist flexion, and wrist extension) using low, moderate, and high force, respectively. Muscle synergies were extracted from the EMG using the alternating nonnegativity constrained least squares and active set (NNLS) algorithm. Three analytic strategies were adopted to examine whether muscle synergies were conserved under different force levels. Our results consistently showed that there exists fixed, robust muscle synergies across force levels. This outcome would provide valuable insights to the implementation of muscle- synergy-based assistive technology for the upper extremity.
Relationship between Vowel Sound Features and Pharyngeal Airway Cross-Sectional Area during Normal Breathing*

Assessment of the pharyngeal airway is becoming important for delivering personalized treatment and better management of sleep apnea. However, evaluation of the pharyngeal airway area is difficult in the current state of the art. It is essential to use simple and accessible technology to measure the pharyngeal airway area. As vowel sounds are generated by vocal cords vibration and characterized by the pharyngeal airway, vowel sounds have the potential to evaluate the pharyngeal airway area. The objective of this study was to investigate the relationship between acoustic features of vowel sounds and the pharyngeal airway cross-sectional area (PAXSA) between soft palate and glottis. Twenty subjects were included in this study whose PAXSA was measured by acoustic pharyngometry. Vowel sounds were recorded with a microphone while lying supine. Vowel sound average power was calculated in different frequency ranges of 100-3000 Hz, 100-500 Hz, 500-1000 Hz, 1000-1500 Hz, 1500-2000 Hz, 2000-2500 Hz and 2500-3000 Hz. Statistical analysis showed that the decreases in the PAXSA were strongly correlated with the higher average power of vowel sounds in all frequency ranges. These results showed that individuals with low PAXSA might articulate the vowel in higher intensity.Clinical Relevance—This study demonstrates that the pharyngeal airway cross-sectional area during normal breathing has a significant effect on vowel articulation. Thus, vowel sound features can be used to estimate the resting pharyngeal airway cross-sectional area.
Hybrid RNN-ANN Based Deep Physiological Network for Pain Recognition

Quantitative assessment of pain is vital progress in treatment choosing and distress relief for patients. However, previous approaches based on self-report fail to provide objective and accurate assessments. For impartial pain classification based on physiological signals, a number of methods have been introduced using elaborately designed handcrafted features. In this study, we enriched the methods of physiological-signal-based pain classification by introducing deep Recurrent Neural Network (RNN) based hybrid classifiers which combines auto-extracted features with human-experience enabled handcrafted features. A bidirectional Long Short-Term Memory network (biLSTM) was applied on time series of pre-processed signals to automatically learn temporal dynamic characteristics from them. The handcrafted features were extracted to fuse with RNN-generated features. Finely selected features from biLSTM layer output and handcrafted features trained an Artificial Neural Network (ANN) to classify the pain intensity. The handcrafted features enhance the RNN classification performance by complementing RNN-generated features. With our accuracy reaching 83.3%, comparison results on an open dataset with other methods show that the proposed algorithm outperforms all of the previous researches with higher classification accuracy. Therefore, this research is a good demonstration of introducing hybrid features for pain assessment.
Learning Erosional Probability Maps for Nuclei Instance Segmentation

Nuclei instance segmentation is an essential but challenging topic in computer vision and medical image analysis due to the ambiguity of nuclei boundaries, the severe border adhesions, and the diversity of nuclei shapes and sizes. In this paper, we propose a simple yet effective proposal-free framework for accurate nuclei instance segmentation. Specifically, we adopt a deep regression model to learn the mapping from the input images to the nuclei erosional inside probability maps and obtain the final instance segmentation results through straightforward dilation post-processing. We evaluate our framework on the open available 2018 Data Science Bowl nuclei instance segmentation dataset and outperform the state-of-the-art methods.
Approximate Gács-Körner Common Information

We propose to exploit the structure of the correlation between two random variables X and Y via a relaxation on the Common Information problem of Gács and Körner (GK Common Information). Consider two correlated sources X and Y generated from a joint distribution PX,Y . We study embeddings of X into discrete random variables U, such that H(U|Y) ≤ δ, while maximizing I(X; U). When δ = 0, this reduces to the GK Common Information problem. However, unlike the GK Common Information, which is known to be zero for many pairs of random variables (X, Y), we show that this relaxation allows to capture the structure in the correlation between X and Y for a much broader range of joint distributions, and showcase applications for some problems in multi-terminal information theory.
Uptake of radioactivity by body fluids and tissues in rhesus monkeys after intravenous injection or intranasal spray of tritium-labelled oestradiol and progesterone.

The uptake of radioactivity by body fluids and tissues in rhesus mon keys after intravenous injection or intranasal spray of tritium-labelled estradiol and progesterone was investigated. 8 healthy intact adult female monkeys in unknown stages of menstrual cycles were either sprayed intranasally or injected intravenously with . 1 mCi of either tritiated estradiol-17 beta or tritiated progesterone. Blood and cerebral spinal fluid (csf) were drawn before and at various intervals after administering the hormones. The animals were sacrificed 1 hour after administering the hormones and 18 different tissues were analyzed for radioactivity. Both steroids entered csf by 1 minute after the administration by either route. Considerable amounts of radioactivity were detected in the plasma of the estradiol sprayed monkeys while the amount of radioactivity in the plasma of the progesterone-sprayed monkeys was negligible. The amount of radioactivity was much higher in the csf in the sprayed monkeys as compared with that in the injected ones. In the sprayed monkeys the peripheral target tissues such as the liver ovary uterus vagina and fallopian tube showed much lower amounts of radioactivity in comparison with those injected. The olfactory bulb olfactory mucosa and respiratory mucosa showed much higher activity in sprayed monkeys but the amount of radioactivity in the lungs was low as compared with that found in the lungs of the injected animals. These results support a general inference that the steroids sprayed reach tissue known to contain neural mechanisms regulating gonadotropin secretion.
A Transfer Learning Framework for RSVP-based Brain Computer Interface*

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this paper, we collect an RSVP-based electroencephalogram (EEG) dataset, which includes 11 subjects. The experimental task is image retrieval. Also, we propose a multi-source transfer learning framework by utilizing data from other subjects to reduce the data requirement on the new subject for training the model. A source-selection strategy is firstly adopted to avoid negative transfer. And then, we propose a transfer learning network based on domain adversarial training. The convolutional neural network (CNN)-based network is designed to extract common features of EEG data from different subjects, while the discriminator tries to distinguish features from different subjects. In addition, a classifier is added for learning semantic information. Also, conditional information and gradient penalty are added to enable stable training of the adversarial network and improve performance. The experimental results demonstrate that our proposed method outperforms a series of state-of-the-art and baseline approaches.
Estimation of respiration rate and sleeping position using a wearable accelerometer

Wearable inertial sensors offer the possibility to monitor sleeping position and respiration rate during sleep, enabling a comfortable and low-cost method to remotely monitor patients. Novel methods to estimate respiration rate and position during sleep using accelerometer data are presented, with algorithm performance examined for two sensor locations, and accelerometer-derived respiration rate compared across sleeping positions. Eleven participants (9 male; aged: 47.82±14.14 years; BMI 30.9±5.27 kg/m2; AHI 5.77±4.18) undergoing a scheduled clinical polysomnography (PSG) wore a tri-axial accelerometer on their chest and upper abdomen. PSG cannula flow and position data were used as benchmark data for respiration rate (breaths per minute, bpm) and position. Sleeping position was classified using logistic regression, with features derived from filtered acceleration and orientation. Accelerometer-derived respiration rate was estimated for 30 s epochs using an adaptive peak detection algorithm which combined filtered acceleration and orientation data to identify individual breaths. Sensor-derived and PSG respiration rates were then compared. Mean absolute error (MAE) in respiration rate did not vary between sensor locations (abdomen: 1.67±0.37 bpm; chest: 1.89±0.53 bpm; p=0.52), while reduced MAE was observed when participants lay on their side (1.58±0.54 bpm) compared to supine (2.43±0.95 bpm), p<0.01. MAE was less than 2 bpm for 83.6% of all 30 s windows across all subjects. The position classifier distinguished supine and left/right with a ROC AUC of 0.87, and between left and right with a ROC AUC of 0.94. The proposed methods may enable a low-cost solution for in-home, long term sleeping posture and respiration monitoring.
Eficiência de recipientes no desenvolvimento de mudas de Cedrela fissilis Vell. (Meliaceae)

Efficiency of different containers on the development of Cedrela fissilis Vell. (Meliaceae) seedlings). Shape and size of container for seedling production influence directly on field survivor. We aimed to evaluate the efficiency of different containers on the development of Cedrela fissilis (cedro) seedlings for application in forest restoration projects. Seedlings were transplanted into five different containers: large and small plastic bags; tubes of three different sizes. After 120 days of acclimation, seedlings development was evaluated in relation to growth patterns, biomass allocation and levels of chlorophyll a, b and total. We observed plant growth reduction toward decreasing containers sizes and volumes, although to the tubes, these differences were not significant. Plastic bags showed higher dry matter accumulation. Seedlings growing in tubes of 100 cm³ showed both higher accumulation of a chlorophyll and root biomass than shoots, which enabled them for better acclimatization to field conditions. In order to fast produce of large seedlings, large plastic bags seem to represent the best alternative due to better seedling development. However, tubes of 100 cm³ are an important alternative to large scale production and with higher acclimation under field conditions.
Comparison of Histogram-based Textural Features between Cancerous and Normal Prostatic Tissue in Multiparametric Magnetic Resonance Images

In the last decade, multiparametric magnetic resonance imaging (mpMRI) has been expanding its role in prostate cancer detection and characterization. In this work, 19 patients with clinically significant peripheral zone (PZ) tumours were studied. Tumour masks annotated on the whole-mount histology sections were mapped on T2-weighted (T2w) and diffusion-weighted (DW) sequences. Gray-level histograms of tumoral and normal tissue were compared using six first-order texture features. Multivariate analysis of variance (MANOVA) was used to compare group means. Mean intensity signal of ADC showed the highest showed the highest area under the receiver operator characteristics curve (AUC) equal to 0.85. MANOVA analysis revealed that ADC features allows a better separation between normal and cancerous tissue with respect to T2w features (ADC: P = 0.0003, AUC = 0.86; T2w: P = 0.03, AUC = 0.74). MANOVA proved that the combination of T2-weighted and apparent diffusion coefficient (ADC) map features increased the AUC to 0.88. Histogram-based features extracted from invivo mpMRI can help discriminating significant PZ PCa.