Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic-based Soft Tactile Sensors with Deformable Continuous Force Transfer Medium for Resolving Contact Locations in Robotic Grasping and Manipulation

The resolution of contact location is important in many applications in robotics and automation. This is generally done by using an array of contact or tactile receptors, which increases cost and complexity as the required resolution or area is increased. Tactile sensors have also been developed using a continuous deformable medium between the contact and the receptors, which allows few receptors to interpolate the information among them, avoiding the weakness highlighted in the former approach. The latter is generally used to measure contact force intensity or magnitude but rarely used to identify the contact locations. This paper presents a systematic design and characterisation procedure for magnetic-based soft tactile sensors (utilizing the latter approach with the deformable contact medium) with the goal of locating the contact force location. This systematic procedure provides conditions under which design parameters can be selected, supported by a selected machine learning algorithm, to achieve the desired performance of the tactile sensor in identifying the contact location. An illustrative example, which combines a particular sensor configuration (magnetic hall effect sensor as the receptor, a selected continuous medium and a selected sensing resolution) and a specific data-driven algorithm, is used to illustrate the proposed design procedure. The results of the illustrative example design demonstrates the efficacy of the proposed design procedure and the proposed sensing strategy in identifying a contact location. The resulting sensor is also tested on a robotic hand (Allegro Hand, SimLab Co) to demonstrate its application in real-world scenarios.
Estimating PM2.5 Emission from Brick Kiln Industry Over Northern India With Numerical Model And Remote Sensing Observation

This study examines contribution of brick kiln (BK) industry to PM2.5 emission over northern part of India. We analyze NASA’s MODIS and JAXA’s GCOM-C/SGLI land-atmospheric products with 3887 BK locations over the region in the period of the highest PM2.5 concentration during December 2016 to February 2017. Our preliminary results show that more than 60% of BK occupied silt-rich areas, with more than 80% of them were located over croplands on the outskirts of urban areas, indicating proxies for PM2.5 emission over Northern India. Initial WRF-Chem model simulations showed prominent diurnal variation of the pollutant with averaged peak concentration exceeding 1μg/m3 over the capital city of New Delhi. Future works will introduce the use of Google Earth Engine (GEE) cloud platform for identifying BK locations and incorporate them into the model to improve the simulation results.
The Energy Efficient Power Allocation for Multiple Relay-Aided D2D communication in 5G networks Using Iterative algorithm

Device-to-Device Communication can generally be defined as, communication of any two devices without the involvement or participation of the Base station.D2D communication may suit well for small range of communication. In case of long range communication and also to get a good Quality of Service we may go for the Relay-Aided Device to Device communication. In this paper, we propose an Energy Efficient power allocation for multiple Relay-Aided Device to Device communication using iterative and Power Efficient Relay Selection algorithm. The main aim is to reduce the power allocation and to optimize the Energy Efficiency. The power allocation is reduced to 10%, and the Energy Efficiency is been boosted.
Are Linear Measurements of the Nucleus Basalis of Meynert Suitable as a Diagnostic Biomarker in Mild Cognitive Impairment and Alzheimer Disease?

BACKGROUND AND PURPOSE: Cell loss within the nucleus basalis of Meynert is an early event in Alzheimer disease. The thickness of the nucleus basalis of Meynert (NBM) can be measured on structural MR imaging. We investigated NBM thickness in relation to cognitive state and biochemical markers. MATERIALS AND METHODS: Mean bilateral nucleus basalis of Meynert thickness was measured on coronal T1-weighted MR imaging scans from the Alzheimer’s Disease Neuroimaging Initiative dataset. Three hundred and fifteen scans (80 controls, 79 cases of early mild cognitive impairment, 77 cases of late mild cognitive impairment and 79 cases of Alzheimer disease) were assessed. Alzheimer’s Disease Assessment Scale-Cognitive scores, CSF tau, and amyloid quantification were extracted. Group differences in NBM thickness, their correlates and measurement reliability were assessed. RESULTS: Mean NBM thickness ± SD progressively declined from 2.9 ± 0.3, 2.5 ± 0.3, and 2.3 ± 0.3 to 1.8 ± 0.4 mm in healthy controls, patients with early mild cognitive impairment, late mild cognitive impairment and Alzheimer disease respectively (P < .001). NBM thickness was negatively correlated with Alzheimer’s Disease Assessment Scale-Cognitive scores (r = –0.53, P < .001) and weakly positively correlated with CSF amyloid (r = 0.250, P < .001) respectively. No association with CSF tau was found. NBM thickness showed excellent diagnostic accuracy to differentiate Alzheimer disease (area under the curve, 0.986) and late mild cognitive impairment from controls (area under the curve, 0.936) with excellent sensitivity, but lower specificity 66.7%. Intra- and interrater reliability for measurements was 0.66 and 0.47 (P < .001). CONCLUSIONS: There is progressive NBM thinning across the aging-dementia spectrum, which correlates with cognitive decline and CSF markers of amyloid-β pathology. We show high diagnostic accuracy but limited reliability, representing an area for future improvement. NBM thickness is a promising, readily available MR imaging biomarker of Alzheimer disease warranting diagnostic-accuracy testing in clinical practice.
Engineering structural and optical properties of 3D chiral dielectric nanostructures

In this work we analyzed arrays of 3D dielectric nano-helices for nanophotonic devices made by focus electron beam induced deposition. By means of this technique it is possible to vary the geometrical parameters of the periodic structures enabling large values of circular dichroism in the visible spectral range. The possibility to modify the structural parameter allows the realization of different geometrical arrangement ranging from chiral photonic crystal-like, to metamaterial-like structures across intermediate regions between them, opening the way to new nanophotonic applications.
Exposure to infant images enhances attention control in mothers

ABSTRACT Viewing cute images has been reported to promote performance on tasks requiring carefulness, possibly related to an enhanced positive emotional state. However, it is unclear whether viewing infant images also enhances attention control in mothers. Therefore, this experimental study examined whether exposure to images of infants affected mothers’ performance on a visual search task, studying associations with happy facial expressivity. Mothers (N = 101, Mage = 30.88) were randomly assigned to one of two conditions in which they either viewed images of infants or images of adults. Before and after viewing images, mothers performed a visual search task. Mothers’ happy facial expressions at baseline and when viewing images were analysed. Viewing images of infants, in contrast to viewing images of adults, improved task performance indexed by accurateness, but not the number of correct responses. Images of infants elicited happy facial expressivity, which was associated with the number of correct responses on the visual search task. This study showed that viewing images of infants evokes happy facial expressions in mothers and can improve mothers’ performance on a perceptual-cognitive task requiring attention control. Mothers’ responses to infant images may be explained as an attentional preparedness for caregiving.
Embranchment Cnn Based Local Climate Zone Classification Using Sar And Multispectral Remote Sensing Data

In this study, a Local Climate Zone (LCZ) classification framework is established using a Densenet based embranchment Convolutional Neural Network (CNN). Both synthetic aperture radar (SAR) and multispectral data are employed for feature fusion, specifically, considering about the difference in imaging mechanism between SAR and multispectral data, features from both resources are extracted in different branches separately according to the physical properties of each band. Significant accuracy improvement can be achieved when evaluate the proposed method by Sentinel-1 and Sentinel-2 dataset, and the comparison results show the superiority of the proposed embranchment CNN framework over the conventional methods.
Pigmented Skin Lesions Classification Using Data Driven Subsets of Image Features

In this paper we present an architecture for identification of pigmented skin lesions from dermatoscopic images. The architecture used a large number of image features and was evaluated with several classification algorithms on different feature subsets as extracted from feature ranking. The best performing classification algorithm was the support vector machines using polynomial kernel function with classification accuracy equal to 74.69% and the most precisely classified skin lesion type between seven different skin pathologies was nevus with accuracy equal to 94.38%.
A Review on Deep Learning Applications in Prognostics and Health Management

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.
Compact Polarimetry for Agricultural Mapping and Inventory: Preparation for Radarsat Constellation Mission

Agriculture and Agri-Food Canada (AAFC) has combined RADARSAT-2 C-band dual polarization data with optical imagery to map crop types across the agricultural extent of Canada yearly since 2009. In preparation for the launch of the RADARSAT Constellation Mission (RCM) primary research has been focused on incorporating similar-mode dual polarization RCM data in the operational system. The availability of the compact polarimetry (CP) mode with continuous coverage has important implications for crop mapping and inventory. CP mode on RCM has a circular transmit and two orthogonal linear receive structure and maintains phase information. The addition of CP data to AAFC’s operational crop type mapping will expand the information that the current dual polarization Synthetic Aperture Radar (SAR) component provides. This will increase the SAR contribution from the simple intensity of backscatter to capturing the scattering characteristics of the target. There are many parameters and decompositions that can be derived from CP data. Many of these parameters are highly correlated or may not provide information that are important for crop type identification. The goal of this research was to derive twenty-four CP parameters and decompositions for a growing season of SAR imagery and to assess the importance and contribution of these features to an overall classification of crops in southern Manitoba, Canada.
MonoFENet: Monocular 3D Object Detection With Feature Enhancement Networks

Monocular 3D object detection has the merit of low cost and can be served as an auxiliary module for autonomous driving system, becoming a growing concern in recent years. In this paper, we present a monocular 3D object detection method with feature enhancement networks, which we call MonoFENet. Specifically, with the estimated disparity from the input monocular image, the features of both the 2D and 3D streams can be enhanced and utilized for accurate 3D localization. For the 2D stream, the input image is used to generate 2D region proposals as well as to extract appearance features. For the 3D stream, the estimated disparity is transformed into 3D dense point cloud, which is then enhanced by the associated front view maps. With the RoI Mean Pooling layer, 3D geometric features of RoI point clouds are further enhanced by the proposed point feature enhancement (PointFE) network. The region-wise features of image and point cloud are fused for the final 2D and 3D bounding boxes regression. The experimental results on the KITTI benchmark reveal that our method can achieve state-of-the-art performance for monocular 3D object detection.
Generation of PECAM‐1 (CD31) conditional knockout mice

Platelet endothelial cell adhesion molecule 1 (PECAM‐1) is an adhesion and signaling receptor that is expressed on endothelial and hematopoietic cells and plays important roles in angiogenesis, vascular permeability, and regulation of cellular responsiveness. To better understanding the tissue specificity of PECAM‐1 functions, we generated mice in which PECAM1, the gene encoding PECAM‐1, could be conditionally knocked out. A targeting construct was created that contains loxP sites flanking PECAM1 exons 1 and 2 and a neomycin resistance gene flanked by flippase recognition target (FRT) sites that was positioned upstream of the 3′ loxP site. The targeting construct was electroporated into C57BL/6 embryonic stem (ES) cells, and correctly targeted ES cells were injected into C57BL/6 blastocysts, which were implanted into pseudo‐pregnant females. Resulting chimeric animals were bred with transgenic mice expressing Flippase 1 (FLP1) to remove the FRT‐flanked neomycin resistance gene and mice heterozygous for the floxed PECAM1 allele were bred with each other to obtain homozygous PECAM1 flox/flox offspring, which expressed PECAM‐1 at normal levels and had no overt phenotype. PECAM1 flox/flox mice were bred with mice expressing Cre recombinase under the control of the SRY‐box containing gene 2 (Sox2Cre) promoter to delete the floxed PECAM1 allele in offspring (Sox2Cre;PECAM1 del/WT), which were crossbred to generate Sox2Cre; PECAM1 del/del offspring. Sox2Cre; PECAM1 del/del mice recapitulated the phenotype of conventional global PECAM‐1 knockout mice. PECAM1 flox/flox mice will be useful for studying distinct roles of PECAM‐1 in tissue specific contexts and to gain insights into the roles that PECAM‐1 plays in blood and vascular cell function.
Unsupervised Temporal-Adaptation with Multiple Geodesic Flow Kernels for Hyperspectral Image Classification

The miniaturization of hyperspectral sensors and the popularity of the unmanned aerial vehicle (UAV) make it possible to obtain a series of hyperspectral images (HSIs) in the same geographical area at different time-points by same or different sensors. When classifying these multi-temporal HSIs, temporal-adaptation is required to deal with the spectral drift and band inconsistency problems. Since most studies focus on semi-supervised domain adaptation (DA) strategy, and spatial features are usually absent during most of the DA procedure, an unsupervised temporal-adaptation method is realized by spatial-spectral multiple Geodesic Flow Kernels (S2-GFKs) to classify bi-temporal HSIs. Experiments conducted on two real HSI datasets and compared with several well-known methods demonstrate the availability of the proposed model.
A Novel Temperature Controlled Broadband Metamaterial Absorber for THz Applications

A broadband switchable metamaterial absorber is investigated in this paper. The switchable response is achieved by utilizing the phase transition property of vanadium dioxide (VO2) that is thermally controlled. A novel band extension scheme is presented by introducing capacitive coupling effects among the resonators. By exploiting the coupling effect, near octave bandwidth is achieved when referred to an absorptivity of 90%. Further, by integrating the temperature controlled VO2 film into the dielectric layer, the proposed absorber can be switched between a frequency band from 0.32 THz to 0.56 THz and another band from 0.356 THz to 0.682 THz that is achieved by changing the operation temperature. Due to the symmetrical structure, the studied absorber features polarization insensitive and a wide incident angle of up to 50°. The broadband and switchable properties are discussed based on the resonant structure, surface current distributions, electric field distributions and impedance matching. It is noted the presented method can be scaled to other adjacent THz frequency band.
Performance Analysis of FSO Link under Diferent Conditions of Fog in Delhi, India

Free space optical communication (FSO) is one of the most effective technology having advantages like free license spectrum, high data rate, swift ability to expand and easy deploy-ability. In FSO communication atmosphere is used as a channel, therefore atmospheric turbulence leads to the degradation in performance of the communication system. In this paper, one of the atmospheric turbulence condition that is atmospheric fog attenuation has been analyzed to study its effect on the performance of an FSO link. This paper also presents a comprehensive survey on attenuation due to fog conditions in Delhi region. The atmospheric fog attenuation used for simulation in the system is calculated by using Kim’s model for two specific months i.e. January and November. Q-factor has been analyzed by varying the transmission power and range of the FSO system. The simulation results show that for error free transmission of data during the fog months, the transmitting power between 25-35dBm is recommended to be used for the optimal transmission range from 6-8Km.
Leader-following Discrete-time Bipartite Consensus on Signed Networks

Distributed averaging has applications in a wide range of distributed algorithms. Previous studies are mostly concentrated on the networks with only non-negative weighted edges. This paper examines the leader-following discrete-time bipartite consensus problem under signed networks, namely, both positively and negatively weighted edges are allowed. It is shown that the absolute value of the steady-state of the agents is upper and lower bounded by the largest and the smallest absolute value of external input, respectively. Specifically, for structurally balanced networks, it is shown that the absolute value of the bipartite consensus converges to the absolute value of the external input. Furthermore, a quantitative characterization of the bipartite consensus on structurally balanced networks is provided according to the sign pattern of the influence exerted by external input. Simulation results are finally provided to demonstrate the theoretical results.
Sexually Transmitted Infections and Behavioral Determinants of Sexual and Reproductive Health in the Allahabad District (India) Based on Data from the ChlamIndia Study

Background: Sexually transmitted infections (STIs), like Chlamydia trachomatis and Neisseria gonorrhoeae (CT and NG, respectively) are linked to an important sexual and reproductive health (SRH) burden worldwide. Behavior is an important predictor for SRH, as it dictates the risk for STIs. Assessing the behavior of a population helps to assess its risk profile. Methods: Study participants were recruited at a gynecology outpatient department (OPD) in the Allahabad district in Uttar Pradesh India, and a questionnaire was used to assess demographics, SRH, and obstetric history. Patients provided three samples (urine, vaginal swab, and whole blood). These samples were used to identify CT and NG using PCR/NAAT and CT IgG ELISA. Results: A total of 296 women were included for testing; mean age was 29 years. No positive cases of CT and NG were observed using PCR/NAAT. A 7% (22/296) positivity rate for CT was observed using IgG ELISA. No positive association was found between serology and symptoms (vaginal discharge, abdominal pain, dysuria, and dyspareunia) or adverse pregnancy outcomes (miscarriage and stillbirth). Positive relations with CT could be observed with consumption of alcohol, illiteracy, and tenesmus (p-value 0.02–0.03). Discussion: STI prevalence in this study was low, but a high burden of SRH morbidity was observed, with a high symptomatic load. High rates of miscarriage (31%) and stillbirth (8%) were also observed among study subjects. No associations could be found between these ailments and CT infection. These rates are high even for low- and middle-income country standards. Conclusion: This study puts forward high rates of SRH morbidity, and instances of adverse reproductive health outcomes are highlighted in this study, although no associations with CT infection could be found. This warrants more investigation into the causes leading to these complaints in the Indian scenario and potential biases to NAAT testing, such as consumption of over-the-counter antimicrobials.
Electrocardiographic changes during acute mental stress

Background: Electrocardiogram (ECG) waveforms are influenced by physiological, pathological, and psychological factors. Acute mental stress affects the hypothalamic–pituitary–adrenal axis resulting in alterations in various physiological functions of the body particularly the cardiovascular system. Objective: To study the ECG changes during acute mental stress and also to understand the reasons for the changes in ECG caused by acute mental stress. Materials and Methods: ECG was recorded in relaxed state and in acute stress state in healthy male subjects in the age group of 18–24 years. ECG recordings in relaxed state were compared with those in acute stress state. Result: Increased heart rate, decreased PR interval, decreased QT interval, and prolonged QTc interval were observed in ECGs in acute stress state which were statistically significant. Also an increase in QRS duration and change in QRS axis were observed during acute stress which were statistically not significant. Conclusion: Increased sympathetic activity caused by acute mental stress may be the cause for this altered electrical activity of the heart.
Hyperspectral Classification Via Low-Rank Component Induced Spatial-Spectral Kernel

Spatial-spectral kernel (SSK) has proven to be one of state-of-the-art tools for producing precise classification results for hyperspectral images (HSIs). However, how to exactly identify the neighborhood pixels within a given cubic patch of HSI is one of the critical tasks for constructing an accurate spatial-spectral kernel (SSK). In this paper, a novel low-rank component induced SSK (LRCISSK) method is proposed to deliver more accurate classification results for HSI. It explores the low-rank properties within each HSI patch in spectral domain to adaptively identify the precise neighborhood pixels with regards to the centroid pixel. Then, the neighborhood pixels associated with the centroid pixel are embedded into the SSK framework to easily map the spectra into the nonlinear complex manifolds and enable the support vector machine (SVM) classifier to effectively discriminate them. Experiments on Indian Pines and Pavia University datasets demonstrate the superiority of the proposed LRCISSK classifier when compared to other state-of-the-art approaches.