Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of seasonal efficacy of different evaporation retardants in semi-arid tropics.

An experiment was conducted in 2007–08 at Arjia, Rajasthan to evaluate the efficacies of different evaporation retardants viz., Maize stover (6 kg m -2 ), Burnt oil (2 mm thick film), Poly film (50 micron), Cetyle alcohol (2.2 kg ha -1 ) and Control in reducing evaporation from surface of water bodies. Results revealed that there was significant relation between evaporation retardants and control treatments in pre-monsoon, monsoon and post-monsoon seasons. Due to application of different evaporation suppressants, on an average, maize stover resulted in lowest evaporation of 2.75 mm day -1 , followed by poly film (2.94 mm day -1 ) and burnt oil (3.89 mm day -1 ). As compared to control, cetyle alcohol as surfactant resulted in the lowest mean reduction (31.3%) in evaporation. On an average, the maize stover caused mean reductions in evaporation to the tunes of 59 and 58% over different seasons in comparison with open pan evaporation and control treatments, respectively. Thus it is concluded that application of maize stover is proved to be the most effective method for suppression of evaporation from free-water surfaces.
OrgaNet: A Robust Network for Subcellular Organelles Classification in Fluorescence Microscopy Images

Automatic identification of subcellular compartments of proteins in fluorescence microscopy images is an important task to quantitatively evaluate cellular processes. A common problem for the development of deep learning based classifiers is that there is only a limited number of labeled images available for training. To address this challenge, we propose a new approach for subcellular organelles classification combining an effective and efficient architecture based on a compact Convolutional Neural Network and deep embedded clustering algorithm. We validate our approach on a benchmark of HeLa cell microscopy images. The network both yields high accuracy that outperforms state of the art methods and has significantly small number of parameters. More interestingly, experimental results show that our method is strongly robust against limited labeled data for training, requiring four times less annotated data than usual while maintaining the high accuracy of 93.9%.
U-net combined with CRF and anatomical based spatial features to segment white matter hyperintensities

White matter hyperintensities (WMH) are important biomarkers for cerebral small vessel disease and closely associated with other neurodegenerative process. In this paper, we proposed a fully automatic WMH segmentation method based on U-net architecture. CRF were combined with U-net to refine segmentation results. We used a new anatomical based spatial feature produced by brain tissue segmentation based on T1 image, along with intensities of T1 and T2-FLAIR images to train our neural network. We compared 8 forms of automated WMH segmentation methods, range from traditional statistical learnng methods to deep learning based methods, with different architecture and used different features. Results showed our proposed method achieved best performance in terms of most metrics, and the inclusion of anatomical based spatial features strongly increase the segmentation performance.
Posture Control of a Four-Wheel-Legged Robot With a Suspension System

To achieve posture control and ride comfort (vibration isolation performance) of a robot in unstructured terrain, a novel four-wheel-legged robot (FWLR) with an actively-passively suspension system is first designed. In the suspension system, the active parts are responsible for posture control and the passive parts are responsible for vibration isolation. Then, a closed-loop and decoupled posture control model with 11 DOF are proposed, with which we designed the posture controller with a second-order low-pass filter (SLPF). To test the posture control performance of FWLR in unstructured terrain, both simulation and experiment are carried out, the simulation and experimental results show that the posture angles in unstructured terrain are reduced by 54.65% and 59% on average, respectively. In addition, the frequency response shows that the posture angles are reduced by more than 50% in low-frequency unstructured terrain. Finally, to validate the ride comfort of FWLR, dynamic models with different degrees of freedom (DOF) are established and simulated, and the results present that the ride comfort can be improved with the posture angular acceleration is reduced by 15.83% and 46.7% on average. Generally, with the actively-passively suspension system proposed in this article, FWLR can be equipped with excellent ride comfort and posture control in unstructured terrain. The research in this article has potential reference value and practical value for enriching the posture control of robots and vehicles.
Improving Steady-State Performance of the UT-ZA-PNLMS Algorithm for Sparse Systems

For identifying sparse systems, a recently proposed algorithm called upper threshold based zero attracting proportionate normalized least mean square (UT-ZA-PNLMS) algorithm has shown improved performance in terms of both the convergence rate and steady-state error in comparison to the ZAPNLMS algorithm. The UT-ZA-PNLMS algorithm employs adaptive threshold based gain function in order to improve convergence rate of the active taps, especially the taps with low magnitude, and appends zero attracting term in the update equation in order to bring the inactive taps to their optimum zero level. However, as the UT-ZA-PNLMS algorithm uses uniform shrinkage for that zero attraction, the active taps experience significant bias which limits overall steady-state performance. In this paper, we introduce selective shrinkage for the zero attracting term so that the inactive taps get strong attractive force whereas the active taps would experience negligibly small attractive force, and thus the bias in the active tap is reduced. In particular, we propose three different algorithms incorporating log-sum, $\ell_{p^{-}}$ norm and $\ell_{0}$-norm penalties to the cost function of the upper threshold based PNLMS algorithm. The resulting algorithms are studied extensively and the simulation results show their improved steady-state performances.
Low Power Quaternary Adder Using CNFET

Binary logic and MOS devices have been in use since the inception of the design era, but now due to advancement in VLSI industry binary logic has become tedious and complicated. To overcome this challenge Multi-valued logic (MVL) such as ternary and Quaternary Logic (QTL) can be used. MVL designs have an advantage over binary logic designs with respect to the chip area and the complexity of interconnection. In this paper, we present the design and performance analysis of QTL Full Adder (QFA) using Carbon Nano-tube Field Effect Transistor (CNFET). For design purposes, we have used the Stanford Virtual-Source Carbon Nanotube Field Effect Transistor Model version 1.01 with sub 10nm CNFET technology. The design tool used for simulation is Cadence Virtuoso. The proposed QFA design has been compared against the existing CNFET based QTL designs and it is found that the proposed QFA design is 97-98% better in terms of Power Delay Product (PDP) and Energy Delay Product.
System Design and Experimental Research of Neural Signal Blocking based on Spike Trapping Principle *

Different neural signal blocking technologies provide potential therapy for various diseases caused by pathological or involuntary neural activities. In this paper, a novel neural signal blocking concept based on spike-trapping principle is introduced. The blocking system was designed and implemented. In the system, a compound action potential was induced by a proximal stimulus, and a blocking signal was generated after a neural signal was detected, which was applied to the distal end of the nerve to block the nerve conduction. The reliability of the system was verified on bullfrog sciatic nerve gastrocnemius muscle specimens. The optimal blocking signal parameters, the relationship between nerve diameter and blocking threshold voltage, and the nerve safety of blocking signals were explored. The experimental results show that the system is possible to block a pathological or involuntary neural signal automatically. The average voltage of block threshold and the minimum pulse width are -1.66 V and 120 ms, respectively. The blocking threshold decreases as the diameter of the nerve increases. The blocking signals at the threshold level are safe for the stimulated nerve.
Real-world speed estimation using single trunk IMU: methodological challenges for impaired gait patterns*

Walking speed (WS) is recognized as an important dimension of functional health and a candidate endpoint for clinical trials. To be adopted as a powerful outcome measure in clinical assessment, WS should be estimated pervasively and accurately in the real-life context. Although current state of the art points to possible solutions, e.g., by using pairing of wearable sensors with dedicated algorithms, the accuracy and robustness of existing algorithms in challenging situations should be carefully considered. This study highlights the main methodological issues for WS estimation using single inertial sensor fixed on trunk (chest/low back) and data recorded in a sample of stroke patients with impaired mobility.
A Low-Profile Hybrid Multi-Permittivity Dielectric Resonator Antenna With Perforated Structure for Ku and K Band Applications

A wideband hybrid dielectric resonator antenna (DRA) consisting of a rectangular slot patch and a perforated stacked cylindrical dielectric resonator (DR) is proposed. A rectangular slot was etched on the grounding side of a microwave laminate ( $\epsilon _{s}=3.38$ ) to excite the hybrid resonator at a high frequency. The stacked DR used consists of three different layers of permittivity, in which air-cavity was introduced internally to form a perforated structure. With a proper stacking arrangement of the perforated DRs on top of the rectangular slot, their operating frequencies were merged together to produce a wideband hybrid DRA. It was found that the combination of the stacked DR with perforated structure in the hybrid element had yielded an impedance bandwidth of as wide as 75.8% (12.2 GHz - 27.1 GHz). Huge improvement in bandwidth was successfully achieved in this study in comparison to that without a perforated structure of only 48.9%. Simulation of the antenna was performed in time domain using Computer Simulation Technology (CST) and was subsequently verified with the measurement results. The average simulated and measured directivity of the antenna were recorded to be 6.05 dBi and 5.65 dBi, respectively, with a stable broadside radiation throughout the operating range of frequency. The radiation characteristics were seen to be broadside in both the E-plane and H-plane.
Strain maps of the left atrium imaged with a novel high-resolution CINE MRI protocol*

To date, regional atrial strains have not been imaged in vivo, despite their potential to provide useful clinical information. To address this gap, we present a novel CINE MRI protocol capable of imaging the entire left atrium at an isotropic 2-mm resolution in one single breath-hold. As proof of principle, we acquired data in 10 healthy volunteers and 2 cardiovascular patients using this technique. We also demonstrated how regional atrial strains can be estimated from this data following a manual segmentation of the left atrium using automatic image tracking techniques. The estimated principal strains vary smoothly across the left atrium and have a similar magnitude to estimates reported in the literature.
Battery Energy Storage Systems for Primary Frequency Regulation in Island Power Systems

This paper presents the analysis of the impact of frequency droop control on the primary frequency regulation (PFR) with the use of battery energy storage system BESS in insolated systems. The presented analysis is based on the characteristic the output power and frequency of the batteries that is used in this type of control. The analysis of behavior of frequency in insolated systems is an aspect that must be considered for a correct adjustment of the operation of the BESS in the PFR and its lifetime. The performance results show that not is necessary a small dead band in the characteristic output power and frequency of batteries for have a good response in the PFR and this protects the life of the batteries. The variations in the dead band of the characteristic only has impact on the first transient of the frequency while with a wide dead band and a small droop (R) is possible obtained a good control and to protect the lifetime of the BESS.
Secure Information Exchange for Omniscience

We consider the problem of exchanging sensitive information in public and provide a general formulation that can unify and extend various existing scenarios of information exchange, such as the problems of private information extraction and information bottleneck. The formulation also gives rise to a new scenario called secure omniscience (SO), where users want to exchange all their private information with minimum leakage to a wiretapper with side information. Single-letter lower and upper bounds are obtained for the minimum leakage, and the bounds are shown to be tight under the finite linear source model with two users. The bounds are derived in terms of the solutions of the closely related problems of communication for omniscience (CO) and secret key agreement (SKA). However, we find examples where the bounds are not tight, and so the connections to CO and SKA are not precise. In particular, it is possible that any optimal CO scheme that minimizes communication does not minimize leakage, and any optimal SO scheme that minimizes leakage does not attain the capacity for SKA. Nevertheless, we identify a useful notion of information alignment that can modify an optimal CO scheme to reduce leakage for SO.
Modelling the Neurons Activated by Transcranial Magnetic Stimulation

We present a computational models to couple the electric field induced by magnetic stimulation to neuronal membranes. The conventional cable equations are used to simulate magnetic stimulation of long peripheral nerves by Magstim 70mm coil. Our analysis provides a rigorous theoretical foundation and implementation methods for the use of the cable equation to model neuronal response to magnetically induced electric fields.
Efficient Decoding of RSCC over Ka-band Satellite Channel in Severe Weather Conditions

Communication systems performance over the Ka-band satellite channel is sensitive to the severity of weather conditions, particularly in the event of rainfall, so the use of robust channel coding with superior results is a crucial way to mitigate the effect of rainfall. As satellites are known to have power limitations, it will be efficient to use such strategies but with a decrease in complexity. RSCC Codes are standard forward error correction strategies for space communication systems that can fix errors and endure rainfall but with extra processing time and power consumption for the decoder. In this paper, we are introducing an overview of the rain fade impact throughout Ka-band satellite communication and developing a model of the rainfall fade channel. Besides, we suggest a reduced complexity Viterbi algorithm built upon a modified adaptive Viterbi algorithm. The current work achieves similar error performance as the conventional RSCC and significantly reduces computing loads and memory demands.
Pose Refinement of Occluded 3D Objects Based on Visible Surface Extraction

In this paper, we propose a pose refinement method based on the visible surface extraction of 3D object. Given a rough estimation of object pose, the algorithm of iterative closet point (ICP) is often used to refine the pose by aligning the object model with test scene. To avoid the interference of invisible points on the ICP process, we only use the visible surface for pose refinement. It is especially necessary when occlusion occurs in the scene. Combining the technologies of image rendering and depth consistency verification, the visible surface can be effectively extracted. During the process of pose refinement, hypothesis verification methods are also used to eliminate unreasonable hypothetical poses as early as possible. The proposed method is evaluated on the public Tejani dataset. The experimental results show that our method improved the average F1-score by 0.2062, which proves that our method can obtain pose estimation results of high accuracy, even in the occluded scene.
Multiple Access Channel Resolvability Codes From Source Resolvability Codes

We show that the problem of code construction for multiple access channel (MAC) resolvability can be reduced to the simpler problem of code construction for source resolvability. Specifically, we propose a MAC resolvability code construction that relies on a combination of multiple source resolvability codes, used in a black-box manner, and leverages randomness recycling implemented via distributed hashing and block-Markov coding. Since explicit source resolvability codes are known, our results also yield the first explicit coding schemes that achieve the entire MAC resolvability region for any discrete memoryless multiple-access channel with binary input alphabets.
Next‐generation sequencing analysis of rat heart left ventricle: novel microRNAs, novel isomiRs and transmural distribution of microRNA expression

MicroRNAs (miRNAs) have a significant role in regulating gene expression in heart disease. They can exist as isoforms (isomiRs) that are potentially (patho)physiologically important. The heterogeneity of cardiomyocyte physiology across the left ventricular wall has an important influence on heart (patho)physiology, and may well be affected by transmural miRNA expression gradients. With the aims of determining rat heart isomiR profiles and left ventricular transmural miRNA expression we investigated epi/midmyo/endocardial tissue samples using Illumina next‐generation sequencing. We detected significant quantities of 146 known rat miRNAs and 68 novel orthologs of known miRNAs. Many novel isomiRs were detected at a higher frequency than the corresponding mature sequence, and have predicted targets different from those of the corresponding canonical miRNA. We identified a novel rat miR‐1 analogue from a second miR‐1 gene and a novel rat miRNA similar to miR‐676. We also cloned and sequenced the rat miR‐486 gene. Several important cardiac intracellular signalling pathways are predicted targets of the most highly detected miRNAs. Most miRNAs are not expressed in a gradient across the ventricular wall; the exceptions include miR‐10b, miR‐21, miR‐99b and miR‐486. These findings suggest new mechanisms of cardiac gene regulation in health and disease. Funded by British Heart Foundation PG110/039/28365.
Detecting Carotid Intima-Media From Small-Sample Ultrasound Images

Cardiovascular diseases are the biggest threat to human being’s health all over the world, and carotid atherosclerotic plaque is the leading cause of ischemic cardiovascular diseases. To determine the location and shape of the plaque, it is of great significance to detect the intima-media (IM). In this paper, a new IM detection method based on convolution neural network (IMD-CNN) is proposed for the detection of IM of blood vessels in longitudinal ultrasonic images. In IMD-CNN, firstly the region of interest (ROI) is automatically extracted by morphological processing, then the patch-wise training data are constructed, and finally a simple CNN is trained to detect the IM. The experimental results obtained on 23 images show that the test accuracy of IMD-CNN is over 86% and the performance of IMD-CNN is also visually proved to be effective.
The Error Probability of Maximum-Likelihood Decoding over Two Deletion/Insertion Channels

This paper studies the problem of reconstructing a word given several of its noisy copies. This setup is motivated by several applications, among them is reconstructing strands in DNA-based storage systems. Under this paradigm, a word is transmitted over some fixed number of identical independent channels and the goal of the decoder is to output the transmitted word or some close approximation. The main focus of this paper is the case of two deletion channels and studying the error probability of the maximum-likelihood (ML) decoder under this setup. First, it is discussed how the ML decoder operates. Then, we observe that the dominant error patterns are deletions in the same run or errors resulting from alternating sequences. Based on these observations, it is derived that the error probability of the ML decoder is roughly $\frac{{3q - 1}}{{q - 1}}{p^2}$, when the transmitted word is any q-ary sequence and p is the channel’s deletion probability. We also study the cases when the transmitted word belongs to the Varshamov Tenengolts (VT) code or the shifted VT code. Lastly, the insertion channel is studied as well. These theoretical results are verified by corresponding simulations.
Relationship of the Respiration Waveform to a Chest Worn Inertial Sensor

Demand of portable health monitoring has been growing due to increasing cardiovascular and respiratory diseases. While both cardiovascular monitoring and respiratory monitoring have been developed independently, there lacks a simple integrated solution to monitor both simultaneously. Seismocardiography (SCG), a method of recording cardiac vibrations with an accelerometer can also be used to extract respiratory information via low frequency chest oscillations. This study used an inertial measurement unit which pairs a 3-axis accelerometer and a 3-axis gyroscope to monitor respiration while maintaining optimum placement protocol for recording SCG. Additionally, the connection between inertial measurement and both respiratory rate and volume were explored based on their correlation with a Spirometer. Respiratory volume was shown to have moderate correlation with chest motion with an average best-case correlation coefficient of 0.679 across acceleration and gyration. The techniques described will assist the design of future SCG algorithms by understanding the sources behind their modulation from respiration. This paper shows that a simplified processing technique can be added to SCG algorithms for respiration monitoring.
Motor Imagery Classification of Finger Motions Using Multiclass CSP

Electroencephalogram (EEG) data during motor imagery tasks regarding small-scale physical dynamics such as finger motions have low discriminability because capturing the spatial difference of the motions is difficult. We assumed that more discriminative features can be captured if spatial filters maximize the independence of each class data. This study constructed spatial filters named multiclass common spatial pattern (CSP), which maximize an approximation of mutual in-formation of extracted components and class labels, and applied them to a five-class motor-imagery dataset containing finger motion tasks. By applying multiclass CSP, the classification accuracies were improved (Mean SD: 40.6 ± 10.1%) compared with classical CSP (21.8 ± 2.5%) and no spatial filtering case (38.7±10.0%). In addition, we visualized learned spatial filters to assess the trend of discriminative features of finger motions. For these results, it was clear that multiclass CSP captured task-specific spatial maps for each finger motion and outperformed multiclass motor-imagery classification performance about 2% even when the tasks are small-scale physical dynamics.