Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of real-time tracking filters implementation in FPGA

This paper presents real time tracking hardware based on field programmable gate arrays (FPGA). Traditionally, tracking system is simulated in MATLAB environment and an equivalent hardware description language (HDL) code is written for FPGA. But, in this paper a simulated and verified MATLAB code is directly converted into HDL which is used in FPGA. This method improves the efficiency of the real time tracker and flexibility in usage of FPGA. Moreover, the simulated and hardware outputs will be consistent. Three types tracking algorithm namely Kalman filter (KF), extended Kalman filter (EKF), and αβγ filter are implemented to validate the proposed approach.
Estradiol-Mediated Axogenesis of Hypothalamic Neurons Requires ERK1/2 and Ryanodine Receptors-Dependent Intracellular Ca2+ Rise in Male Rats

17β-estradiol (E2) induces axonal growth through extracellular signal-regulated kinase 1 and 2 (ERK1/2)-MAPK cascade in hypothalamic neurons of male rat embryos in vitro, but the mechanism that initiates these events is poorly understood. This study reports the intracellular Ca2+ increase that participates in the activation of ERK1/2 and axogenesis induced by E2. Hypothalamic neuron cultures were established from 16-day-old male rat embryos and fed with astroglia-conditioned media for 48 h. E2-induced ERK phosphorylation was completely abolished by a ryanodine receptor (RyR) inhibitor (ryanodine) and partially attenuated by an L-type voltage-gated Ca2+ channel (L-VGCC) blocker (nifedipine), an inositol-1,4,5-trisphosphate receptor (IP3R) inhibitor (2-APB), and a phospholipase C (PLC) inhibitor (U-73122). We also conducted Ca2+ imaging recording using primary cultured neurons. The results show that E2 rapidly induces an increase in cytosolic Ca2+, which often occurs in repetitive Ca2+ oscillations. This response was not observed in the absence of extracellular Ca2+ or with inhibitory ryanodine and was markedly reduced by nifedipine. E2-induced axonal growth was completely inhibited by ryanodine. In summary, the results suggest that Ca2+ mobilization from extracellular space as well as from the endoplasmic reticulum is necessary for E2-induced ERK1/2 activation and axogenesis. Understanding the mechanisms of brain estrogenic actions might contribute to develop novel estrogen-based therapies for neurodegenerative diseases.
Emerging Strategies to Combat ESKAPE Pathogens in the Era of Antimicrobial Resistance: A Review

The acronym ESKAPE includes six nosocomial pathogens that exhibit multidrug resistance and virulence: Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. Persistent use of antibiotics has provoked the emergence of multidrug resistant (MDR) and extensively drug resistant (XDR) bacteria, which render even the most effective drugs ineffective. Extended spectrum β-lactamase (ESBL) and carbapenemase producing Gram negative bacteria have emerged as an important therapeutic challenge. Development of novel therapeutics to treat drug resistant infections, especially those caused by ESKAPE pathogens is the need of the hour. Alternative therapies such as use of antibiotics in combination or with adjuvants, bacteriophages, antimicrobial peptides, nanoparticles, and photodynamic light therapy are widely reported. Many reviews published till date describe these therapies with respect to the various agents used, their dosage details and mechanism of action against MDR pathogens but very few have focused specifically on ESKAPE. The objective of this review is to describe the alternative therapies reported to treat ESKAPE infections, their advantages and limitations, potential application in vivo, and status in clinical trials. The review further highlights the importance of a combinatorial approach, wherein two or more therapies are used in combination in order to overcome their individual limitations, additional studies on which are warranted, before translating them into clinical practice. These advances could possibly give an alternate solution or extend the lifetime of current antimicrobials.
Energy-Efficient Hybrid Precoding for mmWave Massive MIMO Systems

In millimeter Wave (mmWave) massive multi-input multi-output (MIMO) systems, the novel sub-connected architecture has been introduced which can further reduce power consumption compared with the fully-connected architecture. In this paper, we propose an energy-efficient hybrid precoding design with quality of service (QoS) constraints for sub-connected architecture. Firstly, the total energy efficiency optimization problem with nonconvex constraints is decomposed into two separate optimization sub-problems whose solution is a optimal single precoder respectively. Then, the first optimization sub-problem analog domain concerned is solved by a cross-entropy-based algorithm according to the machine learning theory. Finally, the second optimization sub-problem digital domain concerned is solved by an iterative optimization algorithm from fractional programming theory. Simulation results demonstrate that the performance of the proposed precoding algorithm. The performance of the proposed algorithm is capable of achieving near optimal solution. It is also demonstrated that the proposed algorithm can enhance the energy efficiency of networks while guaranteeing the QoS of users.
Study of the Symbol Error Rate/Bit Error Rate in Non-orthogonal Multiple Access (NOMA) Systems

Recently, the non-orthogonal multiple access (NOMA) and the related power division multiple access (PDMA) techniques have attracted broad attentions, since they could efficiently improve the throughput of current networks. In NOMA/PDMA, parallel data from different information streams (or services) are encoded and modulated concurrently and then are superposed together for transmission. As a result, it is difficult to analyze the related symbol error rate (SER) and bit error rate (BER) which play a key role in system control, e.g., power allocation. To circumvent such an issue, we derive both SER and BER given by closed-form expressions in conditions of Gray coding and quadrature phase shift keying (QPSK) modulation. According to our outcomes, it is shown that SER and BER could be reduced efficiently, when the information streams are processed with Gray coding jointly. Furthermore, it is also indicated that the jointly Gray coding could improve the independence among the signals which composite a superposition signal. Consequently, our results are helpful to improve the performance of the downlink transmission in NOMA/PDMA.
Kelp: QoS for Accelerated Machine Learning Systems

—Development and deployment of machine learning (ML) accelerators in Warehouse Scale Computers (WSCs) demand signiﬁcant capital investments and engineering efforts. However, even though heavy computation can be ofﬂoaded to the accelerators, applications often depend on the host system for various supporting tasks. As a result, contention on host resources, such as memory bandwidth, can signiﬁcantly discount the performance and efﬁciency gains of accelerators. The impact of performance interference is further ampliﬁed in distributed learning, which has become increasingly common as model sizes continue to grow. In this work, we study the performance of four production machine learning workloads on three accelerator platforms. Our experiments show that these workloads are highly sensitive to host memory bandwidth contention, which can cause 40% average performance degradation when left unmanaged. To tackle this problem, we design and implement Kelp, a software runtime that isolates high priority accelerated ML tasks from memory resource interference. We evaluate Kelp with both production and artiﬁcial aggressor workloads, and compare its effectiveness with previously proposed solutions. Our evaluation shows that Kelp is effective in mitigating performance degradation of the accelerated tasks, and improves performance by 24% on average. Compared to previous work, Kelp reduces performance degradation of ML tasks by 7% and improves system efﬁciency by 17%. Our results further expose opportunities in future architecture designs.
Real-Time Attack Detection on Robot Cameras: A Self-Driving Car Application

The Robot Operating System (ROS) are being deployed for multiple life critical activities such as self-driving cars, drones, and industries. However, the security has been persistently neglected, especially the image flows incoming from camera robots. In this paper, we perform a structured security assessment of robot cameras using ROS. We points out a relevant number of security flaws that can be used to take over the flows incoming from the robot cameras. Furthermore, we propose an intrusion detection system to detect abnormal flows. Our defense approach is based on images comparisons and unsupervised anomaly detection method. We experiment our approach on robot cameras embedded on a self-driving car.
A Temporal Neural Trace of Wavelet Coefficients in Human Object Vision: An MEG Study

Wavelet transform has been widely used in image and signal processing applications such as denoising and compression. In this study, we explore the relation of the wavelet representation of stimuli with MEG signals acquired from a human object recognition experiment. To investigate the signature of wavelet descriptors in the visual system, we apply five levels of multi-resolution wavelet decomposition to the stimuli presented to participants during MEG recording and extract the approximation and detail sub-bands (horizontal, vertical, diagonal) coefficients in each level of decomposition. Apart from, employing multivariate pattern analysis (MVPA), a linear support vector classifier (SVM) is trained and tested over the time on MEG pattern vectors to decode neural information. Then, we calculate the representational dissimilarity matrix (RDM) on each time point of the MEG data and also on wavelet descriptors using classifier accuracy and one minus Pearson correlation coefficient, respectively. Given the time-courses calculated from performing the Pearson correlation between the wavelet descriptors RDMs and MEG decoding accuracy in each time point, our result shows that the peak latency of the wavelet approximation time courses occurs later for higher level coefficients. Furthermore, studying the neural trace of detail sub-bands indicates that the overall number of statistically significant time points for the horizontal and vertical detail coefficients is noticeably higher than diagonal detail coefficients, confirming the evidence of the oblique effect that the horizontal and vertical lines are more decodable in the human brain.
Performance Improvement in Cognitive Radio using Even-Odd technique in QAM based K-spectrum Sensing Scheme

In the present scenario, the number of users and their spectrum usage has increased excessively. Cognitive Radio (CR) has emerged as a novel technology that enables utilization of unused spectrum within a given band. CR works with two essential components: spectrum sensing and spectrum allocation. CR classifies users into two categories named Primary Users (PUs) and Secondary Users (SUs). A SUs detects the absence of Pus and hence find the vacant bands in the available spectrum. The most dominating challenge for spectrum sensing is to have a very high sampling rate, which in turn requires high-end processors which results in high hardware cost, increased power consumption, and computational complexity. Compressing spectrum sensing solves these issues too much extent. In this paper, a distributed cooperative detection scheme with even-odd technique proposed for MIMO underlay CR network. The proposed Even-Odd method improves detection probability and evaluates the delivery ratio of SUs. Thus, the normalized throughput of the SUs increases as compared to the existing techniques.
Risk factors associated with oral manifestations and oral health impact of gastro-oesophageal reflux disease: a multicentre, cross-sectional study in Pakistan

Objective Gastro-oesophageal reflux disease (GORD) is a relatively common disorder and manifests with extraoesophageal symptoms, such as dental erosions (DE), cough, laryngitis, asthma, and oral soft- and hard-tissue pathologies. This study aimed (1) to identify oral soft and hard-tissue changes in patients with GORD and (2) to evaluate these oral changes as indices for assessing GORD and its severity. Setting This cross-sectional study was conducted at four major tertiary care government hospitals, in two metropolitan cities of Pakistan. Participants In total, 187 of 700 patients who underwent oesophago–gastro–duodenoscopy and having GORD were included in the study. Patients with GORD were divided according to the presence of DE into group A (with DE, chronic/severe GORD) and group B (without DE, mild GORD). Patients who were unconscious and had extremely limited mouth opening were excluded. Primary and secondary outcome measures Abnormal conditions and lesions of the oral mucosa were recorded. The impact of oral hard and soft-tissue changes on the oral health-related quality of life was assessed using the Pakistani (Urdu) version of the validated Oral Health Impact Profile-14 (OHIP-14) instrument. Results Oral submucous fibrosis (66.3%), ulceration (59.4%) and xerostomia (47.6%) were significantly more common in group A (p<0.05). The prevalence of GORD was 26.7%, within which the prevalence of DE was 35.3%. Unhealthy dietary pattern, nausea/vomiting, oesophagitis, xerostomia, ulceration, gingivitis and angular cheilitis showed a statistically significant association with chronic GORD and DE. All subscales of OHIP-14 were positively correlated (p<0.05) in patients with GORD and DE, with notable impact on psychological discomfort (rs=0.30), physical disability (rs=0.29), psychological disability (rs=0.27) and functional limitation (rs=0.20). Conclusion Patients with GORD and DE presented with more severe oral manifestations than did those with GORD and no DE. We recommend timely dental check-ups to assess the severity of both systemic and oral disease.
Crop and Weeds Classification for Precision Agriculture Using Context-Independent Pixel-Wise Segmentation

Precision agriculture is gaining increasing attention because of the possible reduction of agricultural inputs (e.g., fertilizers and pesticides) that can be obtained by using hightech equipment, including robots. In this paper, we focus on an agricultural robotics system that addresses the weeding problem by means of selective spraying or mechanical removal of the detected weeds. In particular, we describe a deep learning based method to allow a robot to perform an accurate weed/crop classification using a sequence of two Convolutional Neural Networks (CNNs) applied to RGB images. The first network, based on an encoder-decoder segmentation architecture, performs a pixelwise, plant-type agnostic, segmentation between vegetation and soil that enables to extract a set of connected blobs representing plant instances. We show that such network can be trained also using external, ready to use pixel-wise labeled data sets coming from different contexts. Each plant is hence classified between crop and weeds by using the second network. Quantitative experimental results, obtained on real world data, demonstrate that the proposed approach can achieve good classification results also on challenging images.
Object Recognition for Robotics Based on Planar Reconstructed B-Rep Models

Current robotic systems in small and medium-sized enterprises as well as in households need a perception system to enable an interaction of the robot with the environment. One necessary ability of a perception system is a method to recognize objects in a given scene. We contribute an approach to object recognition based on the input of an eye-in-hand depth camera, which uses boundary representation (B-Rep) models as the internal data format. Our method applies three steps: generate B-Reps from point clouds, generate and select hypotheses from an object database, as well as build up a world model. In the evaluation, we discuss the classification rate and conclude, that our approach is suitable for robotic applications, as it is an anytime, surface-based method without the need for a prior object segmentation.
Model-Based Control Architecture for a Twin Jet Unmanned Surface Vessel

The aim of this study is to implement a modelbased control architecture for a twin jet Unmanned Surface Vessel (USV) using the line-of-sight (LOS) path algorithm for straight-line. The modeling and design of the motion control system are studied using MATLAB-Simulink and the real-time implementation is done by using Robot Operating System (ROS). To simplify the real-time implementation and thus, programming files, the necessary code generation is done by generating a Standalone ROS Node from Simulink software, making the process easier for researchers with non-advanced programming skills. Simulation and field test results are shown to validate the model-based control architecture.
A Connectivity-Aware Caching Algorithm for Vehicular Content Centric Networks with Cache-Enabled Vehicles

Recently, cache-enabled vehicles have been introduced to improve the efficiency of content delivery in vehicular content centric networks (VCCNs). Content requests can be satisfied by vehicular caches without causing backhaul cost. However, it takes caching cost to store contents in participating vehicles. Moreover, the vehicle-to-vehicle (V2V) content delivery highly depends on the inter-vehicle connectivity. In this paper, a connectivity-aware content caching algorithm is proposed to help cache-enabled vehicles manage theirs caches. According to the content popularity distribution, connectivity, and current caching status, cache-enabled vehicles store some chunks of contents to maximize the total caching utility. Simulation results prove that the proposed connectivity-aware caching algorithm is superior to the existing AlwaysCaching algorithm and probabilistic caching (ProCaching) algorithm in the total caching utility.
Estimation of Lightweight Object's Mass by a Humanoid Robot During a Precision Grip with Soft Tactile Sensors

The estimation of the intrinsic properties of an unknown object is a very challenging problem, mainly due the limitations on the tactile technology. In this article we present a method to estimate an object’s weight during a precision grip made by a humanoid robot. Tactile sensors on the fingertips provide information on the 3D force vector during a movement of grasping and lifting a cup filled with different masses (30100g). Using the force measurements across time, we were able to successfully calculate the object weight for 8 different masses, with repeatable measurement and low deviations from the real value, especially for higher object masses. Keywords—weight estimation, humanoid robot, tactile sensors, precision grip.
Advancing Research for the Management of Long-Lived Species: A Case Study on the Greenland Shark

Long-lived species share life history traits such as slow growth, late maturity, and low fecundity, which lead to slow recovery rates and increase a population’s vulnerability to disturbance. The Greenland shark (Somniosus microcephalus) has recently been recognized as the world’s longest-lived vertebrate, but many questions regarding its biology, physiology, and ecology remain unanswered. Here we review how current and future research will fill knowledge gaps about the Greenland shark and provide an overall framework to guide research and management priorities for this species. Key advances include the potential for specialised ageing techniques and demographic studies to shed light on the distribution and age-class structure of Greenland shark populations. Advances in population genetics and genomics will reveal key factors contributing to the Greenland shark’s extreme longevity, range and population size, and susceptibility to environmental change. New tagging technologies and improvements in experimental and analytical design will allow detailed monitoring of movement behaviours and interactions among Greenland sharks and other marine species, while shedding light on habitat use and susceptibility to fisheries interactions. Interdisciplinary approaches, such as the combined use of stable isotope analysis and high-tech data-logging devices (i.e. accelerometers and acoustic hydrophones) have the potential to improve knowledge of feeding strategies, predatory capabilities, and the trophic role of Greenland sharks. Measures of physiology, including estimation of metabolic rate, as well as heart rate and function, will advance our understanding of the causes and consequences of long lifespans. Determining the extent and effects of current threats (as well as potential mitigation measures) will assist the development of policies, recommendations, and actions relevant for the management of this potentially vulnerable species. Through an interdisciplinary lens, we propose innovative approaches to direct the future study of Greenland sharks and promote the consideration of longevity as an important factor in research on aquatic and terrestrial predators.
Individualised active communication education (I-ACE): another clinical option for adults with hearing impairment with a focus on problem solving and self-management

Abstract Objective: This clinical note describes the Individualised – Active Communication Education (I-ACE) programme designed to improve problem solving and self-management in adults with hearing impairment. Design: The I-ACE was offered to adult clients seeking help for the first time and effects were measured for participants using self-report questionnaires: the Client Oriented Scale of Improvement (goal attainment), the Hearing Handicap Questionnaire (hearing disability), and the International Outcome Inventory – Alternative Interventions (outcomes) immediately after programme completion and 3 months later. Participants also provided qualitative feedback about I-ACE. Study sample: Twenty-three participants completed I-ACE, with 22 completing all self-report questionnaires and 23 participants providing qualitative feedback. Results: The participants reported positive outcomes and goal attainment, but no change in hearing disability post-programme. The effects were maintained 3 months later. Qualitative feedback indicated that I-ACE supported participants in recognising and increasing awareness of their hearing difficulties and in developing potential solutions to these difficulties. Participants also enjoyed the opportunity to involve communication partners. Conclusion: I-ACE is an appropriate option for adults with hearing impairment who wish to become more aware of their hearing difficulties and how to solve them.