Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Indoor Localization with Range-Free Weighted Localization Algorithm

Currently, Ultra-wide band has important in the role of the short-range communication, especially in, an indoor localization. However, there are more factors of the indoor environment caused to reduce the precise localization and also increase the error of distance. The presents a new weighted to evaluate the UWB technology for the indoor localization. The proposed methods are using received signal strength indicator (RSSI) to compute the error of distance based on weighting algorithms. Whereby the weight centroid localizer (WCL) is proposed in this paper. As the results, the cumulative distribution function (CDF) probability indicates the error of distance as properly, and this method can help to increase the precision of range based localization method in an application of indoor environment.
Centralized Wavelet Multiresolution for Exact Translation Invariant Processing of ECG Signals

Dyadic wavelet transform is useful in analyzing electrocardiogram (ECG) signals due to its fast computation and its multiresolution ability. In order to improve the feature extraction performance of dyadic wavelet transform, a new construction example of centralized multiresolution (CMR) is proposed. The proposed CMR example consists of two elements, namely, a dyadic part and a non-dyadic part. The dyadic part, based on the maximal overlap second generation wavelet packet transform (SGWPT), generates dyadic wavelet packets. The non-dyadic part engenders ensemble wavelet packets by postprocessing on the dyadic part. The produced wavelet packets and ensemble wavelet packets are combined to realize continued spectral refinement around fixed central analysis frequencies. Numerical simulation and a case study of ECG signal decomposition are utilized to validate the enhancements of the proposed CMR example. The processing results of the CMR example are compared with those of the dual tree complex wavelet transform and the conventional SGWPT. It is validated this CMR example achieves better feature extraction performances due to the presence of the exact translation invariance property.
Spatial Community Search Using PageRank Vector

Community detection task aims to find communities for a given network while community search focuses on finding a densely connected community with some given query nodes. Community search methods have the advantage of speed as they find a community for a set of query nodes instead of finding all the communities. They also have better performance on handling dynamic networks compared to community detection algorithms. However, current studies of community search are not well designed to handle spatial sensitive networks. We propose a new community search method, Spatial-based Search using PageRank Vector (SSPPV), which aims to find a community for a given query node over a large spatial social network. SSPPV finds a community with not only good cluster quality but also a smaller spatial size of the resulting community, compared to existing methods.
Practical User Selection With Heterogeneous Bandwidth and Antennas for MU-MIMO WLANs

User selection is one of the most important components for next generation multi-user multiple-input-multiple-output wireless local area networks. However, state-of-the-art approaches neglect the heterogeneity of users in the available bandwidth and the number of antennas, which diminishes their performance considerably. To tackle this challenge, we formulate a novel integer optimization framework to select the antennas of heterogeneous users simultaneously. With estimated signal-to-interference-and-noise ratio of users via channel vector projection, we propose a low-complexity branch-and-prune algorithm to search for the near-optimal combinations of user antennas. Our algorithm is compatible with legacy 802.11ac and is implemented on the software defined radio system. Extensive experiments show that our algorithm achieves around 95% of the optimal throughput and outperforms a benchmark scheme with a $1.18\boldsymbol \times $ gain in realistic indoor environments.
Position Detection for Badminton Tactical Analysis based on Multi-person Pose Estimation

In the badminton game, the identification of the players position plays an important role in tactical analysis. This paper proposes to automaticly extract player positions for tactical analysis in badminton videos using computer vision techniques. Particularly, the pose estimation problem was formulated to obtain the accurate body parts of players. This problem can be solved by using deep learning and graph-based techniques. Then, the positions of a player are calculated through the positions of both feet and the nine-cell badminton court. Experimental evaluation on the self-constructed database illustrates that, proposed method obtains satisfied performance in most situations, such as near and far court, International competition and training, and male and female.
Inner Product Based Particle Swarm Optimization

Standard Particle Swarm Optimization (SPSO)is a well-known and very competitive swarm optimization approach, which is designed by Particle Swarm Central. In all PSO variants, the relative position relation between the individual and the global optimal position has important influences on the performance of algorithms. In this paper, an alternative Standard Particle Swarm Optimization (SPSO 2007)is proposed, which is based on the inner product of difference vectors. One particle will confuse which solution it should learn from when the global best and the personal best positions have comparable attractions to different directions during its velocity updating process. Even the oscillation phenomenon will appear that the global best solution draws the particle close to it at one generation and the personal best solution draws the particle back to it at next generation. In order to overcome this phenomenon particle adopts different velocity update strategies when the angle between difference vectors is either acute or obtuse of two directions in this paper. Two difference vectors refer to the current particle to the global and the personal best solutions. The vector level and the component level inner product based PSOs are proposed, denoted as IPSPSO2007V and IPSPSO2007C respectively. They are analyzed firstly and then compared with SPSO2007 with IEEE CEC2015 benchmarks, which indicate that two inner product based PSOs show promising performance.
Noniterative Hyperspectral Image Reconstruction From Compressive Fused Measurements

Compressive spectral imaging (CSI) enables the acquisition of spectral and spatial information of a scene using fewer projected measurements than traditional scanning approaches. Recently, research efforts have focused on obtaining high-resolution spectral images via expensive detectors and sophisticated CSI devices. Alternatively, high-resolution spectral images can be obtained using side information or fusion of compressed measurements, without significantly increasing acquisition costs. Indeed, these approaches retrieve improved resolution images applying iterative and computationally expensive algorithms. This paper proposes the fusion of compressed measurements obtained from two state-of-the-art CSI systems, the single-pixel camera (SPC) and the three-dimensional coded aperture snapshot imaging system (3D-CASSI), such that high-resolution images can be obtained by exploiting detailed spectra provided by the SPC and high spatial resolution of the 3D-CASSI. Specifically, a noniterative reconstruction algorithm is proposed, based on the fact that the spatial–spectral data lie in a low-dimensional subspace. In contrast to related works, the proposed approach relies on implementable CSI systems. Simulations and experimental results show the effectiveness of the proposed method compared to similar approaches, both in reconstruction quality and complexity. Specifically, the proposed method is up to 5.6 times faster than its counterparts and provides comparable quality of attained reconstructions.
Direct Numerical Simulation of Gas–Particle Flows with Particle–Wall Collisions Using the Immersed Boundary Method

We investigated particulate flows by coupling simulations of the three-dimensional incompressible Navier–Stokes equation with the immersed boundary method (IBM). The results obtained from the two-way coupled simulation were compared with those of the one-way simulation, which is generally applied for clarifying the particle kinematics in industry. In the present flow simulation, the IBM was solved using a ghost–cell approach and the particles and walls were defined by a level set function. Using proposed algorithms, particle–particle and particle–wall collisions were implemented simply; the subsequent coupling simulations were conducted stably. Additionally, the wake structures of the moving, colliding and rebounding particles were comprehensively compared with previous numerical and experimental results. In simulations of 50, 100, 200 and 500 particles, particle–wall collisions were more frequent in the one–way scheme than in the two-way scheme. This difference was linked to differences in losses in energy and momentum.
THE IMPACT OF HEALTHY DIET ON COVID-19 PANDEMIC USING CONTINENTS, GROSS DOMESTIC PRODUCT AND POPULATION

Many researchers have focused on COVID-19 Pandemic which was a global phenomenon and has brought about by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) which started in late 2019, this disease has spread throughout all globes and has affected the entire world and is one of the most leading cause of death in countries and continents. The aim of the study is to ascertain the relationship that exist between COVID-19 Pandemic Continents, Gross Domestic Product and Population between November 2019 to September 2020. Design: The researchers studied 170 countries and the dataset was extracted from the Kaggle database. The factors analyzed Qualitatively were data distribution of Continents Table 1. , GDP per capita Table 2. , and Population Category Table 3. Also Quantitatively data of COVID-19 variables (Confirmed, Death, Recovered, and Active cases) and Obesity Table 4. , Correlation was also established to test the relationship between GDP per capita & COVID 19 Variables Table 5. Regression for the joint contributions of independent variables (GDP growth, Population, GDP per capita & Obesity) to the prediction of COVID 19 Death Table 6 and Relative effect of the independent variables (GDP growth, Population, GDP per capita & Obesity) to the prediction of COVID 19 Death Table 7 were also analyzed. Results: Based on the period we considered 170 Countries. There were 27.1 % (46) out of 170 Countries which was allotted to Africa Participants and the p-value < 0.05 which shows that Africans are majority of the data collected, GDP per capita 31.2%(53) under $ 0- $2499, Population category showed highest category to be 35.9% (61) under 988,003- 9,904,896, COVID-19 variables (Confirmed, Death, Recovered, and Active cases) and Obesity shows the outbreak of pandemic, a total of 331 cases was confirmed, 6 death cases, 238 recovered cases, active cases is 92, while 3124 of the sample across continents were found to be obese. Correlation Test used show that the relationship between GDP per capita & COVID 19 Variables, GDP per capita is positively correlated with number of COVID Variables Confirmed cases (r = .477, p<0.01), number of Covid 19 Death cases (r = .363, p<0.01), Covid 19 Recovered cases (r = .272, p<0.01) and number of Covid 19 Active cases (r= .416, p< 0.01). Regression Analysis showed a coefficient of multiple regressions R = 0.514 and multiple R-square = 0.264, it means that 26.4% variation in dependent variables (COVID Death) is explained by the independent variables (GDP growth, Population, GDP per capita & Obesity ) and their exist a joint contributions of independent variables to the prediction of COVID 19 Death and Regression Analysis performed reveals that (GDP per capita Beta = .201, t= 2.751, p<0.01). and Obesity (Beta = .415, t= 5.465, p<0.01)) are potent predictors of COVID 19 Death among patients While GDP growth (Beta = .105, t= 1.524, P>0.01) and Population (Beta = .020, t = .288, p>0.01) are not significant predictors of Covid 19 Death.
Performance Analysis of Flexible Duplexing-enabled Heterogeneous Networks Exploiting Multi Slope Path Loss Models

The increasing demand for data traffic can be met by effective utilization of spectrum through flexible duplexing in heterogeneous networks (HetNets). By effectively switching the transmission modes between full duplex (FD) and half duplex (HD), an elevated system performance can be achieved as HD mode provides better results in regions where self interference for FD is much higher than the desired power. We, in this paper, investigate a distance-based duplexing scheme in a two-tier HetNet that exploits dual slope path loss model in both tiers. In our proposed scheme, a user connects to a base station (BS) based on maximum received power after which it decides to operate in either FD or HD depending upon a thresholding distance. Simulation results show that the performance of the system depends upon the thresholding distance for flexible duplexing mode and the path loss exponents of dual slope model. Results for sum rate and outage probabilities are provided for both uplink and downlink transmissions.
Optimal Cache Budget Distribution for Hierarchical ICN Networks

Caching facilities can be deployed by all or some of the ICN nodes on the path of delivering data items from a content source to users. However, some inconsistent conclusions have been made from different studies regarding the benefits of in-network caching. To investigate the benefits of in-network caching, we propose an analytical model that optimally distributes a total cache budget among the nodes of a given ICN network in an environment that does not follow the Independent Reference Model (IRM). The cache budget distribution problem is studied with respect to optimizing system-centric and user-centric metrics, using a small number of synthetic and realistic topologies as case studies. Our findings reveal the benefits of in-network caching as well as the optimal distribution of the cache budget in ICNs with respect to our selected objective function. Although the efficiency of in-network caching on user-centric metrics strongly depends on topologies and the strength of temporal locality, in-network caching is very helpful in optimizing the ISP-centric metrics for all network and traffic settings.
Low Serum Pyridoxine Levels Worsen Seizure Control in Adult Epilepsy Patients

Background: Vitamin B6 (pyridoxine) is an important cofactor in the process by which glutamic acid decarboxylase (GAD) converts the excitatory, pro-epileptogenic neurotransmitter, glutamate, into the inhibitory, anti-epileptogenic neurotransmitter, gamma-aminobutyric acid (GABA). This concept has been established in infants with pyridoxine-dependent epilepsy as well as adult patients with other epilepsy subtypes who presented with medication-resistant status epilepticus, with both patient groups experiencing cessation of seizure activity following pyridoxine administration. Given our knowledge of the role of vitamin B6 in the conversion of glutamate to GABA, its effect on seizure control in infants with specific epilepsy subtypes, reports of adult-onset seizures associated with vitamin B6 deficiency, and vitamin B6’s role in terminating status epilepticus in adult patients with other types of epilepsy, we suspect that low vitamin B6 levels in adult epilepsy patients may correlate with poor seizure control across all epilepsy subtypes. This study seeks to determine whether there is a relationship between pyridoxine levels and the level of seizure control in adults with epilepsy, regardless of their seizure type. Methods: After obtaining institutional review board approval, we prospectively enrolled 32 patients (age range: 25-57 years) with epilepsy who presented to our clinic. Patients who did not meet the study criteria or who were diagnosed with psychogenic non-epileptic seizures (PNES) were excluded from the study (n = 2). Patients were classified as well-controlled (WC) or poorly controlled (PC) based on the absence or presence of a seizure within the last three months, respectively. After classification as WC or PC, pyridoxine serum levels and anti-seizure medication (ASM) levels were drawn in that clinic visit, following patient consent. All patients were contacted regarding pyridoxine and serum ASM levels, and patients that were found to be deficient in pyridoxine were treated with appropriate supplementation. At the end of the recruitment period, we performed analyses to determine if there was a statistically significant relationship between PC status and serum pyridoxine levels. Results: Of 32 patients, two patients were diagnosed with psychogenic non-epileptic events and were subsequently excluded. Of 30 patients, 10 had PC epilepsy. Median (interquartile range) serum B6 levels were 35.8 (26.8-54.2) in patients with WC epilepsy and 17.5 (10.1-41.3) in patients with PC epilepsy (P = 0.11). In the PC group, 6/10 (60%) of the patients demonstrated low serum pyridoxine compared to 3/20 (15%) in the WC group (P = 0.03). Conclusion: There was a statistically significant relationship between serum pyridoxine levels and seizure control. If appropriate, pyridoxine supplementation should be considered, especially in critically ill adult patients with refractory or PC seizures despite good adherence to ASMs.
Concise Convolutional Neural Network for Crowd Counting

Utilizingconvolutional neural network (CNN) for estimating the crowd count in a still image has taken some progress. But the existing models or algorithms are too complex for actual applications and their real-time performance have not been effectively verified. In this paper, we propose a concise and effective CNN model with only five convolutional layers. Our proposed model allows the input image to be of any size or resolution. The model maps the input image to its crowd density map. By integrating the density map we can get the crowd count. The true density map is computed based on geometry-adaptive kernels which can alleviate the perspective problems. We report the performance in terms of mean absolute error, which is a measure of accuracy of the method. We conduct extensive experiments on major crowd counting datasets to verify the effectiveness of the proposed model and apply it to the actual situation successfully. In addition, we created a new dataset to verify the transfer learning performance and real-time performance of our model. Experiments show that it has great transfer learning performance and real-time performance.
Classification of Reservoir Fracture Development Level by Convolution Neural Network Algorithm

Identifying and classifying fractures is an important task in the study of fractured oil and gas reservoirs. The most common solution is to identify it by artificial interpretation or synthetically probability methods, and to classify them according to the degree of fracture development. In order to improve the accuracy and reduce the man-made or computational errors, this study introduces the convolutional neural network (CNN) algorithm, one of the deep learning algorithms, to distinguish the degree of fracture development while constructing a new model which can automatically identify cracks and determine the category of fractured reservoirs in the meantime. Firstly, the logging curves with strong sensitivity to fractures are selected as the input data of convolution neural network, and the crack category is quantified as the output label of the network. A CNN model which is suitable for the classification of cracks is designed, whose parameters is continuously optimized through a small batch gradient descent method in the training stage. Then the trained convolutional neural network is applied to process the logging data of an oil field. The comparison of the result of crack classification by convolutional neural network with that by the traditional BP neural network indicates that the unique convolutional weight sharing structure of convolutional neural networks can extract the most effective features and greatly improve the accuracy of the fracture classification in dealing with complex nonlinear problems such as the classification of fractured reservoirs.
Diagnosis of multiple sclerosis: revisions of the McDonald criteria 2017 – continuity and change

Purpose of review The purpose of this review is to describe the new 2017 revisions of the McDonald diagnostic criteria for multiple sclerosis and review first experiences in their application to different patient populations. Recent findings The 2017 revisions agreed on by an international expert panel, as the precursors, define criteria needed to fulfill dissemination in time and space in the clinically isolated syndrome after exclusion of alternative diagnoses. One major change is the inclusion of cerebrospinal fluid (CSF) oligoclonal bands as evidence of dissemination in time in a patient with dissemination in space gathered by clinical or magnetic resonance examination. The distinction between asymptomatic and symptomatic lesions in counting for evidence of dissemination in space or time in supra, infratentorial, and spinal cord syndrome has been abandoned. Finally, cortical lesions can be used to demonstrate dissemination in space. Major differential diagnoses, in particular, the still-evolving concept of neuromyelitis optica spectrum disorders and the myelin oligodendrocyte glycoprotein-IgG-related demyelinating central nervous system disorders. Summary The new 2017 revisions will simplify the application of the MRI criteria for dissemination in space and include CSF findings as evidence for dissemination in time in clinically isolated syndrome.