Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloning and characterization of nutrient deficiency and salinity stress responsive TaCBL 4 gene from bread wheat ( Triticum aestivum L . )

Environmental stresses, such as drought, salinity, extreme temperatures and nutrient deficiencies affect plant productivity adversely. Calcineurin B-like proteins (CBLs) and CBL-interacting protein kinases (CIPKs) are components of calcium mediated stress response pathways in many plants. In the present study, TaCBL4 gene homologous to salt stress responsive Arabidopsis CBL4 (AtSOS3 ) was cloned from wheat and sequence characterized. The predicted protein of TaCBL4 showed 95, 83, 70 and 61% similarity with amino acid sequences of barley, rice, maize and Arabidopsis CBL4, respectively, demonstrating that CBL4 genes are conserved between dicots and monocots. TaCBL4 protein consists of three EF-hand calcium binding motifs, a characteristic feature of calcium sensor proteins. The modelled image of TaCBL4 protein was highly identical to the already resolved structure of the AtCBL4 protein. In addition to salinity stress, the expression of TaCBL4 gene was also up-regulated by deficiency of major nutrients, namely, nitrogen, phosphorus and potassium. This suggests that TaCBL4 may serve as node for signaling crosstalk between salinity and nutrient deficiency stress signaling pathways and nutrient homeostasis under salinity stress. The present study lays foundation for functional studies on role of TaCBL4 gene in nutrient use efficiency and for its further utilization in breeding new wheat varieties with enhanced nutrient use efficiency and salinity tolerance.
Growing radial basis neural networks

This paper proposes a framework for constructing and training growing radial basis function (GRBF) neural networks. The GRBF network grows in the process of training by splitting one of the prototypes that determine the locations of the radial basis functions. Two splitting criteria are proposed to determine which prototype to split at each growing cycle. The proposed hybrid learning scheme provides the framework for incorporating existing algorithms in the training of GRBF networks. A supervised learning scheme based on the minimization of the localized class-conditional variance is also proposed. GRBF neural networks are evaluated and tested on pattern classification applications with very satisfactory results.
Evaluation of the Efficiency of Chinese Makerspace Operation Based on Double-creation Background

In recent years, China’s economy in the global economic downturn in the environment, still in a relatively rapid growth, and China launched a "mass entrepreneurship, innovation" policy is inseparable. At the same time, makerspace is a new generation of incubator platform which is put forward under the background of double creation, and the inefficient operation is the problem to be solved urgently. In this paper, the Data Envelopment Analysis (DEA) method is used as the research tool, according to the statistic data of the latest 2017 China Torch Statistics Yearbook, this paper evaluates and analyzes the efficiency of the makerspace operation in 30 provinces and cities of China in 2016 from four aspects of technical efficiency, pure technical efficiency, scale efficiency and projection analysis. The results show that: the technical efficiency of 30 provinces and cities in China is 0.847, the overall level is good, but not DEA effective accounted for the total area of 63.33%, of which scale efficiency is the main cause of technical efficiency. On the other hand, there are 14 provinces with varying degrees of input redundancy or insufficient output, the number of practitioners, the number of working places, the number of entrepreneurial instructors and other aspects of the input redundancy is the main cause of the non-DEA effective. Based on the above conclusions, this paper mainly puts forward 4 key countermeasures for the Government and related enterprises to provide reference for the operation and management of the Chinese provinces and cities.
Political Sentiment Assessment through Social Media

The world wide web including social sites, blogs, forums etc. generate a huge amount of data in the form of views, emotions, opinions and arguments regarding different products, social activities, brands and politics. This has prompted increasing interests in analytical domain. Thus, all these comments have a great influence on readers, vendors & politicians. In political domain, political parties and other experts make use of facebook, twitter and blogs to communicate with the people and to check voice of public. Hence, an appropriate model which gives correct analysis and results about peoples’ sentiments related to political parties and political diplomats is built. Data is captured from social media sites and pre-processing is carried out. Word2vec model is used for word embedding process. For final evaluation neural network is used. After detailed study and comparison efficient neural network model is chosen and positive/negative sentiments are identified. Experimental evaluation signifies the effectiveness of the proposed model. This system is also helpful for understanding people’s response to particular political decision which will help in better decision making during elections and political campaigns.
Classification Using Unmixing Models in Areas With Substantial Endmember Variability

Hyperspectral imagery (HSI) can significantly contribute to habitat monitoring which is essential to ecosystem management. Specifically, spectral unmixing in HSI is an important tool for habitat conservation status assessment. However, an issue found is the spectral variability of the endmembers that can be addressed by the Extended Linear Mixing Model (ELMM), which considers this spectral variability for obtaining accurate abundance maps. An analysis is performed in a mountainous ecosystem with high spectral variations. Classification maps are obtained from the abundance maps to assess the unmixing models. Results are very satisfactory, plausible abundances are estimated with accurate characterization of variability within the scene and overall accuracies over 83%are obtained. ELMM and Robust ELMM allow studying the features of each pixel, including additional information about characterization of mixed pixels, by considering spectral variability and being more robust to the absence ofpure pixels as well as to noise.
Appraisal of Pre-processing Techniques for Automated Detection of Diabetic Retinopathy

Retinopathy is a retinal vascular disease caused by abnormal blood flow that damages the retina and it accounts for 5% of blindness worldwide. There are different causes of Retinopathy as retinopathy of prematurity, diabetic retinopathy and hypertensive retinopathy; this paper mainly focuses on pre-processing techniques employed for the detection of diabetic retinopathy. In this work, literatures reporting different methods for automated diabetic retinopathy detection in color images of retina are analyzed for their strength and shortcoming. Extensive techniques for retinal image screening have been published so far thus this paper presents the evaluation of these methods on a common database of image screening. Different image pre-processing techniques are implemented on fundus images and their performance is compared using performance metrics like mean square error, peak signal to noise ratio and root mean square error. Pre-processing steps used in this work can also be applied to other images of interest to identify and recognize the objects in an image.
A New Simulation Method for Complicated Successive Power System Faults in Extreme Weather

Extreme weather might lead to complicated successive power system faults, which would threaten the security and stability of power system. In order to minimize the harm of extreme weather, a new method to simulate the complicated successive faults is proposed in this paper. Firstly, all of the candidate fault transmission lines in the power system are merged as one single virtual line. With the principle that fault probability is equal along the whole length of the virtual line, fault lines are selected from all of the candidate lines in a roulette way. Secondly, the fault location on the selected line is determined randomly. The fault type, however, is determined according to the historical statistics data of extreme weather power system faults. Thirdly, the time sequence for all of the determined faults is constructed based on actual extreme weather faults scenes. The simulation scenes are generated by arranging the determined faults with the time sequence. Then, the power system analysis software PSS/E is used to simulate the generated simulation scenes. Three terminating criteria are proposed for the simulating process. Finally, the proposed simulation method is applied in an actual regional power system. The results show that the proposed method can effectively simulate extreme weather scenes and examine the stability of power system.
Application of Deep Neural Network for Predicting River Tide Level

In riverine countries like Bangladesh, residents have to depend on rivers for many things for their daily life. If the river has river port or intercostals waterways or estuaries than it becomes more significant to country. So, knowing the upcoming water height or tide level at the river becomes an important fact to people for planning their daily activities, like: fishing, water way communication, port activities controlling etc. Moreover, people living at the bank of the river can anticipate sudden flooding by having updated information about tide height at the river. The aim of this study is to propose a machine learning model which will be able to percept short term future tide level at a river. For undertaking the experiments, we have collect 10 years (2007–2017) of historical dataset of the Karnaphuli river from Chittagong Port Authority's (CPA) hydrographic department. It is the mainstream river of Bangladesh having four tide gauge stations named Kalurghat, Canal-10), Canal-18 and Sadarghat. We have designed our study using Support Vector Machine (SVM), Artificial Neural Network with back propagation (BP-ANN) and Deep Neural Network (DNN). After careful and meticulous analysis we have found DNN model outperformed the others with almost 99% accuracy in future water level prediction.
Compressive Sensing Algorithms for Multiuser Detection in Uplink Grant Free NOMA Systems

Non-orthogonal multiple access (NOMA) scheme has been found to exhibit the ability of supporting massive connectivity of multiple users by employing the same, non-orthogonal, spectrum resources. Grant-free transmission mode in NOMA system allows users to randomly initiate transmission at any time slot, and this mode of transmission is highly desirable for the uplink NOMA system. This paper focuses on comparing three compressive sensing algorithms, that track user activity and detect users' signals in a scenario where the active user set is assumed unchanged in several continuous time slots, with another two compressive sensing algorithms that perform similar tasks when the active user sets changes in several continuous time slots within a whole frame. The five algorithms-based multi-user detectors (MUDs) are employed for multiuser detection processes in a grant-free NOMA system. The simulation results indicate that the compressive algorithm employed while the active user sets changes in several continuous time slots within a whole frame, the dynamic compressive sensing (DCS)-based multi-user detection (MUD), outperformed all the other MUDs. The traditional subspace pursuit (SP)-based MUD employed when the active user sets remains unchanged in several continuous time slots within a whole frame performs in a similar way with its variant, the modified SP-based MUD employed when the active user sets changes in several continuous time slots within a whole frame. The worst performing MUDs are the orthogonal matching pursuit (OMP)-based MUD and its variant, the computational efficient OMP-based MUD that exhibits similar performance with the OMP-based MUD.
Analysis of Five Clustering Algorithms in Quantitative Method Research

In the course of research using quantitative methods, cluster analysis is often used for scientific research. However, we often face some complicated clustering algorithms during the analysis process. Choosing the right clustering algorithm is a very necessary research issue. Therefore, based on the characteristics of adjacency matrix data, we analyze the commonly used clustering algorithms in scientometrics. Firstly, we analyze the procedure of clustering. Then, the characteristics of several common clustering algorithms are summarized. Third, based on the validity and correctness of clustering, the effect of clustering algorithms are discussed. Finally, we implement the algorithm by MATLAB, and make an empirical analysis of three data sets cases. According to the characteristics of clustering algorithm, the correctness of clustering and the validity of clustering, we choose the clustering algorithm suitable for our use.
Millimetre Wave Propagation Reverse Measurements for 5G Urban Micro Scenario

The double-direction channel describes the angular pairing of rays connecting a base station (BS) to its user terminal (UE). This paper presents double-directional experimental measurements for an Urban Micro-cellular (UMi) campus environment at millimetre wave frequencies. Results showed that multiple Angles of Departure (AoD) from a given UE position often result in few (often one) Angles of Arrival (AoA) at the BS. Similarly different UE locations can often share a common angle of arrival at the BS. Three AoAâ€™s covered all the dominant transmissions from 9 of the 11 UE sites evaluated in the test location. Such situations can limit spectral efficiency by reducing both MIMO and spatial division multiple access performance.
Investigations on designs of Dielectric Resonator Antennas for WiMax & WLAN applications

DRA emerged as a good option to go for designing at low as well as high frequencies as it show good diversity performance, good isolation performance, compact size, light weight and good bandwidth coverage with excellent gain. This paper basically gives an overview of various DRA designs based upon the DRA's performance in WiMax/WLAN applications. As today is the real of miniaturization, so some of designs of DRA have been studied & analysis of key parameters like DRA shape, material, feed type, size, substrate material, bandwidth & peak gain is done. There is a possibility to use multiple ports on a single dielectric structure to achieve diversity. This has to be explored for WiMax/WLAN applications.
An Accurate Weighted Time-Reversal Approach for Passive Indoor Localization

In recent years, passive localization has attracted widespread attentions from academia for it is able to locate user without the user carrying any device, e.g., mobile phone. Considering the complex indoor environments, how to improve the resolution and the accuracy of passive localization become the focuses of researches. In this paper, an accurate weighted Time Reversal (TR) passive localization system named WTRPL is proposed. In WTRPL, we first introduce the TR method into passive localization to achieve high resolution localization. Furthermore, by fully utilizing information of multiple antennas, a confidence level based weighted scheme is employed to increase localization accuracy. We test the proposal in two typical indoor environments and experimental results confirm the effectiveness of the proposal.
Zone Segmentation of a Text Line Printed in Gurmukhi Script Newspaper

Newspapers consist of essential information and many efforts have been done for digitization and recognition of newspaper text. Few Gurumukhi script newspaper articles are available in digital form but searching of text is not possible on digital images. So, text processing is required for making searching possible on the text and also indexing of headlines. To recognize any text, segmentation of text into individual line is an important phase. As per characteristics of Gurmukhi script, text line can be divided into three zones namely upper zone, middle zone and lower zone. So, segmentation of an individual line into different zones is a preliminary phase for segmentation of text. Zone division is possible through detection of headline and baseline. Baseline detection is a tedious task because of the uneven presence of on and off pixels in a baseline. In this paper, authors have presented an algorithm for zone segmentation of Gurmukhi script newspaper text based on headline and baseline.
Machine Learning in Finance

The modern developments in machine learning have increased the diversity of its usage. With the help of machine learning it has become easy for mankind to analyse and decode the proper information. In this paper we have created an algorithm which can predict the analysis of financial market. The method used in algorithm is Logistic Regression to multiple variables. In this method, with the help of previous datasets of financial market we can predict the future analysis, which will surely decrease the human efforts and margin of error. The proposal of this algorithm is to create an impact in the near future and to make easy for humans to study the financial market.
Optimization Management of Charitable Organizations’ Participation in Social Assistance in China

Social assistance is the basic guarantee for maintaining social stability, optimizing urban governance and promoting sustainable development. The participation of charitable organizations in social assistance is a trend around the world. However, charitable organizations start late in China. There are many difficulties for charitable organizations to participate in social assistance. In this paper, 36 representative charitable organizations are selected as samples, and the method of Data Envelopment Analysis (DEA) is used to analyze the relative efficiency of charitable organizations’ participation in social assistance during 2013-2016. The results show that although the overall efficiency of charitable organizations has been improved during the study period, the average efficiency is low. Further, the panel regression model is used to analyze the factors that affect Chinese charitable organizations’ participation in social assistance. Based on the results, several measures are suggested to be taken to optimize charitable organizations’ participation in social assistance, including enhancing fund-raising capacity of charitable organizations, increasing government’s support, improving the quality of staff of charitable organizations, and strengthening the supervision of charitable organizations’ participation in social assistance*.
MEASUREMENT AND ANALYSIS OF NON-LINEAR DISTORTION OF ELECTRO-ACOUSTIC AND AUDIO SYSTEMS

A novel technique for analysis of nonlinear distortion of electro-acoustic and audio systems is presented. The method is based on the combination of a Sweep sine Wave signal Technique (SWT) and a Multiple Input Single Output (MISO) nonlinear modeling. The signal driving a system under test is a logarithmic sine wave sweep. The response of the system under test is acquired and used in a convolution with swept sine wave "inverse filter" response to obtain a set of transfer functions. This set of transfer functions provides information about harmonic distortion. These transfer functions are further processed. Then the system under test can be modeled as a MISO nonlinear model, consisting of a parallel combination of nonlinear branches containing filters and memory-less power-law distortion functions. Such a model facilitates determination of a nonlinear response to the input signal. This method was used to analyze transfer functions of various components of electro-acoustic systems. It was applied to testing of recording and reproduction equipment for audio signals, including equipment for their further digital signal processing. During testing of multichannel systems, the method enables evaluation of not only individual channels, but also their mutual interaction. A good correlation with traditional methods of distortion measurement has been shown.
Efficient Analog Beamforming with Dynamic Subarrays for mmWave MU-MISO Systems

Analog beamformer with large-scale antenna arrays has been widely considered in millimeter wave (mmWave) communication systems because of its superiority in hardware cost and energy consumption compared with traditional fully digital beamforming schemes. In this paper, we introduce an efficient dynamic subarray analog beamforming architecture with lowresolution phase shifters (PSs) for mmWave multiuser multipleinput single-output (MU-MISO) systems. In an effort to mitigate the performance loss due to the use of low- resolution PSs, each user can dynamically select a non-overlap subarray from total transmit antennas and use corresponding subarray analog beamformer to transmit signals. This dynamic subarray analog beamforming architecture can utilize the multi- antenna/multiuser diversities by dynamically adapting to channel state information (CSI) of users. An efficient dynamic subarray analog beamformer design algorithm is also presented, which aims at maximizing the sum-rate of the MU-MISO system. Simulation results demonstrate that the proposed dynamic analog beamforming solution can significantly outperform the conventional fixed-subarray schemes.
Review on Video Refereeing using Computer Vision in Football

Football is, without a doubt, one of the biggest and most popular sports in the world with an estimated fanbase of 3.5 million individuals. England’s "Premier League" has 20 teams and each of these teams earn roughly 40 million a year just through tv broadcast sales. Add to that jersey sales, tickets and sponsorship money, one slowly begins to understand just how popular this sport is. However, it is not immune to controversy and one of the biggest problem plaguing the sport is the referral system which is inconsistent and prone to mistakes. This paper presents a system to perform the job of refereeing in the sport of football by taking an input of the live video file and using computer vision and image processing to understand what is happening. Computer Vision is the science aiming at automating the process of information extraction, analysis and understanding information from a sequence of images. It works at providing the capability of vision to a computer. The system makes decisions such as goal, foul or offside. It then conveys this message to the on field official. It does so by tracking the players and the ball and analyzing their position at every instant.
Performance Analysis of a Compact Dual-Mode Antenna Operating at UWB and ISM Band for Wireless Medical Applications

In this article, a new design of a flexible dual mode off/in-body antenna is proposed. The main objectives of this proposed antenna refer to its dual mode operating at 5.7808 GHz & 4.1968 GHz, miniaturized dimension, impressive return loss, higher bandwidth that makes it suitable for WBAN applications. The size of the antenna is 12 mm x 10 mm x 0.52 mm which is appropriate for implantable as well as off-body applications. The performance parameters were scrutinized for functioning the proposed antenna in Industrial, Scientific and Medical (ISM) (5.725 GHz-5.875 GHz) for Off-body communications and Ultra-Wideband (3.1 GHz-10.6 GHz) for implantable applications. A three layers of human phantom model is developed as simulation environment where the antenna is inserted inside the muscle for In-body operation. Biocompatibility test, flexibility and related performance measurements have been utilized using CST Microwave Studio software in both curved and planar states by following different types of dielectric property matching with human tissue model. Finally, Specific Absorption Rate (SAR) is assessed to check its feasibility and workability in wireless medical applications.