Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time and Accurate Indoor Localization with Fusion Model of Wi-Fi Fingerprint and Motion Particle Filter

As the development of Indoor Location Based Service (Indoor LBS), a timely localization and smooth tracking with high accuracy are desperately needed. Unfortunately, any single method cannot meet the requirement of both high accuracy and real-time ability at the same time. In this paper, we propose a fusion location framework with Particle Filter using Wi-Fi signals and motion sensors. In this framework, we use Extreme Learning Machine (ELM) regression algorithm to predict position based on motion sensors and use Wi-Fi fingerprint location result to solve the error accumulation of motion sensors based location occasionally with Particle Filter. The experiments show that the trajectory is smoother as the real one than the traditional Wi-Fi fingerprint method.
RESPONSE OF COWPEA TO INOCULATION WITH STRAINS OF Bradyrhizobium spp. AND TO NITROGEN FERTILIZER UNDER PROTECTED CULTIVATION

RESPOSTA DO FEIJÃO-CAUPI A INOCULAÇÃO COM ESTIRPES DE BRADYRHIZOBIUM E ADUBAÇÃO NITROGENADA EM CULTIVO PROTEGIDOA inoculação de sementes de feijão-caupi com bactérias fixadoras de nitrogênio atmosférico pode ser uma alternativa de manejo mais sustentável. Neste estudo, objetivou-se avaliar os efeitos da inoculação e doses de N sobre características do feijão-caupi, em cultivo protegido. Utilizou-se o delineamento experimental em blocos ao acaso, arranjados em esquema fatorial 4 x 4, com quatro fontes de N, caracterizadas por três estirpes (BR 3262, BR 3267 e BR 3299) e uma testemunha sem inoculação e sem N mineral, e quatro doses de adubação nitrogenada (0, 20, 40 e 60 kg ha-1). As características avaliadas foram: altura de plantas, massas secas da parte aérea, da raiz e de toda a planta, número de nódulos, massa seca de nódulos e eficiência relativa dos tratamentos. No tratamento inoculado com a estirpe BR 3267, a massa seca da parte aérea, raiz e de toda a planta aumentaram, com doses crescentes de N até 30 kg ha-1. Entretanto, a nodulação foi inibida, com o aumento da dose de N. A eficiência simbiótica da estirpe BR 3262 foi semelhante à da testemunha, sendo as demais inferiores, indicando que a população nativa de rizóbios, por si só, é capaz de suprir a demanda da planta.Palavras-chave: Bradyrhizobium; fixação biológica de nitrogênio; nutrição mineral; Vigna unguiculata (L.) Walp. ABSTRACT: Inoculating cowpea seeds with atmospheric nitrogen-fixing bacteria may be a more sustainable management alternative. The objective of this study was to assess the effects of inoculation and N fertilizer rates on characteristics of cowpea grown in greenhouse. A randomized complete block design was used. Treatments were laid out in a 4 x 4 factorial experiment consisting of four N sources: three inoculant strains (BR 3262, BR 3267 and BR 3299) and a non-inoculated control without mineral N supply; and four N fertilizer rates (0, 20, 40 and 60 kg ha-1). The following characteristics were evaluated: plant height, shoot dry weight, root dry weight, total dry weight, number of nodules, nodule dry weight, and relative efficiency of treatments. For the treatment inoculated with strain BR 3267, shoot dry weight, root dry weight and total dry weight increased with increasing N rate up to 30 kg ha-1. However, nodulation was inhibited with increasing N rate. The symbiotic efficiency of strain 3262 was similar to that of the control and lower than the remaining strains, indicating that the indigenous population of rhizobia, by itself, is able to meet the plant’s N demand.Keywords: Bradyrhizobium; biological nitrogen fixation; mineral nutrition; Vigna unguiculata (L.) Walp.
Analysis of Human EMF Exposure in 5G Cellular Systems

Increasing concerns of communications at a frequency spectrum higher than 6 GHz have gained international alarm that suggests more research is needed before it is deployed suc-cessfully. In this context, in the ﬁrst part of this thesis, we investigated the human electromagnetic ﬁeld (EMF) exposure in indoor and outdoor environments from ﬁfth-generation (5G) downlink communications and compared its impacts with the present cellular technologies considering the features that the 5G will likely adopt. The second part focuses on mitigation of human exposure for both indoor and outdoor environments with two different methods adopted. Our simulation results suggest that while the impacts from 5G communications cross the regulatory borders for a very short separation distance between base stations (BSs) and user equipment (UE), the exposure level remains high throughout the network compared to the present systems. This work also highlights the signiﬁcance of considering SAR for the measurement of exposure compliance in downlinks.
Genetic Detection of Neurogenesis and Astrocytic Transformation of Radial Glia

The adult mammalian brain was long believed to be incapable of generating new neurons after the developmental stage. It is now known that specialized stem cell niches produce neurons throughout life important for sensation, memory, and response to injuries. However, questions on the origins of the stem cells responsible for adult neurogenesis as well as critical questions concerning the development of the various neural lineages in the brain remain unanswered, and better tools are needed to better appreciate the formation of the most complex structure in biology. Cell proliferation is traditionally detected by dosing with labeled thymidines. Furthermore, administration of two thymidine analogues at different times is an excellent tool for determining cell-cycle kinetics, but current methods of distinguishing the two molecules is technically complicated. Here we report a simple way of distinguishing BrdUand IdU-labeled cells by immunofluorescence. This method identifies BrdUand IdU-incorporation as doubly and singly labeled cells respectively by using two monoclonal antibodies with varying specificity to BrdU. We use this method to show that the standard dose of BrdU is sufficient to label the majority of proliferative populations in the S-phase. Because DNA synthesis may occur in events unrelated to neurogenesis methods other than BrdU are needed to detect adult neurogenesis. Here we describe bitransgenic Nestin-CreER/EGFP-Reporter mice which labels nestinpositive subventricular zone (SVZ) progenitors with EGFP in the adult after tamoxifen induction. Although there is robust labeling of hippocampal neurons by
Automatic Habitat Mapping using Machine Learning

Habitat mapping is an important task to manage ecosystems. This task becomes most challenging when it comes to marine habitats as it is hard to get good images in underwater conditions. In order to do habitat mapping in a vast coastal region, we combine the use of sonar and camera sensors embedded in Autonomous Underwater Vehicles that can accurately locate itself in underwater environments and the data it retrieves. Side Scan Sonar images are not easy to analyze as they are distorted by the movement of the robots. In our approach, human specialists annotate a subset of the optic images and machine learning is applied to automatically identify other images. Moreover, optic images are associated with Sides Scan Sonar imagery which is partially co-located. A tool (Hijack) has been specifically developed to help biologists in the annotation phase. This tool interacts with the user to quickly analyze each image and associate it with a habitat type from the European Nature Information System taxonomy. The optic images are enhanced using Computer Vision techniques, to help specialists to be certain of the habitats they are observing. Later we employ Convolutional Neural Networks to automatically classify habitats with the annotations of specialists by predicting the remaining imagery, both optical and sonar. With this approach, we present a method to perform marine habitat mapping, associating optical images with Side Scan Sonar images with a Machine Learning predictive model.
To twist, roll, stroke or poke? A study of input devices for menu navigation in the cockpit

Modern interfaces within the aircraft cockpit integrate many flight management system (FMS) functions into a single system. The success of a user's interaction with an interface depends upon the optimisation between the input device, tasks and environment within which the system is used. In this study, four input devices were evaluated using a range of Human Factors methods, in order to assess aspects of usability including task interaction times, error rates, workload, subjective usability and physical discomfort. The performance of the four input devices was compared using a holistic approach and the findings showed that no single input device produced consistently high performance scores across all of the variables evaluated. The touch screen produced the highest number of ‘best’ scores; however, discomfort ratings for this device were high, suggesting that it is not an ideal solution as both physical and cognitive aspects of performance must be accounted for in design. Practitioner summary: This study evaluated four input devices for control of a screen-based flight management system. A holistic approach was used to evaluate both cognitive and physical performance. Performance varied across the dependent variables and between the devices; however, the touch screen produced the largest number of ‘best’ scores.
Algorithm to Solve Dirichlet Problem for Laplace’s Equation

The various researches and developments in the parallel computing Apache Spark framework allows to process petabyte-scale data and possesses properties such as scalability, fault tolerance, load balancing and mechanisms of in the memory computations across the nodes of the cluster. So, the features are much attractive for high performance of scientific computations. As the Hadoop platform is not much suitable for the iterative computing due to some typicality then Apache Spark with new distributed data structure (RDD) is much suitable. Here we are using the method and algorithm described by researchers from time to time for Hadoop-based algorithm to solve the Dirichlet problem for Laplace’s equation. The comparative figures are drawn with respect to time to verify the performance. By seeing graphs and other details, we can say that the Spark based implementation is much suitable for solving the Dirichlet problem for their improved performance as compared to Hadoop-based implementation. Keywords:- Apache Spark; Dirichlets problem; Hadoop; Laplace’s equation; RDD.
A Model for Blood Cell Transport and Separation in a Magnetophoretic Microsystem

We present a model for predicting the transport and separation of red and white blood cells in plasma in a magnetophoretic microsystem. The microsystem consists of an inlet that feeds a single microfluidic channel with multiple outlets. An array of integrated soft-magnetic elements is embedded adjacent to the microchannel, and once magnetized, these elements produce a magnetic force on blood cells as they flow through the channel. In whole blood, white blood cells (WBCs) behave as diamagnetic microparticles, while deoxygenated red blood cells (RBCs) exhibit paramagnetic behavior. We model the transport of these cells through the microsystem taking into account magnetic, fluidic and gravitational forces. Our analysis indicates that the microsystem is capable of separating WBCS, deoxygenated RBCs and plasma into respective outlets.
In vitro Antibacterial Potential of Rosa Damascena and Terminalia Chebula against Bacterial Peritonitis

Twenty two bacterial isolates were collected from hundred peritoneal samples taken from patients that were admitted to Tanta university hospital, Egypt. All the investigated isolates were identified morphologically and biochemically. The twenty two isolates were subjected to in vitro evaluation for antibiotic sensitivity test using antibiotics from different classes. Rosa damascena and Terminalia chebula were extracted using different solvents (Methanol and acetone) and were investigated for antibacterial activity against the 22 bacterial isolates. The sensitivity was determined using agar well diffusion method and the inhibition zones were compared with the standard drug gentamicin. The extracts showed a wide spectrum of inhibition against the tested isolates. Acetone extracts of the two plants were proven to have the strongest antibacterial activity than the methanol extracts. The minimum inhibitory concentrations of extracts were determined. GC-MS and FT-IR analyses were carried out for the T. chebula acetone extract as the best antibacterial agent.
Designing unique words for reliable channel estimation in multi-antenna block transmission systems

Single-carrier, multiple-input multiple-output wireless communication systems generally require knowledge of the channel to equalize a received message. In block transmission systems, such as those that utilize the frequency domain to facilitate channel equalization, short training sequences known as unique words (UWs) can be inserted into the data stream, thus providing a means for estimating and tracking the state of the channel. The problem of designing the UWs can be difficult when certain constraints are placed on the sequences. In this paper, a nonlinear optimization approach is taken to design near-optimal UWs under given constraints, such as a limit on the peak-to-average power ratio (PAPR) of the sequences. Optimality of the sequences is defined with respect to the Cramer-Rao bound on performance for an unbiased estimator
Research on Chinese Semantic Role Labeling with Hierarchical Syntactic Clues

As a key task in the process of natural language understanding, semantic role labeling has been widely used in the field of natural language processing at a higher level, such as information extraction, text analysis and machine translation. This paper adopts the current mainstream semantic character annotation method based on syntactic tree analysis, proposes a Chinese semantic role labeling method that integrates hierarchical syntactic cues, and implements a hierarchical annotation model based on conditional random field classifier. On the basis of the model, the influence of different syntactic features on system performance is discussed. Different feature sets are formulated for verb predicate and noun predicate respectively, and the contribution of each feature to the system is analyzed. The experimental results show that the introduction of phrase syntax analysis can effectively improve the recognition effect of semantic roles. The results obtained in this study have a good reference value for future research.
Determining message delivery delay of controller area networks

The main object of distributed real-time systems is to satisfy the timing requirements of hard and soft real-time communication. That is to say the deadlines of hard real-time traffic (HRT) must be guaranteed, while those of soft real-time traffic (SRT) can occasionally be missed without major consequences. The main issue considered in this paper is to provide a method to calculate the message delivery delay time of controller area networks, which is a key step to determine the HRT and SRT timing constraints.
Special issue on “Multi‐modal information learning and analytics of cross‐media big data”

We are living in the era of data deluge. Meanwhile, the world of big data includes a rich and complex set of cross ‐ media content, including text, images, video, audio, and graphics. To date, substantial research efforts have been dedicated to big data processing and cross ‐ media mining, yielding good theoretical underpinnings and great practical success. However, studies that jointly consider cross ‐ media big data analytics are relatively sparse (Zhang, Liu, Deng, Xu, and Choo (2018); Zhang, Wei, Wang, and Liao (2018)). This research gap needs more attention since it will benefit many real ‐ world applications. Despite its significance, it is important to analyse cross ‐ media big data due to the heterogeneity (Zhang, Zhao, Li, Chen, and Yuan (2015)), large ‐ scale volume, increasing size, lack of structure, correlations, and noise. Multi ‐ modal information learning (Demertzis, Iliadis, Avramidis, and El ‐ Kassaby (2017)), which can be treated as the most significant breakthrough in the past 10 years, has greatly affected the methodology of computer vision and achieved substantial progress in both academia and industry. Additionally, deep learning has been adopted in all kinds of computer vision applications, and many breakthroughs have been achieved in sub ‐ areas, such as DeepFace in the LFW competition for face verification and GoogleNet for the ImageNet Competition for object categorization. We can expect that more and more computer vision applications will benefit from multi ‐ modal information learning. This special issue focuses on learning methods to achieve high ‐ performance multi ‐ modal information analysis and understanding under uncontrolled environments on a large scale, which is also a very challenging problem. This problem has attracted considerable attention from both academia and industry. We hope this topic will aggregate top ‐ level research on new advances in multi ‐ modal information from cross ‐ media data. The purpose of this SI is to provide a forum for researchers and practitioners to exchange ideas and progress in related areas. After several rounds of review, six papers have been accepted for publication. Zhu,
Flaw recognition based on principal component analysis

A new feature extraction method based on principal component analysis is proposed to solve deficiencies in traditional manual definition method and wavelet decomposition method.This approach can reduce not only the amount of data,but also the computational complexity,and it makes sure the compressed data can effectively represent the original data features as well.A feature vector is built up with the features extracted,which is tested in an experiment based on fuzzy pattern recognition and compared to the present two methods.The results show that the new method is superior at both efficiency and accuracy than present methods in recognizing of various patterns.
PERFORMANCE OPTIMIZATION OF LUT IN FPGA USING CNFET

A Leakage power dissipation is becoming a concern in field-programmable gate arrays (FPGAs) due to scaling in FPGA technology. Field programmable gate arrays (FPGAs) are the implementation platform of choice when it comes to design flexibility. However, the high power consumption of FPGAs (which arises due to their flexible structure), make them less appealing for extreme low power applications hence it is important to investigate ways of reducing FPGA power consumption. This paper proposes an energy efficient dual-threshold CarbonNanotube Field Effect Transistor (CNFET) based architecture of 4-input Look-up Table (LUT), a building block of Field programmable gate arrays. HSPICE simulation based on Berkeley Predictive Technology Model (BPTM) for 32nm channel length, in the CNFET based LUT delay is improved by 95% and is 98% more leakage power efficient than the LUT implemented in the bulk CMOS.
Predictive Control of Quad-Rotor Delivering Unknown Time-Varying Payloads Based upon Extended State Observer

In this paper, robust control problem is addressed for quad-rotor delivering unknown time-varying payloads. Firstly, the model of a quad-rotor carrying payloads is built. Dynamics of the payloads are treated as disturbances and added into the model of the quad-rotor. Secondly, to enhance system robust-ness, the extended state observer (ESO) is applied to estimate the disturbances from the payloads for feedback compensation. Then a type of predictive controller targeting multiple-input-multiple-output (MIMO) system is developed to degrade the influences caused by sudden changes from load-ing/dropping of the payloads. Finally, by making comparison with the con-ventional cascade proportional-integral-derivative (CPID) and the sliding mode control (SMC) approaches, superiority of the scheme developed is va-lidated. The simulation results indicate that the CPID method shows poor performance on attitude stabilization and the SMC shows input chattering phenomenon even it can achieve satisfied control performances.
Highly uniform monolayer graphene synthesis via a facile pretreatment of copper catalyst substrates using an ammonium persulfate solution

The demand for large-area, high-quality synthesis of graphene with chemical vapor deposition (CVD) has increased for the realization of next-generation transparent and flexible optoelectronic applications. In conventional CVD processes, various synthesis parameters can strongly affect the quality of the resultant graphene. In particular, surface engineering of a copper catalyst substrate is one of the most promising pathways for achieving high-quality graphene with excellent reproducibility. For this purpose, simple wet chemical etching of a catalyst substrate without toxic fume byproducts or metal ion residues is desired. Here, we suggest a facile method for preparing a pretreated copper catalyst substrate for highly uniform, large-area CVD graphene growth. This pretreatment method involves a wet copper etchant, ammonium persulfate (APS) solution, and gentle ultrasonication (100 W), which do not produce unwanted or toxic fume byproducts during their reaction. Moreover, this approach does not leave metal ion residue on the cleaned copper substrates that serves as residual nucleation sites and leads to multilayer graphene growth. To evaluate the quality of the synthesized monolayer graphene on the cleaned copper catalyst substrates, we used various characterization techniques, such as Raman spectroscopy and sheet resistance, optical transmittance, and FET characterization.
Monodispersed Pt3Ni Nanoparticles as a Highly Efficient Electrocatalyst for PEMFCs

A facile strategy is proposed to synthesize monodispersed Pt3Ni nanoparticles. Such a kind of electrocatalyst shows a larger electrochemical surface area (98.9 m2 gpt−1) and double the mass activity of the oxygen reduction reaction activity compared to commercial Pt/C catalyst. The results show that the suitable addition of Ni and triethylamine in the reduction process plays an important role in controlling the size and dispersion of Pt3Ni nanoparticles. A further membrane electrode assembly test proves that as-prepared Pt3Ni nanoparticles can greatly enhance the electrochemical performance of a proton exchange membrane fuel cell, which exhibits a great potential of application in fuel cells.
A Low-Complexity Massive MIMO Detection Based on Approximate Expectation Propagation

Among various massive multiple-input multiple-output (MIMO) signal detection schemes, expectation propagation (EP) achieves superior performance in high-dimensional systems with high-order modulations and flexible antenna configurations. However, the inevitable matrix inversion in each iteration of EP brings unbearable computational burden, which hinders the efficient implementation. Several reduced-complexity variants of EP are proposed recently, which effectively alleviate the computational cost but at the expense of unacceptable performance loss. In this paper, a low-complexity massive MIMO detection is first proposed based on approximate EP, which relieves the computational complexity of the exact EP while maintaining the good performance. Particularly, the EP moment matching equations are reformulated to simplify the sequential updating procedure. In addition, an approximation based on the channel-hardening phenomenon is proposed to eliminate the matrix inversion at each iteration. Numerical results show that, for high-dimensional MIMO the proposed detector approaches the exact EP in term of bit-error-rate (BER) by a small number of iterations. No matter with symmetric or asymmetric antenna configuration, it outperforms other EP variants, Gaussian tree approximation, and channel-hardening exploiting message passing. An analysis of computational complexity reveals the high efficiency of the proposed detection compared to the state-of-the-art with flexible antenna configurations.