Miracle-dz/UniFAR-FTUs huggingface dataset:
Principles of Biological Echolocation Applied to Radar Sensing: Applying biomimetic sensors to achieve autonomous navigation

This article introduces the application of principles from biological echolocation to radar sensing. A novel biomimetic radar sensor is presented whose features are based on the relevant morphology of a bat. Signal properties accessible to bats as well as biologically feasible processing techniques are discussed, and we show how they translate to the domain of pulseecho radar. We demonstrate that by applying these techniques, our radar system can achieve a 3D localization of reflectors, and in combining that localization with a custom subsumption control architecture, we have realized a robotic platform capable of autonomous navigation through an unknown environment. Finally, we employ a specialized simultaneous localization and mapping (SLAM) algorithm during autonomous navigation and show that it is capable of building an accurate topological map of the environment using radar as the only source of exteroceptive information.
A Multidirectional-Difference-Hash-Based Image Clutter Metric for Targeting Performance

Quantification of image clutter plays an important role in predicting target acquisition performances of a photoelectric imaging system due to the strong effect of optoelectronic image clutter. Accuracy in predicting the targeting performance of previous reported clutter metrics was relatively low because of disadvantages, such as lack of ability to accurately quantify the image with complex clutters and threshold selection problem. To address this problem, a novel multidirectional-difference-Hash-based (MDHash-based) image clutter metric is proposed in this paper. Initially, an image similarity measure method based on multidirectional difference hash is established. Then, this method is applied to the quantification of image clutter, and an MDHash-based image clutter metric is obtained. A comparative experiment is conducted using Search_2 dataset. Results show that the proposed clutter metric correlates effectively with probability of detection, false alarm rate, and search time of observers.
Services Integration for Cyber Physical Systems

Cyber Physical Systems (CPS) address the interaction between the computation and the physical processes and entities, integrating them within the "cyber" world. Computers systems monitor and control the physical processes, through devices like sensors and actuators. Recent researches pointed out that middleware architecture are advantageous for CPS development. At their turn, physical devices have limited resources and computation power and are not always able to execute complex computation tasks. To assure adequate computational and physical resources, able to handle complex processes, some approaches proposed a Service-Oriented Architecture (SOA) for CPS development. Using of Smart communication devices proved also to be efficient, due to their sensor type modules included. SOA provides to the users an integrated access and communication protocol which interacts with physical and software elements and facilitates the building of flexible systems, while preserving efficiency, scalability and security. This paper presents a CPS integrated transaction platform, which can be accessed using Smart devices, through mobile and wireless communication networks.
Fast Resource Allocation for Downlink NOMA Based on Revenue and Chordal Graphs

Non-orthogonal multiple access (NOMA) is a promising technology for future mobile networks due to superior spectrum efficiency. In this paper, we model the interaction between the base station and multiple users as a Stackelberg game and devise a fast resource allocation method consisting of resource block allocation and power allocation. The objective is to serve as many users as possible at rates beyond their requirements, while seeking to enhance total revenue. We derive a closed-form formula for optimal power allocation. Besides, we convert the resource block allocation problem into the problem of finding a maximum weight independent set for a chordal graph, which only takes linear time. Simulation results show that the fast method we propose outperforms existing algorithms in total throughput and the number of users whose rate demands are attained.
A Modified Cascaded Inverter for PV Integration into Grid Without Transformer

There has been an increasing interest in transformer less inverter for the integration of the PV system into the grid. Cascaded Multilevel Level Inverter is most suitable for this integration, as the separate DC sources can be directly fed by the PV arrays and it is suitable for medium to high power applications. However one of the technical challenges of transformer less inverter is the issue of leakage current which has to be addressed carefully. This paper describes a Modified Cascaded Multilevel Inverter, appropriate for PV grid integration. This analysis has emphasis on transformer less feeding, leakage current minimization and reduction in switching and conduction loss. This topology reduces the high frequency fluctuation in terminal and common mode voltages and thereby achieves the minimization of leakage current as per the German DIN VDE 0126-1-1 standard. The analysis is carried out with Matlab simulation platform.
Study of Slow Wave Structure with Double Corrugated Waveguide Shielded by Photonic Crystals

A Double Corrugated Waveguide Shielded by Photonic Crystal (PhC) wall is proposed for effective beam-wave interaction with a circular beam at terahertz frequency. Numerical results reveal that the The phase velocity and Pierce interaction impedance for Double Corrugated Waveguide Shielded by Photonic Crystals are higher in the frequency of Photonic band gap compared with conventional Double Corrugated Waveguide.
Cloud Security Issues - A Survey

Cloud computing is the one which offers infrastructure to the client on a pay as you use model by leveraging on the Internet technology. As this model depicts the electricity billing it is often termed as utility computing. It offers various services to the client based on the need. The need of the resources can be scaled up or down on the basis of the requirement. The IT sectors started using cloud service models. As the use of cloud increases day by day the concerns of cloud also gets increased. There are number of data breaches that are happening day by day that affect the cloud growth. The major concern in adapting cloud is its security. The companies have lot of data that gets increased each day enlarges the complication of data privacy and security. The data that we talk can be classified as either stagnant data i.e., for simple storage as offered by Amazon’s Simple Storage Service (S3) or it can be vibrant that changes often. The solution available to protect the stagnant data will be to simply encrypt the data before it gets stored in the server. In order to solve the issues of vibrant data the techniques are being proposed to work out on the encrypted. This paper is a survey of different security issues that affects the cloud environment and related work that are carried out in the area of integrity.
A Cultural Examination Of Self-Complexity

This article examines self-complexity in cultural samples known to differ in individualism and collectivism. Individualism and collectivism are linked, respectively, to independent and interdependent patterns of self-construal. These patterns of self-construal may promote corresponding cultural differences in self-complexity. However, this possibility is contingent on self-complexity arising from the same interpersonal processes that contribute to self-construal. If, instead, self-complexity arises from intrapersonal processes like cognition and memory, then it is possible self-complexity may not vary across cultures. In Study 1, we compared a sample of Chinese-born students studying in the U.S. to U.S.-born students and found that the Chinese students had lower self-complexity. In Study 2, we compared adult Indian respondents to adult U.S. respondents and found no differences. These results are discussed in relation to cultural theories and intrapersonal, cognitive explanations.
Data security key establishment in AODV

The adhoc on demand distance vector (AODV) protocol enables dynamic, self-starting, multihop routing between participating mobile nodes wishing to establish and maintain an adhoc network. The use of AODV remains a concerned in the situations where security/trust are the major requirements. The use of AODV is also restricted in the networks characterized by large processing, heavy data flow and uncontrolled overheads. The mechanism proposed in [1] addresses the issues related to overheads and processing in AODV but its use is restricted to network layer only. An endeavor has been made in this paper to propose a more versatile and secure AODV which can be used at network and upper layers.
A Cooperative Scheme for Unmanned Aerial Vehicles in Malfunction Areas

This paper studies the application of the unmanned aerial vehicles (UAVs) to a malfunction area. Note that the ground base stations (GBSs) inside the malfunction area are broken, whereas the GBSs outside work well and are distributed according to a homogeneous Poisson point process (HPPP). A user-centric cooperative scheme is proposed to serve the user equipments(UEs) in the malfunction area. Specifically, based on the UEsâ€™ connections to the UAV and nearest GBS, the users in the malfunction area can be served by the UAV only, both the UAV and the nearest GBS or the nearest GBS only. The proposed scheme can be tuned by a parameter î'. Analytical framework is developed to obtain the expression for the coverage probability achieved by a UE in the malfunction area, which is verified by computer simulations.
An Intelligent Personalized Fashion Recommendation System

Creating an outfit is a problem that is based on the preferences of each person and it can be difficult even for the best experts. This paper presents an automated system that can recommend a full outfit based on a cloth item considering also user's preference. Two convolutional neural networks based on the AlexNet model are used to identify cloth items and attributes associated with each item. After that, two types of scores are used in order to evaluate the user's preference for combination of different items, that are continuously updated in order to obtain recommendations that are more suitable for each user.
Raya Muttarak A Tale of Disaster Experience in Two Countries : Does Education Promote Disaster Preparedness in the Philippines and Thailand

Preparing for a disaster can substantially minimize loss and damages from natural hazards. Amongst other socio-demographic determinants, disaster experience and education are found to be key predictors of individual disaster preparedness. This paper explores the pathways through which education enhances disaster preparedness and the interplay between education and experience in shaping preparedness behaviours. Data analysis is based on face-to-face interviews in two disaster-prone countries: the Philippines and Thailand. While education raises the propensity to prepare against disasters, we further find that the effect of education on disaster preparedness is mainly mediated through social capital and disaster risk perception in Thailand but there is no evidence that education is mediated through other observable channels in the Philippines. This in turn suggests that the underlying mechanisms explaining the education effects are highly context-specific. Furthermore, we show that education raises disaster preparedness only for the households that have not been affected by a disaster in the past. One explanation could be that education improves abstract reasoning and anticipation skills such that the better educated undertake preventive measures without needing to first experience the harmful event and then learn later.
Effect of marketing strategies on export performance of agricultural products: the case of saffron in Iran.

The trend of export in some developing countries such as Iran shows that, in addition to exogenous factors such as exchange rates volatility and government policies, endogenous variables also affect the volume and value of products export. Among endogenous factors, the role of marketing strategies is very important. In the current study, the role of marketing strategies of market penetration, market development, product development, and differentiation on the export performance of saffron exporting companies in Khorasan Razavi Province in Iran was investigated using spatial panel data regression model. In order to calculate the export performance index, the four components of firm’s profitability, sales volume, sales growth, and export intensity were considered. Data and information used in the study were obtained from a census of 14 saffron exporting companies during 2011-2016. The results show that marketing strategies of differentiation, market development, and product development had a significant positive effect on the export performance of saffron companies in Iran. Therefore, by applying appropriate marketing strategies in different markets, export performance could be enhanced.
Performance Factor Analysis and Scope of Optimization for Big Data Processing on Cluster

Use of computational cluster for large-scale Big Data processing has attracted attention as a technology trend for its time efficiency. Modern cluster equipped with latest multi, many-core distributed shared architecture, high speed interconnect and file system, ensures high performance using message passing and multi-threading parallel approaches, also handles batch, micro-batch and stream processing of high dimensional massive dataset but running data-intensive Big Data application on compute-centric cluster imposes challenges to its performance because of several runtime overheads. In order to alleviate these bottlenecks and exploit full potential of the cluster a state of the practice, performance-oriented technical analysis covering all relevant aspects is presented in the context of Terascale Big data processing on TeraFLOPS cluster PARAM-Kanchenjunga, with identification of major factors influencing the performance or sources of these overheads related to computation, communication or IPC, memory, I/O contention, scheduling, load imbalance, synchronization, latency and network jitter; by determining their impact. As existing approaches found insufficient, to achieve possible speedup advance methods with a variety of alternatives as RDMA enabled libraries, PFS, MPI-Integrated extensions, loop tiling, hybrid parallelization are provided to consider for optimization purposes. This paper will assist to prepare performance aware design of experiments and performance modeling.
Performance Evaluation of Classification Algorithm in Weka using Parallel Performance Profiling and Computing Technique

At present, the size of the data is growing rapidly. In such a situation, it is necessary to keep an eye on the speed of the data without undermining it. It is also important to note that while processing the data, its quality remains intact. This is the reason that data mining technology has become very important in the field of predictions in the scientific mining area, commercial and environment sectors. In this case, the need for parallel processing becomes important. Therefore, the aim of this paper is to analyze and perform computation times of different classification algorithms on many datasets using parallel profiling and computing techniques. Performance analysis is based on many factors, such as the unique nature of the dataset, the size, and type of the class, the diversity of the data in the data set, and so on. Many researchers are working on the optimization of classification algorithms which are not showing accurate results according to the processor (core) capacity. So, in this paper, we have displayed some simulation results which discuss the processor's size, efficiency, and workload as well as the complexity of input instructions in the group; Which will help researchers to optimize the code for maximum use of the core. At the end of the paper, we have given a comparative study of the optimized algorithm based on a parallel approach and tuned algorithm based on parallel performance.
Compact Ground-Based Interferometric Synthetic Aperture Radar: Short-range structural monitoring

Recently, structural monitoring by radar remote sensing has become more necessary for both economic and security reasons. Infrastructure monitoring with no incorporated deformation sensors (e.g., old generation water dams for which regulations did not impose monitoring capabilities) is usually performed by regular in situ topographic surveys. However, these surveys cannot be performed very often, and alternative methods are desirable. A feasible nonintrusive way to monitor such a structure is with interferometric synthetic aperture radar (SAR) data that can be acquired with monostatic/bistatic sensors.
ICASE A CRITICAL EVALUATION OF TWO-EQUATION MODELS FOR NEAR WALL TURBULENCE

A variety of two-equation turbulence models including several versions of the K e model as well as the KoJ model are analyzed critically for near wall turbulent flows from a theoretical and computational standpoint. It is shown that the K -_ model has two major problems associated with it: the lack of natural boundary conditions for the dissipation rate and the appearance of higher-order correlations in the balance of terms for the dissipation rate at the wall. In so far as the former problem is concerned, either physically inconsistent boundary conditions have been used or the boundary conditions for the dissipation rate have been tied to higher-order derivatives of the turbulent kinetic energy which leads to numerical stiffness. The K w model can alleviate these problems since the asymptotic behavior of co is known in more detail and since its near wall balance involves only exact viscous terms. However, the modeled form of the w equation that is used in the literature is incomplete an exact viscous term is missing which causes the model to behave in an asymptotically inconsistent manner. By including this viscous term and by introducing new wall damping functions with improved asymptotic behavior a new K -_model (where I" 1/w is turbulent time scale) is developed. It is demonstrated that this new model is computationaUy robust and yields improved predictions for turbulent boundary layers. *This research was supported by the National Aeronautics and Space Administration under NASA Contract No. NAS1-18605 while the author was in residence at the Institute for Computer Applications in Science and Engineering (ICASE), NASA Langley Research Center, Hampton, VA 23665.
Development of Reference Korean Organ and Effective Dose Calculation Online System

Recently High-Definition Reference Korean-Man (HDRK-Man) and High-Definition Reference Korean-Woman (HDRK-Woman) were constructed in Korea. The HDRK phantoms were designed to represent respectively reference Korean male and female to calculate effective doses for Korean by performing Monte Carlo dose calculation. However, the Monte Carlo dose calculation requires detailed knowledge on computational human phantoms and Monte Carlo simulation technique which regular researchers in radiation protection dosimetry and practicing health physicists do not have. Recently the UFPE (Federal University of Pernambuco) research group has developed, and opened to public, an online Monte Carlo dose calculation system called CALDOSE_X(www.caldose.org). By using the CALDOSE_X, one can easily perform Monte Carlo dose calculations. However, the CALDOSE_X used caucasian phantoms to calculate organ doses or effective doses which are limited for Korean. The present study developed an online reference Korean dose calculation system which can be used to calculate effective doses for Korean.
The Effective Classification of the Chines e Web Pages Based on KNN

In order to improve the efficiency and accuracy of classifying the Chinese web pages and help users to locate pages of their interest quickly, this paper presents an efficient feature selection method. We assign weights to different HTML tags and compute the final weight of each word occurred in the document, and then select the representative feature words to describe the document. The method combing the KNN classification algorithm can classify the Chinese web pages effectively. Experimental results demonstrate that the method can reduce the dimension of space and improve precision and recall obviously.
A memory optimal BFGS neural network training algorithm

This paper considers the implementation of a novel memory optimal neural network training algorithm which maximises performance in relation to available memory. Mathematically, it is similar to the full memory BFGS training when there are no constraints on memory and to the variable memory (VM) BFGS when memory is limited. However, it requires less computations per iteration than VM and uses a much better strategy for discarding old curvature information when memory is limited.
A framework for interlacing test and design

This paper presents a test framework which interlaces the test process with the development process in the domain of embedded control systems. The framework shows the following characteristics: • Strong integration of test and development process by solving test management issues inside development tools instead of specialized test management tools • Test in very early development stages by assuming a model-based development process where executable models are available • Reuse of test sequences at different abstraction levels and tool flexibility by tool-coupling The paper demonstrates concept and realisation of the test framework, which offers a manual but comfortable test suite derivation where test cases can be archived in the requirements database and interlinked with the requirement specification modules. Once the test sequences are defined, an automatic test instrumentation, execution and evaluation can be done. The test instrumentation is flexible, such that the system under test can be either a model or the realized hardware/software
Beyond the Legislation for Sickle Cell Disease Prevention - Getting the Right Outcomes

This study aimed at highlighting benefits derivable from legislation for Sickle cell disease (SCD) prevention using the three levels of health legislative power (primary, secondary and tertiary). It was an evidence-based integrative review of 54 references related to SCD, epidemiology, management, and legislation for its control over the past three decades (1979-2018). Two keywords (SCD and Legislation/Policy) were used as search strategy to identify the problem, intervention, and outcome evaluation of the research questions. MeSH, Medline, CINAHL, PubMed, Cochrane Database Systematic Review, Google Scholar and African Journal Online were search databases reviewed. The expected outcomes were categorized into three levels of benefits. They were primary, secondary and tertiary categories of benefit. While the primary category would institutionalize pre-genetic counseling in the health institutions, the secondary and tertiary groups would establish screening (prenatal and newborn)/comprehensive care and curative interventions (i.e., investigational therapies and stem cell transplantation) respectively for people living with SCD. These interventions will enhance SCD surveillance, screening, auditing, health needs assessment, treatment, control, and prevention in the target audience. Conclusions The legislation for Sickle Cell Disease prevention in the target population would promote early disease detection, treatment, and prevention. We expect that with an increase in case ascertainment, the disability-adjusted life year (DALY) would reduce so also other complications. This legislation will diffuse new innovations in SCD management and research which will ultimately improve the health indices of people living with the disease globally.