Miracle-dz/UniFAR-FTUs huggingface dataset:
FPGA Based Design of Artificial Neural Processor Used for Wireless Sensor Network

In this paper,Â a simulation ofÂ artificial intelligent system has been designed for processingÂ the incoming data ofÂ sensorÂ units and then presenting proper decision. The Back-propagation Neural Network BPNN has been used as the proposedÂ intelligent system for this work, whereas the BPNN is considered as a trained network in conjunction with an optimization method for changing the weights and biases of the overall network. The main two features of theÂ BPNN are: high speed processing, and producingÂ lowest Mean-Square-Error MSE ( cost function ) in few iterations. The proposed BPNN has used the linear activation functions 'Satlins' and 'Satline' for the hidden and output layer respectively, and has used the training function 'Traingda' ( which is gradient descent with adaptive learning rate)Â as a powerful learning method. It is worth to mention, that no previous research used these three functions together for such analysis. The MATLAB software package has been used forÂ designing and testing the proposed system. An optimal result has been obtained in this work, where the value ofÂ Mean-Square-Error has reached to zeroÂ Â in 87 epochs, and the real and desired outputs have been fitted. In fact, there isÂ no previous work has reached to this optimal result. Â The proposed BPNN has been implemented in FPGA, which is fast, and low power tool.
Influence of defect structure on colour tunability and magneto optical behaviour of WO3 nanoforms

The present study reports the impact of thermal annealing on the structural, optical and magnetic properties of WO3 nanostructures, synthesized using an acid precipitation method by, employing various spectroscopic and magnetic measurements. The X-ray diffraction and Raman measurements confirmed the orthorhombic structure of as dried WO3·H2O and monoclinic structure of WO3 nanopowders annealed at or above 500 °C. The morphological characterization shows the formation of different microstructures like nanosheets, nanoplatelets and nanocuboids in the micro-scale with the variation of annealing temperatures. The optical band gap has been calculated using the Kubelka–Munk function. The room temperature photoluminescence (PL) spectra recorded at different excitation wavelengths show intense near ultraviolet (NUV) emission which might be due to the presence of localized states associated with oxygen vacancies, and the surface states in the conduction band. The emissions in visible region correspond to the structural defects such as oxygen vacancies present within the band gap and band to band transitions. The spectral chromaticity colour coordinates indicate that the light emitted from the prepared samples shows shift from violet to red region with the change of excitation wavelength. Magnetic measurements show decrease in room temperature ferromagnetism (FM) with annealing temperature. The X-ray absorption spectroscopy (XAS) measurements at O K-edge show the significant change in the W–O hybridizations. The decrease in PL intensity and ferromagnetic ordering with increase in annealing temperatures are directly correlated with the filling up of oxygen vacancies in the samples. The oxygen vacancies based F-Center exchange model is discussed to understand the origin of FM in WO3 nanostructures.
Statistical Analysis and Modeling Health Data: A Longitudinal Study

Lung cancer has been considered one of the leading causes of deaths while cancer remains the second most common cause of deaths in the USA. Understanding the behavior of a disease over time could yield important information to make decisions about the disease. Statistical models could provide crucial clues and help to make a decision about the disease, budget allocation, evaluation, and implement prevention. Longitudinal trend analysis of the diseases helps to understand long term effects and nature. Cholesterol level is one of the most contributing risk factors for Coronary Heart Disease. Studying cholesterol statistically helps to know more about its nature and provides crucial information to mitigate its effectiveness in diagnosing its impact to public health. In our study, we have analyzed lung cancer mortality in the USA based on age at death, period at death, and birth cohort to investigate its nature in longitudinal effects. The attempt has been made to estimate mortality rate based on age for different age groups and to find the relative risk of mortality due to period effect and relative risk due to birth cohort for lung cancer in the United States. Our statistical analysis and modeling are based on the data obtained from Surveillance Epidemiology and End Results (SEER) program of the United States. We have also investigated the probabilistic behavior of average cholesterol level based on gender and ethnicity. The study reveals significant differences with respect to the distribution they follow and their basic inferences which could be beneficial to draw conclusions in various ways in addressing related issues. At the same time, the change of cholesterol level over time for an individual might be a good source to study the association of cholesterol level, coronary heart disease and their effects on age. The cholesterol data is obtained vi from inter-university Consortium for Political and Social Research and National Health and Nutrition Examination Survey (NHANS) of the United States. Understanding the average change in total serum cholesterol level over time as people get older could be vital to explore it. We have studied the longitudinal behavior of the association of sex and time with cholesterol level. It is observed that age, sex, and time have an individual effect and can impact differently upon collective considerations. Their adverse effect in increasing cholesterol level could promote to worsen the cholesterol related issues and hence heart related diseases. We believe our study pivots knowing more about target population of cholesterol level and helps to have the useful inference about cholesterol levels for public health. Finally, we also analyzed the average cholesterol data using a functional data analysis approach to understand its nature and effect on age. Since functional data analysis approach presents more flexibility in modeling, it could provide more insight in studying cholesterol level.
Intelligent Vision Based Pneumatic Wall Painting Machine an ANN Approach

Painting the wall is normally done manually, which is very difficult and troublesome for humans to work in an upright position and also very dangerous for eyes and skin. Due to fatigue and surrounding environment, painting might not even all over the wall. To overcome these difficulties, an intelligent pneumatic wall painting machine has been designed and fabricated using vision and neural system.nbspThe machine has the arm which can extend up to 25 feet by carrying the pneumatic spray gun. The camera in the arm captures the image of the wall and the obtained image has been processed and gives as the input to the trained artificial neural network. The output from the network is used to control the pneumatic pressure supplied to the air gun. A part from the automatic control, manual control switches are also used for the operation.nbspThus the developed intelligent machine is a low cost machine with automatic and manual control for perfectly painting the wall with reduced human fatigue, time and paint, even with an un uniformly painted wall.
High Performance Distributed On-Chip Voltage Regulation for Modern Integrated Systems

Distributed on-chip voltage regulation where multiple voltage regulators are distributed among different locations of the chip demonstrates advantages as compared to on-chip voltage regulation utilizing a single voltage regulator. Better on-chip voltage noise performance and faster transient response can be realized due to localized voltage regulation. Despite the advantages of distributed on-chip voltage regulation, unbalanced current sharing issue can occur among each voltage regulator, which has been demonstrated to deteriorate power conversion efficiency, stability, and reliability of the power delivery network. An effective balanced current sharing scheme that can be applied to most voltage regulator types is proposed to balance the current sharing. Furthermore, a relatively high on-chip temperature induced by increased power density leads to prominent voltage regulator performance degradations due to aging. The emerging type of digital low-dropout regulator is investigated regarding aging induced transient and steady state performance degradations. Reliability enhancement techniques for digital low-dropout regulators are developed and verified. Such techniques introduce negligible power and area overhead and do not affect the normal operations of digital low-dropout regulators. Reliability enhancement techniques also reduce the area overhead needed to mitigate aging induced performance degradations. Area overhead saving further translates into more space for increased number of distributed on-chip voltage regulators, enabling scalable on-chip voltage regulation.
Study of EBCOT in Group-of-Column Skipping in Two Scans

In Motion JPEG2000, embedded block coding with optimal (EBCOT) consists of Tier 1 and Tier 2 coding. In Tier 1, every bit-plane is scanned three times and coded in three channels. This means a long operation time, which may account for over 50% of total time of EBCOT. To reduce the time of compression and coding of JPEG2000, it is necessary to study the mechanism of Tier 1 coding, especially in the three-channel scanning mode. It has been found that validity flag ] [j i  is the first to be determined in the first and the second scan. If the validity flag is 1, then magnitude refinement (MR) coding is done; if it is 0, we need to determine whether the coding in validity channel is to be done. However, we propose the feasibility of combining the two codings together and perform scanning once. Then we only have to determine whether the coding is to be done for the pixels in the validity channel or in the MR channel. During the first scan, the value of validity flag i[j] will be determined only once, thus reducing the number of judgments. Therefore, the proposed process of EBCOT consists of 2 scans and coding in 3 channels. This reduces the number of judgment and scans and saves the time.
CMOS Analog Integrated Circuits

High-speed and power-efficient analog integrated circuits can be used as standalone devices or to interface modern digital signal processors and micro-controllers in various applications, including multimedia, communication, instrumentation, and control systems. New circuit architectures and the ever-decreasing size of complementary metal oxide semiconductor (CMOS) transistors have accelerated the trend toward systemon-a-chip design, which merges analog circuits with digital components onto a single chip. CMOS Analog Integrated Circuits: High-Speed and Power-Efficient Design describes the important aspects in designing these analog circuits and provides a comprehensive and in-depth examination of design techniques and circuit architectures, emphasizing practical aspects of integrated circuit implementations.
The prediction model based on errors-in-variables and its application

Prediction is a main aspect of statistical practical application.Though the multivariate linear regression prediction is one of the very good methods and is applied widely to various kinds of actual fields,but its limitation and insufficient is obvious.This paper understands data by one new viewpoint that the observation of all variables contains the error,and thinks that the explanation variable chosen cautiously needn’t delete,For this reason,this paper put forward a kind of new multivariate prediction method—a multivariate linear EIV prediction. Bt considering an illustration of this new prediction model and comparing the relative deviation between EIV prediction model and multivariate regression prediction model, this paper shows that the multivariate linear EIV prediction has better precision of prediction.
Intelligent pseudo‐location recommendation for protecting personal location privacy

Individuals' right to privacy includes control over access to their location information. With the advent of location‐based services and personal transport services (such as ridesharing), the risk of location privacy breaches is increased greatly. The potential negative effects of location privacy leakages include spam location‐based service flooding, threats to personal safety (such as physical attacks), and intrusion related to access to private places (such as homes and hospitals). Therefore, protecting the privacy of users' real locations is becoming increasingly important. This is often achieved using a pseudo‐location near the real location, but existing pseudo‐location generators, such as NRand and the uniform random method, suffer from statistical inference, which can infer the obfuscation domain to cover the real location. In this paper, we propose an intelligent pseudo‐location recommendation (IPLR) method to reduce the risk of a statistical inference attack. In IPLR, we generate a random substitute of the real location to attract the adversary and thus hide the real location. Then, the pseudo‐location is generated in the neighborhood of the random substitute location following a normal distribution; the random substitute location is changed frequently to confuse attackers. In particular, we define three levels of location privacy, ie, address level, street level, and district level, to evaluate the effectiveness of the IPLR method. Our experimental study using simulation data demonstrates that the proposed IPLR method achieves lower risk of location privacy leakage and higher probabilities of safety in all three levels of location privacy than NRand and the random method. It also demonstrates the effectiveness of the proposed IPLR to balance location privacy and service quality.
Power combining amplifiers for mobile millimetre wave block up converters

The use of power combining techniques allows the realization of millimetre wave solid state amplifiers with output power levels that are not achievable from a single solid state source. The need for efficient and low cost amplifiers for mm-wave systems has propitiated an intense area of research during the last years in power combining techniques. This thesis presents the development of compact and efficient power amplifiers based on new power combining structures using solid state devices. In addition, the implementation of a mm-wave block up-converter (BUC) is included, with the aim to evaluate the performance of the amplifier in a real system. The developed power combiner is based on a travelling wave architecture, which is demonstrated to be a very convenient solution at mm-wave frequencies because of its high efficiency and compactness. The new structure proposed, consists of a double side waveguide geometry which provides a 50% size reduction compared to existing solutions. This structure is validated by manufacturing a 12-way power combiner and amplifier prototype. In order to achieve the goal of testing the amplifier in a real system, an L-to-Ka band block up-converter is then designed and implemented. The performance of the block up-converter is demonstrated for a single and a double conversion architectures. Finally, the performance of the BUC is demonstrated under digital modulated signals.
No Free Lunch Theorem for concept drift detection in streaming data classification: A review

Many real‐world data mining applications have to deal with unlabeled streaming data. They are unlabeled because the sheer volume of the stream makes it impractical to label a significant portion of the data. The data streams can evolve over time and these changes are called concept drifts. Concept drifts have different characteristics, which can be used to categorize them into different types. A trade‐off between performance and cost exists among many concept drift detection approaches. On the one hand, high accuracy detection approach usually requires labeled data, possibly involving high cost for labeling. On the other hand, a variety of methods have been devoted to the topic of concept drift detection with unlabeled data, but these approaches often are most suited for only a subset of the concept drift types. The objective of this survey is to present these methods, categorize them and give recommendations of usage based on their behaviors under different types of concept drift.
Handover Mechanisms in Internet of Vehicles (IoV)

Vertical handover is one of the key technologies that will enable the connected and autonomous vehicles deployment. The emergence of vehicular networks—V2V, V2I, V2X—communications has enabled new applications, such as cooperative intelligent transport systems (C-ITS), real-time applications. However, these networks are characterized by a high level of mobility and dynamic change in the topology, which generates scattered networks. To address this problem and ensure a high level of performance, a new concept denoted heterogeneous vehicular networks (HVN) emerged, which is a key concept of the internet of vehicles (IoV). It consists in a hybridization the vehicular network (IEEE 802.11p) and cellular networks (3G/LTE/4G). In this chapter, authors introduced this new concept of IOV and its architectures and communication layers. Then they explored the different existing data relaying mechanisms in order to propose a new classification of handover approaches. After that, they presented the support of handover mechanisms in LTE and finally highlighted some handover challenges and issues.
Implementation of a Web based Weather Monitoring Station and Data Storage System

 Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA), being the country’s weather bureau, is the one presently monitoring weather conditions. PAGASA has many observation sites and one is the Science Garden which is located in Agham Road, Quezon City. The present system in the Science Garden requires an observer to go to the instrument shelter to take readings and then translate it to weather parameters. Due to the different environmental conditions, readings could be affected as it is taken manually. Safety concerns also pose a threat as observers are required to take readings in the open field. An AWS was made to address such problems as it is automated. Being automated, the weather parameters are sent automatically to the observers PC via SMS. This ensures that during harsh weather conditions, the safety and well-being of the observers are taken care of. Given that technology will drive towards AWS in the future, our system could be considered as an extra node that can be deployed to any part of the country. The hardware and software components of this study include a GSM module, Sensor modules and software such as Apache Web server, MySQL Database and PHP Scripts.
Optimization of Task Scheduling Algorithm Using Modified Mean Grey-Wolf

: Task Scheduling (TS) is used to improve the performance of Cloud Computing (CC) and optimizing the Quality of Service (QoS) of the cloud. The main objective of this research work is to optimize the TS by minimizing the MakeSpan (MS) and energy consumption. The scheduling issues are overcome by Modified Mean Grey Wolf Optimization algorithm (MGWO) and it enhances the overall performance of the system. In this MGWO, the leadership hierarchy is simulated by four grey wolves such as alpha, beta, delta and omega for the major steps like hunting, searching, encircling and attacking the prey. Here, hunting and encircling modified using the mean value to improve the effectiveness of GWO. CloudSim toolkit is used for evaluating the objective of the Modified-MGWO in standard workload (left-skewed & right-skewed dataset). The proposed Modified-MGWO algorithm reduced energy consumption nearly 10% when compared with the existing algorithms such as Particle Swarm Optimization (PSO) and standard GWO. Moreover, the MS of the Modified-MGWO method reduced nearly 13% when compared with existing methods for both datasets.
Using Apriori Algorithm on Students’ Performance Data for Association Rules Mining

—With the development of information technology, many colleges and universities have established student information management system. The long-term operation of the student information management system will generate big data for colleges and universities. Moreover, there exists valuable information in the huge amount of data. Hence, it is necessary to use the data mining method to mine the massive data and get some valuable reference information so as to improve the teaching and management of students. In this paper, the Apriori algorithm is used to mine association rules of 34 courses of 100 students majoring in computer science and technology, so as to find out the correlation between courses and the factors that lead to the high or low grades of courses. R is used to conduct the experiment to discover the association rules, and the association rules are analyzed and discussed. The results of data mining on students’ achievements in this work are expected to provide a reference for improving the teaching quality of computer science and technology courses.
An Efficient Distributed Frequent Itemset Mining Algorithm Based on Spark for Big Data

: Frequent item exploration is a fundamental element in many data mining problems aimed at finding interesting models in the data. Recently, the PrePost algorithm, a new algorithm for extraction frequent element sets based on the idea of N-lists, which in most cases surpasses other current state-of-the-art algorithms, has been introduced. The PrePost algorithm's performance deteriorates when it comes to handling big data. Nevertheless, the current existing PrePost algorithms in place implemented with the MapReduce model are not sufficiently powerful for iterative computation. To reduce IO overhead and take advantage of cluster memory, this article offers an enhanced version of PrePost, the Distributed PrePost (DisPrePost), a parallel algorithm built on the Spark framework, which incorporates the concept of resilient distributed datasets and performs in-memory processing to optimize the execution time of operation, that also utilises a HashMap to further refine the N-list creation process. Experience has shown that the DisPrePost algorithm is more efficient and scalable than the two advanced state-of-the-art methods HPrePostPlus and the well-known algorithm HFIM.
A practical shading model for ray tracing

Photorealistic rendering relies on informative descriptions of its scenes. In particular, the material properties of 3D objects is important to achieve convincing results. The material of an object details how it interacts with light, for instance the color, transparency, and the sharpness of its reflections. What arises in practice is finding a brief description that allows for a wide range of materials to be represented, while preserving photorealism - in other words, a practical material system. These constraints mean that the material parameters should be few and easy to understand, and creating the material definitions themselves must be simple. This report outlines a shading model that attempts to deal with these issues, and presents a collection of base material types that exposes only a few, but powerful, parameters for artists to work with.
Use of BGP for dissemination of ILA mapping information

Identifier-Locator Addressing [I-D.herbert-nvo3-ila] relies on splitting the 128-bit IPv6 address into identifier and locator parts to implement identifier mobility, and network virtualization. This document proposes a method for distributing the identifier to locator mapping information using Multiprotocol Extensions for BGP-4 [RFC4760].