Miracle-dz/UniFAR-FTUs huggingface dataset:
An Accurate and Robust Approach of Device-Free Localization With Convolutional Autoencoder

Device-free localization (DFL), as an emerging technology that locates targets without any attached devices via wireless sensor networks, has spawned extensive applications in the Internet of Things (IoT) field. For DFL, a key problem is how to extract significant features to characterize raw signals with different patterns associated with different locations. To address this problem, in this paper, the DFL problem is formulated as an image classification problem. Moreover, we design a three-layer convolutional autoencoder (CAE) neural network to perform unsupervised feature extraction from raw signals followed by supervised fine-tuning for classification. The CAE combines the advantages of a convolutional neural network (CNN) and a deep autoencoder (AE) in the feature learning and signals reconstruction, which is expected to achieve good performance for DFL. The experimental results show that the proposed approach can achieve a high localization accuracy rate of 100% for a reasonable grid size on the raw real-world data, i.e., the collected raw data without added Gaussian noise, and is robust to noisy data with a signal-to-noise ratio greater than −5 dB. Additionally, its time cost for the classification of a single activity is 4 ms, which is fast enough for the IoT applications. The proposed approach outperforms the deep CNN and AE in terms of localization accuracy and robust ability against noise.
Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

A full performance assessment of 3D mapping–aided (3DMA) GNSS in dense urban areas is presented. This first part of a two‐part paper focuses on the effects of algorithm design and user equipment, based on data collected in London using Leica Viva GS15 and u‐blox EVK‐M8T GNSS receivers and a Nexus 9 Android tablet. Best performance is obtained by combining shadow matching with likelihood‐based 3DMA GNSS ranging using hypothesis‐domain integration. Improved versions of the algorithms, together with a comprehensive tuning process, are described. Root mean square horizontal position errors obtained using data from Leica, u‐blox, and Nexus receivers are 3.5, 4.7, and 4.9 m, respectively, compared with 23.6, 26.4, and 31.0 m using conventional GNSS positioning, about a factor of six improvement. Optimal algorithm tuning depends on the environment and the impact of varying grid spacing of the candidate positions is assessed. Algorithms have also been shown to operate in real time on both a Raspberry Pi 3 and a Samsung Galaxy S8+ Android smartphone.
Built in Test Compression Capabilities in Programmable PRPG

This paper presents a low power pseudo random pattern generator with desired preselected toggling activity. The proposed architecture has a linear feedback shift register or a ring generator (linear finite state machine) which drives phase shifter and it produces binary sequences with low switching rate. This will have the best test coverage achievable compared with the best-to-date conventional BIST-based PRPGs. In this proposed system we introduce with negligible impact on test application time and deterministically guide the test pattern generator towards test sequences which will improve the ratio of fault-coverage-to pattern-count. The above proposed hybrid system efficiently combines test compression with Logic BIST to deliver high quality tests. The system is implemented using HDL and the simulation and synthesis reports are shown. Keywords-LFSR, Optimization, Low Power, Test Patterns, pseudorandom test pattern generators (PRPGs), test data
Fingerprint Recognition Strategies Based on a Fuzzy Commitment for Cloud-Assisted IoT: A Minutiae-Based Sector Coding Approach

In parallel with the rapid developments of cloud-assisted IoT, the corresponding security and privacy issue emerges as a challenge. Biometric recognition technologies are interesting and promising to reinforce traditional cryptographic and personal authentication systems for cloud-assisted IoT. However, the biometric information, if compromised, cannot be canceled and substituted easily. In this paper, based on fuzzy commitment protocol, a fingerprint recognition scheme using minutiae-based sector coding strategy is proposed for cloud-assisted IoT. In our approach, the minutiae of a fingerprint are classified into many designed sectors and then encoded according to their features. With the idea of fuzzy commitment, the key encryption process is accomplished by using BCH codes and Hash mappings. Our scheme, not only provides a flexible way to realize the fingerprint recognition between the complexity and security by designing error-correcting codes with different parameters, but also offers a good balance between the genuine accept rate (GAR) and the false acceptance rate (FAR) with customized sector coding strategies.
Autonomous wheelchair under a predefined environment

Article history: Received 23 June 2018 Received in revised form 25 January 2019 Accepted 13 February 2019 This research work proposes a prototype of low-cost smart wheelchair design. The significance of this model of the wheelchair is to move autonomously around a known environment, following a definite path. Existing systems include control action using a joystick, voice recognition, head movements, and eye motion. However, these systems include limitations in terms of operating complexity, the environment in which it is used and cost. Most importantly, the limitation of the existing system is the need for the continuous effort of the user in controlling the wheelchair. Hence, this work targets to aid the physically challenged persons to have improved mobility without much of human effort. An automatic wheelchair model is proposed that empowers navigation without continuous steering of a wheelchair by the user in a known environment. The model includes a set of sensors, infrared, ultrasonic, RFID reader and tags. The other significant components are the compass module, ATmega microcontroller, and motors. This system was designed and developed to enable low cost, reduced time and effort of the user. This paper provides a comprehensive overview of the design and validation of the autonomous wheelchair system. Further, the sophistication towards the GPS enabled autonomous system could be extended, which works well in different new environments.
A Comparison Between Traditional Machine Learning Approaches And Deep Neural Networks For Text Processing In Romanian

This paper presents a comparison between traditional machine learning approaches (decision trees and multilayer perceptron) and the latest trend in artificial intelligence, deep neural networks for three separate tasks of text processing in Romanian. The tasks we examine are: lexical stress assignment, syllabification and phonetic transcription. The evaluation is performed on large manually transcribed lexicons and uses simple input features derived strictly from the orthographic form of the words. Results show that, depending on the task, the performance of each of the algorithms can vary, and that in some limited cases, the decision trees can outperform the deep neural networks.
Conceptual design and dimensional optimization of the linear delta robot with single legs for additive manufacturing

Because of remarkable characteristics such as superior speeds and accelerations, high stiffness and good dynamic performance, parallel robots are being increasingly adjusted to different task requirements in the manufacturing field. Their parallel structures made by closed-loop kinematic chains are better suited to develop new curved and multidirectional fabrication strategies in Additive Manufacturing. Based on this application, the conceptual design and dimensional optimization of a new structure of the linear delta parallel robot for Additive Manufacturing (three-dimensional printing) is presented. The new structure uses an innovative concept of delta mechanism with single legs and rotational joints, which consists of 12 links (three single parallel legs), three prismatic joints, and 11 revolute joints. A particular feature of the proposed mechanism is that it contains a joint common to all the kinematic chains instead of a mobile platform. Quality function deployment is used as a methodology for conceptual design. Then the kinematics of the mechanism is described in detail, including mobility analysis, inverse and direct kinematics, and a study of dimensional optimization. A method of efficient optimization based on genetic algorithms is used to find the minimum dimensional parameters of the robot, considering the maximization of the useful workspace as main performance index. Finally, a prototype of the robot is developed to validate the design concepts and functionality of the machine.
Effectiveness of Ambiguity-aware Web Search Algorithm for Mobile Phone

We have proposed a fast Web search method for mobile terminals such as mobile phones. We need to input some terms for Web searching usually. A full QWERTY keyboard is used for input of terms. It is not easy to press the intended key because the key size is small on a mobile phone. Moreover, a user needs to press a few keys per Kana character since one Kana character generally consists of a few alphabets in Japanese. A flick input method has been developed and is used for input of Kana-characters on a touch panel of a smart phone. Because the method needs only about 12 keys, the key size is larger than a full QWERTY keyboard. However, the flick operation is not easy. In our proposed method, a user is able to easily input terms because our proposed method needs only one keystroke per character using only 12 keys without flick operations. Character-strings inputted by the user are ambiguous because each string corresponds to not only the intended term but also other terms. However, the ambiguous strings are not translated into the intended terms in our proposed method. The system based on our proposed method directly searches Web pages by the ambiguous strings and outputs the searched result based on co-occurrence. Thus, we are able to find the intended Web page on a mobile phone rapidly and easily. In the result of the evaluation experiment, the Web search accuracy was 80[%] and the system was able to search Web pages by the correct terms at 90[%]. It was proved that our proposed method was effective for mobile terminals.
The Relationship between Female Labour Force Participation and Violent Crime in Malaysia: An ARDL Approach

Crime has been on the rise in Malaysia, and this can pose a great challenge for the country to attract more tourist arrivals and even foreign direct investment. Numerous studies have investigated various factors in crime in Malaysia. However, none of them focused on female labour force as a potential determinant. Hence, it is vital to understand the effects of female labour force participation on violent crime in Malaysia. Therefore, this study aims to delve into the effects of the female labour force on Malaysia's violent crime using economic approach, namely as ARDL bounds testing approach. This study analyses annual data ranging from 1982 to 2017. The results demonstrate that the female labour force can negatively affect violent crime in the long run while it has an insignificant effect on violent crime in the short run. Thus, this study offers some important insights into formulating female participation policies in the labour force to combat violent crimes in Malaysia. Further research can be strengthened by having panel approach of cross-states analysis on these two variables for a more comprehensive perspective on violent crime in Malaysia.
A new length-biased power Lindley distribution with properties and its applications

Abstract The present paper introduces a new model namely length biased power Lindley distribution with two parameters. The survival function, failure rate and moments of the proposed distribution are derived. The maximum likelihood estimators of the parameters and their asymptotics are discussed and also the order statistics are derived. The potentiality of the proposed model has been tested by using two real life time data sets.
PERANCANGAN APLIKASI KESEHATAN IBU DAN ANAK (KIA) BERBASIS ANDROID

Penelitian ini dilaksanakan dengan tujuan untuk membangun aplikasi berbasis Android Mengenai kesehatan ibu dan anak (KIA). Untuk pengumpulan data primer, Penelitian ini mengunakan teknik wawancara, sementara untuk data skunder, penelitian ini melakukan pengumpulan data dengan cara dokumentasi atau riset perpustakaan, internet, serta buku-buku yang memiliki hubungan dengan penelitian. Metode pengembangan sistem yang penulis gunakan dalam penelitian ini adalah mengunakan Metode waterfall yang memiliki kelebihan yaitu ketika semua sistem dapat didefenisikan secara utuh dan benar diawal proyek, maka software engineering dapat berjalan dengan baik tanpa masalah. Dari hasil penelitian yang telah dilakukan didapatkan sebuah permasalahan yang menuntut penulis untuk mengembangkan sebuah aplikasi berbasis Android kesehatan ibu dan anak (KIA). Dimana dalam pengembangan sistem yang telah dilakukan penulis mengunakan alat bantu pembuatan program yaitu Eclipse. Dari aplikasi yang telah dibuat, diperoleh sebuah aplikasi yang memiliki kelebihan yaitu kalkulator perhitungan perkiraan masa kehamilan, perhitungan berat badan ideal ibu hamil dan anak, perhitungan perkiraan masa subur, serta informasi mengenai kesehatan ibu dan anak (KIA).
EMG Muscle Activation Pattern of Four Lower Extremity Muscles during Stair Climbing, Motor Imagery, and Robot-Assisted Stepping: A Cross-Sectional Study in Healthy Individuals

Background Stair climbing can be a challenging part of daily life and a limiting factor for social participation, in particular for patients after stroke. In order to promote motor relearning of stair climbing, different therapeutical measures can be applied such as motor imagery and robot-assisted stepping therapy. Both are common therapy measures and a positive influence on the rehabilitation process has been reported. However, there are contradictory results regarding the neuromuscular effect of motor imagery, and the effect of robot-assisted tilt table stepping on the EMG activation compared to stair climbing itself is not known. Thus, we investigated the EMG activity during (1) a stepping task on the robot-assisted tilt table Erigo, (2) motor imagery of stair climbing, and (3) real stair climbing in healthy individuals for a subsequent study on patients with lower limb motor impairment. The aim was to assess potential amplitude independent changes of the EMG activation as a function of the different conditions. Methods EMG data of four muscles of the dominant leg were recorded in m. rectus femoris, m. biceps femoris, m. tibialis anterior, and m. gastrocnemius medialis. The cross-correlation analysis was performed to measure similarity/dissimilarity of the EMG curves. Results The data of the study participants revealed high cross-correlation coefficients comparing the EMG activation modulation of stair climbing and robot-assisted tilt table stepping in three muscles except for the m. gastrocnemius medialis. As the EMG activation amplitude did not differ between motor imagery and the resting phase the according EMG data of the motor imagery condition were not subjected to a further analysis. Conclusion Robot-assisted tilt table stepping, but rather not motor imagery, evokes a similar activation in certain leg muscles compared to real stair climbing.
Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain–computer interface

Nonstationary learning refers to the process that can learn patterns from data, adapt to shifts, and improve performance of the system with its experience while operating in the nonstationary environments (NSEs). Covariate shift (CS) presents a major challenge during data processing within NSEs wherein the input-data distribution shifts during transitioning from training to testing phase. CS is one of the fundamental issues in electroencephalogram (EEG)-based brain-computer interface (BCI) systems and can be often observed during multiple trials of EEG data recorded over different sessions. Thus, conventional learning algorithms struggle to accommodate these CSs in streaming EEG data resulting in low performance (in terms of classification accuracy) of motor imagery (MI)-related BCI systems. This chapter aims to introduce a novel framework for nonstationary adaptation in MI-related BCI system based on CS detection applied to the temporal and spatial filtered features extracted from raw EEG signals. The chapter collectively provides an efficient method for accounting nonstationarity in EEG data during learning in NSEs.
The Anatomy of A Large-Scale Hyper Textualweb Search Engine

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford. edu/ To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they
Strengths and Weaknesses of the ETSI Adaptive DCC Algorithm: A Proposal for Improvement

This letter studies the adaptive decentralized congestion control (DCC) algorithm defined in the ETSI TS 102 687 V1.2. 1 specification. We provide insights on the parameters used in the algorithm and explore the impact of those parameters on its performance. We show how the algorithm achieves good average medium utilization while protecting against congestion, but we also show how the chosen parameters can result in slow speed of convergence and long periods of unfairness in transitory situations. Finally, we propose a modification to the algorithm which results in significant improvements in the speed of convergence and fairness.
Proximal Support Vector Machine-Based Hybrid Approach for Edge Detection in Noisy Images

Abstract We propose a novel edge detector in the presence of Gaussian noise with the use of proximal support vector machine (PSVM). The edges of a noisy image are detected using a two-stage architecture: smoothing of image is first performed using regularized anisotropic diffusion, followed by the classification using PSVM, termed as regularized anisotropic diffusion-based PSVM (RAD-PSVM) method. In this process, a feature vector is formed for a pixel using the denoised coefficient’s class and the local orientations to detect edges in all possible directions in images. From the experiments, conducted on both synthetic and benchmark images, it is observed that our RAD-PSVM approach outperforms the other state-of-the-art edge detection approaches, both qualitatively and quantitatively.
Performance Evaluation of FSC Routing Protocol in Wireless Sensor Networks

Minimization of energy consumption in nodes as well as uniform energy depletion of the nodes, are critical parameters in order to increase the lifetime of a network. The major cause for energy depletion is due to the need for transmitting the sensed data from the sensor nodes (SNs) to remote sinks. These data are typically relayed using ad hoc multi-hop routes in the WSN. A sideeffect of this approach is that the SNs located closer to the sink are heavily used to relay data from all network nodes; hence, their energy is consumed faster, leading to a nonuniform depletion of energy in the WSN. Because of more data collections and more packet transmission, the usage of energy of the system is more. For performing best clustering process Fan – Shaped Clustering (FSC) is used, where the large scale network is partitioned into fan-shaped clusters. In this approach performed with various combination of energy saving methods such as selection of cluster head and relay node, implementation of re-clustering and an efficient routing method and hotspot solution. For reducing signaling cost, localization re-clustering process performed. Selection of CH, Initially central area has been introduced. The usage of CH selection can optimize the intra-communication cost and reduce re-clustering frequency. Every CH pass the packet to their relay node, then the relay node transferred the packets to the next inner cluster group. All this process enabled the network to achieve good performance. Finally analyze the performance based on the different criteria such as the total alive nodes, the total residual energy and the packet collection rate with the some important parameters radius of central area, the energy threshold for
Properties of the gold price : an investigation using fractional Brownian motion and supervised machine learning techniqes

This thesis investigates properties of the gold price. Two different aspects have been analyzed using two different methods. The concept of fractional Brownian motion has been utilized in the search for evidence of long memory in the gold price. Further, the machine learning techniques Gradient Boosting Machine and XGBoost are used in the investigation of the relative importance of financial and economic variables in a prediction of the gold price. Both aspects are studied across multiple time periods in order to examine the potential change. We find evidence of long memory in the gold price, however not in all examined time periods from 1979 until now. The first ten years from 1979, as well as the ten years from the start of the 2008 financial crisis to today, appear to have the property of persistence, while the years in between display no evidence of long memory. The second part of the analysis, the relative importance of variables in a prediction, is more focused on the years before and after the 2008 financial crisis. We find that variables such as crude oil and durable goods orders are relatively important before the crisis, while crude oil and US 10-year treasury bonds are relatively important after. Both parts of the analysis indicate that the properties of the gold price change over time.
Power Cables

From simple to sculptural, the original cable patterns explored in this must-have resource create a foundation of techniques for designing signature knitwear. Basic twisted stitches, complex interpretations of cables, reversible cables, adding texture and color, turning stitches around, constructing cables with I-cord, and wrapping stitches to create the illusion of cables are some of the integrated techniques detailed in this guide. Contained within are more than 15 original cable patterns for pullovers, jackets, bags, socks, and accessories. Also included is information on a new charting system for predicting cable behavior as well as tips on cabling without a cable needle, choosing the best yarns for specific cable effects, and designing original cable patterns.