Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel microstrip diplexer design with tunable bandwidths and switchable channels for 4.5G applications

In this paper, a novel microstrip diplexer with tunable bandwidths and switchable channels is presented by using dual-mode square loop resonators (DMSLRs). Two resonators at different electrical lengths are coupled to input and output ports with lumped capacitors, and the isolation between the output ports is optimized by means of these capacitors. In order to obtain tunable bandwidth in each channel, varactor diodes are located at the orthogonal corners of the proposed resonators. In addition, the proposed resonators have reference patch elements at the lateral arms to provide convenience of varactor diode capacitances, such as perturbation elements. Depending on the capacitances of the varactor diodes, the bandwidths of each channel can be tuned. Furthermore, each channel can be switched off. The designed diplexer has two channels at the center frequencies of 1.8 and 2.6 GHz, which are allocated to 4.5G applications. Three-dB bandwidths can be tuned between 60 and 160 MHz for the first channel and between 150 and 350 MHz for the second channel. Isolation between the output ports is obtained as better than 18 dB in each channel. The designed diplexer was fabricated and measured, and the measured results were consistent with the predicted results.
A Snapshot on Conversational Analysis

This article is exposed to scrutinize one of salient ideas in pragmatics. In this niche, we are centrally concerned with the organization of conversation. Key definitions will come up along the article, and the conversations analyzed here are borrowed from a readily available textbook of linguistics, in which two or more participants freely alternate in speaking, which generally happens outside specific institutional settings. This article tries to look into conversations, pragmatic phenomena. This also proposes a theo-retical phase for a conversational analysis and therefore helps students understand the procedures in analyzing co-present conversational participants. To a certain extent, presupposition may be seen in some basic ways organized around a conversational setting: the way in which information has to be presented if it is to be introduced to particular participants with specific share assumptions and knowl-edge about the world they are in. Key words: Pragmatics, Conversational Analysis, Language Usage.
Cheminformatics-Aninsilico pharmacoinformatics approach in Drug Discovery

Cheminformatics is a sub discipline of computer molecular science that deals with the information retrieval from chemical databases, three-dimensional molecular and crystal structure modeling, chemical reaction pathway prediction, molecular functional groups and docking sites [1]. Cheminformatics originally emerged as a vehicle to help in drug discovery and development process. But now it has numerous application in numerous disciplines such as life sciences, chemistry, biochemistry, structural biology etc [2]. This area of research mainly deals with small molecules, whereas bioinformatics covers genes, proteins, and other larger chemical compounds [3]. However both the disciplines are inter-related and quiet novel. Since Cheminformatics is a new field therefore still in its initial stages of development.
Saturation Throughput And Delay Aware Asynchronous Noc Using Fuzzy Logic

Due to scaling in recent trends in modern semiconductor industries, critical instance such as voltage spikes, temperature, interlink between IC circuits has been improvised under the design. The saturation throughput and average message delay are used as performance metrics to evaluate the throughput. We introduced the fuzzy logic based asynchronous NoC to attain the high throughput and diminish the delay between the inter logic circuits aim to select the less congested paths to produce load balance in the network especially under realistic traffic loads. Our Fuzzy logic based routing algorithm is an adaptive, low cost, and scalable. It outperforms against different adaptive routing algorithms in the average delay and saturation throughput for various traffic patterns.
Personalized exercise training Chatbot based on wearable fitness devices

This report presents a personalized exercise training chatbot for individual users based on data collected from the Internet of Things (IoT), particularly wearable fitness devices. The chatbot is designed with our goal of motivating users to exercise more by discussing exercise statistics with the user, such as whether their daily steps have increased, decreased, or remained steady. In this work I first survey a few examples of how increased interest in fitness and the promotion of healthy lifestyles is driving demand for personalized artificial intelligence, wearable computing, and ubiquitous computing applications. Next, I describe the design of a data-driven ”personal trainer” chatbot. I then develop a prototype persuasion system based on interactive dialogs delivered via a front-end application, that collects data from wearable equipment using back-end data loggers that I instrumented as a mobile application. Finally, I describe the process of deploying and demonstrating this prototype along with technical challenges and early findings. The overall system consists of (1) the back-end Coach agent, an Android application that collects data from all wearable instruments, and (2) the front-end Me agent, which initiates and continues conversations with the user using notifications that are in turn based on data from the Coach agent. This data-driven ensemble reminds the user to exercise and also gives the user a chance to provide feedback via human/agent interactive dialogs. In this publication, I used only one wearable device, the MI Band 2, and get real-time steps and weekly step aggregates from it. The human/agent dialogues are deployed via the Slack groupware platform. Google Sheets is used as a web service for updating and exchanging data.
An Analysis of WhatsApp Forensics in Android Smartphones

WhatsApp is mobile application which allows exchange of messages, videos, audio’s and images via Smartphone. The increased use of IM on Android phones has turned to be goldmine for mobile and computer forensic investigators. This paper focuses on conducting forensic data analysis by extracting useful information from WhatsApp and from similar applications installed on Android platform.
Symbolic Computation of the Minimum Buffer Sizes for Dataflow Graphs (Extended Version)

Synchronous dataflow graphs are widely used to design digital signal processing and real-time streaming applications. A benefit of that model is to allow static analyses to predict and guarantee the performances (e.g., throughput, memory requirements) of an application. Performance analyses can either be performed at compile time (for design space exploration) or at run-time (for resource management and reconfigurable systems). However, these algorithms, which often have an exponential time complexity, may cause a huge run-time overhead or make design space exploration unacceptably slow. In this paper, we argue that symbolic analyses are more appropriate since they express the system performance as a function of parameters (i.e., input and output rates, execution times). Such functions can be quickly evaluated for each different configuration or checked w.r. t. many different non-functional requirements. We first provide a symbolic expression of the maximal throughput of acyclic synchronous dataflow graphs. We then perform an analytic and exact study of the minimum buffer sizes needed to achieve this maximal throughput for a single parametric edge graph. Based on this investigation, we define symbolic analyses that approximate the minimum buffer sizes needed to achieve maximal throughput for acyclic graphs. We assess the proposed analyses experimentally on both synthetic and real benchmarks.
AUTOMATED FINGERPRINT IDENTIFICATION SYSTEM (AFIS) DENGAN TEKNIK MINUTIAE-BASED

Perkembangan komputer telah berhasil mengembangkan sistem pengenalan sidik jari, salah satunya adalah Automated Fingerprint Identification System . Input AFIS berupa sidik jari, outputnya berupa identitas orang yang mempunyai sidik jari tersebut. AFIS pada dua dekade terakhir ini telah digunakan dengan luas dalam bidang pengenalan individu. Karena sistem pengenalan sidik jari menawarkan sejumlah kelebihan untuk menambah kualitas dan kegunaan dari identifikasi. Modul terakhir dari identifikasi sidik jari adalah teknik penyesuaian atau perbandingan sidik jari ( matching ). Salah satu teknik perbandingan yang banyak digunakan adalah minutiae-based, karena mempunyai keakuratan tinggi dan waktu komputasi yang cepat. Perancangan dan pembuatan sistem perangkat lunak untuk AFIS dengan teknik minutia-based menggunakan 1 tabel yaitu tabel Pemilik. Sistem ini terdiri dari 2 proses utama yaitu proses registrasi dan identifikasi. Pada masing-masing proses dilakukan terlebih dahulu pre-processing yang terdiri dari segmentasi, low pass filtering, local equalization histogram, dan binerisasi. Setelah itu, dilakukan proses penipisan dan deteksi minutiae . Setelah dilakukan pendeteksian minutiae untuk proses registrasi data minutiae disimpan dalam basis data, dan untuk proses identifikasi data minutiae dibandingkan dengan semua data yang berada dalam basis data. Dari uji coba yang dilakukan nilai keakuratan yang didapatkan adalah 88.00% dengan standar deviasi 17.25%. Hal ini terjadi karena karakteristik citra yang berbeda-beda, pendeteksian minutiae yang salah ( false minutiae ), dan posisi minutiae yang berubah-ubah.
Deep CNN Sparse Coding Analysis

Theorem: Let X̂ (l−1) be consistent with the D-CSC model, with ‖V‖P (l) 2,∞ ≤ ζl and ‖X‖ (l) 0,∞ ≤ Sl for all l = 0, . , L−1. Furthermore assume that D (l) is a random diagonal matrix with independent Rademacher random variables on the diagonal entries, drawn independent of the dictionaries A. Finally suppose the estimate at each layer is as in (1) and denote as ZL the event that the activation path is successfully recovered. Then
Seamless Navigation via Dempster Shafer Theory Augmented by Support Vector Machines

In any military/civilian application—be it on ground, air or underwater—a sense of navigation (consisting of position, velocity and attitude parameters) is critically important. Global Positioning System (GPS) have been the prominent technology to fulfill this demanding requirement for reliable navigation over extended periods of time, covering any part of the world during day or night. However, in a number of scenarios, standalone GPS measurements may be completely unreliable (like in canyons or forests) due to the signal blockages or multipath effects etc (in urban areas). Under these circumstances, alternative information sources, like Inertial Navigation System (INS), need to be employed to bridge the gap of times when GPS signal reception is unavailable. In this paper we implement a novel hybrid methodology based on Dempster Shafer theory (DS) augmented by Support Vector Machine (SVM) to integrate INS and GPS measurements; thereby enhancing the accuracy of the low-cost Micro-Electro-Mechanical-Systems (MEMS) based navigation system. Traditionally, Kalman Filtering (KF) based approaches have been developed to fuse the INS and GPS data. KF is an optimal filter for linear systems with Gaussian noises but is not applicable to non-linear systems. For non-linear models, EKF (i.e., linearized KF) can be implemented but may cause filter divergence under high dynamic conditions. Alternatively, Particle Filtering (PF) and Artificial Neural Network (ANN) based approaches have been proposed. The major challenge of basic PF is the computational load, while ANN suffers from local minimization and overfitting problems. Hence, in order to effectively fuse GPS and INS data for land vehicle navigation application, we propose an efficient hybrid methodology based on Dempster Shafer theory augmented by Support Vector Machine (DS-SVM). Our field test results clearly indicate that the proposed DS-SVM algorithm effectively compensated and reduced positional inaccuracies over the regular ANN model during GPS availability and outage conditions for low-cost inertial sensors.
Compositional Source Word Representations for Neural Machine Translation

The requirement for neural machine translation (NMT) models to use fixed-size input and output vocabularies plays an important role for their accuracy and generalization capability. The conventional approach to cope with this limitation is performing translation based on a vocabulary of sub-word units that are predicted using statistical word segmentation methods. However, these methods have recently shown to be prone to morphological errors, which lead to inaccurate translations. In this paper, we extend the source-language embedding layer of the NMT model with a bi-directional recurrent neural network that generates compositional representations of the source words from embeddings of character n-grams. Our model consistently outperforms conventional NMT with sub-word units on four translation directions with varying degrees of morphological complexity and data sparseness on the source side.
A Visual Velocity Impedance Controller

Successful object insertion systems allow the object to translate and rotate to accommodate contact forces. Compliant controllers are used in robotics to provide this accommodation. The impedance compliant controller is one of the more researched and well known compliant controllers used for assembly. The velocity filtered visual impedance controller is introduced as a compliant controller to improve upon the impedance controller. The velocity filtered impedance controller introduces a filter of the velocity impedance and a gain from the stiffness. The velocity impedance controller was found to be more stable over larger ranges of stiffness values than the position based impedance controller. This led to the velocity impedance controller being more accurate and stable with respect to external forces. The velocity impedance controller was also found to have a better compliant response when tested on various insertion geometries in various configurations, including a key insertion acting against gravity. Finally, a novel kinetic friction cone compliance model is introduced for the velocity impedance controller. It was determined that the new compliance model provided a more reliable insertion than the standard insertion model by increasing the error tolerance for failure.
The Impact of the Floorplan of Functional Units on 3D Multi-core Processors

— Interconnection delay is one of the most critical constraints in improving the performance of multi-core processors. In order to reduce the interconnection delay in the multi-core processor, 3D integration technology has been applied in designing multi-core processors. The 3D multi-core processor is composed of vertically stacked cores which are connected by through-silicon vias, leading to improved interconnection and power efficiency by reducing the physical wire length significantly. However, 3D multi-core processors have severe temperature problems caused by higher power density compared to 2D Multi-core processors. In this paper, we propose the thermal-aware floorplan schemes to solve the thermal problems in 3D multi-core processors by changing the location of functional units. According to our experimental results, the proposed floorplan schemes reduce the peak temperature by 12ºC on average with 3% performance gain.
Trust based key management framework for MANET

i PREFACE ii GLOSSARY OF SYMBOLS AND ABBREVIATIONS v LIST OF FIGURES vii LIST OF TABLES ix CHAPTER 1: INTRODUCTION 1.1 Mobile Ad hoc Network (MANET). 1 1.2 Attacks on MANETs . 4 1.2. 1 Attacks against Routing. 7 1.3 Security techniques for MANET. 10 1.3. 1 Intrusion detection System . 14 1.3. 2 Key Management . 15 1.3. 3 Certification system. 15 1.4 Key Management techniques . 16 1.4. 1 Issues. 19 1.5 Self-Organized key management . 20 1.5. 1 Issues. 21 1.6 Certificate chaining approach and its Issues. 22 1.6. 1 Limitations of Certificate Chaining. 23 1.7 Certificate exchange and revocation. 23 1.8 Motivation. 26 1.9 Problem Statement. 29 1.10 Research Objectives. 29 1.11 Thesis Overview. 30 CHAPTER 2: LITERATURE REVIEW 2.
Link prediction in social network based on local information and attributes of nodes

Link prediction is essential to both research areas and practical applications. In order to make full use of information of the network, we proposed a new method to predict links in the social network. Firstly, we extracted topological information and attributes of nodes in the social network. Secondly, we integrated them into feature vectors. Finally, we used XGB classifier to predict links using feature vectors. Through expanding information source, experiments on a co-authorship network suggest that our method can improve the accuracy of link prediction significantly.
Applications of Grobner Bases to Linear Codes.

Put A = F , [ z i , . ,Za], and le t / be an ideal o f . 4. Let be a ll the Fç-rational points o f V { I ) . Define a map ip : A / 1 — F " by (p{f) = ( / ( P i ) , . , / (P „ ) ) , where / is any preimage o f / under the canonical map from A to A / / . Let { / , | i E N } be a basis o f A / / as an F ,-vector space. Define the affine variety code C and its dual C'*" by ^ ~ V^((/l7 • • • ) fm))'i is the orthogonal complement o f C w ith respect to the usual inner product in F ". We show tha t any linear code can be expressed as an affine variety code. When a code C is represented as an affine variety code, problems o f de­ coding and find ing the m inim um distance o f C may be expressed as questions about polynom ial ideals. Using the theory o f Grobner bases, along w ith computer programs th a t cal­ culate Grobner bases, we show how to decode and find the m inim um distance o f any linear code.
Using Requirement Templates to Automate Requirements Formalization

Proving correctness of safety-critical software systems is potentially as important as the development of these systems. Verification tools can be used to automatically analyze a system, but this requires requirements to be specified in a suitable formalism. Currently, requirements are often separately specified in a natural language and subsequently formalized. This is time and effort intensive and error-prone, mostly because the natural language requirements might be ambiguous. In this research, we present a framework (FRET) that allows users to specify unambiguous requirements in natural language, by using requirement templates that capture patterns between requirements. Furthermore, we discuss its functionality to automatically formalize these requirements into predetermined formalisms. Through two case studies using ten realistic requirement specifications, we have evaluated this framework in comparison to the current situation of requirements specification and formalization. We found that FRET is currently capable of specifying all of the provided requirements and formalizing 94% of the requirements that could be manually formalized. Furthermore, FRET allows users to specify and formalize requirements in less time and with less effort and specification and formalization in FRET is less error-prone due to the effect of requirement templates on ambiguity, when comparing it to the current situation of requirements specification and formalization. Finally, we have established that two different formalizations of a default template are in fact equivalent to each other. Thus, we are capable of determining whether a template is consistent with regard to its set of formalizations.
Validation of Automatic Segmentation Algorithms for Short-axis Cine Cardiac Magnetic Resonance

Introduction Modern short-axis (SA) cine cardiac magnetic resonance (CMR) images usually consist of 15–50 phases at 10–15 slices (150–750 images). These images are used for quantification of various ventricular functional parameters, which requires delineation of all cardiac contours. As manual delineation of these contours is prohibitively time consuming, many algorithms have been developed for automatic delineation. However, for clinical practice these algorithms are not yet accurate enough and semi-automatic segmentation algorithms are therefore often used. In this study, we have evaluated the accuracy of three automatic segmentation algorithms [1,2,3] at end diastole (ED) for a large number of patients. Additionally, we have evaluated the accuracy of the resulting segmentation from propagating the automatically obtained ED segmentation using the contour propagation algorithm described in [4].
Disease pattern discover in institutional data via cluster analysis

In this presents study to discover the disease patterns by using statistical approach on institutional database. This study, help to know about the number of patients attended in OPD with different ailments for every year. By the help of data mining technique, the study is designed to discover the patterns and hidden relationships in dataset. Actually data mining technique is used to extract information from a data set and transform it into an understandable structure for further uses. The main aims of this study to provide profiling of patients, discover dominant disease and dominant month via cluster analysis. A cluster is a collection of data objects which are similar to one another within the same cluster and are dissimilar to the objects in other clusters. In this regard, clustering is used to profile patients according to their month of GOPD in the institute. The gap statistic used to find the optimum numbers of clusters in dataset. Using this, a number of clusters are formed on the basis of type of disease acquired by patients, demographic and socioeconomic characteristics beside that patients are grouped into different clusters according to their disease.
A review of the empirical generations at work research: implications for school leaders and future research

Most schools currently employ three generations of teachers and leaders: Baby Boomers (1946–65), Generation X (1966–80) and Generation Y (1981–2003). However, the implications for school leaders of multi-generational schools remain relatively unexplored. This paper examines the empirical multi-disciplinary generations at work evidence to identify differences and similarities in how generational cohorts approach work/life balance, authority, collaboration and careers. The paper defines generational characteristics and introduces the review methods employed. The findings are organised within three important leadership actions: stimulating professional growth and capacity, building collaborative cultures, and establishing work conditions. The conclusion presents future research directions.
The Value of Magnetic Resonance Myelography in the Diagnosis of Nerve Roots Compression in Prolapse of Lumbar Intervertebral Disc

Purpose To evaluate the value of MRM in the diagnosis of nerve roots compression in prolapse of lumbar intervertebral disc (PLID). Materials and Methods A successive observation was conducted to 52 patients with PLID and with the symptoms of unilateral nerve roots compression. Diagnosis of the intervertebral disc lesions was made by using MRI and MRM, respectively. The consistency and diagnosability between these two examinations and clinical diagnosis were evaluated. Results The consistence between the clinical diagnosis and MRI diagnosis was poor (Kappa = 0.36), but which was good between MRM and clinical diagnosis (Kappa = 0.85). According to clinical orientation standards, diagnosis of nerve roots compression by MRM was significantly better than MRI in specificity, positive predictive value, negative predictive value and accuracy (P 0.05). Sensitivity of MRM was also higher than MRI, but the difference was not statistic (P 0.05). Conclusion MRM has a great value and is superior to MRI for patients with PLID in identificationand exclusion the lesion of intervertebral disc. MRM can be used as one of the routine diagnosis method of PLID in clinic.