Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection and Classification of Continuous Volcano-Seismic Signals With Recurrent Neural Networks

This paper introduces recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) to detect and classify continuous sequences of volcano-seismic events at the Deception Island Volcano, Antarctica. A representative data set containing volcano-tectonic earthquakes, long-period events, volcanic tremors, and hybrid events was used to train these models. Experimental results show that RNN, LSTM, and GRU can exploit temporal and frequency information from continuous seismic data, attaining close to 90%, 94%, and 92% events correctly detected and classified. A second experiment is presented in this paper. The architectures described above, trained with data from campaigns of seismic records obtained in 1995–2002, have been tested with data from the recent seismic survey performed at the Deception Island Volcano in 2016–2017 by the Spanish Antarctic scientific campaign. Despite the variations in the geophysical properties of the seismic events within the volcano across eruptive periods, results provide good generalization accuracy. This result expands the possibilities of RNNs for real-time monitoring of volcanic activity, even if seismic sources change over time.
Analysing Public Sentiment on GST Implementation in India

Goods and Service Tax(GST) was proposed by the government of India as a ‘one tax one nation scheme’ replacing all cascading tax into a single tax called as GST. The GST is India's biggest tax reform which impacts people belonging to every class. Therefore, GST was discussed lot among people in the society. By analyzing social data related to GST we can identify public opinion towards this scheme, which can further help the government to do some amendment to implement this scheme effectively. Now a day millions of people are active in online microblogging sites such as Twitter and share their opinion about an entity, service or scheme. Sentiment Analysis provides a way to determine the emotions behind social media discussions. In this paper, we present sentiment analysis of GST by using the textual content of Twitter to determine the public opinion about GST. People’s opinion may play a very important role in implementing this amendment effectively. After the execution of this work, we observe that the people of India are not giving a warm response to GST due to difficulty in implementing this scheme and confusion.
Part-of-speech Tagging and Named Entity Recognition Using Improved Hidden Markov Model and Bloom Filter

Natural Language Processing (NLP) makes interaction with computer easier by making use of human language understanding. Part-of-speech (POS) tagging is first task in every NLP application. The complexity of computing POS tag lies in the number of computational steps an algorithm uses for determining POS tag for a given sentence. If the number of comparisons to do POS tagging can be reduced, then it will benefit all NLP applications. Identifying the entity i.e. Named Entity Recognition (NER) is the basic operation in NLP. NER is done by the help of POS tags. Research work presented here focus on the number of comparisons made by 4-gram Hidden Markov Model (HMM) for POS tagging and how it can be reduced. In this paper, we present a method to decrease the number of comparisons for POS tagging in 4-gram HMM model. In addition, a method has been put forward for NER using Bloom Filter.
70 Gb/s Low-Power DC-Coupled NRZ Differential Electro-Absorption Modulator Driver in 55 nm SiGe BiCMOS

We present a 70 Gb/s capable optical transmitter consisting of a 50 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m long GeSi electro-absorption modulator (integrated in silicon photonics) and a fully differential driver designed in a 55 nm SiGe BiCMOS technology. By properly unbalancing the output stage, the driver can be dc-coupled to the modulator thus avoiding the use of on-chip or external bias-Ts. At a wavelength of 1560 nm, open eye diagrams for 70 Gb/s after transmission over 2 km standard single-mode fiber were demonstrated. The total power consumption is 61 mW, corresponding to 0.87 pJ/b at 70 Gb/s. Bit-error rate measurements at 50 Gb/s and 56 Gb/s (performed both back to back and with up to 2 km standard single-mode fiber) demonstrate large (0.4 UI at a BER of <inline-formula><tex-math notation="LaTeX">$10^{-12}$</tex-math></inline-formula>) horizontal eye margins. This optical transmitter is ideally suited for datacenter applications that require densely integrated transceivers with a low power consumption.
Analyzing the performance of the Indian Cricket Team using Weighted Association Rule Mining

This paper presents a Weighted Association Rule Mining algorithm for analyzing the Indian cricket team in one day international cricket matches against Sri Lanka and South Africa. This analysis is used by the team for framing game winning plans. The factors such as result of toss, taking part in home condition or away condition, first bat or second bat, and the game result are taken into consideration.
Individual Regret Bounds for the Distributed Online Alternating Direction Method of Multipliers

We consider a distributed online optimization problem where, at each time, a group of agents choose their individual states, after which an individual cost function is revealed to each of them. The whole network then faces a regret according to the cumulative sum of costs incurred by the agents’ chosen states, and each agent faces an individual regret according to the cumulative sum of costs incurred by the agent's state estimation, perceived as the whole network's chosen state. In order to tackle the minimization of the individual regret using only local information, we assume that the group of agents communicate over a fixed undirected connected graph. We then propose an online version of the alternating direction method of multipliers algorithm, distributed over the communication graph, which allows each agent to drive its individual average regret over time to zero.
A low complexity secure force encryption based multiple image watermarking in NSCT domain

Wavelet domain image watermarking has gained popularity as it provides an adaptive way for embedding and recovery of image watermarks. Image watermarking is one of the important solution that deals with the multimedia security. To prove a better solution for image information security, a multiple image watermarking method in wavelet domain with SF (secure force) encryption has been proposed. The proposed work integrates NSCT (nonsubsampled contourlet transfrom), RDWT (redundant discrete wavelet transform), SVD (singular value decomposition)and SPIHT (set partitioning in hierarchical tree). For better security, a low complexity SF encryption technique has been used to encrypt the watermarked image. Shift invariance nature of RDWT and NSCT makes them more appropriate for watermarking. Analysis of the proposed method has been done with a number of standard cover images with multiple watermarks. Experiments have shown the effectiveness of the proposed algorithm over others quantitatively with different performance metrics.
Indonesian Affective Word Resources Construction in Valence and Arousal Dimension for Sentiment Analysis

Research in the field of text analysis will always be related to words, either the selection of words to be used or the position of the words in a sentence. Furthermore, a hypothesis that each language difference can cause different meanings, makes some researchers interested in doing research classifying words based on emotion or affective words. Research focuses on affective states as a continuous numerical value to the dimensions of valence and arousal. Sentiment analysis that is usually done with positive and negative category approaches, nowadays, the dimensional approach can provide more analysis of grained sentiments. On the other hand, the affective words dataset with valence and arousal rating are still very rare, especially for the Indonesian language. Therefore, this research does an affective lexicon dataset called Indonesian Valence and Arousal Words (IVAW) containing 1024 words by Self-Assessment Manikin (SAM) surveys. Furthermore, for the next study, we will also crawls status in twitter based on selected words from IVAW to get Indonesian Valence and Arousal Text (IVAT). To predict VA rating for obtaining the advance of annotation quality, experiment will be compared by brain signal using EEG tool.
Illumination-Robust Subpixel Fourier-Based Image Correlation Methods Based on Phase Congruency

The Fourier-based image correlation technique has been widely concerned due to its accuracy, efficiency, and robustness to image contrast and brightness. Accordingly, a variety of subpixel methods have been proposed. However, the detailed subpixel-level influence of the complicated radiometric variations has yet to be investigated, and few corresponding improvements have been made. This paper presents a novel illumination-robust subpixel Fourier-based image correlation method based on phase congruency. Both the magnitude and orientation information of the phase congruency features are adopted to construct a structural image representation. The image representation is then embedded into the correlation scheme of the subpixel methods, either by linear phase estimation in the frequency domain or by kernel fitting in the spatial domain, achieving two improved subpixel methods. The proposed methods integrate the advantages of the structural image representation and the original correlation scheme, and make full use of both global and local phase information to achieve illumination-robust correlation. Experiments undertaken with both simulated and real radiometric differences were carried out with ground-truth subpixel shifts. The performances of the proposed methods and the other state-of-the-art subpixel Fourier-based correlation methods were evaluated and compared. The experimental results indicate that the proposed methods outperform the other methods in the presence of diverse radiometric variations, in both accuracy and robustness.
High Efficiency and Small Group Delay Variations 0.18- $\mu$ m CMOS UWB Power Amplifier

A new staggered tuning technique, by optimizing the inter-stage matching circuit, is proposed to realize a power amplifier (PA) with small group delay (GD) variations and excellent gain flatness across the full bandwidth of ultra-wideband (UWB) system. The proposed PA consists of two stages where the first stage is constructed by a current-reuse with shunt RC feedback topology to realize gain flatness and low power consumption. The design is implemented in 0.18 <inline-formula> <tex-math notation="LaTeX">${\mu }\text{m}$ </tex-math></inline-formula> commentary metal-oxide semiconductor (CMOS) technology, fabricated, and tested. The proposed PA has a measured power gain (<inline-formula> <tex-math notation="LaTeX">${|{\mathrm{ S}}_{21}|}$ </tex-math></inline-formula>) of 11.5 ± 0.7 dB, maximum power-added efficiency (PAE) of 26% and an output 1-dB compression point of 9 dBm, respectively, and this is the maximum PAE among CMOS PAs that cover the full bandwidth of UWB system. Besides, the PA has a small GD variations of ± 68 ps which is the lowest till date.
Intensity Estimation of Tropical Cyclones Using the Relevance Vector Machine From Infrared Satellite Image Data

In this study, tropical cyclone (TC) infrared geostationary satellite images obtained by FY-2 satellite scanning are used as reference points in the overall infrared geostationary satellite image, the deviation angle gradient co-occurrence matrix is constructed, and the statistical parameters of the co-occurrence matrix are extracted. Combined with the best track data of tropical cyclones provided by the China Meteorological Administration, the TC center is used as the origin point, and the satellite image is divided into nine 50 km scales, from the origin to a range of 450 km. Relevance vector machines are then used to construct the TC intensity estimation model for each scale. The experimental results show that the optimal radial scale is approximately 250 km. Based on these results, the proposed TC intensity estimation technique can be used for TC intensity prediction. This technique has the potential to improve the accuracy of TC intensity prediction and to provide technical support for disaster reduction.
Floating Bulk Cascode Class-E Power Amplifier

In this brief, the switching behavior of the cascode topology is improved through the floating bulk (FB) technique. Although the cascode structure has the advantage of reducing voltage stress on transistors, its parasitic elements increase power loss. The FB technique has been proposed to alleviate the power loss in the cascode class-E PA topology which results in enhancement of power added efficiency (PAE). In this method, the bulk of the common-gate (CG) transistor is connected to the ground through a resistor. As a result, the parasitic capacitances between the drain and source of the CG transistor create a new path of current that accelerates charging of parasitic capacitance at the drain of the common-source (CS) transistor. This new current path speeds up on-to-off transition of the CG transistor. In addition, the threshold voltage of the CG device is increased during on and off transitions which lowers the drain-source voltage of the CS device. Concurrently, these two phenomena minimize the power loss in both CS and CG transistors. To demonstrate the functionality of the FB technique, a cascode class-E PA is implemented in a 0.18- $\boldsymbol {\mu }\text{m}$ CMOS TSMC process at 1.8 GHz with 52% PAE and 27 dBm maximum output power.
Estimation of Air Pollution in Delhi Using Machine Learning Techniques

Urban air pollution prediction becomes an indispensable alternative to curb its detrimental consequences. Numerous machine learning techniques have been adopted to forecast the air quality. In this paper, we implemented different classification and regression techniques like Linear Regression, SDG Regression, Random Forest Regression, Decision Tree Regression, Support Vector Regression, Artificial Neural Networks, Gradient Boosting Regression and Adaptive Boosting Regression to forecast the Air Quality Index of major pollutants like PM2.5, PM10, CO, NO2, SO2 and O3. The techniques are then evaluated using Mean square error, Mean absolute error and R2, which show that Support Vector Regression and Artificial Neural Networks are best suited for predicting the air quality in New Delhi.
Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities

In recent years, green energy management systems (smart grid, smart buildings, and so on) have received huge research and industrial attention with the explosive development of smart cities. By introducing Internet of Things (IoT) technology, smart cities are able to achieve exquisite energy management by ubiquitous monitoring and reliable communications. However, long-term energy efficiency has become an important issue when using an IoT-based network structure. In this article, we focus on designing an IoT-based energy management system based on edge computing infrastructure with deep reinforcement learning. First, an overview of IoT-based energy management in smart cities is described. Then the framework and software model of an IoT-based system with edge computing are proposed. After that, we present an efficient energy scheduling scheme with deep reinforcement learning for the proposed framework. Finally, we illustrate the effectiveness of the proposed scheme.
Performance Evaluation of Linear and Nonlinear Scale Descriptor Based Algorithms

Feature extraction and object matching is one of the important and significant task in many computer vision and robotic applications. Finding the corresponding points between the two images is very crucial in image analysis. The KAZE and SURF algorithms are found to perform well on feature matching. This paper investigates the performance evaluation of linear scale and nonlinear scale based algorithms namely KAZE and SURF respectively. The experimental results show that KAZE is more efficient than SURF but takes more computational resources.
Performance Analysis of a Wireless Power Transfer System based on Inductive Coupling

Wireless power transmission (WPT) is an economical approach for the transmission of electric energy from source to the destination through atmosphere or vacuum without the use of wire, cables or any conductor. It is useful to transfer electrical energy to places where it is tedious to transmit energy using conventional wires. Dr. Nicola Tesla has performed the pioneering work in the field of wireless power. For a short range, inductive power transfer systems as well as power transfer via magnetic resonance can be obtained. However, a little work has been done to address the problem associated with the coupling coefficient of coils when transferring energy inductively via multiple resonators. The following work provides an insight into the concepts of power transfer via inductive coupling method and the study of power transfer in a simple wireless system. MATLAB is used to evaluate and parameterize distance, efficiency, figure of merit, frequency and coupling coefficient to show the successful power transmission. LTSpice IV is used to encapsulate the voltage signals at the transmitter and receiver end. This concluded that wireless power transmission can be successfully achieved using inductive coupling and the concept of resonance for diverse charging needs.
A Spatiotemporal Fusion Based Cloud Removal Method for Remote Sensing Images With Land Cover Changes

Cloud contamination greatly limits the potential utilization of optical remote sensing images for geoscience applications. Many solutions have been developed to remove the clouds from multispectral images. Among these approaches, the temporal-based methods which borrow complementary information from multitemporal images outperform the other methods. However, the common fundamental supposition of the temporal-based methods decides that they are only suitable for scenes with phenological changes, while they perform poorly in cases with significant land cover changes. In this paper, a cloud removal procedure based on multisource data fusion is developed to overcome this limitation. On the basis of the temporal-based approaches, which employ a cloud-free image as reference, this method further introduces two auxiliary images with similar wavelengths and close acquisition dates to the reference and target (contaminated) images into the reconstruction process. The temporal variability of the land cover is captured from the two auxiliary images through a modified spatiotemporal data fusion model, and thus, the serious errors produced by the temporal-based methods can be avoided. Moreover, a residual correction strategy based on the Poisson equation is used to enhance the spectral coherence between the recovered and remaining regions. The experiments confirmed that the proposed method can perform very well for cases with significant land cover changes. Compared with some state-of-the-art approaches, it produces lower bias and more robust efficacy. In conclusion, our method will act as an important technical supplement to the current cloud removal framework, and it provides the possibility to handle scenes with significant land cover changes.
Hyperspectral Unmixing via Total Variation Regularized Nonnegative Tensor Factorization

Hyperspectral unmixing decomposes a hyperspectral imagery (HSI) into a number of constituent materials and associated proportions. Recently, nonnegative tensor factorization (NTF)-based methods have been proposed for hyperspectral unmixing thanks to their capability in representing an HSI without any information loss. However, tensor factorization-based HSI processing approaches often suffer from low-signal-to-noise ratio condition of HSI and nonuniqueness of the solution. This problem can be effectively alleviated by introducing various spatial constraints into tensor factorization to suppress the noise and decrease the number of extreme, stationary, and saddle points. On the other hand, total variation (TV) adaptively promotes piecewise smoothness while preserving edges. In this paper, we propose a TV regularized matrix–vector NTF method. It takes advantage of tensor factorization in preserving global spectral–spatial information and the merits of TV in exploiting local spatial information, thus generating smooth abundance maps with preserved edges. Experimental results on synthetic and real-world data show that the proposed method outperforms the state-of-the-art methods.
A Novel Approach to Automatic Detection of Chaupai Chhand in Hindi Poems

The availability of material in Indian languages in general and Hindi in particular, on the internet is common now. The material includes the literary work. Unfortunately, very little work has been done to analyze such text. Our aim is to automate the detection of Chaupai Chhand in Hindi poems. This is the maiden attempt to autodetect Chaupai Chhand in Hindi poems. This would help analyze really big poems like RamCharitManas which has more than 85% Chaupais among all pads.
An Integrated Method for Reconstructing Daily MODIS Land Surface Temperature Data

Land surface temperature (LST) is a critical parameter in land surface process. The Moderate Resolution Imaging Spectroradiometer (MODIS) can be used to generate various LST data products, and these data have been widely applied in many studies. Unfortunately, cloud contamination brings about numerous missing or abnormal values, which negatively affect the application of LST data. To reconstruct missing values and improve data quality, this paper proposes an integrated method for reconstructing LST data under two conditions: Clear sky and cloudy sky. For the clear-sky condition, the MODIS eight-day LST (MOD11A2) product is used to be interpolated into the low-quality daily LST dataset using the harmonic analysis of time series (HANTS) algorithm. And then the linear regression algorithm is implemented on the original good-quality pixels of the MODIS daily LST (MOD11A1) product. After that, seamless processing on the reconstructed low-quality daily LST dataset is carried using the Poisson image editing method, and finally the high-quality daily LST dataset under clear-sky condition are then obtained. For the cloudy-sky conditions, the revised neighboring-pixel (NP) algorithm that originates from the surface energy balance theory is used to reconstruct the real LST data. To evaluate the reconstruction performance under clear-sky condition, a simulated dataset is generated from simulated missing pixels with good quality that are randomly chosen from 98 available LST images in the year 2010. Meanwhile, the real LST measurements collected from ground sites are used to assess the reconstructed results under cloudy-sky condition. Satisfactory validation results show that the proposed integrated method effectively reconstructs the missing information and low-quality pixels caused by cloud cover and other factors. The filled data can seamlessly preserve the temporal and spatial consistence of the daily LST data, which do promote the practical utility of the MODIS LST product.