Miracle-dz/UniFAR-FTUs huggingface dataset:
Land use information extraction from remote sensing images based on deep learning

Accurate land use information extraction plays an important role in land management, environmental monitoring, urban and rural planning and development and ecological assessment. High resolution remote sensing images have the characteristics of class similarity. Tradi¬tional methods of extracting ground object information from remote sensing images are limited to specific scenes and data sources, and have low segmentation accuracy and efficiency. In this paper, we shed the light to this research topic. To further improve the accuracy of land use information extraction, this paper takes HRNet(High-level Resolution Network) as the backbone network, which improves the performance of the network by fusing multi-scale feature map information through parallel work. Then, using the feature map information as input, the classification of each pixel is predicted through OCRNet network to achieve semantic segmentation of the image. The results show that: OCRNet-18 segmentation model is superior to other deep learning models in terms of PA, MIoU and Kappa coefficients, and the accuracy of PA (total), Kappa coefficient and MIoU are 0.913, 0.866, and 0.644 respectively.
A review of different Sparse Arrays for DOA estimation using Subspace based algorithm

In this paper, a detailed review is carried out for different sparse array structures for direction-of-arrival (DOA) estimation. We know that there are different sparse arrays like the minimum redundancy array, minimum hole array, nested array, supernested array, coprime array, Cantor array etc. for DOA estimation. Here, we employ the spatial smoothing MUltiple SIgnal Classification (MUSIC) algorithm for DOA estimation. The spatial smoothing technique can be applied to the hole-free part of the difference coarray of the sparse array. We discuss the configuration of each of these arrays and look into the main properties of the arrays with their advantages and disadvantages. We also analyze the environment in which a particular array can be used to give its optimum performance. We then carry out the simulations to compare the performances of the arrays.
Comparative Analysis of Sensors for Soil Moisture Measurement

One of the main steps to achieve sustainability in crop and livestock production through precision agriculture is the development and implementation of modern sensor technologies. This article discusses issues related to the use of low-cost sensor devices for soil moisture monitoring. To select suitable sensors for a certain type of soil, a set of measurements are performed with different types of sensors. The obtained results are presented and an analysis of the capabilities of each of the types of sensors is done.
Synthesizing Spoken Descriptions of Images

Image captioning technology has great potential in many scenarios. However, current text-based image captioning methods cannot be applied to approximately half of the world's languages due to these languages’ lack of a written form. To solve this problem, recently the image-to-speech task was proposed, which generates spoken descriptions of images bypassing any text via an intermediate representation consisting of phonemes (image-to-phoneme). Here, we present a comprehensive study on the image-to-speech task in which, 1) several representative image-to-text generation methods are implemented for the image-to-phoneme task, 2) objective metrics are sought to evaluate the image-to-phoneme task, and 3) an end-to-end image-to-speech model that is able to synthesize spoken descriptions of images bypassing both text and phonemes is proposed. Extensive experiments are conducted on the public benchmark database Flickr8k. Results of our experiments demonstrate that 1) State-of-the-art image-to-text models can perform well on the image-to-phoneme task, and 2) several evaluation metrics, including BLEU3, BLEU4, BLEU5, and ROUGE-L can be used to evaluate image-to-phoneme performance. Finally, 3) end-to-end image-to-speech bypassing text and phonemes is feasible.
Deep And Machine Learning Towards Pneumonia And Asthma Detection

Machine Learning is a branch of artificial intelligence widely used in the medical field to analyze high-dimensional medical data and the early detection of certain dangerous diseases. Lung diseases continue to increase the mortality rate in the world. The early and accurate prediction of lung diseases has become a primary necessity to save patient's lives and facilitate doctor's works. This paper focuses on predicting certain chest diseases such as Pneumonia and Asthma using Deep Learning (DL) and Machine Learning (ML) techniques, respectively, the Deep Neural Network (DNN), and the K-nearest Neighbors (KNN) methods. These approaches are evaluated using a private data set from the pulmonary diseases department of Diyarbakir hospital, Turkey. It consists of 212 samples, 38 input characteristics characterize each one. The results obtained showed the effectiveness of these methods to detect pulmonary diseases, particularly the KNN, by giving a detection accuracy of 95% and 94.3% by using the DNN method.
An ensemble learning framework for distributed resource allocation in inteference channels: The two user case

We focus on the problem of optimal power allocation for a two user interference channel characterized by mixed Channel State Information (CSI), which includes instantaneous information for the direct channels and statistical information for the interference channels. For this model, we introduce a general framework for optimizing the power allocation such as to maximize some generic Quality of Service (QoS) performance metric (or equivalently minimize some cost function). We model this problem as a function approximation problem where the function to be learned is the mapping between CSI and the solution to the optimization problem. We then tackle this problem borrowing ideas from ensemble learning. In particular, using generalized linear models (which are characterized by low complexity and can be implemented even at network nodes characterized by strong computational limitations), we produce different weak learners for learning to solve the considered optimization problem and based on ensemble learning theory, we combine such learners to produce stronger learners. We assess the performance of our framework by applying it on a particular resource allocation problem, and the obtained performance results indicate that the proposed approach can deliver near-optimal performance.
Speech Codec Enhancement With Generative Adversarial Networks

In order to solve the problem of voice quality degradation after the encoding and decoding of various encoders, we propose a processing method for the back-end of various encoders based on a generative countermeasure network. This method uses a generative adversarial network to learn the relational mapping of the speech time domain before and after encoding by the encoder, and conducts training through end-to-end training to restore the quality of the speech encoded by the narrowband encoder. We selected the encoders G.726, G.729 and G723.1 to form a training set with the original speech respectively for training, used a test set composed of speakers that did not appear in the training set for model evaluation to verify the feasibility of the model. It can be seen from the experimental results that the generative adversarial network model we explored improves the encoded speech quality.
Application of Active Biased Integrators for Biosignal Processing

Active biased integrators (ABI) have recently been reported. The ABI is an innovative continuous-time lossy integrator characterized by a very low high-pass cutoff frequency in the millihertz range. ABI is used to amplify signals generated by sources with capacitive output impedance. This paper presents some novel ABI applications for biosignal amplification. It is shown that the heart rate and respiration activity can be easily monitored with piezoelectric sensors directly connected to ABI. The achieved very low high-pass cutoff frequency of 0.05 Hz allows ABI to be successfully used for processing ECG signals acquired with capacitive electrodes.
Effect of Pilot Overhead on Spectral Efficiency in Massive MIMO V2I Networks

Massive multiple-input multiple-output (MIMO) networks are considered to be one of the key technologies of next-generation wireless communication systems because of their ultra-high spectral efficiency (SE), and are expected to be used in machine-type communication fields that require large throughput, such as vehicle-to-infrastructure (V2I) communication. The prerequisite for achieving the claimed performance of massive MIMO is to obtain accurate channel state information. A common approach is to use mutually orthogonal uplink pilots to estimate the channel, thereby reducing pilot contamination. However, in a V2I network, when a large number of vehicle users appear, orthogonal pilots often bring greater pilot overhead and reduce system performance. Based on the highway scenario, this paper simulates the influence of pilot overhead on network SE under different uplink combining strategies. Our numerical results reveal the relationship between the SE metric and the major performance-impacting parameters, which has a certain guidance for designing massive MIMO V2I networks.
Research and Application of Operation and Maintenance Simulation Technology of Provincial Intelligent Distribution Network Based on Data Mining

Based on the actual production in the power industry, this achievement researches the key technologies of operation and maintenance simulation such as horizontal data fusion of multi-service systems of intelligent distribution network, vertical data penetration of provincial multi-level (provincial, city and county), large-scale operation state analysis of intelligent distribution network, data mining, etc., develops a full panoramic simulation environment of intelligent distribution network, builds dynamic real-time monitoring of distribution network multi-service, monitoring and analysis of distribution network equipment, and functional modules of distribution network operation optimization, and thus realizes a panoramic display, multi-dimensional analysis and operation control of intelligent distribution network. This will enable the panoramic display, multi-dimensional analysis and operation control of the operation status of intelligent distribution networks, filling the gap in the field of practical training and assessment of intelligent distribution network operation and maintenance and management in China. It supports the construction of a small-scale energy interconnection research platform with flexible access to all elements of source, network, load and storage, and is compatible with various smart meters, sensors and other IoT devices, collecting all kinds of massive data and supporting relevant scientific research, simulation testing and functional verification.
Sliding Mode Control of Output Voltage in DC-DC Boost Converter Using PI Sliding Surface

The use of DC power supply is currently very widely used, one of reason is due to new renewable energy sources. Some of the basic forms of DC voltage regulators that we are commonly used are buck converter, boost converter, and buck-boost converter. The use of the converter depends on the application designed, for instance is an input voltage step-up, which is boost converter. This converter has instability in the output voltage when there is a sudden change in input voltage or load and a voltage drop on the semiconductor components in its converter. This phenomenon can be reduced by adding a controller using some kind of feedback. The controllers that used in this research are the fuzzy-PID controller and PI-SMC (Sliding Mode Controller). Sliding mode controller using PI structure in sliding surface is a nonlinear controller that can accelerate the system response to reach voltage reference and reduce the steady state error. The boost converter performance using proposed controller has quick response than using fuzzy-PID controller. This can be observed in this research when there is a change in the input voltage and load, it shows small recovery time and low voltage deviation.
A Novel Technique for Dimensional Space Reduction in Passive RFID Transponders

Chipless Radio Frequency Identification (RFID) is one of the most rapidly increasing technologies in the field of identification of systems over the past few years. Tagging technology has an edge over optical barcodes as it does not require line-of-sight communication. In this paper, the design, simulation, and fabrication of two three-bit chipless RFID tags, based on the split-ring resonator (SRR) concept are analyzed. The Radar Cross Section (RCS) and frequency coding techniques are suggested with a comparison, showing the reduction in dimension of the transponders during fabrication. It leads to the cost reduction in tag manufacturing which is suitable for mass industrial production.
UltraZohm—An Open-Source Rapid Control Prototyping Platform for Power Electronic Systems

This paper presents two rapid control prototyping (RCP) use cases facilitated by the open-source platform UltraZohm. The openly available UltraZohm development framework eases the transition from simulation to the test bench. The framework offers the integration of automatic code generation for field-programmable gate arrays (FPGAs), either by using Simulink models based on the HDL Coder, or by synthesizing C++ code into VHDL via the Vivado high-level synthesis tool. The first use case focuses on the implementation details of an on-chip controller-in-the-loop setup, where a permanent magnet synchronous machine is emulated in the FPGA with a sampling frequency of 2 MHz. The second use case presents an efficient real-time implementation of the sphere decoding algorithm employed to solve the long-horizon finite control set model predictive control problem for a three-level neutral point clamped inverter driving an induction machine. Experimental results based on a small-scale prototype confirm that the algorithm can be executed in real time on the FPGA, with an execution time of a few tens of microseconds. Both use cases highlight the benefits of using a high-performance RCP platform for research in power electronics and their control.
Use of infrared thermography to detect heat stress in dairy cows

The aim of the study was to investigate the possibility of using infrared thermography (IRT) as a tool for early and rapid detection of heat stress in dairy cows. According to the values of the temperature-humidity index (THI), the cows are divided into three groups. Thermography images of the udders were also taken, from which their surface temperatures (UST) were extracted with specialized software. The data were processed and analyzed statistically.
Modeling and Simulation of Three-Phase Bidirectional Electric Vehicle Charger

This paper aims to investigate how an electric vehicle (EV) charger with a controller performs and affects the EV battery, integrated into the grid. A bidirectional electric vehicle charging system is designed and modeled in MATLAB/Simulink to evaluate its functionality & performance. The key questions of the study are how well the charger interacts with the grid and its effects on the battery voltage & current. The study is conducted via theoretical analysis and simulation work. The power electronics control algorithm is investigated based on total harmonic distortion (THD) analysis. Moreover, some methods for the design are presented and evaluated regarding how to improve the performance of the overall electric vehicle charger station integrated into a dynamic grid.
Text summarization technique for punjabi language using neural networks

In the contemporary world, utilization of digital content has risen exponentially. For example, newspaper and web articles, status updates, advertisements etc. have become an integral part of our daily routine. Thus, there is a need to build an automated system to summarize such large documents of text in order to save time and effort. Although, there are summarizers for languages such as English since the work has started in the 1950s and at present has led it up to a matured stage but there are several languages that still need special attention such as Punjabi language. The Punjabi language is highly rich in morphological structure as compared to English and other foreign languages. In this work, we provide three phase extractive summarization methodology using neural networks. It induces compendious summary of Punjabi single text document. The methodology incorporates pre-processing phase that cleans the text; processing phase that extracts statistical and linguistic features; and classification phase. The classification based neural network applies an activation function- sigmoid and weighted error reduction-gradient descent optimization to generate the resultant output summary. The proposed summarization system is applied over monolingual Punjabi text corpus from Indian languages corpora initiative phase-II. The precision, recall and F-measure are achieved as 90.0%, 89.28% an 89.65% respectively which is reasonably good in comparison to the performance of other existing Indian languages’ summarizers.
Application of the matrix converter to power flow control

Advanced power electronic converters can provide the means to control power flow and ensure proper and secure operation of the future power grid. The small elec- trical energy sources dispersed in electrical power systems referred to as distributed ge- neration are one of the most significant parts of future grids - Smart Grids. The three- phase, direct matrix converter is an alternative solution to the conventional AC-DC-AC converter for interfacing two AC systems in distributed power generation with different voltage and/or frequency parameters. This paper presents a control analysis of a three- phase matrix converter employed as a power interface of future electrical grids. The pro- posed system has been successfully tested for bidirectional power flow operation with different grid operating conditions, such as, frequency and voltage variation.
Novel type of the elements of integrated diffractive optics

Novels diffractive element in the THz waveband offers the potential to realize novel types of devices for communications, sensing, integrated optics, networks, transmission lines, and so on. To this end, diffractive planar elements fabricated on non-flat surfaces make it possible to enrich the "pool of devices" for applications including integrated optics at different waveband, including THz, and to design elements with novel properties and potentials. This can be illustrated most clearly using as an example optical element such as that for optical polychromatic computers. For instance, the diffractive element discussed above can be used as a nonlinear device for polychromatic radiation or multiplexer or a focusing element with selectivity in the multimode regime. Frequency characteristics for such elements are determined by the extent of concavity (convexity) of the surface of the element and by the direction of incidence wave onto it. Therefore, when working on a wavelength λ is not equal to λ0, the position of the focusing area in space (the amount of its displacement) and focusing properties should depend on the direction of incidence of the radiation. Hence, it is possible to distinguish between a signal incident on the "tip" of the element from that falling on its "base," simply by placing radiation receivers at the corresponding points in space. Thus, in this paper we will present detailed simulation results obtained using a parallel FDTD method and the application of the proposed device to focusing and frequency-selective properties of flat conical diffractive elements in THz waveband.
Human activity recognition based on transfer learning with spatio-temporal representations

A Gait History Image (GHI) is a spatial template that accumulates regions of motion into a single image in which moving pixels are brighter than others. A new descriptor named Time-Sliced Averaged Gradient Boundary Magnitude (TAGBM) is also designed to show the time variations of motion. The spatial and temporal information of each video can be condensed using these templates. Based on this opinion, a new method is proposed in this paper. Each video is split into N and M groups of consecutive frames, and the GHI and TAGBM are computed for each group, resulting spatial and temporal templates. Transfer learning with the fine-tuning technique has been used for classifying these templates. This proposed method achieves the recognition accuracies of 96.50%, 92.30% and 97.12% for KTH, UCF Sport and UCF-11 action datasets, respectively. Also it is compared with state-of-the-art approaches and the results show that the proposed method has the best performance.
RocksDB: Evolution of Development Priorities in a Key-value Store Serving Large-scale Applications

This article is an eight-year retrospective on development priorities for RocksDB, a key-value store developed at Facebook that targets large-scale distributed systems and that is optimized for Solid State Drives (SSDs). We describe how the priorities evolved over time as a result of hardware trends and extensive experiences running RocksDB at scale in production at a number of organizations: from optimizing write amplification, to space amplification, to CPU utilization. We describe lessons from running large-scale applications, including that resource allocation needs to be managed across different RocksDB instances, that data formats need to remain backward- and forward-compatible to allow incremental software rollouts, and that appropriate support for database replication and backups are needed. Lessons from failure handling taught us that data corruption errors needed to be detected earlier and that data integrity protection mechanisms are needed at every layer of the system. We describe improvements to the key-value interface. We describe a number of efforts that in retrospect proved to be misguided. Finally, we describe a number of open problems that could benefit from future research.
Planning Support Systems: Integrating Geographic Information Systems,Models,and Visualization Tools

From the Publisher: The integration of community concerns with GIS technologies has had the effect of bringing community planners and designers together at the planning table. Planners no longer plan for the people in the communities, they plan with them. With planning support software, citizen planners can move buildings from block to block, tear them down, build complete subdivisions, run new highways in and around town, analyze any number of scenarios, and see with their own eyes the consequences of each action. This reference offers new possibilities and discusses the most important aspects of computer-aided land-use planning. Topics covered include urban modeling, simulation and scenario construction, collaborative planning, and visualization. Author Biography: Richard K. Brail is a professor at Rutgers University Edward J. Bloustein School of Planning and Public Policy. He lives in New Brunswick, New Jersey. Richard E. Klosterman is a professor at the University of Akron. He lives in Akron, Ohio.
Rhodeus amarus (Bloch, 1782): a new potential threat for Margaritifera auricularia (Spengler, 1793) (Unionoida, Margaritiferidae)

Rhodeus amarus is a cyprinid fish that spawns in the mantle cavity of freshwater mussels, resulting in a fitness cost to the host mussel. Rhodeus amarus is expanding its geographic range in Europe, hence the exposure of different freshwater mussel species to R. amarus is also rising. This expansion of contact creates potential risk to affected mussels, and especially for mussel species already facing extinction risk. Here, we review the expansion of R. amarus in Europe and the mussel species that are known hosts for R. amarus. We discuss the potential danger of this expansion into new areas that are inhabited by spatially restricted mussel species, and we confirm that the critically endangered Margaritifera auricularia is now a host for R. amarus.