Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Analysis of Citrus Tree Classification from UAV Images

Nowadays, agricultural producers are increasingly attempting to combine computer vision methods with the output from unmanned aerial vehicles (UAVs) to automate farm operations aiming at speed, precision, and cost. In this context, time-consuming tasks such as crop monitoring may be solved in a more efficient and less error-prone manner. In particular, for estimating productivity and managing pests, a key task that may be automated is the characterization of crop regions into full-grown trees, tree seedlings, tree gaps, and background. In this paper, we address the classification of aerial images into these classes, targeting at samples from a citrus plantation acquired by UAVs. Although Deep Learning-based approaches have been used successfully for many classification problems, little is discussed on the effects of parameters tuning (involving features, classifiers, and sampling methods) for each specific problem. Therefore, our goal is to solve this task while addressing explainability, by running an experimental analysis that explores classical methods as well as Deep Learning-based approaches.
Improvement of small signal stability of solar integrated power system by UPFC based optimal controller

In this work a parallel fuzzy PID controller is proposed for enhancing small signal stability of variable solar integrated power system. The controller parameters are tuned by DE-GWO technique. With increase in solar penetration the system oscillations are found to be more aggravated subject to sudden variation in solar output. Time domain simulation has been performed with detail eigen value analysis to justify the effectiveness of controller. The system response predict that proposed controller can damp system oscillations much heavily in contrast to single fuzzy PID controller optimized by DE and GWO techniques.
B-DRRN: A Block Information Constrained Deep Recursive Residual Network for Video Compression Artifacts Reduction

Although the video compression ratio nowadays becomes higher, the video coders such as H.264/AVC, H.265/HEVC, H.266/VVC always suffer from the video artifacts. In this paper, we design a neural network to enhance the quality of the compressed frame by leveraging the block information, called B-DRRN (Deep Recursive Residual Network with Block information). Firstly, an extra network branch is designed for leveraging the block information of the coding unit (CU). Moreover, to avoid a great increase in the network size, Recursive Residual structure and sharing weight techniques are applied. We also conduct a new large-scale dataset with 209,152 training samples. Experimental results show that the proposed B-DRRN can reduce 6.16% BD-rate compared to HEVC standard. After efficiently adding an extra network branch, this work can improve the performance of the main network without increasing any memory for storing.
Automatic Adjustment of Image Sharpness in Relief Shading

Relief shading is a technique to depict topography on maps in a way that it is intuitively understood by the user. While relief shading was traditionally performed manually, nowadays digital elevation models are used as basis for analytical (i.e., automatic) relief shading methods. This Master thesis aims at the creation of a new method to perform automatic contrast adjustment in analytical relief shading. This is one of the core tasks of Swiss-style relief shading and relies on applying the aerial perspective effect. The aerial perspective effect should imitate manual relief shading – the gold standard – as close as possible. The new method was implemented in a Geographic Information System (GIS) environment (ArcGIS), since currently there is no standardized method to employ atmospheric corrections in GIS. As a first step in developing a novel method, two existing approaches for analytical relief shading were implemented as ArcGIS geoprocessing models. Then, the novel method was developed by making the following improvements: (1) emphasizing sharpness of ridges and (2) placing the haze effect in the lower elevations. The novel approach is designed to constrain the aerial perspective effect to an area of a watershed, treating each area locally as it is done in Swiss-style manual relief shading. The results demonstrate eligibility of both global and local watershed approaches for quality relief representation. Although this is a step closer to a fully-automated method to create the aerial perspective effect, it still requires userdefinable parameters. Nevertheless, this approach is quantitative, consistent, reliable, and more objective than older techniques.
Multi-Level Performance Instrumentation for Kokkos Applications Using TAU

The TAU Performance System® provides a multi-level instrumentation strategy for instrumentation of Kokkos applications. Kokkos provides a performance portable API for expressing parallelism at the node level. TAU uses the Kokkos profiling system to expose performance factors using user-specified parallel kernel names for lambda functions or C++ functors. It can also use instrumentation at the OpenMP, CUDA, pthread, or other runtime levels to expose the implementation details giving a dual focus of higher-level abstractions as well as low-level execution dynamics. This multi-level instrumentation strategy adopted by TAU can highlight performance problems across multiple layers of the runtime system without modifying the application binary.
Forecast of Rainfall Quantity and its Variation using Environmental Features

Rainfall plays a crucial role in the lives of an ordinary man. Developing a prediction model that captures sudden fluctuations in rainfall has always been a challenging task. The paper aims at developing three models which predict monthly rainfall for all districts in Tamil Nadu, India and also drawing a district-wise comparison among them to find the best model for prediction. The models developed are District-Specific Model, Cluster-Based Model and Generic-Regression Model. The District-Specific Model trains on data from a particular district, the Cluster-Based Model groups districts based on the climatic conditions and trains on data from a particular cluster and the Generic-Regression Model trains on combined data from all the districts. The paper also aims at finding the monthly variation of rainfall across geographical regions.
Design and FE Analysis of a Planar Transformer for a Compact 5 kW Isolated DC-DC Converter

This paper discusses the design, analysis and experimental demonstration of a planar transformer for a compact 5 kW isolated dc-dc converter. In the proposed converter, a 200 kHz planar transformer is used with a turnâ€™s ratio of 8:1. Analytical evaluation of planar transformer alone becomes more complicated and time consuming. In order to evaluate the performance of such transformer designs, a finite element simulation software is often required. In this paper, COMSOL Multiphysics simulation along with experimental verification are been presented to illustrate the performance of the proposed planar transformer.
GPADRlex: Grouped Phrasal Adverse Drug Reaction lexicon

Identification and monitoring of adverse drug reactions (ADRs) of marketed drugs from digital media can complement traditional pharmacovigilance activities. A phrasal lexicon is the need of systems that would monitor the ADRs from digital media. In this study, we compiled a large phrasal lexicon from the FDA Adverse Event Reporting System (FAERS) that is specific for ADRs, descriptive in nature and wherein the phrases representing the same ADR are grouped together using semantic similarity based hierarchical clustering. We explored six different methods to compute the phrasal semantic similarity and seven different linkage methods. We compared our automatic clustering with manually curated clusters on a subset of a previous ADR lexicon. Our results show that nine clustering schemes produced significantly better clusters than random clustering. Moreover, we provide a mechanism to generate user-defined k clusters from the 19,585 phrasal ADRs in our lexicon.
Analysis of Boardgames using Eye-tracking: Case Study with Gomoku

Gomoku (also called ”Five in a row”) is one of the earliest popular checkerboard games invented by humans. In computing research, a tree-based data structure and its branching factor is the common technique of analyzing such board game. While eye-tracking techniques have been used for decades in interpreting human behaviors and improving human-computer interaction, it had rarely been utilized in analyzing engagement in game playing. Utilizing a game refinement theory alongside eye-tracking technique, the objective of this paper is to propose a new algorithm to measure the branching factor and quantify personalized challenge in playing a game. In addition, investigating the eye-tracking parameters may also provide means of measuring the players ability, further supporting the main objective of this paper. The findings showed that the proposed algorithm is a promising approach in order to help the game developer to design a game with a personalized challenge that changes according to the players ability; thus, possibly improving the players engagement and entertainment in games as well as other domains in the future.
Dynamically tunable perfect absorbers based on periodic microstructures

Arising from condensed-matter excitations, epsilon-near-zero (ENZ) modes are a new path for enhancing light-matter interactions at nanoscale with their unique and interesting optical properties when probed at wavelengths corresponding to their zero permittivity crossing points. In this work, a highly efficient tunable absorber can be achieved by integrating an ultra-thin indium tin oxide (ITO) film into the periodically patterned metal-insulator-metal (MIM) structure. Particularly strong absorption modulation could be achieved when the ITO is operated in the ENZ spectral range. This work can make a significant contribution to direct application in a range of optoelectronic devices that require active control over light absorption and energy conversion.
Optical fiber methane sensor using refractometry

One potential application of fiber refractometers is gas detection. In this work, a multimode fiber (MMF) refractomer has been designed as a CH4 sensor by de-cladding 2 cm of the MMF and then functionalizing this region using a polymeric thin film of cryptophane-A supramolecules incorporated within a styrene acrylonitrile (SAN) host. CH4 concentration is subsequently measured by observing the variation of the transmitted optical power along the MMF compared to an unfunctionalized MMF used as reference. This variation is induced by the modification of the refractive index of the functionalized film due to CH4 concentration variation. The proposed sensor is potentially of lower cost than alternative sensors and can detect CH4 concentration with a resolution of 186 ppm at a relatively fast response of ~25s.
Renewable energy harnessing by implementing a dc-dc boost converter and a three-phase multilevel inverter with fuzzy controller

This paper presents a multilevel topology implemented to conquer losses possessed with the aid of the current renewable energy harnessing techniques. Thereby making it more dependable and efficient output strength go with the flow to the grid. Multilevel inverters are used as it offers an extraordinary method in power digital interfacing. It additionally eliminates the cause of transformer in the tool, thereby disposing of several transformer losses and the location of set up. This can increase the efficiency of the output strength generated to the grid. The device implements a dc to dc enhance converter to improve the output dc energy acquired from the renewable supply deliver. It additionally implements a fly again capacitor which removes voltage spikes generated. This inverter also can be introduced proper into a PV cellular for in addition extension. The whole harmonic distortion is measured, and the simulation outcomes are acquired and additionally using fuzzy controller by way of closed loop system.
X-ray Detection using Single Photon Avalanche Diodes

We present the first reported use of a CMOS Single Photon Avalanche Diode (SPAD) arrays for the detection of high energy photons (X-rays), utilizing a 3D-stacked backside illuminated (BSI) SPAD image sensor, and cabinet biological X-ray irradiator. We detected photon peak energies from 30 to 120 keV, and an increase in SPAD bias voltage results in higher relative average counts per pixel. Furthermore, after an absorbed radiation dose of 3.75 Mrad, the difference in DCR was found to not be statistically significant, with a calculated two-sided P-value of 0.787. This unveils new applications in X-ray detection.
Evaluation of Processing Distribution for Application Program and OS in Microkernel OS

To effectively use a multicore processor, processes should be distributed to each core. Furthermore, circumstances under which the distribution effect does or does not appear must be clarified. Operating systems (OSes) have two types of architectures: microkernel and monolithic kernel. A microkernel OS implements minimal OS functions as the kernel and most other OS functions as the processes, which are referred to as OS servers. A monolithic kernel OS implements all OS functions as the kernel. These two types of OSes have different distribution forms for processing. This paper evaluates the basic performance of the microkernel OS; further, it compares distributed processing between microkernel and monolithic kernel OSes. We employed AnT as the microkernel OS and Linux as the monolithic kernel OS. AnT is a microkernel OS developed for a multicore environment and Linux is a widely used monolithic kernel OS. Finally, this paper reports that microkernel OSes surpass monolithic kernel OSes in terms of performance of distributed processing in a multicore environment.
MMW Wireless Communication System Based On QPSK Format Using Photomixer and Coherent Detection

Millimeter wave (MMW) frequencies (30 - 300 GHz) are promising solution for the increasing demand for high data rate, UHD multimedia, HD gaming, security and surveillance. The generation of CW signal at MMW band and above is essential. We experimentally demonstrate MMW wireless communication system at W-band (75 - 110 GHz) with on-off keying (OOK) modulation format using optical heterodyning technique and digital coherent detection. The generation of MMW carrier signal and the wireless photonic transmission system enabled by using commercial off-the-shelf, ultra wideband and antenna integrated InGaAs p-i-n photomixer. The MMW carrier signal received and detected using Schottky barrier diode (SBD) integrated with MMW antenna.
Experimental study of transversal-stress-induced polarization crosstalk behaviors in polarization maintaining fibers

For the first time, we experimentally study the transversal-stress (T-stress) induced polarization crosstalk behaviors in polarization maintaining fibers (PMFs) including the linearity, sensitivity, response time and recovery time, using a distributed polarization crosstalk analysis (DPXA) system. Using two Panda PMFs with or without polyacrylate buffer coating and one Bow-tie PMF with golden polyimide coating as experimental samples, we find that: I) the polarization crosstalk can be highly linear with the T-stress for PMFs no matter with or without coating; II) the polyacrylate coating can reduce the crosstalk sensitivity of naked PMFs by more than hundreds of times, while replacing the polyacrylate coating with polyimide coating can increase the sensitivity by tens or even hundreds of times; III) the polyacrylate coating can induce a significant recovery time of crosstalk when a T-stress is removed after a long loading time compared with that in naked PMFs or golden PMF with polyimide coating, however the crosstalk response speed is too fast to be measured by the DPXA system. Additionally, we also find that the current polyimide coating technique still needs to be improved further to reduce the crosstalk base level. This work will be very useful for PMF-based distributed sensing applications and sensing PMF manufacturing.
SE block-based Single Shot Multibox Detector

We present SE block-based Single Shot Multibox Detector (SE-SSD), a framework that improves the accuracy of the Single Shot Detector (SSD [9]). As a currently popular method, increasing the number of feature maps or the fusion of different feature layers can improve the performance of the deep network. Other approach such as DSOD [16] can learn a good object detectors from scratch. In this paper, we introduce the “Squeeze-and-Excitation” (SE) block, and analyze how to use SE block effectively to refine the performance of the SSD. The improved performance is enhanced by adaptively recalibrating channel-wise feature responses, than increasing the depth of convolutional layers. The proposed network explicitly model the nonlinear interactions between channels, and selectively emphasize or suppress the informative features of different channels. When Our network tested on Pasal VOC, it can achieve 82.0% mAP. In addition, the result on Pascal VOC 2012 test and COCO can also achieve 76.4% mAP and 27.9% mAP separately. The result of proposed network outperforms the state-of-the-art methods R-FCN and SSD on each dataset. And the speed of SE-SSD is still faster than Faster-RCNN and RFCN.
Approximate Approach to Finding Generic Utility of Sequential Patterns

Majority of utility pattern mining (UPM) literature assumes that the utility of particular patterns is known beforehand. At the same time, in frequent pattern mining (FPM), all patterns assume the same value. A problem with many datasets is that information about the utility of patterns is not easily or at all available and the utility is not directly proportional to the frequency of the specific pattern. One approach to finding the utility could be formulated in terms of how a particular pattern contributes to effectiveness of a specific machine learning (ML) algorithm. In this paper, we focus in particular on sequential patterns, e.g. time series. We propose an approximate approach to finding the generic utility of sequential patterns based on intergroup separation calculated using subsequence Dynamic Time Warping (DTW) similarity. Such utility could then be used in supervised algorithms and with some limitations in unsupervised algorithms. It can be applied both for regular and streaming application. We provide a proof of effectiveness of our approach using PAMAP2 Physical Activity Monitoring Data Set, an open dataset from the UCI Machine Learning Repository. For dissimilar activities (e.g. lying and ascending the stairs) we identify subsequences with normalized utility exceeding 0.5, where 1 would be a difference between total lack of heart rate and maximal heart rate all the time. For very similar activities (e.g. Nordic walking and ascending stairs) we find subsequences with utility exceeding 0.12.
The Periodic MLWA With Non-Uniform Aspect Ratios Based on Trapezoid DSPSL With Back-Firing to End-Firing Beam-Scanning Capacity

A periodic microstrip leaky wave antenna (MLWA) constructed of non-uniform aspect ratios microstrip patch periods is presented in this communication. Effective equations and simulations for the relevant antennas are used to obtain the propagation constants of the proposed antenna and design it. However, due to the presence of an open stop-band (OSB) in the broadside direction, double-side parallel-strip lines are then introduced in the antenna design. Truncating and interlacing the unit cells are developed to make them become impedance matched structures with the elimination of the OSB and the improvement of the radiation patterns. As a result, a new antenna configuration is created, and a prototype of the antenna is fabricated and measured. Continuous main beam scanning is observed from back-firing to end-firing with the operating frequency band of 4.8–18 GHz. Excellent agreements between the predicted and measured results are obtained. The measured peak gain of the prototype is 14 dBi, and the gain exceeds 6 dBi over the frequency band.
Robot-Assisted Therapy for Children with Delayed Speech Development: a Pilot Study

This paper presents a study that aims to investigate the effects of Robot-Assisted Therapy (RAT) on children who have a form of verbal development retardation such as Delayed Speech Development (DSD). To this end, we developed a number of applications for a humanoid robot NAO with the aim to engage children during RAT sessions. We conducted an evaluation of these applications with DSD children who interacted with the robot on a few occasions. Our findings demonstrate the utility of such applications for the therapy of DSD children which was both engaging and entertaining. Similar approach could be utilized for the therapy of children with Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder.
FragmentFusion: A Light-Weight SLAM Pipeline for Dense Reconstruction

We present FragmentFusion, a real-time dense reconstruction pipeline that combines sparse camera tracking with image-space volumetric fusion. The tracking is based on ORB-SLAM, which constructs and optimizes a sparse global map of 3D points and keyframes. We transform each of these keyframes into decimated meshes and render them from the estimated viewpoint. Fusion is performed in the pixel shader by exploiting atomic operations on a packed data structure. This eliminates the need of a 3D voxel grid making FragmentFusion very flexible at large scenes and varying scales. Moreover, since all keyframes are fused on-the-fly, we can use bundle adjustment and loop-closure without expensive volumetric re-integration. FragmentFusion is lightweight in terms of compute power and memory consumption. It can easily fuse several hundreds of keyframes in real time, in a quality comparable to other approaches. We achieve real-time frame rates on a notebook at around 20% CPU and GPU utilization and low memory consumption.
Zeroth-Order Resonator Based on Mushroom-like Structure for Liquid Mixture Concentration Sensing of Sodium Chloride Solution

This paper presents the liquid-mixture sensor using a zeroth-order resonator (ZOR) based on mushroom-like structure. The mushroom-like structure is designed using Jerusalem shape for reducing the size of a resonator and providing a high Q-factor that operating frequency at 5.90 GHz. By using the perturbation theory, each dielectric properties of the liquid-mixture affect to the resonance frequency. In this paper, four conditions, only zeroth-order resonator sensor, with empty Teflon tube, inserting deionized water and inserting Sodium Chloride solution (NaCl), are investigated the sensor performance with the reflection coefficient,| S11|, technique. The measured can classify the liquid under test (LUT) by detecting the resonant frequency change of the reflection coefficient,| S11|. The zeroth-order resonator sensor also offers other advantages, e.g., non-destructive method, real-time monitoring and no life-cycle limitation.
The Effect of Gendhing Gamelan toward the Reducing of Anxiety on Pregnancy

listening to music will not only have a soothing and uplifting effects on someone but also a positive influence on the unborn baby. The positive vibes that will be acquired from listening to music will create a prenatal stimulation that will help pregnant mother to bond with their unborn baby. It will enable them to lower their stress and anxiety levels and also reduce any stress that the unborn baby may feel in the womb. Previous researches claimed the ability of music, especially western music, in reducing anxiety and stress toward pregnant woman. Instead of using western music, the present study investigated the effect of listening to gamelan music toward the reducing of anxiety on pregnancy. In a pilot experiment, 60 healthy volunteers (mean age = 29.0; SD = 5.0) evaluated their anxiety responses to a Javanese traditional musical piece using a Slendro Sanga scale. The research shows significant effect $(\mathrm{p}\lt 0.05$; F=5.340) of Javanese traditional music to reduce anxiety in pregnant woman thus the hypotheses is accepted.