Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Forest Leaf Area Index Using Height and Canopy Cover Information Extracted From Unmanned Aerial Vehicle Stereo Imagery

Leaf area index (LAI) is an important forest structural parameter in the process of photosynthesis. Most studies have investigated the estimation of forest LAI using the spectral information of optical remote sensing images or the height information from LiDAR data. This paper explored the estimation of forest LAI using canopy cover and forest height information extracted from stereo imagery acquired by cameras onboard an unmanned aerial vehicle (UAV). UAV remote sensing has gradually become practical in recent years. Two alternative methods were proposed to extract forest height information. The height indices method extracted forest height indices within each forest plot based on the vertical histogram of the canopy height model derived from stereo imagery, while the segmentation method characterized forest plots using tree numbers and average tree heights based on the individual tree segmentation. The results showed that canopy cover and forest height are complementary in the estimation of forest LAI no matter what method was used. The combined use of canopy cover and forest height information extracted by the segmentation method had a better estimation accuracy of forest LAI with R2 = 0.833 and RMSE = 0.288. This paper demonstrated a new approach to predict forest LAI using UAV optical images.
Large-Signal Modeling of a Scalable High- ${Q}$  AlGaN/GaN High Electron-Mobility Varactor

In this paper, a new large-signal model for gallium nitride high electron-mobility varactors is presented. Compared to former models, this model is scalable and describes the whole range of operation from breakdown to forward conduction. The model consists of a passive parasitic shell, an ideal intrinsic core, and takes dispersion into account. The main extraction is done on an eight-finger device with a tuning ratio of 4.3 and a minimum quality factor <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> above 30 for frequencies below 2 GHz. The model is verified for a large range of bias points and frequencies. Load–pull measurements provide evidence even at high RF input power up to 23 dBm.
Comparison of Perceptual Differences Between Users and Designers in Mobile Shopping App Interface Design: Implications for Evaluation Practice

This paper presents a case study of end users and designers and how they perceive different mobile shopping app user interfaces (UIs). This paper aims to explore the UI preference differences between users and designers and elucidate specified UI attributes that are reflective of user preferences. This paper contains three procedures: 1) eliciting users’ and designers’ UI perceptual similarity by sorting to identify UI perceptual similarity space; 2) eliciting users’ and designers’ overall preferences by rating, which displays the preference dimensions; and 3) eliciting users’ and designers’ idiosyncratic perceptions by using the repertory grid technique (RGT), which indicates the UI perceptual differences between the users and the designers. The specified inter-group perceptual differences were detected by comparing RGT-elicited perceptions. The UI design features were explored, which influenced the designers’ and users’ subjective evaluations. The results showed four typical mobile shopping app UIs, and the distribution of ideal preference UIs for users and designers was evaluated. Through the experiments, rich qualitative insights were quantified, and valuable information from the participants was obtained. The specified design attributes of UI on the mobile shopping app, which improved user satisfaction, were found out. Moreover, those attributes will help designers know which important attributes should be noted.
One-Pot Synthesis of Copper Nanoparticles Using Underwater Plasma

In this paper, we propose an innovative method for synthesizing copper nanoparticles (Cu-NPs) using underwater plasma. The proposed method using underwater plasma has advantages such as one-pot synthesis, fast reaction time, and high throughput. We obtained yields as high as 15 g of Cu-NPs powder from 25 g of copper hydroxide, for 10-min a reaction time. In addition, we confirmed that the Cu-NPs produced are spherical and range in size between 40 and 80 nm. We also verified that the synthesized Cu-NPs are almost pure Cu powder, close to 100 %, through high-resolution transmission electron microscopy and X-ray diffraction analysis.
Revisiting spectral envelope recovery from speech sounds generated by periodic excitation

We propose a set of new accurate spectral envelope recovery methods for speech sounds generated by periodic excitation based on a set of interference-free power spectrum representations. The proposed methods outperform our previous spectral recovery models used in legacy-STRAIGHT, TANDEM-STRAIGHT and WORLD VOCODERs. We introduce several design procedures of paired time widows which remove interferences caused by signal periodicity in the time domain or in both time and frequency domains. In addition to this interference-free representation, we introduce post and pre-processing to improve recovery accuracy around spectral peak regions. We conducted a set of evaluation tests using voice production simulator and natural speech samples. Finally, we discuss the application of the proposed method on revising high-quality VOCODERs.
Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features

The present study tackles the problem of automatically discovering spoken keywords from untranscribed audio archives without requiring word-by-word speech transcription by automatic speech recognition (ASR) technology. The problem is of practical significance in many applications of speech analytics, including those concerning low-resource languages, and large amount of multilingual and multi-genre data. We propose a two-stage approach, which comprises unsupervised acoustic modeling and decoding, followed by pattern mining in acoustic unit sequences. The whole process starts by deriving and modeling a set of subword-level speech units with untranscribed data. With the unsupervisedly trained acoustic models, a given audio archive is represented by a pseudo transcription, from which spoken keywords can be discovered by string mining algorithms. For unsupervised acoustic modeling, a deep neural network trained by multilingual speech corpora is used to generate speech segmentation and compute bottleneck features for segment clustering. Experimental results show that the proposed system is able to effectively extract topic-related words and phrases from the lecture recordings on MIT OpenCourse Ware.
Data augmentation with moment-matching networks for i-vector based speaker verification

This paper proposes an i-vector generation scheme with conditional generative moment-matching networks (MMNs) for speaker verification. In this scheme, multiple i-vectors for each enrollment speaker are randomly generated from trained MMNs and noise distributions. The randomly generated i-vectors are assumed to represent diverse variations for each enrollment speaker. Since this paper is aim to provide new possibility of the i-vector augmentation with MMNs, i-vector-based preliminary speaker verification evaluation with support vector machine (SVM) are performed. The results of SVM classification show that the generated i-vectors are contributed for estimation of the accurate SVM classifiers of enrollment speakers. From the experimental results, we also compare the distributions of the generated i-vectors with those of the original ones and discuss them.
Generative adversarial networks for generating RGB-D videos

Generative adversarial networks(GANs) have been successfully applied for generating high quality natural images and have been extended to the generation of RGB videos and 3D volume data. In this paper we consider the task of generating RGB-D videos, which is less extensively studied and still challenging. We explore deep GAN architectures suitable for the task, and develop 4 GAN architectures based on existing video-based GANs. With a facial expression database, we experimentally find that an extended version of the motion and content decomposed GANs, known as MoCoGAN, provides the highest quality RGB-D videos. We discuss several applications of our GAN to content creation and data augmentation, and also discuss its potential applications in behavioral experiments.
People Groups Analysis for AR Applications

Automatically characterizing groups and crowds of people plays an important role in different domains such as psychology, architecture or entertainment. In the engineering field, people grouping is important in social signal processing but also videosurveilance. Our challenge is to introduce such solutions into the field of augmented reality, where people get added content on real groups of people.As a preliminary work, this paper presents a system which provides a live visual feedback in virtual reality augmenting real groups of people with added information such as their ID, group ID or group coherence. The scene was analyzed with mainly one classical RGB camera and enhanced with a smartphone that a holder points towards the other persons.This paper goes through the proposed system, which is capable of tracking people, performing people grouping, analyzing groups and augmenting those groups in a virtual world.The first qualitative results show the feasibility of an augmented crowd environment and provide a set of interesting practical insights on the different modules of this system in the context of real-life scenes.
Class I histone deacetylases localize to the endoplasmic reticulum and modulate the unfolded protein response

Post‐translational modification through protein acetylation is emerging as an important mode of cellular regulation. We have previously demonstrated the role that glucose‐regulated protein 78 kDa (GRP78) acetylation and subsequent activation of the unfolded protein response (UPR) play in the antitumor activity of class I histone deacetylase (HDAC) inhibitors, which primarily target class I HDACs. In this study, we explored the contributory role these class I HDACs may play in UPR regulation. Binding studies were performed using immunoprecipitation/immunoblotting following dual‐transfection with HA‐tagged GRP78 and FLAG‐tagged HDACs. Subcellular localization was performed using Western blot of fractionated cell lysates and confocal microscopy. Individual HDACs were inhibited using RNA interference. We identified the potential of HDACs 1, 2, and 3 to bind to GRP78. These HDACs colocalized with GRP78 in the endoplasmic reticulum (ER). Inhibition of individual HDACs resulted in GRP78 acetylation and selective activation of the UPR. Although traditionally viewed as nuclear enzymes, we demonstrate that Class I HDACs localize to the ER, bind to GRP78, and selectively activate the UPR, representing a novel mode of UPR regulation and mechanism of action of HDAC inhibitors. —Kahali, S. , Sarcar, B. , Prabhu, A. , Seto, E. , Chinnaiyan, P. Class I histone deacetylases localize to the endoplasmic reticulum and modulate the unfolded protein response. FASEB J. 26, 2437‐2445 (2012). www.fasebj. org
The Various Angles of Outphasing PAs: Competitiveness of Outphasing in Efficient Linear PA Applications

The demand for linear and highly efficient RF amplifiers has continued to rise without showing signs of stopping, as the world looks to the implementation of 5G. Modern communication signals gave rise to the demand, as the desire to efficiently utilize the limited electromagnetic spectrum led to widespread use of amplitude- and phase-modulated signals, e.g., LTE and LTE Advanced, that use carrier aggregation to achieve broader bandwidths. The defense industry may provide even more applications, as electronic warfare techniques make use of multitone and sometimes noise-like signals that have statistics similar to communication signals. Radars are required to limit emissions in adjacent bands, but traditional rectangular pulses have high out-of-band emissions. Gaussian pulse shaping can be used to improve spectral efficiency, limiting emissions and sidelobes, while adding amplitude modulation [1].
Single Channel Properties of Heterotetrameric Mutant RyR1 Ion Channels Linked to Core Myopathies*

Skeletal muscle excitation-contraction coupling involves activation of homotetrameric ryanodine receptor ion channels (RyR1s), resulting in the rapid release of Ca2+ from the sarcoplasmic reticulum. Previous work has shown that Ca2+ release is impaired by mutations in RyR1 linked to Central Core Disease and Multiple Minicore Disease. We studied the consequences of these mutations on RyR1 function, following their expression in human embryonic kidney 293 cells and incorporation in lipid bilayers. RyR1-G4898E, -G4898R, and -ΔV4926/I4927 mutants in the C-terminal pore region of RyR1 and N-terminal RyR1-R110W/L486V mutant all showed negligible Ca2+ permeation and loss of Ca2+-dependent channel activity but maintained reduced K+ conductances. Co-expression of wild type and mutant RyR1s resulted in Ca2+-dependent channel activities that exhibited intermediate Ca2+ selectivities compared with K+, which suggested the presence of tetrameric RyR1 complexes composed of wild type and mutant subunits. The number of wild-type subunits to maintain a functional heterotetrameric channel differed among the four RyR1 mutants. The results indicate that homozygous RyR1 mutations associated with core myopathies abolish or greatly reduce sarcoplasmic reticulum Ca2+ release during excitation-contraction coupling. They further suggest that in individuals, expressing wild type and mutant alleles, a substantial portion of RyR1 channels is able to release Ca2+ from sarcoplasmic reticulum.
Optimal path tracking of a quadrotor in the presence of obstacle using the League Championship Algorithm

Nowadays, cost reduction and optimization has attracted attention of researchers and scientists in many fields. Optimal path tracking for vehicles, which reduces fuel consumption, as well as the distance traversed by the vehicle, has mostly caught attention. Besides, Unmanned Arial Vehicles are in the spotlight because of their great performance in doing a vast majority of jobs, especially those which are perilous for human. One of the most prevailing issues in this field is optimal path tracking. Although different algorithms are presented and despite their acceptable performance, none of them has focused on reducing energy and distance. In this paper a new optimization algorithm is utilized for the latter optimizations. This algorithm is called the Championship Algorithm by its Iranian developer. Combining it with the path tracking problem of the unmanned aerial vehicles, it is shown that its performance is much better than one of the most important existing algorithms (GA). Optimal trajectory tracking problem is also solved to reduce fuel and energy consumption.
Vehicle Detection and Classification based on Deep Neural Network for Intelligent Transportation Applications

This paper proposes an optimized vehicle detection and classification method based on deep learning technology for intelligent transportation applications. We optimize the Convolutional Neural Network (CNN) architecture by fine-tuning the existing CNN architecture for the intelligent transportation applications. The proposed design achieves the accuracy of miss rate around 10% when FPPI is 0.1. Realized on nVidia Titan-X GPU, the proposed design can reach the performance about $720\times 480$ video under different weather condition (day, night, raining) at 25fps. The proposed model can achieve 90% accuracy on three target vehicle classes including small vehicles (Sedan, SUV, Van), big vehicles (Bus) and Trucks.
A novel upper-limb rehabilitation robot with 4 DOFs: Design and prototype

In recent years, robotic rehabilitation has been utilized in treatment and recovery of patients with disabilities. In this paper, a novel design and kinematic analysis of a 4-DOF robot for upper-limb rehabilitation are proposed. The main novelty of this design is its mechanism for wrist and fingers motions which is added to the shoulder and elbow mechanism without any noticeable weight increase in the moving parts of the robot. For this purpose, a cable driven mechanism is implemented at the robot end-effector to move the wrist and fingers parts, and the corresponding actuators are placed at the base of the robot.
Automatic Detection and Segmentation of Lentil Crop Breeding Plots From Multi-Spectral Images Captured by UAV-Mounted Camera

Unmanned Aerial Vehicles (UAVs) paired with image detection and segmentation techniques can be used to extract plant phenotype information of individual breeding or research plots. Each plot contains plants of a single genetic line. Breeders are interested in selecting lines with preferred phenotypes (physical traits) that increase crop yield or resilience. Automated detection and segmentation of plots would enable automatic monitoring and quantification of plot phenotypes, allowing a faster selection process that requires much fewer person-hours compared with manual assessment. A detection algorithm based on Laplacian of Gaussian (LoG) blob detection and a segmentation algorithm based on a combination of unsupervised clustering and random walker image segmentation are proposed to detect and segment lentil plots from multi-spectral aerial images. Our algorithm detects and segments lentil plots from normalized difference vegetative index (NDVI) images. The detection algorithm exhibited an average precision and recall of 96.3% and 97.2% respectively. The average Dice similarity coefficient between a detected segmented plot and its ground truth was 0.906.
A High-Efficiency Interleaved Ultra-High Step-Down DC–DC Converter With Very Low Output Current Ripple

A nonisolated ultra-high step-down interleaved dc–dc converter with low voltage stress and common ground between the input and output ports is proposed in this paper. In order to extend the extremely narrow duty cycle, series capacitors and transformers are applied to the four-phase interleaved buck converter. In addition to extending the duty cycle, the series capacitors clamp the voltage stress across the switches and diodes, which results in using low voltage rating semiconductor components. Additionally, the main switches are turned on under a soft-switching condition. Hence, the conduction and switching losses are reduced and the overall efficiency of the designed converter is improved. Other important strengths of this topology are very low output current ripple due to interleaving and automatic uniform current sharing. The validity of theoretical analysis and converter operation is confirmed by the implementation of a 150 W prototype at 400 V input voltage and 12 V output voltage.
EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model

With the rapid development of brain–computer interfaces (BCI), the number of applications that use BCI technology is increasingly thick and fast. Prediction of age and gender of a person through EEG analysis is a new application of BCI that has been proposed in this paper. An industry standard EEG recording device has been used to record cerebral activities of 60 subjects (both male and female) in relaxed position with closed eyes. Deep BLSTM-LSTM network has been used to construct a hybrid learning framework for the aforementioned analysis. Accuracy of 93.7% and 97.5% have been recorded for age and gender classification problems respectively. These values are better than the state-of-the-art methods. Our analysis also reveals that the beta band frequencies are better in predicting the age and gender as compared to other frequency bands of the EEG signals. The proposed method has several applications, including biometric, health-care, entertainment, and targeted advertisements.
7.6 A 65nm 236.5nJ/Classification Neuromorphic Processor with 7.5% Energy Overhead On-Chip Learning Using Direct Spike-Only Feedback

Advances in neural network and machine learning algorithms have sparked a wide array of research in specialized hardware, ranging from high-performance convolutional neural network (CNN) accelerators to energy-efficient deep-neural network (DNN) edge computing systems [1]. While most studies have focused on designing inference engines, recent works have shown that on-chip training could serve practical purposes such as compensating for process variations of in-memory computing [2] or adapting to changing environments in real time [3]. However, these successes were limited to relatively simple tasks mainly due to the large energy overhead of the training process. These problems arise primarily from the high-precision arithmetic and memory required for error propagation and weight updates, in contrast to error-tolerant inference operation; the capacity requirements of a learning system are significantly higher than those of an inference system [4].
Using Surface Engineering to Modulate Superconducting Coplanar Microwave Resonator Performance

Superconducting microwave resonators are important components of superconducting quantum information and astronomy detector systems. In this paper, we show how to modify the microwave resonator performance after fabrication through surface engineering. In particular, we focus on titanium nitride (TiN)/silicon (Si) resonators because they have shown potential for achieving high-quality factors <inline-formula><tex-math notation="LaTeX">$(\text{Q}_{i}\text{s})$</tex-math></inline-formula>. Depending on the type of surface treatment, chemical- or plasma-based, we found <inline-formula><tex-math notation="LaTeX">$\text{Q}_{i}\text{s}$</tex-math></inline-formula> that vary by approximately a factor of 18. We used inductively coupled plasma (ICP) combined with reactive ion etching (RIE) for the plasma surface treatment. We found that the microwave resonator performance depends on the type of plasma environment, such as single gas (oxygen) or gas mixtures [argon/hydrogen (Ar/H<sub>2</sub>), argon/octafluorocyclobutane (Ar/C<sub>4</sub>F<sub>8</sub>), and argon/sulfur hexafluoride (Ar/SF<sub>6</sub>)], and the plasma processing conditions, such as treatment time, ICP power, and RIE power. Of the plasma surface treatments, the Ar/SF<sub>6</sub> environments with no or low ICP power showed the highest potential to improve <inline-formula><tex-math notation="LaTeX">$Q_{i}$</tex-math></inline-formula>. The processing conditions determined the chemistry and roughness of the Si and TiN surfaces, TiN film thickness, and the overall TiN/Si resonator structure (edge and sidewall). Our results can be used as a guideline for optimizing the microwave resonator performance using surface treatments.
Clustering and Classification of a Qualitative Colorimetric Test

In this paper, we present machine learning based detection methods for a qualitative colorimetric test. Such an automatic system on mobile platform can emancipate the test result from the color perception of individuals and its subjectivity of interpretation, which can help millions of populations to access colorimetric test results for healthcare, allergen detection, forensic analysis, environmental monitoring and agricultural decision on point-of-care platforms. The case of plasmonic enzyme-linked immunosorbent assay (ELISA) based tuberculosis disease is utilized as a model experiment. Both supervised and unsupervised machine learning techniques are employed for the binary classification based on color moments. Using 10-fold cross validation, the ensemble bagged tree and k-nearest neighbors algorithm achieved 96.1% and 97.6% accuracy, respectively. The use of multi-layer perceptron with Bayesian regularization backpropagation provided 99.2% accuracy. Such high accuracy system can be trained off-line and deployed to mobile devices to produce an automatic colourimetric diagnostic decision anytime anywhere.