Miracle-dz/UniFAR-FTUs huggingface dataset:
Pattern Characterization in Second Order Memristor Networks

Second order memristors are two terminal devices which present a conductance depending on two orders of variables, namely the geometric parameters and the internal temperature. They have shown to be able to mimic some specific features of neuron synapses, specifically Spike-Timing-Dependent-Plasticity (STDP), and consequently to be good candidates for neuromorphic computing. In particular memristor crossbar structures appear to be suitable for implementing locally competitive algorithms and for tackling classification problems, by exploiting temporal learning techniques. On the other hand spiking networks have been intensively studied in the context of unsupervised, supervised and reinforcement learning. In this manuscript we briefly present a simplified model of second order memristors, that we have derived in a previous paper. Then we focus on two main results. As a first step we show that the model is capable of accurately reproducing a correct synaptic response to complex inputs, like cycles of spike triplets and quadruplets at different frequencies. Then we show that, exploiting such a simple model, complex spatio-temporal patterns can be almost analytically characterized, in memristor networks connecting an arbitrary number of presynaptic and postsynaptic neurons.
An Accelerated and Robust Partial Registration Algorithm for Point Clouds

Partial registration for point clouds plays an important role in various fields such as 3D mapping reconstruction, remote sensing, unmanned driving, and cultural heritage protection. Unfortunately, partial registration is challenging due to difficulties such as the low overlap ratio of two point clouds and the perturbation in the orderless and sparse 3D point clouds. Thus, a variety of the 3D shape context descriptors are introduced for finding the optimal matching. However, extracting geometric features and descriptors are time consuming and easily degenerated by noise. To overcome these problems, we introduce a parallel coarse-to-fine partial registration method. Our contributions can be summarized as: Firstly, a robust coarse trimmed method is proposed to estimate the coarse overlap area and the initial transformation via fast bilateral denoising and parallel point feature histogram (PPFH) descriptor aligning. Secondly, an accelerated fine registration procedure is conducted by a parallel trimmed iterative closest point (PTrICP) method. Moreover, most parts of our coarse-to-fine workflow are accelerated under the Graphics Processing Unit (GPU) parallel execution mode for efficiency. Thirdly, we extend our method from the rigid registration to the isotropic scaling registration, which improves its applicability. Experiments have demonstrated that our method is feasible and robust in various situations, including the low overlap ratio, outlier, noise and scaling.
Association Loss for Visual Object Detection

Convolutional neural network (CNN) is a popular choice for visual object detection where two sub-nets are often used to achieve object classification and localization separately. However, the intrinsic relation between the localization and classification sub-nets was not exploited explicitly for object detection. In this letter, we propose a novel association loss, namely, the proxy squared error (PSE) loss, to entangle the two sub-nets, thus use the dependency between the classification and localization scores obtained from these two sub-nets to improve the detection performance. We evaluate our proposed loss on the MS-COCO dataset and compare it with the loss in a recent baseline, i.e. the fully convolutional one-stage (FCOS) detector. The results show that our method can improve the <inline-formula><tex-math notation="LaTeX">$\mathrm{AP}$</tex-math></inline-formula> from 33.8 to 35.4 and <inline-formula><tex-math notation="LaTeX">${\rm AP}_{75}$</tex-math></inline-formula> from 35.4 to 37.8, as compared with the FCOS baseline.
Diversity for Resistance to Stem and Leaf Rusts in Indian Wheat Germplasm

In India, three wheat species -Triticum aestivum L. emend. Fiori & Paol (bread wheat), T. durum Desf. (durum wheat) and T. dicoccum Schrank (khapli wheat) are under cultivation, which are attacked by stem rust (Puccinia graminis Pers. f. sp. tritici Eriks. & Henn.) and leaf rust (P. triticina Eriks). Out of 120 genotypes each of durum and bread wheats tested including released varieties, advanced generation lines, genetic stocks and landraces, 67 bread wheat and 71 durum genotypes showed resistance to stem rust; and 45 bread wheat and 50 durum genetic stocks showed resistance to leaf rust, based on field scores of five successive crop seasons (2004–2009) under rust epiphytotic conditions. On the basis of patterns of their seedling responses to 24 pathotypes of stem rust and 40 of leaf rust, at least 17 diverse groups among bread wheats and 18 among durums could be recognised for resistance to stem rust, while 12 groups among bread wheats and nine among durums could be identified for leaf rust resistance, indicating wider resistance base of Indian wheat germplasm. In all, 31 bread wheat and 36 durum genotypes showed resistance to both the rusts, which can serve as donors in wheat crop improvement.
Model Predictive Control of a Convertible Tiltrotor Unmanned Aerial Vehicle

This paper presents a Model Predictive Control (MPC) based control structure for a convertible Unmanned Aerial Vehicle (UAV) with fixed wings and tiltrotor thrust vectoring. The controller encompasses full flight envelope trajectory tracking, thereby optimally exploiting the aircraft's Vertical Take Off and Landing (VTOL) and cruising-forward flight capabilities. An adaptive control allocation is designed to handle the changing control authorities of the actuators and efficiently distribute the required control actions between propellers, tilt servos and control surfaces. Preliminary simulation results show the feasibility of the proposed control approach.
Marine Predators Algorithm for Parameters Identification of Triple-Diode Photovoltaic Models

The precise electrical modeling of photovoltaic (PV) module is crucial due to the large-scale permeation of PV power plants into electric power networks. Therefore, a triple-diode photovoltaic (TDPV) model is presented to address all PV losses. However, the TDPV is mathematically modelled by a nonlinear $I$ - $V$ behavior, including nine-parameters that cannot be directly determined from the PVs datasheet due to the lack data offered by the PV manufacturers. This article presents a new application of the marine predators algorithm (MPA) to properly extract the electrical parameters of the TDPV model of a PV panel. The validity of the MPA-based TDPV model is widely appraised by the numerical analyses, which are carried out under various temperatures and solar irradiations. The optimal nine-parameters achieved using the MPA are compared with that realized by different optimization approaches-based PV model. For a realistic study, the numerical results and the measured data are compared for the marketable Kyocera KC200GT and Solarex MSX-60 PV panels. The efficacy of the MPA-based TDPV model is properly executed by checking its current error with that obtained from various models. With the MPA technology, a highly accurate model of any marketable PV module can be attained, which represents a new contribution to the sector of PV power systems.
A smart energy management power supply unit for low-power IoT systems

A lot of research has been contributed towards smart energy harvesting, efficient energy management and energy storage/supply capabilities, as they are considered a major bottleneck in Wireless Sensor Networks (WSNs). Similarly, there is an extreme interest to design new algorithms and protocols regarding energy harvesting prediction, load's energy consuming profiling, etc. Although those techniques improve energy efficiency, it still remains to solve the fundamental problem of energy provisioning in a more practical, real-life manner, as the majority of the hardware solutions choose to produce simple and robust implementations. In this paper, we present a smart energy management platform for low-power IoT systems that implements both energy harvesting and storage technologies but dynamically sets different power modes based on online monitoring measurements and energy harvesting prediction. With respect to power specifications our solution succeeds to both supply and inform the load system for future energy provisioning capability. Thus, our prototype can be characterised as a load agnostic device w.r. t. specification values, that can adjust to different conditions and use cases towards an effective system energy provisioning.
Surface potential behavior of corona charged polyimide films under controlled humidity conditions

The behavior of surface potential decay (SPD) of Kapton HN polyimide films (thickness: 50 µm) under an environmental humidity conditions has been investigated after applying a negative dc corona discharge. The evolutions of SPD have been recorded for different relative humidity values (RH %). The experimental results show clearly that the relative humidity of the environment affects the SPD. The decay rate $(Dr)$ increases with the increase of relative humidity inside the climatic chamber. For high relative humidity RH = 70 %, the SPD rate is most important at all charging voltages. It is complemented by calculating the electric conductivity which indicates that the absorbed humidity has a great influence on the bulk conductivity.
Speech Activity Detection Using a Fusion of Dense Convolutional Network in the Movie Audio

Speech activity detection (SAD) is a critical preparation process for speech-based applications. The speech activity detection is used to identify the speech in an audio recording. This paper aims to propose a speech activity detection on the entertainment media domain based on CNN. The fusion of two Dense Convolutional Network (DenseNet) with different feature extraction by using Dempster-Shafer theory (DS theory) was used to classify the speech segment. We combined acoustic features, which are the logmel spectrogram (LM), mel frequency cepstral coefficient (MFCC), chroma, spectral contrast, and tonnetz as the input feature. The combination of acoustic features operates on the raw speech signal and delivers it into a convolution neural network for classifying the speech. The result in this work shows that the proposed speech activity detection can achieve better performance (+1% Accuracy, +8% Precision, and +5% F1 score) than previous work in a more complicated noise environment.
Low power mixer-first receiver with second-order baseband filtering TIA

A second-order transimpedance amplifier (TIA) has been proposed to strongly suppresses out-of-band blockers and enhance linearity in mixer-first receivers. TIA synthesis has been described in a step-by-step manner and the resulting network is then used in a prototype receiver. Receiver was designed in 16nm CMOS and simulation shows that it has 22.7 dB RF gain and NF of 3.1 dB. NF degrades by only 0.9 dB in the presence of a OdBm blocker. Blocker 1dB compression point for the receiver is +13 dBm and its out-of-band IIP3 is +27 dBm. Receiver consumes only 17.2 mW in the TIA circuit and 20.8 mW in the LO path (fLO = 1.5 GHz).
HP: A light-weight hybrid algorithm for accurate data partitioning

This paper introduces a hybridization of the k- means and k-medoids paradigms. The new algorithms is named HP (hybrid partitioning) algorithm. Specifically, we improve on a recently developed scalable version of k-means (k-means-lite), by introducing the PAM algorithm into it in such a way that the high accuracy of the latter is absorbed without inheriting its high inefficiency. K-means-lite runs standard k-means on the combination of intermediate centroids obtained by initially feeding $\pmb{n}$ samples into k-means. In HP, instead of k-means, PAM is used to cluster the combination of centroids obtained from the samples. This PAM component is fast because it is run on very small data, precisely of size $\pmb{nk}$. Experiments show that this modification improves not only the accuracy of k-means-lite but also outperforms the accuracy of k-means, without losing much k- means-lite's efficiency. Keywords- k-means, k-means-lite, k-medoids, clustering, accurate, efficient, PAM
A Review of Multi-Sensor Fusion System for Large Heavy Vehicles Off Road in Industrial Environments

Industry 4.0 or fourth industrial revolution elevates the computerization of Industry 3.0 and enhances it with smart and autonomous systems driven by data and Machine Learning. This paper reviews the advantages and disadvantages of sensors and the architecture of multi-sensor setup for object detection. Here we consider the case of autonomous systems in for large heavy vehicles off-road in industrial environments with the use of camera sensor, LiDAR sensor, and radar sensor. Understanding the vehicles surroundings is a vital task in autonomous operation where personnel and other obstacles present significant hazard of collision. This paper review further discusses the challenges of time synchronisation on sensor data acquisition in multi-modal sensor fusion for personnel and object detection, and details a solution implemented in a Python environment.
Mitigation Effects of Decoupling Cells on Full Adders Process Variability

To mitigate variability effects, this work explores decoupling cells on different adder topologies using a 7nm FinFET technology. Process variability shows an impact of up to 20% on nominal voltage and superior to 50% at near-threshold operation. After the insertion of DCells on the evaluated full adders, the circuits operating at nominal voltage present an increase of up to 20% on the robustness to delay variability and 40% on power consumption variability, with less than 20% of penalty in power and delay. Moreover, this technique adoption has a high impact on the results of circuits operating at near-threshold voltage; in some cases, reaching more than 40% reduction in performance for a small reduction in variability.
Infrared Thermography for Correlating Blood Pressure and Temperature

Infrared Thermography is a non-invasive clinical imaging procedure for detecting and monitoring several diseases and physical injuries by showing thermal abnormalities present in the body. This imaging procedure is done with the aid of a thermal camera. In the preliminary work, the main aim is to derive a correlation and an empirical relationship between temperature and blood pressure. The objective is to measure blood pressure using mercury sphygmomanometer and automatic sphygmomanometer; whereby diastolic and systolic pressures along with pulse is noted down. Two regions of interest in the forearm has to be defined where the variations in temperature with variations in blood pressure is noted as well as graphically plotted. Also, a scatter plot is plotted to determine and find out the empirical equation correlating the blood pressure and temperature. Blood pressure correlation with temperature is done for the whole population of the study set consisting of 20 subjects within the age group of 18-35 years [with men (10 in number) and female (10 in number)]. The key findings of this work are that temperature at the regions of interest increases with an increase in blood pressure and vice versa. And, during inflation and deflation i.e., with increasing and decreasing blood pressure, males showed higher temperature variation than females at both R1 and R2 region. The future works that can be predicted to happen based on the preliminary work is the development of an IR based sensor which would assist in measuring the blood pressure of a body from the surface of the skin, in a non-contact manner.
Lung Cancer Diagnosis Extraction from Clinical Notes Written in Spanish

The wide adoption of electronic health records (EHRs) offers a potential source to support research. Lung cancer is one of the most common cancer in the world. Although several tools have been developed to automatically extract concepts from oncology clinical notes, still there is a gap between concept extraction and concept understanding. The high number of clinical notes for the same patient, use of negation and proper date annotations lays in the root of the problem. In this paper, we propose an approach to accurate Lung cancer diagnosis extraction from clinical notes written in Spanish. The approach deals with a disambiguation process required to extract the correct date and diagnosis of a patient having hundreds of clinical notes and consequently hundreds of annotations. Results obtained on an annotated database of 1000 patients show an F-score of 90%.
Fault detection in a 3-DOF laboratory helicopter using wavelet packets and subspace identification in frequency subbands

This paper proposes a wavelet-packet approach for fault detection using subspace identification. The wavelet-packet decomposition tree is used to define the frequency subbands at which the plant models are created. The best decomposition tree is chosen by using a dynamic programming algorithm that achieves a trade-off between accuracy and parsimony. A recently published framework is adopted to allow for the identification of subband models with a subspace approach. The proposed technique is validated with experimental data acquired with a Quanser™ 3-DOF laboratory helicopter and presents better results than those of a standard time-domain fault-detection technique.
Copula-Based Analysis of Physical Layer Security Performances Over Correlated Rayleigh Fading Channels

This paper studies performance analysis of physical layer security in a Rayleigh fading wiretap channel, where, the main (transmitter-to-legitimate receiver) and eavesdropper (transmitter-to-eavesdropper) channel coefficients are correlated. By exploiting the novel approach called Copula theory, we derive closed-form expressions for average secrecy capacity (ASC), secrecy outage probability (SOP), and secrecy coverage region (SCR). Moreover, to more evaluate the impact of channel correlation, the asymptotic behavior of SOP in high signal-to-noise ratio (SNR) regime and other scenarios is studied, and finally, the analytical results are illustrated numerically under the positive dependence, independence, and negative dependence structures. Based on the insights from this analysis, we found that the effect of correlated fading on the performance of physical layer security can be helpful or harmful in different scenarios, depending on the structure of dependency between the main and eavesdropper channels.
Fully-Automated Analysis of Scoliosis from Spinal X-Ray Images

Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Radiography is the most cost-effective and accessible modality for imaging the spine. Conventional spinal assessment, diagnosis of scoliosis, and treatment planning relies on tedious and time-consuming manual analysis of spine radiographs that is susceptible to observer variation. A reliable, fully-automated method that can accurately identify vertebrae, a crucial step in image-guided scoliosis assessment, is presently unavailable in the literature. Leveraging a novel, deep-learning-based image segmentation model, we develop an end-to-end spine radiograph analysis pipeline that automatically provides an accurate segmentation and identification of the vertebrae, culminating in the reliable estimation of the Cobb angle, the most widely used measurement to quantify the magnitude of scoliosis. Our experimental results with anterior-posterior spine X-ray images indicate that our system is effective in the identification and labeling of vertebrae, and can potentially provide assistance to medical practitioners in the assessment of scoliosis.
2020–2021 Florida Citrus Production Guide: Planting New Citrus Groves in Florida in the Era of Citrus Greening

The Institute of Food and Agricultural Sciences (IFAS) is an Equal Opportunity Institution authorized to provide research, educational information and other services only to individuals and institutions that function with non-discrimination with respect to race, creed, color, religion, age, disability, sex, sexual orientation, marital status, national origin, political opinions or affiliations. For more information on obtaining other UF/IFAS Extension publications, contact your county’s UF/IFAS Extension office. U.S. Department of Agriculture, UF/IFAS Extension Service, University of Florida, IFAS, Florida A & M University Cooperative Extension Program, and Boards of County Commissioners Cooperating. Nick T. Place, dean for UF/IFAS Extension. In this article, we summarize the results of an analysis to examine the profitability of three tree densities under different production and market conditions. We found that establishing a new grove with a tree density similar to that of the state’s average is not profitable under current market conditions. In addition, such density only attains a modest return under potentially higher prices. Despite the higher level of investment required for planting higher-density groves, such investments are profitable under the assumptions and scenarios analyzed. Our results should prove useful to citrus growers looking to invest in alternatives that have the potential to improve their profitability.
A New Species of the Genus Murina (Chiroptera: Vespertilionidae) from the Central Highlands of Vietnam with a Review of the Subfamily Murininae in Vietnam

The subfamily Murininae has high species diversity in Vietnam, but taxonomic studies are limited. In this paper, we describe a new species of the genus Murina based on a specimen collected from Ngoc Linh Nature Reserve, Kon Tum Province in the Central Highlands of Vietnam. It is a medium-sized species with ‘suilla-type’ dentition. A taxonomic review of Murininae from Vietnam was also conducted based on combined morphological, DNA, and karyological characteristics. Molecular phylogenetic analyses based on the mitochondrial cytochrome c oxidase subunit (COI) gene supported the subfamily Murininae, while the genus Murina proved to be paraphyletic in relation to the genera Harpiocephalus and Harpiola. Fourteen species of the genus Murina, one species of Harpiocephalus, and one species of Harpiola are recognized from Vietnam. Murina tiensa is regarded as a junior synonym of M. harrisoni; strong sexual dimorphism was observed in M. harrisoni. Relations between forearm length and total length of skull showed different trends among species and sexes. Karyotypes of Murina huttoni, M. cyclotis, M. lorelieae, M. beelzebub, M. feae, and Harpiola isodon were 2n = 44, FN = 50, while that of Harpiocephalus harpia was 2n = 44, FN = 52.
Disputes and their management in the workplace: a survey of British employers

Almost all organisations experience disputes as part of their day-to-day activities. But despite being commonplace and seen as timeconsuming and costly (CIPD 2015), workplace disputes are challenging to precisely define since they take such a wide range of forms. A dispute in the workplace encompasses conflict over values meaning what is right and wrong, but also over competing individual and group interests within an organisation. It can also result from stereotypes and rumours which negatively affect