Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Spectrum Sensing Over Weibull and Hoyt Fading Channels using Centralized and Distributed Schemes

Multipath fading, hidden terminal and shadowing are problems faced by users in non-cooperative type spectrum sensing techniques. A solution to this problem is Cooperative Spectrum Sensing (CSS) technique. CSS allows users to collaborate to perform spectrum sensing which may be managed by a common receiver. Further, it has two categories Distributed CSS (DCSS) and Centralized CSS (CCSS). In this paper these two schemes are compared with each other and different fusion rules are applied over them. Mathematical formulas for detection probability (Pd) of two fading channels namely Weibull and Hoyt are shown and further different fusion rules are applied on cluster-based CSS to explore the performance of centralized and distributed CSS. This concept has been earlier used for the conventional fading channels i.e. Rayleigh, Rician, Nakagami-m. In this paper results for cluster-based CSS are examined for comparatively lesser studied Weibull and Hoyt fading channels. It is noticed for different fusion rules, Weibull fading channel generates better performance as compared to Hoyt fading channel. This paper focuses on the implementation of four different fusion rules such as AND-AND, AND-OR, OR-AND along with centralized AND for these two fading channels. Results have shown that at SNR<5dB Weibull fading channel attains a higher probability of detection (Pd) than the Hoyt fading channel for all four fusion rules. It is noticed that the slope of the curve for Hoyt fading channel is steep as compared to Weibull fading channel and reaches to 0.9 Pd at SNR>5dB for OR-AND and AND-OR rule, while with these fusion rules, Weibull fading channel attains 0.9 Pd at SNR less than 0dB. OR-AND fusion rule gives the best result amongst all 4 fusion rules for both the channels.
Real-Time EEG–EMG Human–Machine Interface-Based Control System for a Lower-Limb Exoskeleton

This article presents a rehabilitation technique based on a lower-limb exoskeleton integrated with a human–machine interface (HMI). HMI is used to record and process multimodal signals collected using a foot motor imagery (MI)-based brain–machine interface (BMI) and multichannel electromyographic (EMG) signals recorded from leg muscles. Current solutions of HMI-equipped rehabilitation assistive technologies tested under laboratory conditions demonstrated a great deal of success, but faced several difficulties caused by the limited accuracy of detecting MI electroencephalography (EEG) and the reliability of online control when executing a movement by patients dressed in an exoskeleton. In the case of lower-limb representation, there is still the problem of reliably distinguishing leg movement intentions and differentiating them in BMI systems. Targeting the design of a rehabilitation technique replicating the natural mode of motor control in exoskeleton walking patients, we have shown how the combined use of multimodal signals can improve the accuracy, performance, and reliability of HMI. The system was tested on healthy subjects operating the exoskeleton under different conditions. The study also resulted in algorithms of multimodal HMI data collection, processing, and classification. The developed system can analyze up to 15 signals simultaneously in real-time during a movement. Foot MI is extracted from EEG signals (seven channels) using the event-related (de)synchronization effect. Supplemented by EMG signals reflecting motor intention, the control system can initiate and differentiate the movement of the right and left legs with a high degree of reliability. The classification and control system permits one to work online when the exoskeleton is executing a movement.
Mudanças temporais na composição florística e na diversidade de um cerrado sensu stricto do Brasil Central em um período de 18 anos (1985-2003)

Inventories were conducted at three-year intervals in 19 permanent plots of 1000 m2 located at Fazenda Agua Limpa (FAL) - DF. Three events of disturbance by fire occurred in the area. A total of 69 species, 54 genera and 35 families were registered. Floristic composition was compared using the modified Morisita-Horn index, a quantitative similarity index, and Sorensen, a qualitative index. Both indices showed high similarity between inventories with little change in species composition over time. Changes in richness caused differences in the values of Simpson's index, and Shannon's index according to Hutcheson's t-test (P < 0.05). Floristic composition has not changed over time under the current fire regime. But diversity increased after a longer interval without fire. In this nine-year period free from fire, new species appeared and others returned to the plots, increasing diversity, but not up to its initial values.
Interplay Between Malware Epidemics and Honeynet Potency in Industrial Control System Network

The Industrial Control System (ICS) is widely used in industrial processes, such as power grids, water conservancy, natural gas, petrochemical and so on. More and more cyber attacks are targeting the ICS worldwide. This paper presents a novel honeynet-based epidemic model in ICS network. The honeynet is an active approach that can attract malware attacks and provide sample information and immunization strategy of the malware. An epidemic model with immunization and quarantine in ICS network is formulated to explore the dynamics of the malware propagation, and the honeynet potency is analyzed as well. Theoretical analysis reveals the disease-free and endemic equilibrium of our model, then the local and global stability of the disease-free (endemic) equilibrium are examined by the basic reproduction number. Furthermore, numerical experiments show that the honeypot with more system vulnerabilities is conducive to suppress the malware epidemic, and the honeynet with lower average degree power low index can be more effectively. In addition, simulation experiments provide the actual behavior of malware propagation in the ICS network and verification of our derivations.
3D-ReConstnet: A Single-View 3D-Object Point Cloud Reconstruction Network

Object 3D reconstruction from a single-view image is an ill-posed problem. Inferring the self-occluded part of an object makes 3D reconstruction a challenging and ambiguous task. In this paper, we propose a novel neural network for generating a 3D-object point cloud model from a single-view image. The proposed network named 3D-ReConstnet, an end to end reconstruction network. The 3D-ReConstnet uses the residual network to extract the features of a 2D input image and gets a feature vector. To deal with the uncertainty of the self-occluded part of an object, the 3D-ReConstnet uses the Gaussian probability distribution learned from the feature vector to predict the point cloud. The 3D-ReConstnet can generate the determined 3D output for a 2D image with sufficient information, and 3D-ReConstnet can also generate semantically different 3D reconstructions for the self-occluded or ambiguous part of an object. We evaluated the proposed 3D-ReConstnet on ShapeNet and Pix3D dataset, and obtained satisfactory improved results.
Free energy and enthalpy of ATP hydrolysis in the sarcoplasm

A rigorous calculation of the free energy available in vivo from ATP hydrolysis requires the following information which is not all available, namely: (i) intracellular pH, (ii) activities of all the species of reactants and products in sarcoplasm, (iii) thermodynamic data for all the reactions involved, including values for ionic strength and temperature dependence, and (iv) the extent of deviation from equilibrium conditions, i. e. during contraction. We shall discuss each of these factors in turn and state the assumptions made that allow the approximate calculation of the free energy made available by the following net reaction in the sarcoplasm: ATP +H2O → ADP + Pi + H+. (1) Although it can only be an approximation this calculation is useful since it will take into account recent thermodynamic measurements in vitro.
Graph Convolution-Based Deep Clustering for Speech Separation

Deep clustering is a promising technique for speech separation that is crucial to speech communication, acoustic target detection, acoustic enhancement and speech recognition. In the study of monophonic speech separation, the problem is that the decrease in separation and generalization performance of the model in the case of reducing the variety of the training data set. In this paper, we propose a comprehensive deep clustering framework that construction the structural speech data based on GCN, named graph deep clustering (GDC) to further improve the separation performance of the separation model. In particular, embedding features are transformed into graph-structured data, and the speech separation mask is achieved by clustering these graph-structured data. Graph structural information aggregates nodes within a class, which makes feature representations conducive to clustering. Experimental results demonstrate that the proposed scheme can improve the clustering performance. The SDR of the separated speech is improved by about 1.2 dB, and the clustering accuracy is improved by 15%. We also use the perceptually motivated objective measures for the evaluation of audio source separation to score the speech quality. The target speech quality and the overall perceptual score are improved by 10.7% compared with other speech separation algorithms.
Características nutricionais e utilização do resíduo de batata-doce em dietas de frangos de crescimento lento

Three experiments were conducted in the poultry sector of the Federal University of Tocantins - Campus of Araguaina, to determine the energy and nutritional values of the residue of ethanol production from sweet potato (RBD) and its use in slow growth broiler diets. In the first experiment was conducted a metabolism trial to determine the apparent metabolizable energy (AME) and corrected for nitrogen balance (AMEn) and apparent metabolizable coefficients of the residue using the method of total excreta collection, in a completely randomized design with two treatments: basal diet and test diet (70% reference diet + 30% inclusion of the residue of sweet potato) four replications and ten birds per experimental unit. The RBD used showed: 24.64% of crude protein (CP), and 2732 kcal / kg and 2547 kcal / kg as the determined values for AME and AMEn on the natural material respectively. The others experiments were conducted to evaluate the inclusion (0%, 6%, 12% and 18%) of RBD in slow growth poultry diets, evaluating the performance characteristics and metabolism. The inclusion levels did not affect feed intake, but there was a reduction in weight gain(WG) and worse feed conversion(FC). The coefficients of metabolizable nitrogen and mineral matter presented quadratic performance with maximum points from 6.38% and 5.17% respectively. The RBD can be included in slow growth poultry diets to the level of 1,041%.
Lake Chad : ecology and productivity of a shallow tropical ecosystem

Historical background. - I. The lacustrine environment and its evolution. - 1. Paleolimnology of an upper quaternary endorheic lake in Chad Basin. - 2. The lacustrine environment. - 3. Physical and chemical characteristics of the waters. - 4. Hydrochemical regulation of the lake. - II. The main types of communities and their evolution during a drought period. - 5. The aquatic vegetation of Lake Chad. - 6. The phytoplankton. - 7. The zooplankton. - 8. The benthic fauna: ecology, biomass and communities. - 9. The fauna associated with the aquatic vegetation. - 10. Fish communities of Lake Chad and associated rivers and flood-plains. - III. The balance of a lacustrine ecosystem during `Normal Chad' and a period of drought. - 11. Phytoplankton production. - 12. Secondary production (zooplankton and benthos). - 13. The exploitation of fish stocks in the Lake Chad region. - IV. Trophic relations. - 14. Trophic relations between the phytoplankton and the zooplankton. - 15. Trophic relations of fishes in Lake Chad. - 16. The impact of birds on the lacustrine ecosystem. - V. - 17. The lacustrine ecosystem during the `Normal Chad' period and the drying phase. - Systematic index. - General index.
Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

Considering the low satisfaction rate and low efficiency of product service plans, a personalized product service plan recommendation method adopting the degree of trust and cloud model is proposed. The recommendation algorithm mainly includes calculating the similarity and the prediction results of the scheme. First, by fully considering the user’s subjective characteristics, the user’s trust is used to improve the traditional similarity. Second, considering data sparsity and discreteness, the cloud drop distance similarity calculation method is introduced in the process of calculating the trust similarity, and a new similarity is generated via the weighted synthesis to predict and fill in the gaps in the data. Then, when the new user does not have the cold start problem caused by the historical score record, the neural network method can be used to classify the users based on the user characteristics. The method is proposed and introduced to predict sparse user interest features and obtain similar user sets based on feature classification. The corresponding program offers recommendations. Finally, the effectiveness and rationality of the proposed method are verified by using the recommendations for a machine tool product for a manufacturing enterprise as an example.
Min-SINR Maximization for mmWave Massive MISO-NOMA System With Randomly Directional Beamforming

Based on the high-direction characteristic of millimeter wave (mmWave) transmission, randomly directional beamforming (RDB) can be used for the mmWave massive multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system to reduce the number of radio frequency (RF) chains. However, the impact of RDB on the signal-to-interference-plus-noise ratio (SINR) of each user is related to the corresponding beamforming gain and interference from other users. Thus, in this paper, we investigate the max-min SINR among all users to evaluate user fairness. In particular, we focus on the single-cell downlink mmWave MISO-NOMA system with RDB, where single-antenna users are divided into multiple NOMA clusters according to their azimuth angles. We formulate the minimum achievable SINR maximization problem associated with power allocation and propose the sum of power allocation coefficients based iterative algorithm (SPACIA) to find the max-min SINR. We also prove that the max-min SINR monotonically decreases as the number of paired users in an NOMA cluster increases as well as the number of beams in the cell with large-scale base station (BS) antenna array increases. Moreover, we derive the upper bound of the max-min SINR. Simulation results verify our theoretical analyses and demonstrate that the proposed algorithm guarantees user fairness, thus outperforming existing schemes.
Accelerated testbenches

The relative verification effort compared to the overall design effort increases with the complexity of digital circuits, which in turn increases factor 4x every 3 years according to Moore's law. Simulation is work horse in verification even if formal methods and static models and design analysis gain momentum. Major effort in simulation is the building of so called testbenches or environment models imitating the environment of the digital circuit. This testbenches imitate the environment by applying events to,and checking events from, the interface signals of the model of the digital circuit. To cope with the dramatically increasing simulation time, which results from digital circuit complexity as well as the longer window, which is simulated, more and more hardware based simulator accelerators, emulators or FPGA based prototypes are used. The benefit of both modeling approaches for testbenches, the relatively small modelling effort for abstract environment models and the high execution speed of hardware like models can be combined by synthesizing abstract environment models.
FLDNet: Light Dense CNN for Fingerprint Liveness Detection

Fingerprint liveness detection has gained increased attention recently due to the growing threat of spoof presentation attacks. Among the numerous attempts to deal with this problem, the Convolutional Neural Networks (CNN) based methods have shown impressive performance and great potential. However, there is a need for improving the generalization ability and reducing the complexity. Therefore, we propose a lightweight (0.48M parameters) and efficient network architecture, named FLDNet, with an attention pooling layer which overcomes the weakness of Global Average Pooling (GAP) in fingerprint anti-spoofing tasks. FLDNet consists of modified dense blocks which incorporate the residual path. The designed block architecture is compact and effectively boosts the detection accuracy. Experimental results on two datasets, LivDet 2013 and 2015, show the proposed approach achieves state-of-the-art performance in intra-sensor, cross-material and cross-sensor testing scenarios. For example, on LivDet 2015 dataset, FLDNet achieves 1.76% Average Classification Error (ACE) over all sensors and 3.31% against unkown spoof materials compared to 2.82% and 5.45% achieved by state-of-the-art methods.
Phosphorus Seed Coating Enhancement on Early Growth and Yield Components in Oat

Availability of P reserves for seedling growth in germinating oat (Avena saliva L.) grain is likely to be reduced in comparison with that in other temperate cereals. Environmental concerns have resulted in reduced P and N fertilizer application, which may, with transformation of P into unavailable forms in soil, result in suboptimal P availability especially at seedling growth. A field experiment was arranged with oat cultivars (Belinda, Fiia, and Salo) in main plots and P seed coating in subplots. The experiment was done in 2003 and 2004 at two locations in Finland that differed in soil type and were all low in soil P content. A third experiment also having low soil P content included three cultivation methods (1, control, with 80 kg N and 18 kg P ha -1 and herbicide application; 2, 80 kg N and 18 kg P ha -1 and herbicide application, supplemented with 40 kg sulfur (S) ha -1 ; and 3,80 kg N and 18 kg P ha -1 and herbicide application, supplemented with 40 kg S ha -1 and fungicide application). It was sown with two oat cultivars (Belinda and Fiia) with and without P seed coating. Seedling emergence, biomass accumulation, relative growth rate, growth duration, grain yield, and 20 plant stand structure traits were measured. Seed coating with P resulted in alterations to many of the recorded plant stand structure traits. All significant changes represented improvements over the control. The effect, however, changed depending on experiment, location, cultivation method, and cultivar. Most often, enhanced biomass accumulation (up to 22%) and grain set (up to 15%) occurred. This was not, however, associated with increased grain yield in any of the experiments, even in cases where biomass and grain yield per unit area were increased by P coating. Small kernels were probably lost during harvest, as in most cases grain weight of hand-threshed samples was lower (by 8% in average) than that in harvested yield. In conclusion, P seed coating promoted early oat growth without increasing economic yield.
Evaluating the Impact of Ionizing Particles on FinFET -based SRAMs with Weak Resistive Defects

Fin Field-Effect Transistor (FinFET) technology enables the continuous downscaling of Integrated Circuits (ICs), using the Complementary Metal-Oxide Semiconductor (CMOS) technology in accordance with the More Moore domain. Despite demonstrating improvements on short channel effect and overcoming the growing leakage problem of planar CMOS technology, the continuity of feature size miniaturization allowed by FinFETs tends to increase sensitivity to Single Event Upsets (SEUs) caused by ionizing particles, especially in blocks with higher transistor densities as Static Random-Access Memories (SRAMs). Variation during the manufacturing process has introduced different types of defects that directly affect the SRAM's reliability, such as weak resistive defects. As some of these defects may cause dynamic faults, which require more than one consecutive operation to sensitize the fault at the logic level, traditional test approaches may fail to detect them and test escapes can occur. These undetected faults associated with weak resistive defects may affect the FinFET -based SRAM reliability during the lifetime. In this context, this paper proposes to investigate the impact of ionizing particles on the reliability of FinFET -based SRAMs in the presence of weak resistive defects. Firstly, a TCAD model of a FinFET-based SRAM cell is proposed in order to allow the evaluation of the ionizing particle's impact. Then, SPICE simulations are performed considering the current pulse parameters obtained with TCAD. In this step, weak resistive defects are injected into the FinFET-based SRAM cell. Results show that weak defects may have either a positive or negative influence on the cell reliability against SEUs caused by ionizing particles.
Deep Model-Based Semi-Supervised Learning Way for Outlier Detection in Wireless Capsule Endoscopy Images

Wireless capsule endoscopy (WCE) has become an irreplaceable tool for diagnosing small intestinal diseases, and detecting the outliers in WCE images automatically remains as a hot research topic. Considering the difficulties in obtaining sufficient labeled WCE data, it is necessary to develop the diagnosis model which works well with only little labeled or even unlabeled training samples. In this paper, a novel semi-supervised deep-structured framework is introduced to solve the problem of outlier detection in WCE images. The key idea of our model is to mine the anomalous graphical patterns existed in the image by analyzing the spatial-scale trends of sequential image regions. Three main contributions are concluded: 1) we integrate a convolutional neural network into long short term memory network, so that the intrinsic differences between outliers and normal instances could be captured. Besides, 2) a assessment model is built by using various signs of anomaly occurrence and fake outliers knowledge learned during the training stage, which enhances the outlier alarm accuracy significantly. Furthermore, 3) a nest-structured training method is proposed, which helps our model achieving efficient training process. Experimental results on the real WCE images demonstrate the effectiveness of our model.