Miracle-dz/UniFAR-FTUs huggingface dataset:
Programmable Fine-Grained Power Management and System Analysis of RISC-V Vector Processors in 28-nm FD-SOI

This letter presents a RISC-V System-on-Chip (SoC) with fully integrated switched-capacitor DC–DC converters, adaptive clock generators, mixed-precision floating-point vector accelerators, a 5-Gb/s serial memory interface, and an integrated power management unit (PMU) manufactured in 28-nm FD-SOI. The vector accelerator improves performance and energy per task on a matrix multiplication kernel by <inline-formula> <tex-math notation="LaTeX">$15\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$13\times $ </tex-math></inline-formula>, respectively, and end-to-end performance on machine learning and graph analytical workloads by <inline-formula> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$12\times $ </tex-math></inline-formula>. Inclusion of microarchitectural counters and fine spatial power-domain granularity facilitate the predictive power-management algorithms that reduce energy per task by 13%–22% compared to the baseline scalar processor. System-level simulations of a range of SoC architectural variations with multiple cores and vector accelerators complement the silicon measurements.
Buck-Plus-Unfolder as the Superior Active Power Decoupling Solution for 400 Vdc/kW-Level Applications

In single-phase ac/dc applications where reliability and/or power-density are critical, active power decoupling (APD) circuits can be employed to reduce the required capacitance on the dc-link. Various APD circuits have been proposed so far, all with their advantages and disadvantages. However, many confusions still exist in the literature on this topic which is mainly attributed to a lack of unified and comprehensive assessment criteria. In this paper, first the decisive criteria for a modern APD circuit are established, and the buck APD is identified as the current state-of-the-art, based on them. Then the buck-plus-unfolder topology with triangular current mode (TCM) modulation is proposed as an improvement, and a simple, yet solid foundation is introduced to choose the superior decoupling solution at different specifications. The operation equations for the APD with TCM modulation are derived next, and the operation of the proposed solution is demonstrated using a hardware prototype.
Which Variables in Forest Survey Data Can Better Distinguish Conserved and Degraded Tropical Dry Forest?

This paper presents the results of a statistical study of forest inventory data for tropical dry forest in Ayuquila River Basin, Jalisco state, Mexico. The field inventory was carried out between May-June of 2019 which is at the end of dry season and the beginning of raining season. The field inventory data were collected in 43 plots of 500 $\mathrm{m}^{2}$ each which were designed in a way to include tropical dry forests in two conditions: degraded and conserved. In each plot, the collected data include DBH, tree height, number of trees per plot, and the density of tree stems. A study was carried out to find out if there are statistically significant differences variables relating to forest structure between degraded and conserved status. The Mann-Whitney test shows that there is significant differences in canopy cover, biomass, tree height, and basal area. This information is important since it helps to understand whether and how forest degradation can be detected using remote sensing data.
Technique and apparatus for Pulsed OSL Readout of YAP:Mn Dosimetric Detectors with Enhanced Dynamic Range

The experimental technique to study the dosimetric response of Mn2+-doped yttrium-aluminum perovskite (YAP:Mn) by means of optically stimulated luminescence (OSL) readout in pulse mode has been developed. A method of synchronous background subtraction has been adopted to extend the lower end of the dynamic range of light flux. A dedicated apparatus has been designed implementing this technique with application specific optical layout, photometric subsystem and the measurement procedure. Some experimental results are presented to demonstrate its features and capabilities for ionizing radiation dosimetry applications.
Multicriteria Analysis For Identifying Forest Fire Risk Zones In The Biological Reserve Of The Sama Cordillera, Bolivia

Forest fires have negative effects on biodiversity, the atmosphere and human health. The paper presents a spatial risk model as a tool to assess them. Risk areas refer to sectors prone to the spread of fire, in addition to the influence of human activity through remote sensing and multi-criteria analysis. The analysis includes information on land cover, land use, topography (aspect, slope and elevation), climate (temperature and precipitation) and socio-economic factors (proximity to settlements and roads). Weights were assigned to each in order to generate the forest fire risk map. The investigation was carried for a Biological Reserve in Bolivia because of the continuous occurrence of forest fires. Five risk categories for forest fires were derived: very high, high, moderate, low and very low. In summary, results suggest that approximately 67% of the protected area presents a moderate to very high risk; in the latter, populated areas are not dense which reduces the actual risk to the type of events analyzed.
WiFi-Based Indoor Positioning by Random Forest and Adjusted Cosine Similarity

WiFi based indoor positioning technology is very popular due to no additional hardware. WiFi location fingerprint is widely used for WiFi-based indoor positioning system. They are collected into fingerprint database to be used for estimating the location. In order to accurately estimate the indoor location, a kind of improved random forest algorithm is proposed in this paper. The proposed algorithm utilizes regional grid division to reduce the maximum error, and adopts adjusted cosine similarity to match the proper grid and fingerprint. Compared to the original random forest, the proposed algorithm reduces 1.15m in terms of maximum error.
Threshold-Based Induction Motors Single- and Multifaults Diagnosis Using Discrete Wavelet Transform and Measured Stator Current Signal

In this article, a threshold-based induction motor fault diagnosis method is proposed using the measured stator current signal. A 0.25-HP three-phase squirrel-cage induction motor fed directly online is tested in the laboratory with various single- and multielectrical faults under six different loading conditions. The discrete wavelet transform (DWT) is chosen as the signal processing technique for the measured stator currents. The threshold and energy values at each decomposition level of the DWT processing results are evaluated. Threshold values appear to be more consistent than energy values at different measured data windows, and thus, the threshold at the decomposition level d8 is chosen as a fault indicator. Curve fitting equations are developed to calculate threshold values for the motor loadings that were not tested in experiments. The suitability using threshold values for induction motor fault diagnosis is further validated using two probabilistic methods, the correlation analysis and the confidence interval estimation.
Research on Tracking Improvement for Electric Vehicle during a Car-following Process

In this paper, the electric vehicle (EV) with an adaptive cruise control (ACC) function is chosen as an object to study the car-following process. Multiple objectives contains safety, tracking, comfort and energy economy are optimized based on the model predictive control (MPC) theory. As distinguished from other studies: a strategy for the weight adjustment is proposed to improve the tracking, in which some weights in MPC can be adjusted according to the relative velocity of two vehicles in real time. The proposed control strategy are evaluated for two different traffic scenarios. The simulation results show that proposed control strategy has better tracking performance than the control strategy with the constant weight. While the tracking is improved, the energy economy is also improved in proposed strategy.
Effects of Dietary Inclusion of Olive Pulp Supplemented with Probiotics on Productive Performance, Egg Quality and Blood Parameters of Laying Hens

This study was conducted to evaluate effects of diet inclusion of olive pulp supplemented by acommercial probiotic mixture (Yeasture®) including Saccharomyces cerevisiae, Lactobacillus acidophilus and L. casei on performance of laying hens, egg quality traits and some blood parameters. A total number of 144 Lohmann LSL-Lite hens were randomly divided in 24 cages. Based on a 3×2 factorial arrangement of treatments, six iso-energetic and iso-nitrogenous experimental diets (ME =2720 Kcal/Kg and CP=15 g/Kg) including: Icorn-soybean meal-based control-1 diet (C1), II-corn-soybean mealbased control-2 diet (C2), and IIIcorn-soybean meal-based diet with 160 g/kg olive pulp (E), with or without probiotic Yeasture® (0.0 and 0.06 g/kg) were formulated. Hens in every randomly-selected four cages (replicates) allotted to feed on each of 6 experimental diets. Collected data of feed intake (FI), egg production (EP), egg mass (EM) and calculated feed conversion ratio (FCR) as well as egg quality traits during this 7week trial period were analyzed based on completely randomized design using GLM procedure of SAS. Diet inclusion of olive pulp did not have significant effect on EP and EM and increased FI and FCR in compared to control groups. Dietary probiotic supplementation decreased EP and EM. Diet inclusion of olive pulp decreased Haugh unit, yolk color and shell weight in the first egg sampling (wk 3) in compared with control diets (P =.05). In second egg Research Article Annual Research & Review in Biology, 4(1): 198-211, 2014 199 sampling (wk7), dietary supplementation by probiotic decreased Haugh unit. Diet inclusion of olive pulp significantly decreased yolk color in second egg sampling (wk7). Including olive pulp to diet significantly decreased serum levels of cholesterol and HDL. Dietary supplementation by probiotic significantly increased cholesterol, HDL and LDL in compared with control groups (P =.05). There was no the significant interaction between dietary olive pulp and probiotic on white blood cell counts, except for monocyte percentage (P=0.01). Dietary treatments did not have significant effect on body weight (BW) and excreta pH. From the results of the present study, it can be concluded that OP can be included in laying hens diets up to 16% with no adverse effect on performance; however, in terms of feed efficiency, Hugh unit, eggshell weight and yolk color, lower percentage of olive pulp in diets would be appreciated.
Poster Abstract: Joint Optimization of Service Function Chain Elastic Scaling and Routing

The main problem of current Virtualization Network Function (VNF) elastic scaling mechanism is that it does not consider the routing cost variation of the whole Service Function Chain (SFC) after scaling operation. In some network environment, the routing cost may increase considerably. In this poster, we address the joint optimization problem of SFC elastic scaling and routing, and propose a greedy algorithm to reduce routing cost caused by scaling operation. The main idea is to force the scaling of VNF, which does not require scaling operations, to optimize the routing cost of traffic forwarding paths. The simulation results indicate that the proposed algorithm can reduce 27% total cost compared with traditional scaling algorithm.
Mvgan Maximizing Time-Lag Aware Canonical Correlation for Baseball Highlight Generation

This paper presents multi-view unsupervised generative adversarial network maximizing time-lag aware canonical correlation (Mv-GAN) for baseball highlight generation. MvGAN has the following two contributions. First, MvGAN utilizes textual, visual and audio features calculated from tweets and videos as multi-view features. MvGAN which adopts these multi-view features is the effective work for highlight generation of baseball videos. Second, since there is a temporal difference between posted tweets and the corresponding events, MvGAN introduces a novel feature embedding scheme considering a time-lag between textual features and other features. Specifically, the proposed method newly derives the time-lag aware canonical correlation maximization of these multi-view features. This is the biggest contribution of this paper. Furthermore, since MvGAN is an unsupervised method for highlight generation, a large amount of training data with annotation is not needed. Thus, the proposed method has high applicability to the real world.
Research on the Algorithm of Initial Alignment for New Generation Air-to-air Missile

As one of the main weapons of the fourth generation fighter, the new generation air to air missile has the characteristics of close combat and over sight air interception, which determines that the transfer alignment of the missile borne moving base must be full of rapidity and high precision. In view of this, the mainly research content of this report is that the missile of navigation system is facing the actual engineering problems before launching, combined with a new generation of air-to-air missile combat mode and is buried in the mount, the establishment of a complete set of moving bade initial nonlinear mathematical model and nonlinear for the algorithm. The model, discusses the extended Kalman filtering algorithm, unscented Kalman nonlinear filter and strong tracking adaptive filtering algorithm in the Kalman nonlinear alignment technology in the application of moving base initial Alignment. The velocity and attitude matching model for the simulation of three kinds of nonlinear filtering algorithm.
A Two-Stream Context-Aware ConvNet for Pavement Distress Detection

Convolutional neural networks (ConvNets) are widely used for pavement distress analysis tasks in which features are typically extracted from a smaller image (e.g. $224\times 224$) that has been cropped from an orthophoto. This paper introduces a ConvNet-based method to classify partitioned segments of orthophotos for pavement distress by incorporating two image input streams, one of which provides contextual information of the segment’s surroundings. The resulting two-stream ConvNet is evaluated to classify segments of the orthophoto with a better performance for pavement distress than single-stream ConvNets, in terms of precision and recall.
Implementation Of Technologies in the Public Service: Geomatics in the Cloud for Monitoring Wetlands in Protected Areas

Wetlands protection has been a constitutive part of the conservation strategies that the Chilean Forest Service has implemented in the Protected Wild Areas of Chile. These ecosystems are highly vulnerable to changes in their hydrological regime, generating direct effects on the biodiversity associated with them. These changes may be related to the rainfall regime, however, the activities of drainage, overgrazing, landfill, or tributary deviation are the greatest threats that these ecosystems face today. For years, long-term satellite information was dismissed in the Chilean public service due to the lack of technical, computational or software capabilities. Nowadays, cloud computing tools with massive geodata analysis have democratized remote sensing, giving way to the development of web applications and online information analysis platforms. An interactive web application was developed in Google Earth Engine (GEE) that uses spectral indexes correlated with the presence of water, generating estimates in “almost real time” on Landsat and Sentinel-2 images. The Surface Water Satellite Monitoring Platform allows the automatic processing through the internet of the images available for the analyzed wetlands, overcoming the time and computational capacity restrictions required with conventional geomatics programs. This has allowed to support the management of these ecosystems, reducing the costs of field work and increasing the efficiency in the monitoring of wetlands in the Protected Wild Areas of Chile.
Classification Based Load Balancing in Content Delivery Networks

The use of content delivery networks is emerging with the increasing population with access to the Internet. Content Delivery Networks bring content closer to the end-user lowering operational expenses and increasing end-user experience. End users consume content from the cache servers using redirection. Optimising the load distributed to these cache servers is crucial to maintain a sustainable network. In this article, we created and evaluated a new routing method with the help of artificial intelligence exploiting Software-defined networking. The new routing methods aim to distribute the load to the delivery servers optimally and maximises utilisation of the network, avoiding bottlenecks along the way. The proposed approach distinguishes and routes the flows in the network on the session layer rather than just following standard routing rules based on prefix matching. Concluding with the evaluation of achieved results and the gained performance increase in this article.
QVAT: QRS Complex Detection based on Variance Analysis and Adaptive Threshold for Electrocardiogram Signal

Heart disease is a disease that has a high level of danger. Somebody who has a history of heart disease must be careful in doing daily activities. Paramedics analyze Electro-cardiogram(ECG) signals to detect heart abnormalities. Some researchers propose automated methods to analyze the heart condition based on ECG signals. One parameter for assessing heart condition is the distance from R peak to R peak, R is the peak of the QRS complex wave. In this research, we proposed QVAT algorithm that automatically detects QRS complexes, then finds R peaks from an ECG signal. The algorithm that we use consists of several steps, namely: band-pass filter, analysis of variance, adaptive threshold and local maxima. Band-pass filters are used to reduce noise that can cause errors in detection QRS waves. Possible noise due to: interference due to electromagnetic wave voltage, noise of muscle movement. The average of variance is used to strengthen the QRS Complex feature at positive x-coordinates, the adaptive threshold is used to localize the QRS complex. The result of adaptive threshold is a region of interest (ROI) of QRS Complex that is used to find the position of R peak. We use the adaptive threshold since the magnitude and slope features of each subject's ECG signal are different. Evaluating the performance of our proposed algorithm, we tested it to detect the QRS complex in the MIT BIH Arrhythmia database. The proposed algorithm QVAT has a sensitivity $Se=\mathbf{99.79}\ \%$ and a positive predictive $+P=\mathbf{99.90}\ \%$. These results indicate sensitivity of QVAT is better than Pantompkins, Garca Rivas and Xiang. The positive predictive parameter is comparable to Xiang method however it is better than Pantompkins, Garca Rivas.
Poster Abstract: Relaxing Network Selection for TCP Short Flows Using SYN Duplication

In the current and next generation of mobile networks, a mobile device intuitively has multiple wireless radios such as Wi-Fi, LTE, 5G New Radio, etc. Moreover, most likely, the mobile device continues to use the TCP/IP stack, which limits the device's network selection. The application traffic may stick to a default network, which may not have the best performance among the associated ones. That harms the performance of TCP short flows, such as web access. This paper proposes using TCP SYN duplication to relax the constraint, aiming to select the best network at the present moment. The device tries to initialize a TCP connection by duplicating and sending SYN packets via all the available networks. The network, which conveys the earliest SYN/ACK response, will be selected for the data transfer. We implement and evaluate the proposal in a testbed with real Internet connections to show its effectiveness.
Feasibility Analysis in Tracking Algorithms for Pattern Recognition Through the Lip Articulators

Problems in speech and basic emission of vocal sounds are commonly observed in children, however the task of identifying the origin of such disability is complex and prolonged, due to the generic aspect, if by irregular learning or neurobiological disorder, thus causing, in some situations, constant social exclusion. Therefore, it is proposed to use Tracking Algorithms consolidated in specific literature (ACF, Boosting, MIL, Median Flow, KCF, KLT, CRST) for a feasibility analysis in complex computational implementations in the anatomy of human vocal tract from sagittal images. These algorithms are used for understanding the kinematic configuration of multiple dynamic objects and their movements, represented by the upper and lower lips (consequently the “lip distance” at the moment of speech), using Brazilian Portuguese language, for the extraction and recognition of patterns automatically. The patterns can be used in innovative applications in therapy for children with poor articulation in the vocal tract, based on “gaps” and specific difficulties in speech.
Multi-UAV Assisted Communication Networks: Towards Max-Min Secrecy Rate

This paper investigates a multi-unmanned aerial vehicle (UAV) assisted network, where multiple UAVs send confidential information to multiple authorized access users (AUs), in the existence of an unauthorized access users. In order to improve the AUs' experience quality, an optimization problem is formulated to maximize the minimum secrecy rate of AUs by jointly optimizing user scheduling, UAVs' trajectories and transmit power allocation, subject to on board energy budget and curvature radius constraints. Since the problem is a mixed integer non-convex optimization problem, an efficient algorithm is proposed based on the block coordinate descent (BCD) and successive convex approximation (SCA) theories, which achieves a good suboptimal solution. Simulation results show that the proposed algorithm converges within several iterations, and significant system performance gain is achieved compared to other benchmarks. Moreover, the larger the curvature radius is, the smoother the UAVs' trajectories are. Although it also yields the lower the secrecy rate, introducing curvature radius into trajectory design is an effective approach to make the optimized UAVs' trajectories closer to practical flight. Besides, the higher the propulsion energy stored at UAVs is, the higher the achieved max-min secrecy rate is. Additionally, the max-min secrecy rate is improved by allowing concurrent transmissions of multiple UAVs.
A General Re-Ranking Method Based On Metric Learning For Person Re-Identification

When Person Re-identification is considered as a retrieval task, re-ranking becomes a critical part of improving the re-identification accuracy. Most of the existing re-ranking methods focus on k -nearest neighbors, which requires a lot of queries and memory. In this paper, we propose a Feature Relation Map based Similarity Evaluation (FRM-SE) model to tackle this problem. The Feature Relation Map is utilized to automatically mine the latent relation between the k -neighbors through convolution operation. The re-ranking distance is learned through the FRM-SE model with metric learning. Further, we optimize the existing re-ranking method to utilize the advantage of the FRM-SE model for maintaining a balance between accuracy and complexity.The proposed approach is validated on two benchmark datasets, Market1501 and CUHK03. Results show that our re-ranking method is superior to the state-of-the-art re-ranking methods. Furthermore, in the transfer learning setting, the model trained on either Market1501 or CUHK03 can achieve a comparable accuracy improvement on the DuekMTMC dataset, which validates the generalization of our SE model.