Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization Design and Experimental Study of Gmapping Algorithm

This paper aims at the issue of simultaneous localization and mapping of indoor mobile robot. In order to solve the defect of large computation and poor real-time performance of traditional particle filter algorithm, the Gmapping algorithm based on Rao-Blackwellized particle filter introduces the improved proposal distribution with high-precision lidar data and adaptive resampling measure to construct two dimensional grid map. With the help of the robot operating system, the map construction of Gmapping algorithm under different parameters is realized in two dimensional robot simulator. The performance of optimized Gmapping algorithm is verified on the platform of differential mobile robot. Finally, the fusion of multi-sensor information and optimization design of Gmapping algorithm are realized by calling the /imu/calibrate_imu service, updated and calibrated inertial measurement units data, and extended Kalman filter package. The simulation and experimental results show that the robot system can construct high precision environment map under the optimal parameter configuration.
A Performance Modelling Approach for SLA-Aware Resource Recommendation in Cloud Native Network Functions

Network Function Virtualization (NFV) becomes the primary driver for the evolution of 5G networks, and in recent years, Network Function Cloudification (NFC) proved to be an inevitable part of this evolution. Microservice architecture also becomes the de facto choice for designing a modern Cloud Native Network Function (CNF) due to its ability to decouple components of each CNF into multiple independently manageable microservices. Even though taking advantage of microservice architecture in designing CNFs solves specific problems, this additional granularity makes estimating resource requirements for a Production Environment (PE) a complex task and sometimes leads to an over-provisioned PE. Traditionally, performance engineers dimension each CNF within a Service Function Chain (SFC) in a smaller Performance Testing Environment (PTE) through a series of performance benchmarks. Then, considering the Quality of Service (QoS) constraints of a Service Provider (SP) that are guaranteed in the Service Level Agreement (SLA), they estimate the required resources to set up the PE. In this paper, we used a machine learning approach to model the impact of each microservice's resource configuration (i.e., CPU and memory) on the QoS metrics (i.e. serving throughput and latency) of each SFC in a PTE. Then, considering an SP's Service Level Objectives (SLO), we proposed an algorithm to predict each microservice's resource capacities in a PE. We evaluated the accuracy of our prediction on a prototype of a cloud native 5G Home Subscriber Server (HSS). Our model showed 95%-78% accuracy in a PE that has 2–5 times more computing resources than the PTE.
Very Short-Term Power System Frequency Forecasting

Power system frequency plays a pivotal role in ensuring the security, adequacy, and integrity of a power system. While some frequency response services are automatically delivered to maintain the frequency within the stipulated limits, certain cases may require that system operators (SOs) manually intervene—against the clock—to take the necessary preventive or corrective actions. As such, SOs can be greatly aided by practical tools that afford them greater temporal leeway. To this end, we propose a methodology to forecast the power system frequency in the subsequent minute. We perform an extensive analysis so as to identify the factors that influence power system frequency. By effectively exploiting the identified factors, we develop a forecasting methodology that harnesses the long short-term memory model. We demonstrate the effectiveness of the proposed methodology on Great Britain transmission system frequency data using comparative assessments with selected benchmarks based on various evaluation metrics.
Review of Substation Grounding System Behavior Under High Frequency and Transient Faults in Uniform Soil

Substations are important parts of electric power systems, and they require well-designed grounding systems. A proper grounding system guarantees the safety of the personnel working in an environment surrounded by grounded equipment from possible electric shock, protects the equipment against unnecessary breakdowns, and conserves the stability of the entire electrical system throughout its operation. Grounding systems developed under power frequency conditions generally react differently under high frequency and transient conditions, such as switching transients and lightning strikes. This work reviews the modeling methods for substation grounding systems and their performance when grounding design parameters change under high frequency and transient fault conditions.
Green Cognitive Internet of Things with Self Supply of Energy and Spectrum in Emergency Communications

In the emergency scenario, wireless communication systems will face spectrum shortage and energy cut-off in case of infrastructure destruction. In this paper, a green cognitive Internet of Things (CIoT) with wireless energy harvesting (WEH) is proposed to share the spectrum licensed to primary user (PU) and harvest the radio frequency (RF) energy of PU's signal, in order to achieve self supply of energy and spectrum. Energy-efficient resource allocation is presented to maximize the average transmission rate of CIoT while guaranteeing the energy saving requirements. The underlay and overlay spectrum access models for CIoT with WEH are described, in which the energy-efficient resource allocations are formulated as joint optimization problems that can be solved using alternating direction optimization (ADO) and Karush-Kuhn-Tucker (KKT) conditions. Results show that the CIoT with WEH can consume less power to achieve larger transmission rate.
Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images

Pancreatic cancer (PC) is a malignant tumor that seriously threatens the survival of patients. Artificial classification has practical difficulties, such as unstable classification accuracy, a heavy workload, and the classification results depend on the subjective judgment of the clinician during the diagnosis and staging of PC. In addition, accurate PC staging could better help clinicians deliver the optimal therapeutic schedule for PC patients of different stages. Therefore, this study proposes a comprehensive medical computer-aided method for preoperative diagnosis and staging of PC based on an ensemble learning-support vector machine (EL-SVM) and computed tomography (CT) images. The least absolute shrinkage and selection operator (LASSO) algorithm was chosen for feature selection. In contrast to no feature selection, the model optimization time decreased by 19.94 seconds while maintaining precision. The EL-SVM learner was used to classify 168 CT images of normal pancreas and different stages of PC. The experimental results demonstrated that the normal pancreas (normal)-pancreatic cancer early stage (early stage) classification accuracy was 86.61%, the normal-pancreatic cancer stage III (stage III) classification accuracy was 87.04%, the normal-pancreatic cancer stage IV (stage IV) classification accuracy was 91.63%, the normal-PC classification accuracy was 87.89%, the early stage-stage III classification accuracy was 75.03%, and the early stage-stage IV classification accuracy was 81.22%, and the stage III-stage IV classification accuracy was 82.48%. Our experimental results prove that our proposed method is feasible and promising for clinical applications for the preoperative diagnosis and staging of PC via CT images.
Multi Variable-layer Neural Networks for Decoding Linear Codes

The belief propagation algorithm is a state of the art decoding technique for a variety of linear codes such as LDPC codes. The iterative structure of this algorithm is reminiscent of a neural network with multiple layers. Indeed, this similarity has been recently exploited to improve the decoding performance by tuning the weights of the equivalent neural network. In this paper, we introduce a new network architecture by increasing the number of variable-node layers, while keeping the check-node layers unchanged. The changes are applied in a manner that the decoding performance of the network becomes independent of the transmitted codeword; hence, a training stage with only the all-zero codeword shall be sufficient. Simulation results on a number of well-studied linear codes, besides an improvement in the decoding performance, indicate that the new architecture is also simpler than some of the existing decoding networks.
WiFi over VLC using COTS Devices

We present a complete Visible Light Communications (VLC) transceiver system consisting of low-cost Commercial-Off-The-Shelf (COTS) components. In particular, we show that COTS IEEE 802.11n (WiFi) devices can be used so that the physical and data link layers of radio frequency WiFi, i.e. 2.4 GHz, are reused for VLC. Moreover, as WiFi is fully integrated with the Linux system, higher protocols from network to transport and application layer can be used and tested in VLC-related experiments. Our approach has the advantage that a VLC experimenter can fully focus on VLC-related low level aspects like the design of novel VLC front-ends, e.g. LED drivers, lenses, and photodetectors and test their impact directly on the full network protocol stack in an end-to-end manner with real applications like adaptive video streaming. We present first results from experiments using our prototype showing the performance of unidirectional VLC transmission. Here we analyze the distortions introduced as well as the relationship between signal strength on frame error rate for different MCS and the maximum communication distance. Experimental results reveal that a data rate of up to 150 Mbps is possible over short ranges.
Improved Data Association Using Buffered Pose Adjustment for Map-Aided Localization

Maps provide an important source of information for autonomous vehicles. They can be used along with cameras and lidars to localize the vehicle. This requires the ability to correctly associate observations to features referenced in the map. The problem is all the more difficult that all observations are not necessarily referenced, and all map features might not be detectable with the embedded sensors. This letter presents an adjustment technique than enables to increase the number of associations that can be made while limiting the chance of obtaining wrong associations. This is achieved by matching observations in batches in a buffer and matching them regularly. Periodically, the observation buffer is used to adjust the trajectory used to match observations. This is done without making any assumption on the association between observations and map features through a likelihood maximization process. The adjusted trajectory then provides the best associations that are used for real-time localization. The method was tested with data recorded on public roads using an experimental vehicle. The results show that the number of associations that can be made is increased, thanks to the trajectory adjustment step and the use of an observation buffer. This also results in greater localization accuracy and consistency with an average error of $0.7\text{ meters}$ at 50 Hz using road markings and traffic signs.
Predation of gypsy moth (Lepidoptera : Lymantriidae) pupae by invertebrates at low small mammal population densities

A census of small mammal populations was taken, and freeze-dried gypsy moth, Lymantria dispar (L.), pupae were placed at 6 sites near the leading edge of moth spread into the southeastern United States. Pupal predation was monitored during 3 consecutive nights and a significant relationship between the number of live small mammals and predation was observed. There was a significant inverse relationship between vertebrate and invertebrate predation levels. Pupal predation by vertebrates increased as small mammal density increased, but invertebrate predation apparently decreased with increasing small mammal density. At all sites, pupae were preyed on beginning the 1st night of exposure, suggesting that the native generalist predator complex should exhibit no time lag in recognizing and accepting gypsy moth pupae as a new food item. The percentage of intact pupae preyed on by vertebrates increased from day 1 to day 2 but then declined on day 3. In contrast, invertebrate predation increased from day 1 to day 2 and remained at the higher rate on day 3. These observations are discussed in relation to behavioral characteristics of the predator populations.
Serological detection of virus diseases of sweet potato in Kenya

Objective: To identify virus diseases attacking sweet potato in the major production areas in Kenya. Methodology and results: A total of 220 symptomatic and 108 asymptomatic sweet potato vines were collected from farmers’ fields, established in an insect-proof screenhouse and tested for viruses by nitrocellulose membrane enzyme-linked immunosorbent assay (NCM-ELISA). The viruses detected were Sweet potato feathery mottle virus (SPFMV), Sweet potato chlorotic stunt virus (SPCSV), Sweet potato mild mottle virus (SPMMV) and Sweet potato chlorotic fleck virus (SPCFV). SPFMV was the most prevalent virus and the most widespread, detected in 67 and 20% of the symptomatic and asymptomatic plants, respectively. SPCSV was the second most common and it was detected in 64 and 13% of the symptomatic and asymptomatic plant samples, respectively. SPMMV was present in 12% of the symptomatic plant samples. SPCFV was rare, being detected in only 4% of the plant samples. Cucumber mosaic virus (CMV), Sweet potato latent virus (SwPLV), Sweet potato caulimo-like virus (SPCaLV), Sweet potato mild speckling virus (SPMSV) and C-6 virus were not detected in any of the samples assayed. SPFMV and SPCSV were detected in all the 15 districts that were surveyed, whereas SPMMV and SPCFV were detected in 9 and 4 districts, respectively. Five different virus complexes were detected in the samples assayed. Dual infection with SPFMV and SPCSV was the most common multiple infection and was detected in 52 and 12% of the symptomatic and asymptomatic plants, respectively. Conclusion and application of findings : This study has provided a quantitative assessment of co-occurrence of viruses in sweet potato plants in Kenya, and highlights the importance of developing resistance specifically targeting SPCSV in either conventional or non-conventional breeding programs as a means of virus disease management.
Current-constrained Finite-time Control Algorithm for Buck Converter

In this paper, the current-constrained problem is studied. In order to track the voltage to the reference voltage quickly, a finite-time control algorithm is designed. Meanwhile, considering that when the voltage changes rapidly, the current in the circuit will increase, which will cause damage to the devices in the circuit. Thus, a current-constraint algorithm must be applied to the controller. The controller designed in this paper can achieve stability in finite-time, and the correctness of the control algorithm is proved. Next, the simulation is carried out to verify the dynamic and steady-state performance of the proposed method.
Brazilian Midwest Native Vegetation Mapping Based on Google Earth Engine

Google Earth Engine (GEE) platform is an online tool, which generates fast solutions in terms of image classification and does not require high performance computers locally. We investigate several data input scenarios for mapping native-vegetation and nonnative-vegetation in the Atlantic Forest region encompassed in a Landsat scene (224/076) acquired on November 28, 2019. The data input scenarios were: I- spectral bands (blue to shortwave infrared); II- NDVI (Normalized Difference Vegetation Index); IIImNDWI (modified Normalized Difference Water Index); IV- scenarios I and II; and V- scenarios I to III. Our results showed that the use of spectral bands added NDVI and mNDWI (scenario V) provided the best performance for the native-vegetation mapping, with accuracy of 96.64% and kappa index of 0.91.
FISH Is in the Limelight Again As More Than a Cytogenetical Technique for Metaphase Chromosomes

Summary Fluorescent in situ hybridization (FISH) has been a powerful technique to reveal chromosomal loci and distribution of DNA sequences in metaphase chromosomes. Three-dimensional FISH (3D-FISH) has also revealed the chromosome territory of interphase chromosomes in the nucleus. Recently, 3D-FISH has played a sup-portive or complementary role to conﬁrm the genome organization and topologically associated domains, which are provided by chromosome conformation capture techniques.
Distributed Deep Learning-based Task Offloading for UAV-enabled Mobile Edge Computing

Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing (MEC) is considered to offer computational capabilities to the resource-constraints end-users. In this paper, we study the task offloading strategy in UAV-enabled MEC systems, where end-users offload the computation-intensive tasks to the UAV to minimize the overall cost in terms of the weighted delay and energy consumption. The end-users either process the task by itself or offload the tasks to the UAV that acts as a computing access point. However, due to the computation bottleneck and limited channel capacity between UAV and the end-users, it becomes a challenging issue to offload the entire tasks to the UAV. Thus, to find the optimal offloading decision for the tasks generated by the end-users, we build a distributed deep neural network (DNN). In the proposed distributed DNN model, we train multiple DNNs in the same training instance, and finally, for validation, we select the DNN that gives the least training loss. For faster convergence of the training process, we use the optimal generated offloading decision using a Quadratically Constrained Linear Program (QCLP) with Semidefinite Relaxation (SDR). The extensive simulation results show that the offloading decision produced by the trained DNN can achieve near-optimal performance with numerous system parameter settings.
DexiNed-based Aluminum Alloy Grain Boundary Detection Algorithm

Grain size measurement is an important link in metallographic analysis, which determines the mechanical properties of aluminum alloy materials such as strength, ductility, plasticity, and fatigue resistance. Accurate and efficient detection of grain boundaries is essential for grain size measurement. This paper proposes an edge detection algorithm based on DexiNed to detect the grain boundaries of aluminum alloy metallographic images. First we build an aluminum alloy grain boundary detection data set to train DexiNed. Then we use the trained DexiNed to perform grain boundary detection on the metallographic image of the aluminum alloy, and report the detection performance on the test set. In addition, using the detected grain boundary images, we further explored the distribution of grain areas in the images. Through comparison experiments with traditional edge detection algorithms, we find that DexiNed can accurately and efficiently detect the grain boundaries of aluminum alloys.
Toxicité respiratoire des particules Diesel : les mécanismes cellulaires et moléculaires.

La qualite de l'air est devenue un enjeu majeur dans la politique environnementale des pays industrialises. En effet, un faisceau de presomptions, a partir des etudes epidemiologiques realisees au cours des vingt dernieres annees, permet d'etablir une relation probable entre certains polluants tels que les particules atmospheriques et la mortalite ou morbidite d'origine respiratoire et cardiovasculaire. Cependant, il n'est pas toujours facile d'etablir une relation causale claire entre un polluant donne et un effet sanitaire. Les particules Diesel ont ete rapidement incriminees dans l'aggravation des maladies respiratoires telles que l'asthme, dans la mesure ou leur petite taille leur permet d'acceder au poumon profond. Des etudes experimentales recentes ont mis en lumiere les mecanismes moleculaires a l'origine de la reponse inflammatoire induite par ces particules. Ceux-ci pourraient passer par la production d'especes activees de l'oxygene qui jouent un role central, actuellement reconnu, dans de nombreuses maladies. Une meilleure comprehension de la participation des differents constituants des particules dans cette reponse inflammatoire devrait permettre de definir des strategies efficaces de depollution. Ces recherches sur la reponse cellulaire et moleculaire a des materiaux complexes tels que les particules Diesel illustrent bien les nouvelles orientations de la toxicologie moderne qui vise de plus en plus a donner une base mecanistique aux evenements toxiques.
Distributed Frequency Controller for MT-HVDC Systems Via Adaptive Dynamic Programming

The exact mathematical models of multi-terminal high-voltage direct current (MT-HVDC) systems are hard to be obtained in the practical application of HVDC technology. To overcome this challenge, a model-free distributed frequency controller for MT-HVDC systems is proposed based on Adaptive dynamic programming (ADP) in this paper. Specifically, for each AC area, only the local and neighboring sampling data of frequency is required without the MT-HVDC system model. Besides, the proposed controller is distributed which effectively balances the communication burden among the AC areas. Moreover, the proposed controller makes the connected AC areas share their power reserves via HVDC grids to compensate load disturbances so that the necessary power reserves can be downsized. Some simulations carried out on a five-terminal HVDC system evaluate the performance of the proposed controller.
Performance Analysis of Relayed D2D Communications under Power Control

In this paper, we consider a relay-aided device-to-device (RA-D2D) communication link operating within a cellular network. The underlay mode where the D2D users share the uplink frequency resources of the cellular link is considered. An optimal algorithm for allocating the appropriate D2D transmit powers that maximize the ergodic capacity of the D2D link while ensuring the cellular communication requirements is proposed. The performance of the proposed scheme is then analyzed in terms of the ergodic capacity of the RA-D2D link. The improvement in the spectral efficiency is compared with the half-duplex (HD) and the full-duplex (FD) modes of D2D communications. Results show that the relay-aided system outperforms the HD system and even the FD system above a certain threshold of self-interference.
Towards a Security Enhanced Virtualised Network Infrastructure for Internet of Medical Things (IoMT)

Internet of Medical Things (IoMT) are getting popular in the smart healthcare domain. These devices are resource-constrained and are vulnerable to attack. As the IoMTs are connected to the healthcare network infrastructure, it becomes the primary target of the adversary due to weak security and privacy measures. In this regard, this paper proposes a security architecture for smart healthcare network infrastructures. The architecture uses various security components or services that are developed and deployed as virtual network functions. This makes the security architecture ready for future network frameworks such as OpenMANO. Besides, in this security architecture, only authenticated and trusted IoMTs serve the patients along with an encryption-based communication protocol, thus creating a secure, privacy-preserving and trusted healthcare network infrastructure.