Miracle-dz/UniFAR-FTUs huggingface dataset:
Wi-Fi Coverage in Indian Homes

We seek to study Wi-Fi coverage and performance in typical Indian homes, and appreciate infrastructure deployment strategies for whole home coverage with throughput guarantees. We prefer experiments in real homes for performance evaluation, and measured coverage and throughput performance in homes of different sizes and types, in 2.4 GHz and 5 GHz band, and with 1x1 and 2x2 client devices of different make and price range. The measurements allowed us to characterize coverage as a function of the size of the home and the band of operation, and characterize throughput as a function of signal strength, bandwidth of operation and radio capability of the devices. We also validated the observations with limited experiments in a static channel. Finally, based on our measurements, we make recommendations on network architecture, AP placement and radio configuration for whole home coverage with 100 Mbps guaranteed throughput.
Joint Association, Transmission Power Allocation and Positioning of Flying Base Stations Considering Limited Backhaul

An integration of flying base stations (FlyBSs) into future mobile network allows to manage scenarios with a highly varying density and requests of user equipments (UEs). While this research topic has received plenty of attention, a backhaul link quality (i.e., the link between a static base station and FlyBS) is either fully disregarded or oversimplified. Nevertheless, to exploit radio resources efficiently, the backhaul link and an access link (i.e., the link between the FlyBS and UE) should be managed together. Thus, in this paper, we introduce a novel power efficient and backhaul-aware association of the UEs to either the FlyBSs or the SBSs to maximize the sum capacity of all UEs. The association of UEs is managed joinlty with the transmission power allocation and the UEs are associated according to the transmission power required at the FlyBSs to serve the UEs and the benefits observed by each UE if it is associated to the particular base station. In this regard, we derive a closed-form expression for the optimal allocation of the FlyBSs’ transmission power to individual UEs to exploit the radio resources at backhaul and access links efficiently. Then, the proposed framework is enhanced by a re-positioning of the FlyBSs and a subsequent re-allocation of the transmission power at the FlyBSs to further improve the overall sum capacity. The simulations show that our proposal significantly increases the sum capacity of the UEs (from 19.6% to 135.3%) with respect to state of the art schemes.
Improving Soil Moisture Spatio-Temporal Resolution Using Machine Learning Method

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. The published soil moisture products, having a low spatial resolution of 25–40 km, and low temporal resolution of 2–3 days, limits their applications at regional scale. In this study, the spatio-temporal resolution of Fengyun (FY) SM products was improved using a machine-learning model named the General Regression Neural Network (GRNN), with the help of selected six high spatial resolution parameters, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat) after gap-filled as input variables. An implements tested over the Tibetan Plateau (TP) showed that the spatio-temporal resolution of FY-3B SM was improved from 0.25° and 2–3 days to 0.05° and 1-day. The high spatio-temporal resolution SM can enhance our understanding of water-energy cycle under climate change.
Effectiveness Evaluation of Arm Usage for Human Quiet Standing Balance Recovery through Nonlinear Model Predictive Control

The computational study of human balance recovery strategy is crucial for revealing effective strategy in human balance rehabilitation and humanoid robot balance control. In this context, many efforts have been made to improve the ability of quiet standing human balance. There are three main strategies for human balance including (i) ankle, (ii) hip, and (iii) stepping strategies. Besides, arm usage was considered for balance control of human walking. However, there exist few works about effectiveness assessment of arm strategy for quiet standing balance recovery. In this paper, we proposed a nonlinear model predictive control (NMPC) for human balance control on a simplified model with sagittal arm rotation. Three case studies including (i) active arms, (ii) passive arms, and (iii) fixed arms were considered to discuss the effectiveness of arm usage for human balance recovery during quiet standing. Besides, the total root mean square (RMS) deviation of joint angles was computed as an index of human motion intensity quantification. The proposed solution has been implemented for a human-like balance recovery with arm usages during quiet standing under perturbation and shows the effectiveness of arm strategy.
Modeling, Gait Sequence Design, and Control Architecture of BADGER Underground Robot

This letter presents the dynamic modeling, the gait sequence design, and the control architecture of the BADGER autonomous underground robot. BADGER is a modular and articulated robotic mechanism which employs inchworm biomimetic motion to drill and propel within the soil. It is used to drill and manoeuvre in the subsurface for building curved tunnels of small diameter in underground spaces, without the need for open-cut excavation. In order to design efficient motion control strategies, a model-based approach is followed. To this end, the kinematic and the lagrangian dynamic models are derived, which take into account the physical interaction of the robot drill-head with the environment, and are used for designing the inchworm gait sequence and for calculating the actuation torques and forces required to realise the trajectory profiles of the gait sequence. The motion control is executed on a simulation platform or on the actual robot hardware using a Robot Operating System (ROS) control architecture. The gait sequence and the motion controller are validated though an experiment where the BADGER is commanded to drill and follow a straight line path in the underground. The gait sequence for following a generic 3D curvilinear path in the underground is demonstrated in a simulation environment.
Web-Based Music Genre Classification for Timeline Song Visualization and Analysis

This paper presents a web application that retrieves songs from YouTube and classifies them into music genres. The tool explained in this study is based on models trained using the musical collection data from Audioset. For this purpose, we have used classifiers from distinct Machine Learning paradigms: Probabilistic Graphical Models (Naive Bayes), Feed-forward and Recurrent Neural Networks and Support Vector Machines (SVMs). All these models were trained in a multi-label classification scenario. Because genres may vary along a song’s timeline, we perform classification in chunks of ten seconds. This capability is enabled by Audioset, which offers 10-second samples. The visualization output presents this temporal information in real time, synced with the music video being played, presenting classification results in stacked area charts, where scores for the top-10 labels obtained per chunk are shown. We briefly explain the theoretical and scientific basis of the problem and the proposed classifiers. Subsequently, we show how the application works in practice, using three distinct songs as cases of study, which are then analyzed and compared with online categorizations to discuss models performance and music genre classification challenges.
Simulation of Propagation Characteristics of PD UHF Signals inside and outside of Switchgear

Partial discharge (PD) detection has proved to be a convenient, reliable and cost-effective technology in determining insulation deterioration within distribution switchgears. The signals will be different of ultra-high frequency (UHF) sensors used for PD detection of switchgear for different installation locations. In this paper, the differences of detection signals are studied for UHF sensor installed inside and outside of the switchgear. The simulation is carried out based on the finite element method and signals of UHF sensor installed on the inside and outside surface of the switchgear are obtained. The results show that the amplitude and arrival time of the UHF sensors are different for different installation locations. If the PD source and the point probes are in the same cabinet, the point probes of internal installation can obtain significantly enhanced signal strength than the point probes of external installation. On the contrary, the signal strength obtained by point probes of internal installation is almost the same with that obtained by the point probes of external installation.
Joint Time and Power Allocation for Cooperative NOMA based MEC System

This paper deals with the joint time and power allocation problem in the cooperative Non-Orthogonal Division Multiple Access (NOMA) based Mobile Edge Computing (MEC) system. We consider a basic three-node MEC system consisting of a Far User (FU), a Near User (NU), and a Base Station (BS) equipped with the MEC server. In the proposed system, the tasks of the users are divided into two parts which are executed locally and at the edge server, respectively. Moreover, the NU would help offload the FU's task together with his own through NOMA transmission. The optimization problem of joint cooperation time slots assignment and power allocation at both NU and FU is formulated to minimize the total energy consumption of users. To efficiently solve it, an algorithm is proposed through employing Lagrange duality method. Simulation results demonstrate that the proposed algorithm can outperform the existing schemes and improve the energy efficiency of the system remarkably.
Learning-in-the-Fog (LiFo): Deep Learning Meets Fog Computing for the Minimum-Energy Distributed Early-Exit of Inference in Delay-Critical IoT Realms

Fog Computing (FC) and Conditional Deep Neural Networks (CDDNs) with early exits are two emerging paradigms which, up to now, are evolving in a standing-alone fashion. However, their integration is expected to be valuable in IoT applications in which resource-poor devices must mine large volume of sensed data in real-time. Motivated by this consideration, this article focuses on the optimized design and performance validation of <inline-formula> <tex-math notation="LaTeX">${L}$ </tex-math></inline-formula>earning-<inline-formula> <tex-math notation="LaTeX">${i}\text{n}$ </tex-math></inline-formula>-the-<italic>Fo</italic> g (<italic>LiFo</italic>), a novel virtualized technological platform for the minimum-energy and delay-constrained execution of the inference-phase of CDDNs with early exits atop multi-tier networked computing infrastructures composed by multiple hierarchically-organized wireless Fog nodes. The main research contributions of this article are threefold, namely: (i) we design the main building blocks and supporting services of the <italic>LiFo</italic> architecture by explicitly accounting for the <italic>multiple</italic> constraints on the per-exit maximum inference delays of the supported CDNN; (ii) we develop an <italic>adaptive</italic> algorithm for the <italic>minimum-energy distributed joint</italic> allocation and reconfiguration of the available computing-plus-networking resources of the <italic>LiFo</italic> platform. Interestingly enough, the designed algorithm is capable to <italic>self</italic>-detect (typically, <italic>unpredictable</italic>) environmental changes and quickly <italic>self</italic>-react them by properly re-configuring the available computing and networking resources; and, (iii) we design the main building blocks and related virtualized functionalities of an <italic>Information Centric</italic>-based networking architecture, which enables the <italic>LiFo</italic> platform to perform the aggregation of spatially-distributed IoT sensed data. The energy-vs.-inference delay <italic>LiFo</italic> performance is numerically tested under a number of IoT scenarios and compared against the corresponding ones of some state-of-the-art benchmark solutions that do not rely on the Fog support.
Synthesis, properties, and biodegradation of poly(1,3-trimethylene carbonate)

Poly(1,3-trimethylene carbonate) (PTMC) synthesized by diethylzinc-catalyzed ring-opening polymerization was characterized by its thermal and mechanical properties, permeability, and susceptibility to hydrolytic chain scission in vitro and in vivo. Mark-Houwink coefficients were derived from viscometry and GPC measurements. The rate of hydrolytic chain scission of solid PTMC in phosphate-buffered saline at 37°C, pH 7.4, was approximately 20 times less than that of poly(e-caprolactone); this rate difference was eliminated when the two polymers were compared in solution in aqueous THF. PTMC implanted subcutaneously in rats was degraded by enzymatic degradation process
A Visual Real-time Fire Detection using Single Shot MultiBox Detector for UAV-based Fire Surveillance

Early fire detection and alarm are significantly important to reduce the losses caused by fire. Conventional methods in fire detection using smoke and heat detectors have disadvantages in accuracy, latency as well as the detection area. In this paper, we propose and implement a real-time fire detection solution for large area surveillance using the unmanned aerial vehicle with an integrated visual detection and alarm system. The system includes a low-cost camera, a light weight companion computer, a flight controller as well as localization and telemetry modules. To achieve real-time detection, Single Shot MultiBox Detector (SSD) algorithm is implemented as the heart of the system. We used MobileNets base model, which more efficient for mobile and embedded vision applications, instead of conventional VGG-16/ResNet model to achieve the mean average precision of 92.7% with the detection speed of 26 FPS.
An FPGA-Based Hardware/Software Design Using Binarized Neural Networks for Agricultural Applications: A Case Study

This work presents an FPGA-based hardware/software design to help the agricultural robot intelligently decide if biological agents need to be applied to the target crops. For target crop recognition, in our global positioning, the selective search integrates with a thresholding scheme to reduce the number of region of interest (ROI) in a captured image. In our local recognition, a binarized neural network (BNN) architecture is presented to help recognize the target crop. Furthermore, an estimation method of pest and disease severity is also presented. Experiments show integrating our presented BNN architecture needs a few extra resources (less than 17% of available FPGA resources in terms of Xilinx Zynq UltraScall+™ MPSoC ZU3EG A484), compared to an existing BNN one. However, the top-1 accuracy rate and the top-5 one can be increased by 32.25% to 32.84% and by 14.99% to 15.17%, respectively. Furthermore, when the presented BNN architecture was also implemented on the ARM Cortex-A53 CPU and the NVIDIA GeForce RTX 2080 GPU, our BNN hardware module on the FPGA can accelerate the frames per second (FPS) by a factor of 3,690.18 and a factor of 1.07, respectively.
An Efficient Low Complexity Edge-Cloud Framework for Security in IoT Networks

Internet of Things (IoT) and its applications are becoming commonplace with more devices, but always at risk of network security. It is therefore crucial for an IoT network design to identify attackers accurately, quickly and promptly. Many solutions have been proposed, mainly concerning secure IoT architectures and classification algorithms, but none of them have paid enough attention to reducing the complexity. Our proposal in this paper is an edge-cloud architecture that fulfills the detection task right at the edge layer, near the source of the attacks for quick response, versatility, as well as reducing the cloud’s workload. We also propose a multi-attack detection mechanism called LCHA (Low-Complexity detection solution with High Accuracy) , which has low complexity for deployment at the edge zone while still maintaining high accuracy. The performance of our proposed mechanism is compared with that of other machine learning and deep learning methods using the most updated BoT-IoT data set. The results show that LCHA outperforms other algorithms such as NN, CNN, RNN, KNN, SVM, KNN, RF and Decision Tree in terms of accuracy and NN in terms of complexity.
Effects of nitrogen and potassium applications on strawberry growth, yield and quality

Abstract The effects of increasing rates of N (as urea) and K on vegetative growth, nutrient uptake, fruit yield and quality of strawberries (Fragaria × anassa Duch.) was investigated in a field trial. In the first and second seasons there were small increases in plant growth and yields with the low rate of N but higher rates caused marked reductions in both growth and yields. With the high N rates accumulation of high levels of ammonium and soluble salts in the soil during spring of the first season was thought to have initially inhibited plant growth. Following nitrification of the accumulated ammonium, soil acidification occurred and consequently toxicities of Al and Mn limited strawberry growth at high N rates. In the second and third seasons there was a positive growth and yield response to the addition of K in the no‐N and low‐N treatments although rate of K had no influence. Increasing rates of K generally increased concentrations of leaf K and decreased those of Mg and Ca. Additions of K had no ef.
Partitioning of organic chemicals at the air-water interface in environmental systems

It is suggested that partitioning of organic chemicals to the air-water interface can be significant and must be quantified in several situations of environmental interest including partitioning of chemicals into small air bubbles in water into small water drops in air (for example, fogs), and between the phases present in relatively dry soils. Experimental data are reported for interface-air partitioning of a variety of chemicals and are consolidated with previous data to show that interfacial partitioning is primarily controlled by the chemical's hydrophobicity. Correlations are derived for interface-air and interface-water coefficients from data for 44 polar and nonpolar chemical
Unsupervised Land Cover Classification of Hybrid Polsar Images Using Deep Network

Deep learning has proved to be highly efficient towards large scale, high dimensional data, rendering it to be an area of interest for researchers. Enormous volumes of satellite imagery enables us to utilise the benefits of a deep framework in the field of remote sensing. In this paper, we propose an unsupervised patch based learning method to cluster hybrid polarimetric SAR images. We extract small patches from the image data set, and train VGG16 model with batch normalization using an entropy based loss function. Initially, the patches are segmented into three classes, namely, surface, volume, and double-bounce, which are defined with reference to the SAR scattering characteristics. We further classify volume into dense vegetation, and agricultural areas. Mixed classes, mainly covering the areas which have settlements surrounded by tall trees, are also observed. This technique gives an average accuracy of 89.70%.
A Compact Answer Set Programming Encoding of Multi-Agent Pathfinding

Multi-agent pathfinding (MAPF) is the problem of finding <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> non-colliding paths connecting <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> given initial positions with <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> given goal positions on a given map. In its sum-of-costs variant, the total number of moves and wait actions performed by agents before they definitely reach the goal is minimized. Not surprisingly, since MAPF is combinatorial, a number of compilations to Boolean Satisfiability (SAT) and Answer Set Programming (ASP) exist. In this article, we describe in detail the first family of compilations to ASP that solve sum-of-costs MAPF over 4-connected grids. Compared to existing ASP compilations, a distinguishing feature of our compilation is that the number of total clauses (after grounding) grow linearly with the number of agents, while existing compilations grow quadratically. In addition, the optimization objective is such that its size after grounding does not depend on the size of the grid. In our experimental evaluation, we show that our approach outperforms search-based sum-of-costs MAPF solvers when grids are congested with agents. We also show that our approach is competitive with a SAT-based approach when follow conflicts are taken into account. We also explore the potential of our solver when finding makespan-optimal solutions, in which makespan is minimized first and then cost is minimized. Our results show that makespan-optimal solutions are slightly suboptimal in most benchmarks. Moreover, our MAPF solver, when run in that mode, is faster and scales better.
Investigating the Impact of Coherent and Incoherent Scattering Terms in GNSS-R Delay Doppler Maps

Forward scattering properties of Earth-reflected Global Navigation Satellite Systems (GNSS) signals are evaluated over land surfaces. The CYclone Global Navigation Satellite System (CYGNSS) End-to-End Simulator (E2ES) is updated. A GNSS-Reflectometry (GNSS-R) model capable of evaluating both the incoherent and the coherent scattering terms is developed based on the Huygens-Kirchhoff principle. The coherent term is studied for incidence angles θi ~ [0, 80]° over a wide range of surface height standard deviation σ = [1, 4] cm and Vegetation Optical Depth VOD = [0, 1.5]. This model is validated over a lake with space-borne data as generated using the CYGNSS raw IF processor. Results demonstrate the impact of higher order Fresnel zones on the spatial resolution of GNSS-R over heterogenous areas, showing “ringing” fluctuations in the reflected power near high contrast boundaries. We expect to contribute to the accurate interpretation of datasets over inland water bodies.
The Determinants of Sustainable Agricultural Technology Adoption

Pertanian memiliki peran penting karena lebih dari 60% populasi dunia bergantung pada pertanian sebagai mata pencaharian. Salah satu faktor penyumbang besar terhadap pertumbuhan produktivitas pertanian adalah penerapan teknologi baru. Teknologi baru pertanian diharapkan berperan sebagai jalan penting untuk keluar dari kemiskinan di sebagian besar negara berkembang. Namun, realita menunjukkan tingkat adopsi teknologi pertanian dianggap masih relatif rendah. Makalah ini merupakan scientific review yang merangkum dan menganalisis hasil-hasil penelitian tentang adopsi teknologi pertanian. Tujuan makalah adalah untuk mengamati pengalaman di sejumlah negara terkait adopsi teknologi pertanian dan menentukan faktor-faktor yang memengaruhi adopsi serta keberlanjutan suatu adopsi teknologi. Hasil studi mengungkapkan bahwa keputusan petani untuk mengadopsi teknologi baru bergantung pada interaksi dinamis antara karakteristik teknologi dan kondisi lingkungannya. Beberapa aspek yang memengaruhi adopsi teknologi pertanian antara lain aspek teknologi, ekonomi dan keuangan, sosial dan kelembagaan, serta usaha pertanian dan karakteristik rumah tangga petani. Namun, penentu adopsi teknologi pertanian tidak selalu tunggal, melainkan kombinasi dari beberapa faktor sehingga untuk memacu adopsi teknologi harus memperhitungkan semua faktor penentunya. Pendekatan yang komprehensif menjadi pilihan terbaik untuk menyebarluaskan teknologi baru pertanian. Pemerintah dapat menjadi fasilitator untuk adopsi teknologi dan memastikan teknologi yang disebarkan bermanfaat bagi petani.