Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling wireless sensors localization in dynamic indoor environments

ii PREFACE. iii TABLE OF CONTENTS.iv LIST OF ABBREVIATIONS. vi 1 Background on Wireless Sensors Networks Localizat ion 1 1.1 Our field of interest . 2 1.2 Problem definition . 2 1.3 Methodology . 4 1.4 Thesis outline.4 2 Localization Approaches in WSN 6 2.1 Range methods . 6 2.1.1 Received signal strength . 8 2.1.2 Time of flight . 9 2.1.3 Beamforming . 10 2.1.4 Radio interferometry . 11 2.2 Range-free methods . 12 2.2.1 Connectivity-based . 13 2.2.2 Proximity-based . 14 2.2.3 Fingerprint-based . 15 2.3 Hybrid measurements and solutions . 15 2.4 Solution approach . 16 3 Development of a Novel Propagation-Based Ranging System 18 3.1 Sources of RSS variability . 18 3.1.1 Extrinsic sources of RSS variability . 18 3.1.2 Intrinsic sources of RSS variability . 21 3.2 Path loss modelling . 22 3.2.1 Distance-dependant signal loss . 22 3.2.2 Multipath effect . 24 3.2.3 Shadow fading . 29 4 Localization Algorithms 33 4.1 Optimization . 34 4.2 Localization algorithm approach . 36 4.3 Problem statement . 37 4.4 Least-squares optimization . 37 4.5 Weights function . 39 4.6 Simulative performance analysis . 42 4.6. 1 Simulations results . 43
Low‐Voltage Oscillatory Neurons for Memristor‐Based Neuromorphic Systems

Neuromorphic systems consisting of artificial neurons and synapses can process complex information with high efficiency to overcome the bottleneck of von Neumann architecture. Artificial neurons are essentially required to possess functions such as leaky integrate‐and‐fire and output spike. However, previous reported artificial neurons typically have high operation voltage and large leakage current, leading to significant power consumption, which is contrary to the energy‐efficient biological model. Here, an oscillatory neuron based on Ag filamentary threshold switching memristor (TS) that has a low operation voltage (<0.6 V) with ultralow power consumption (<1.8 µW) is presented. It can trigger neuronal functions, including leaky integrate‐and‐fire and threshold‐driven spiking output, with high endurance (>108 cycles). Being connected to an external resistor or a resistive switching memristor (RS) as synaptic weight, the TS clearly demonstrates self‐oscillation behavior once the input pulse voltage exceeds the threshold voltage. Meanwhile, the oscillation frequency is proportional to the input pulse voltage and the conductance of RS synapse, which can be used to integrate the weighted sum current. As an energy‐efficient memristor‐based spiking neural network, this combination of TS oscillatory neuron with RS synapse is further evaluated for image recognition achieving an accuracy of 79.2 ± 2.4% for CIFAR‐10 subset.
Novel Production of Magnetite Particles via Thermochemical Processing of Digestate From Manure and Food Waste

Sustainable management of food waste has become a global priority because of the significant environmental impacts associated with conventional disposal methods, including landfilling. Thermochemical processing is a food-waste-to-energy conversion technology in which food waste materials are converted to biofuel in a reduced O2 environment at elevated temperatures. Another conversion technology is anaerobic digestion, in which microorganisms digest biodegradable material, producing biofuel and solid byproducts “digestate. ” We measured the physical properties of “biochar” produced by combining these approaches: digestate was used as feedstock for a commercial-scale thermochemical processing system. Magnetite (Fe3O4) particles were produced during the food-waste-to-energy conversion process. This was particularly unexpected because none of the input materials were ferromagnetic, and no specific Fe precursors were introduced in the process. The Fe3O4 was identified through a combination of X-ray fluorescence and dc magnetometry. Zero-field cooled magnetization-temperature curves reveal a Verwey transition at ∼125 K across samples prepared under various conditions. Room temperature magnetization-field loops show a Langevin-like curve, technical saturation, and coercive fields of $H_{C}$ = 98–130 Oe across various samples. Clear Verwey transition, room temperature hysteresis, and an irreversibility temperature well above room temperature indicate that particles are multidomain. We attribute the presence of Fe3O4 to the relatively high concentration of Fe naturally present in the solid digestate and the operating parameters of the thermochemical conversion process. High surface area magnetic biochar has a variety of potential applications, including the adsorption of heavy metals, wastewater treatment, supercapacitors, and conductive polymer composites.
A Novel Stacked Antenna Configuration and its Applications in Dual-Band Shared-Aperture Base Station Antenna Array Designs

A novel stacked antenna configuration is proposed for the designs of shared-aperture base station antennas. An antenna array consisting of a lower-band antenna element working in 690–960 MHz frequency band and four upper-band antenna elements operating in the 3.5–4.9 GHz band is designed to demonstrate the concept. In the proposed configuration, four upper-band antenna elements are placed over the aperture of the lower-band antenna. Four metallic sheets are introduced to provide capacitive loading for the lower-band antenna and simultaneously offer a ground plane for the upper-band antennas. Therefore, low-profile characteristic and high isolations between the dual-band antennas are guaranteed. The overall height of the antenna array is only $0.17\lambda _{\text {L0}}$ ( $\lambda _{\text {L0}}$ is the free-space wavelength at the center frequency of the lower band), which is much lower than those of conventional designs. The measured results demonstrate that the designed antenna array achieves 32.7% and 33.3% impedance bandwidth in the lower and upper bands, respectively. High port isolations (>30 dB), high gains (>8 dBi), and stable radiation patterns are achieved in the two frequency bands. Owing to its simple structure and compact size, the resultant antenna array is easy for fabrication and is a promising candidate for sub-6 GHz band and 690–960 MHz band antennas.
Building a Colour Image Database to Recommend Architectural Colour Scheme using Case-Based Retrieval Mechanism

The purpose of this study is to develop a digitized Taiwanese colour image database for architectural colour scheme in Taiwan. This paper uses adjectives to present each colour’s contribution to the colour image. The system uses the “colour difference formula” from the CIEDE2000 method, which calculates the difference between the two colour perceptions represented by the two given points. Using the “Group Nearest Neighbour” algorithm, the retrieval mechanism obtains a similarity measurement. This approach can help designers to know the meanings of colours and their associated colour images, which will help them develop the building image.
SQUID microscopy for mapping vector magnetic fields

We mounted a vector-scanning superconducting quantum interference device (SQUID) sensor on a commercial SQUID microscope and successfully improved the sensitivity and spatial resolution of the sensor. Our proposed vector 3D SQUID sensor used multilayered niobium (Nb)-based technology; we realized three SQUID sensors in the structure of a vector pickup coil system on a single chip. The vector pickup coil system was built with three pickup coils that were orthogonal to one another to obtain the X, Y, and Z components of a magnetic field vector. To improve both sensitivity and spatial resolution, we attempted to reduce the inner diameter by increasing the number of windings of the pickup coils using the multilayered Nb process. The design value for the dc SQUID sensors was either the critical current density Jc = 320 A cm−2 for two Josephson junctions (JJs) or the critical current Ic = 12.8 μA for the 2 μm × 2 μm JJs. To measure the current–voltage (I–V) and voltage–flux (V–Φ) characteristics of a sensor, we constructed a homemade measurement system. The fundamental characteristics of our SQUID sensors were in good agreement with the design parameters. We mounted our vector SQUID sensor on a commercial scanning SQUID microscope (SQM2000, Seiko Instrument Inc.) with a single flux-locked loop channel to test one of the three channels. We repeated the measurements three times to obtain the X, Y, and Z componential images of the magnetic field from a single vortex, and we synthesized the 3D mapping of the magnetic field vectors from a single vortex. We proved that our sensor mounted on a SQUID microscope was suitable for measuring the X, Y, and Z components of a vector magnetic field.
Application of Bayesian configural frequency analysis (BCFA) to determine characteristics user and non-user motor X

Configural Frequency Analysis is a method for cell-wise testing in contingency tables for exploratory search type and antitype, that can see the existence of discrepancy on the model by existence of a significant difference between the frequency of observation and frequency of expectation. This analysis focuses on whether or not the interaction among categories from different variables, and not the interaction among variables. One of the extensions of CFA method is Bayesian CFA, this alternative method pursue the same goal as frequentist version of CFA with the advantage that adjustment of the experiment-wise significance level α is not necessary and test whether groups of types and antitypes form composite types or composite antitypes. Hence, this research will present the concept of the Bayesian CFA and how it works for the real data. The data on this paper is based on case studies in a company about decrease Brand Awareness & Image motor X on Top Of Mind Unit indicator in Cirebon City for user 30.8% and non user 9.8%. From the result of B-CFA have four characteristics from deviation, one of the four characteristics above that is the configuration 2212 need more attention by company to determine promotion strategy to maintain and improve Top Of Mind Unit in Cirebon City.
Harmonic Minimization Using Newton Raphson Method

In this paper, a Newton Raphson iterative technique is applied on cascaded multilevel inverter in order to determine the switching angles that eliminates specified higher order harmonics while maintains the required fundamental output voltage. This technique can be applied to any number of levels of multilevel inverters. A nine-level inverter is considered in this paper and the optimized switching angles are calculated in order to eliminate the lower order harmonics using SHE equations. Index Terms – Cascaded H-Bridge Multilevel Inverters (CHB-MLIs), Newton– Raphson method (NR), Selective Harmonic Elimination (SHE), Total Harmonic Distortion (THD), Weighted Total Harmonic Distortion (WTHD).
IntelliTC: intelligent inter-DC traffic controller for the Internet of everything service based on fog computing

As the data size on smart things continue to grow, it becomes inefficient to transfer the massive raw data to centralized cloud datacenters for the analysis of raw data and the deployment of innovative and novel services into various areas such as smart cities, etc. As the concept of Fog Computing, the structure of distributed intelligent infrastructure can address those problems by providing elastic resources and services to end users with smart devices at the edge of network. Hence, through the functions of analytics and flexible control based on the Software-Defined Network (SDN), it is necessary to provide the SDN-driven management intelligence, which can maximize network utilization subject to constraints on service priority and resources fairness by differentiating service flows according to the quality requirements. This paper proposes the SDN controller for intelligent inter-datacenter (inter-DC) cloud networking, which enables optimal traffic loads distribution across the distributed datacenters with the global inter-DC view of link states and flow statistics by applying the network-aware flow scheduling on a policy-driven inter-DC traffic control layer. The proposed intelligent inter-DC controller performs a global resources scheduling mechanism that uses different weights according to the priority of service class in allocating bandwidths based on multi-layers networking interconnecting the distributed datacenters.
Effect of Exponentially Temperature-Dependent Viscosity on the Onset of Penetrative Ferro-Thermal-Convection in a Saturated Porous Layer via Internal Heating

The effect of viscosity depending exponentially on temperature on the onset of penetrative ferro-thermal-convection (FTC) in a saturated horizontal porous layer in the presence of vertical magnetic field is investigated. The bounding surface of the ferrofluid layer is considered to be rigid-rigid and insulated to temperature perturbations. The resulting eigenvalue problem is solved numerically using the Galerkin technique and also analytically by a regular perturbation technique with wave number as a perturbation parameter. The analytical and numerical results are found to be concurrence. The characteristics of stability of the system are strongly dependent on the viscosity parameter B. The effect of B on the onset of ferroconvection in a porous layer is dual in nature depending on the choices of physical parameters and a sublayer starts to form at higher values of B. Whereas, increase in magnetic number M1 and the Darcy number Da is to advance the onset of ferroconvection in a porous layer. The nonlinearity of fluid magnetization M3 is found to have no influence on the onset of ferroconvection.
Turbulence in a stably stratified fluid: onset of global anisotropy as a function of the Richardson number

It is necessary to introduce an external force to induce turbulence in a stably stratified fluid. The Heisenberg eddy viscosity technique should in this case suffice to calculate a space-time averaged quantity like the global anisotropy parameter as a function of the Richardson number. We find analytically that the anisotropy increases linearly with the Richardson number, with a small quadratic correction. A numerical simulation of the complete equations shows the linear behaviour.
Electroweak and QCD aspects in vector boson plus jets associated production with CMS

The study of the associated production of vector bosons and jets constitutes an excellent testbench to check numerous QCD predictions. Measurements of the total and differential cross sections of vector bosons produced in association with jets have been performed at 13 TeV center-of-mass energies with the CMS experiment at the LHC. Differential distributions as function of a broad range of kinematical observables are measured and compared with theoretical predictions. Final states with a vector boson and jets can be also used to study electroweak initiated processes, such as the vector boson fusion production of a Z boson that is accompanied by a pair of energetic jets having large invariant mass.
A study on the dynamic range expansion of the Shack-Hartmann wavefront sensor using image processing

The Shack-Hartmann wavefront sensor is composed of a lenslet array generating the spot images from which local slope is calculated and overall wavefront is measured. Generally the principle of wavefront reconstruction is that the spot centroid of each lenslet array is calculated from pixel intensity values in its subaperture and then overall wavefront is reconstructed by local slope of wavefront obtained by deviations from reference positions. Hence the spot image of each lenslet array has to remain in its subaperture for exact measurement of wavefront. However the spot of each lenslet array deviates from its subaperture area when wavefront with large local slopes enters the Shack-Hartmann sensor. In this research, we propose the spot image searching method that finds area of each measured spot image flexibly and determine the centroid of each spot in its area. Also the algorithms that match these centroids to their reference points unequivocally even if some of them are situated off the allocated subaperture are proposed. Finally we verify the proposed algorithm with the test of a defocus measurement through experimental setup for the Shack-Hartmann wavefront sensor. It has been shown that the proposed algorithm can expand the dynamic range without additional devices.
Un nouvel outil de diagnostic non intrusif d'une pile à combustible basé sur la mesure des singularités de la tension

Nous nous proposons d'exploiter les informations revelees par la morphologie du signal de la tension d'une pile a combustible (PaC) de type PEM afin de developper un outil de diagnostic. L'essentiel de l'information contenue dans un signal " rugueux " se situe souvent dans ses singularites et ses structures irregulieres ; la methode des ondelettes et les outils d'analyse fractale apparaissent alors bien appropries pour l'etude de signaux irreguliers tels que les tensions d'une PaC. Dans nos investigations, nous adoptons l'analyse multi-echelle fondee sur les ondelettes continues pour observer le comportement du signal de tension sur une large gamme d'echelles ; nous mettons en œuvre le formalisme multifractal base sur les maxima locaux de la transformee en ondelettes (MMTO) afin de quantifier la force des singularites et leur distribution pour differentes conditions de fonctionnement de la PaC. La methode developpee permet de discriminer diverses conditions operatoires a l'aide des spectres de singularites calcules sur la seule base des signaux de tension d'une PaC.
Analyzing the Usefulness of the User's Browser History for Generating Query Suggestions

A very useful feature of search engines that helps users while they browse the internet, where, they very often, try to satisfy an information need, is query suggestion. This mechanism shows the user a list of possible queries from where he can choose and be able to perform a search easier and faster. In this paper we tried to assess the usefulness of a user's recent web browsing history for generating new query suggestions. We performed a one month experiment in which we collected browsing history logs of several users and searched query terms submitted by those users to Google (using a Chrome plugin) and found that approximately 32% of the queries submitted can be predicted from the user's browsing history.
A hierarchical extension of a multiple-component scattering model with unitary transformation of the coherency matrix

ABSTRACT The overestimation of volume scattering (OVS) is an essential shortcoming of the model-based polarimetric synthetic aperture radar (PolSAR) target decomposition method. It is likely to affect the measurement accuracy and result in mixed ambiguity of the scattering mechanism. In this paper, a hierarchical extended multi-component decomposition method (ExMCSM) that can adaptively select the refined volume scattering models based on the artificial area extraction result is proposed for overcoming the OVS. First, artificial areas are extracted from the image to distinguish between artificial areas and non-artificial areas. Then, orientation angle compensation (OAC) and helix angle compensation (HAC) are applied to the coherency matrix to reduce the complexity of the operation. Finally, the volume scattering model of each pixel is refined to more effectively adapt to various scattering mechanisms. Spaceborne Gaofen-3 full PolSAR data are used to evaluate the performance of the proposed decomposition method. The experimental results demonstrate that the proposed decomposition method can accurately represent the scattering characteristics of the ambiguous regions and the oriented building areas can be well discriminated as dihedral or odd-bounce structures.
Optimal multipurpose reservoir operation planning using Genetic Algorithm and Non Linear Programming (GA-NLP) hybrid approach

Abstract The major issue in water resource planning and management is determination of the optimal operating policy of a reservoir. In this study, GA-NLP hybrid approach has been applied. Genetic Algorithm (GA) is able to find the near optimal solution easily but for finding global optimal solution it will take too many function evaluations, so hybrid approach is adopted. GA is based on the principle of natural selection, derived from theory of evolution and it is popular for solving optimization problems. Non Linear Programming (NLP) is based on second derivatives of the Lagrangian function. To show practical utility, this approach is applied to an existing reservoir namely, Nagarjuna sagar Reservoir system in India. Maximizing sum of Irrigation releases into left and right main canal from the reservoir while maximizing annual power production is considered as the objective function. The constraints considered for this model are bounds for releases, storage capacity, canal capacity, and power generation capacity. The proposed model derives the optimal rule curves and release policies of the reservoir for different inflow scenario’s and for different priorities. Hence, based on the present case study it can be concluded that GA-NLP hybrid approach has the capability to perform efficiently, if applied in real world reservoir operation problems.
An Enhanced Implementation of Brain Tumor Detection Based on Statistical Features and F-Transform

Brain tumor is an abnormal growth of cells inside brain, which may be cancerous or non-cancerous. This paper describes the proposed approach for detection and extraction brain tumor from MRI scan images. Brain tumor need be detected, diagnosed and estimated in earliest stage. The classification involves classification of images into normal and tumor detected. The medical difficulties become serious if tumor is detected at the later stage. Asymmetry of brain is used for detection of abnormality. Statistical Features were extracted from the detected tumor texture using three kinds of features. The difference between the two halves in that row can judge each data as normal or with suspicious tumors. After detecting the tumor, the segmentation based on F-transform (FuzzyTransform) and morphological operations are performed to delineating brain tumor boundaries and calculate the area of the tumor. The F-transform is an excellent method to extract the salient edges. Experimental results on brain MR images succeed an average accuracy of 96 % and precision of 95% using the proposed algorithm.