Miracle-dz/UniFAR-FTUs huggingface dataset:
Leaf Disease Detection Using Deep Neural Network

The research has concentrated heavily on the utilization of computer vision tasks to identify and categorise phytopathogens. Despite the fact that because herbs are plants too and are prone to disease, the identification of herb diseases has been limited owing to a large amount of study focusing on plant illnesses. A parsley disease detection identification and classification algorithm has been developed in order to recognise and categorise the parsley leaf spot (PLS) sickness according to severity. With 99.5 percentage accuracy rate in both binaries and inter of the PLS sickness, the suggested method employs a neural network convolutional (CNN)-based computational modeling (DL) model to detect 2000 real-time photographs of parsley leaves mixing healthy and PLS unhealthy images. The proposed model beats state-of-the-art pre-trained models in terms of multi-classifying PLS disease, as shown by comparisons between them and the proposed approach.
Sensorless controls of a 7-phase bi-harmonic Surface-mounted PM Machine

This paper deals with the position sensorless controls of a seven-phase bi-harmonic Surface-mounted PM machine. Due to its tooth-concentrated winding and its particular magnet segmentation, this fault-tolerant machine is characterized by an electromotive force (emf) for which the fundamental and the third harmonic are of the same order. This specificity should improve the ability of the machine to operate without position sensor. Furthermore, as the virtual machines (denoted h1, h3 and h5 machines) that are used to control the 7-phase machine have quasi-sinewave back-emfs, several so-called emf-methods to sense the rotor position are available. For h1, h3 and h1h3 controls with a Phase-locked Loop (PLL) to estimate the position, the time simulations of the drive confirm the ability of the machine to operate without position sensor while ensuring low torque ripple and quite unchanged current waveforms. An experimental evidence of this property is given for sensorless h3-control (where the back-emf fundamental is used to sense the position).
Internet of Things via Satellite: A Multiple-Access Performance Study of LoRa and its Enhancements

Among the proposed waveforms for the Internet of things (IoT), long range (LoRa) receives remarkable interest and has been investigated for the use in direct-to-satellite (DtS) scenarios. The large coverage area provided by low Earth orbit (LEO) satellites poses the challenging task to handle multiple-access interference. To shed the light on relevant receiver enhancements compatible with LoRa and able to support a large number of end-devices, a detailed physical layer simulator is developed. Concurrent intra-spreading factor (SF) transmissions are demodulated in parallel relying on the Pyramid technique [1], that we modified and refined. Interference cancellation as well as coherent demodulation are also incorporated in the receiver design. With extensive Monte Carlo simulations, we provide an overview of the benefits of such techniques and identify the most promising ones.
Optimization of Quasi-Halbach Topologies to Maximize the Acceleration of Moving-Magnet Planar Motors

This paper concerns the optimization of eight different quasi-Halbach magnet array structures that are used in a dual-layer moving-magnet planar motor. These magnet arrays vary in number of magnets per pole and contain both cuboidal and trapezoidal prism magnet shapes. The parameters of the magnet structures are optimized to maximize the acceleration of the planar motor. The resulting accelerations and force ripples of the planar motor are compared for the optimal magnet structures. The results show that the studied magnet arrays improve the acceleration of the planar motor compared to the benchmark magnet array with three cuboidal magnets per pole. A maximum increase in acceleration of 10.1 % is achieved, while the force ripple is reduced by 10.2 % in any direction of movement.
Flux Switching Machines- for All-Electric Aircraft Applications

The demand for new and efficient design of electric motors is gaining momentum due to the rapid growth in electric transportation. In particular, all-electric aircraft applications require very high-power density. This paper introduces new motor topologies for a double-rotor flux switching machine with an air-core stator comprising field and armature windings. The field coils are made of yttrium barium copper oxide high temperature superconducting material and aluminum Litz is used for the armature windings. The machine is operated under cryogenic temperatures. The power density obtained with the proposed design is 30.5 kW/kg and an efficiency is 95.2% during take-off is accomplished; satisfying the requirement of electric aviation.
Topology Optimization of Asymmetric PMSM Rotor

This paper investigates the optimization of iron distribution in the rotor of a permanent magnet synchronous machine. The objective is to maximize the average torque for fixed permanent magnets positions, stator, and current feedings. To do so, a density-based algorithm is used with the adjoint variable method to compute sensitivities. It returns non-symmetric geometries, which are related to the interactions between magnets and winding fluxes, as well as the machine operating mode (motor or generator for a given rotation direction). For a given single working point, we obtain asymmetric topologies that outperform the optimized symmetric design, while the symmetric rotor topology is more multifunctional.
Image Captioning Techniques: A Review

Image captioning is the process of generating accurate and descriptive captions. As a recently emerged research area, it is attracting more and more attention. To achieve the goal of image captioning, semantic information of images needs to be captured and expressed in natural languages. Image captions need to identify objects, actions, their relationships, and some salient features that may be missing from the image. After identification, the next step is to generate the most relevant and concise image description. This should be syntactically and semantically correct. Deep learning techniques can handle this process with CNNs and LSTMs. In this survey paper, we first talk about techniques used in early work that are mainly retrieval and template-based. Then, we focus on neural network-based techniques, which offer contemporary results. These techniques are in addition divided into subcategories based on the specific framework. Each subcategory is discussed in detail. After that, state-of-the-art methods are compared on benchmark datasets. Following that, discussions on future research approaches are presented.
Accurate Detection and Location of Insulation Faults and Free Bulk Deformations in Power

This paper presents a procedure to accurately locate the position of insulation faults and free buckling deformations in the windings of a transformer by using small, simple air-core coils that measure fractions of the machine’s leakage flux. This study is based on four previous works by the same authors, in which the method was successfully applied to low voltage transformers with concentric and disc-type windings and to an 800 kVA oil-immersed machine. In previous research, the possibility of locating the fault was only suggested, and the deformation detection was done by FEM simulation. This paper will present a summary of the fundamentals of the method, and the improved and valid experimental procedure for both the detection of insulation faults and deformations, as well as their localization, in terms of position in the machine windings.
A Conceptual Framework for Securing IoT-BIM

Recently, the Internet of Things and building information modeling (IoT-BIM) has captured attention because of its wide range of applications in multiple domains communicating across different layers. The IoT-BIM consists of three layers, namely physical, network, and application layer. This paper provides and discusses security attacks countermeasure for each IoT-BIM layer. In this paper, we discuss various state-of-the-art IoT-BIM security frameworks and propose a unified security framework for IoT-BIM networks, called “Unified Federated Security Framework”. The proposed framework relies on fuzzy cognitive maps for modeling and evaluating trust relationships between the involved entities in federated identity management systems.
Distributed and Decentralized Edge Caching in 5G Networks Using Non-Volatile Memory Systems

Edge caching is an effective way to reduce congestion and latency in 5G networks. Non-volatile memory (NVM) devices are developing fast, with the potential of fast access, and higher endurance versus traditional storage devices, to further boost mobile data offloading efficiency in 5G networks. This paper studies how to effectively use the two-layer storage system (NVM-enhanced) in 5G edge caching. We first model an edge caching optimization problem with NVM storage devices included and develop a parallel distributed algorithm with guaranteed convergence in joint caching and routing decisions. A fully decentralized algorithm for scenarios without any coordination is further developed which also guarantees the convergence. Real-world trace-driven simulations and experiments over a small-scale system demonstrate that NVM significantly boosts the performance of edge caching and the proposed algorithms outperform the existing ones.
Ex-situ Inspection of Concentrated Stator Coils by Means of Impedance Spectroscopy

Broadband Dielectric Spectroscopy (BDS) and Electrochemical Impedance Spectroscopy (EIS) form types of non-destructive testing that allow to examine the dielectric response or the complex impedance responses over a wide frequency range for a component. Several published studies have used spectroscopy to characterize components and circuits or to assess their condition. The present work uses impedance spectroscopy and examines the characteristics of the amplitude, phase, and the Nyquist plots of the complex impedance for a set of concentrated coils from the stator of a high-performance Permanent Magnet (PM) machine from an aerospace application. Having prior knowledge that one of the coils in the set exhibits manufacturing imperfections, their characterization with this type of measurement aims to identify the defective one. The objective is to investigate whether, and to what extent, impedance spectroscopy is a potent candidate for deployment as a means for the ex-situ inspection of stator coils.
Mixing Characteristics of Inclined Fuel Injection via Various Geometries for Upstream-Fuel-Injected Scramjets

Efficient fuel/air mixing plays a crucial role in successful operation of hypersonic airbreathing engines, particularly scramjets, where fuel must be injected into high-speed crossflow and mixed with air at an extremely short timescale. This paper presents the results of a numerical study that investigates the effects of various orifice shapes on fuel mixing characteristics into hypersonic airflow at Mach 5, aiming at the application to scramjet operation with upstream fuel injection at Mach 10. The performance of the injectors at an inclination angle of 45 deg are assessed with respect to various criteria such as the mixing efficiency, streamwise circulation, total pressure recovery, fuel penetration, and spread. Streamwise slot injectors have been found to yield higher mixing efficiency than the other injectors tested (namely, square, circular, diamond, and triangular injectors), owing to the buffering effects. Apparent higher total pressure recovery has been obtained with these rectangular injectors, b.
Colorization of Black & White Videos & Photographs

When a gray image is given as an input, this paper is an offensive problem guessing the audio version of the image. This problem is under the ban, so the previous methods have worked. It may rely on significant user interaction or result in a dull color rendering. We suggest a fully automatic method that produces brightness and realistic coloring. We accept the subtle uncertainty of the problem by setting it as a division function and applying class re-equation to training time to increase color variation in effect. The system is used as a feed pass on CNN during testing and so on trained in over a million color photographs. We test our calculation utilizing a "colorization Turing test," requesting that human members pick between the variety picture of the delivered truth and the base. Our approach is deceptively successful in 32% of the survey, much higher than previous methods. In addition, we show that color can be a powerful motivator learning feature we watch and acts as a multichannel connector. This methodology results in a modern application to several feature learning benchmark.
Towards Elasticity in Heterogeneous Edge-dense Environments

Edge computing has enabled a large set of emerging edge applications by exploiting data proximity and offloading computation-intensive workloads to nearby edge servers. However, supporting edge application users at scale poses challenges due to limited point-of-presence edge sites and constrained elasticity. In this paper, we introduce a densely-distributed edge resource model that leverages capacity-constrained volunteer edge nodes to support elastic computation offloading. Our model also enables the use of geo-distributed edge nodes to further support elasticity. Collectively, these features raise the issue of edge selection. We present a distributed edge selection approach that relies on client-centric views of available edge nodes to optimize average end-to-end latency, with considerations of system heterogeneity, resource contention and node churn. Elasticity is achieved by fine-grained performance probing, dynamic load balancing, and proactive multi-edge node connections per client. Evaluations are conducted in both real-world volunteer environments and emulated platforms to show how a common edge application, namely AR-based cognitive assistance, can benefit from our approach and deliver low-latency responses to distributed users at scale.
Design of a Variable Phase-Pole Induction Machine for Electric Vehicle Applications

The electrification of the transport sector requires significant research in electrical machines and drives to fulfil the application constraints. High torque demands at low speed and high-speed requirements at relatively low torque are posing challenges for the design of efficient machines with a compact and sustainable design. The ability to reach very high speeds makes a variable phase-pole machine attractive for electric traction. Moreover, their dimensions compared to conventional machines has not been sufficiently investigated. This work describes the design and control of a variable phase-pole induction machine with distributed windings for electric vehicle applications. The induction machine is designed to work in two switchable configurations: three-phase/six-pole, and nine-phase/two-pole. The control model is developed based on Harmonic Plane Decomposition. The proposed machine is compared to a conventional induction machine with non-switchable phase-pole configuration, showing a reduction in size for the same coverage of the torque-speed map.
Deep Transfer Learning with RESNET for Remote Sensing Scene Classification

This paper explains a comparative examination of aerial images for Land-Use and Land-Cover (LU&LC) classification, focusing on urban regions where scattering properties are nearly equivalent. To attain better performance metrics, a complete analysis was carried out, in which two ResNet-based models were applied to the RSI-CB128 dataset utilizing three different optimizers: Adam, SGDM, and RMSProp. Both ResNet models have been used on five different classes of the dataset with three different optimisers. RMSprop has the best accuracy with moderate simulation time in the case of ResNetl8, while in the case of ResNet50, SGDM has the best accuracy of 95.8% with the lowest simulation time.
TOWARD UNDERSTANDING THE B[e] PHENOMENON. III. PROPERTIES OF THE OPTICAL COUNTERPART OF IRAS 00470+6429

FS CMa type stars are a group of Galactic objects with the B[e] phenomenon. They exhibit strong emission-line spectra and infrared excesses, which are most likely due to recently formed circumstellar dust. The group content and identification criteria were described in the first two papers of the series. In this paper we report our spectroscopic and photometric observations of the optical counterpart of IRAS 00470+6429 obtained in 2003–2008. The optical spectrum is dominated by emission lines, most of which have P Cyg type profiles. We detected significant brightness variations, which may include a regular component, and variable spectral line profiles in both shape and position. The presence of a weak Li i 6708 Å line in the spectrum suggests that the object is most likely a binary system with a B2–B3 spectral-type primary companion of a luminosity log L/L☉ = 3.9 ± 0.3 and a late-type secondary companion. We estimate a distance toward the object to be 2.0 ± 0.3 kpc from the Sun.
Current Displacement Effects on Copper Losses in PWM Supplied Permanent Magnet Excited Electrical Machines

This paper provides a comparison on the difference of hairpin winding and pull-in winding technology on the ac copper losses of permanent magnet (PM) excited electrical machines. Finite Element Analysis (FEA) is used to study the current displacement effects under pure sinusoidal current and pulse width modulated (PWM) voltage supply. The resulting additional ac copper losses are analyzed for both winding technologies.
Application of Wireless Sensor Networks for Advancements in Non Intrusive Precision Beekeeping

Honey bees play a vital role in many ecological and environmental processes which are of immense value to all life forms. In the last fifty years, their contribution to the pollination of dependent crops has increased three folds, playing a key role in ecological systems, industrial crop production, production of commercial products with medicinal properties. The conventional method involves manual inspection, which requires the bee keeper to visually inspect the activities by opening the hive. This causes agitation among the bees. In addition, this method is purely based on experience of the bee keeper and is prone to human error. These factors have led to the need for advancement in apicultural techniques bringing under its umbrella numerous researchers working on various specializations. This work presents a brief discussion on some of the systems developed based on the concepts of Wireless Sensor Networks which have been deployed to monitor bee health and honey production, detect abnormal activities like swarming, absconding of bees, bee hive robbery and identification of queen-less states.
The future of safe BVLOS drone operations with respect to system and service engineering

Unmanned aerial vehicles (UAVs) have witnessed a dramatic increase in use over the last 20 years, gradually becoming popular tools for a variety of applications, while they contribute to a remarkable number of scientific advances in engineering, remote sensing, and computer science. Drones are an example of an edge device that uses technologies like visual search, image recognition, object detection, and tracking to provide a variety of solutions, ranging from construction, and traffic monitoring to cartography, among others. In order to develop new services and promote creative business models, BVLOS drones are turning into aerial sensing platforms that produce data and fuse data flows coming from current sensors in order to operate. Given this landscape, the combination of cloud and edge computing resources seems to be a promising solution that addresses computation-intensive offloading and latency difficulties.
A Performance Analysis of Reconstruction Algorithms for an Analog-to-Information Converter Architecture

Compressed Sensing (CS) is a technique that allows the sampling of sparse signals with sub-Nyquist sampling rates. A device that implements the CS technique is the Analog-to-Information Converter (AIC), which compresses the input signal while sampling it. This paper presents CS principles and performance analysis of different reconstruction algorithms for a dual-tone sine signal applied to an adapted version of the Random Modulation Pre-Integrator (RMPI) AIC.
IoT and Solar Energy Based Multipurpose Agricultural Robot for Smart Farming

In India, nearly 70% of people depend on agriculture. In the agricultural field, various operations such as seed sowing, grass cutting, pesticide spraying, ploughing are carried out. Automation of agricultural operations is a current demand to increase productivity through the use of tools and technology. At the moment seed sowing, pesticide spraying, and grass cutting are all difficult tasks. The equipment needed for the aforementioned actions is both expensive and inconvenient to use. As a result, India’s agricultural system should be advanced through the development of a system that reduces reliance on human labour and time. The proposed agricultural robot is a user-friendly, Internet of Things (IoT)-based system that can be used in any type of soil. Users can use a web page to monitor the crop’s condition as well as perform some specific operations. The objective of this project is to design, develop, and build a robot that can sow seeds, cut grass, spray pesticides, pluck fruit, and detect soil nutrition levels and irrigation. Solar energy is used to power the entire system. By connecting through wireless modules. the designed model can be controlled via a web page. The web page is used to control the robot’s required mechanism and movement. This improves the efficiency of seed sowing, pesticide spraying, grass cutting, fruit plucking, soil nutrition level detection, and irrigation, as well as reducing the need for manual planting.
Spoken Digit Recognition Using Convolutional Neural Network

This paper presents a spoken digit recognition study based on features extracted from Bark spectrogram and classifies it using convolutional neural network (CNN). In this work, training and test databases of the spoken digits were developed from TI20 corpus. 6,506 spoken digits from 16 individuals have been used in the study. A 198 x 50 feature map has been extracted from each spoken digit to be fed as input to the CNN. A 5-layer convolutional filters have been added as layers in the CNN architecture. Adam optimization algorithm is then used to train the CNN. A confusion matrix resulting from classification on the test database has been presented in this paper. It is shown in this paper that the proposed CNN architecture performing on the test database achieved an average accuracy of over 99% for the known spoken digits and wrongly classify as digit up to 3.4% for the unknown spoken words.
Experimental Analysis of a New Type of Harmonic-Excited Synchronous Machine with Special Consideration of the Core Losses

In this work, a brushless, harmonic-excited wound-rotor synchronous machine without any auxiliary windings which can provide full torque at startup is investigated experimentally. The excitation power is transferred inductively by superimposing an additional harmonic field of different pole-pair number on top of the airgap field. This is achieved by feeding the parallel paths of the stator and rotor winding separately. A prototype for the harmonic-excited synchronous machine has been constructed and experimental results are presented to verify the concept. The main loss contributors are identified and the importance of considering core losses under harmonic excitation is discussed. A general analytical model for harmonic excited synchronous machines is proposed which enables a quick estimation of the iron core flux densities and the core losses generated by the additional harmonic currents.