Miracle-dz/UniFAR-FTUs huggingface dataset:
Wavelet- Sub Pixel Based Single Image Super Resolution

Blind Image Super-Resolution (BISR) is a widely recognized image processing the approach. BISR, which seeks to super-resolving low-resolution images with undetermined deterioration, is gaining prominence due to its usefulness in stimulating real-world applications. In this research, we developed the Wavelet-Subpixel Residual Attention Network (WSRAN) for BISR, which significantly increases SR efficiency without sacrificing performance while enhancing generalized ability to real-world damaged pictures. The subpixel convolution layer converts LR feature maps into reconstructed outputs. We also employ the Residual Attention Block (RAB). Extensive trials indicate that when compared to SR approaches, our WSRAN requires fewer parameters and achieves statistically and subjectively competitive results. In the future, we want to find a means to increase the upscale factor beyond three.
Key Concepts in Geomorphology

PART I: GEOMORPHOLOGY AND ITS TOOLS 1. Earth's Dynamic Surface 2. Geomorphologist's Tool Kit PART II: SOURCE TO SINK 3. Weathering and Soils 4. Geomorphic Hydrology 5. Hillslopes 6. Channels 7. Drainage Basins 8. Coastal and Submarine Geomorphology PART III: ICE, WIND, AND FIRE 9. Glacial and Periglacial Geomorphology 10. Wind as a Geomorphic Agent 11. Volcanic Geomorphology PART IV: THE BIGGER PICTURE 12. Tectonic Geomorphology 13. Geomorphology and Climate 14. Landscape Evolution
Underwater Image Quality Enhancement using Multiscale Image Fusion

Underwater images offer a valuable resource for studying marine life and seafloor environments. However, underwater photography often suffers from poor visibility and color distortions caused by light absorption and scattering, resulting in degraded image quality. The scattering of light also reduces color contrast, and the presence of underwater organisms further complicates image enhancement efforts. To address these challenges, our proposed method utilizes a systematic approach to enhance underwater images. The technique involves color balancing, gamma correction, and sharpening, followed by multiscale image fusion to obtain the desired output. By comparing different sharpening techniques, this study intends to enhance degraded images and produce clearer, more vivid representations of the underwater scene. By applying two stretching models, the lighting issues are addressed and balance color contrast is attained in the photos, resulting in clearer and more vibrant images.
A High-Gain Random Radiation Antenna Assisted by Paraboloid Cylinder Reflector

In this paper, a high-gain random radiation antenna operating within the frequency range of 22 GHz–26 GHz is proposed. The antenna comprises a random modulation radiator and a parabolic cylinder reflector. To achieve random radiation, the radiator is fed by a waveguide port. the radiation is modulated by the metasurface, which consists of 110 randomly arranged metasurface elements with different transmission phase distributions. The metasurface elements in the radiator are designed by modifying the parameters of ten distinct metasurface elements, with the phase distributions covering 360°. In order to bunch the average radiation pattern of the radiator, a parabolic cylinder reflector is proposed to achieve the high-gain radiation. The simulation results demonstrate that the proposed antenna successfully achieves random radiation patterns in the $x-z$ plane when working in the 22-26GHz. The correlation between the radiation patterns in each sub-band is less than 0.4. Moreover, the radiation energy is concentrated within a 2° range in the $y-z$ plane, the S11 of the proposed antenna is less than −10dB, with the gains of the antenna being greater than 25 dB in the operating band.
A Low-Complexity Three-Dimensional Parameter Estimation Algorithm for FMCW Radar

Frequency Modulated Continuous Wave (FMCW) radar has been widely used in the field of vehicle mounted millimeter wave radar to detect the parameters of targets. A low complexity three-dimensional (3D) parameter estimation algorithm for time-division multiplexing multiple-input multiple-output (TDM-MIMO) FMCW radar is proposed to detect the range, velocity, and azimuth angle of targets. The proposed algorithm utilized the data block selection and rank-reduced technique to avoid the high computational burden of the traditional subspace-based algorithm. Moreover, the proposed algorithm solves the problem of velocity ambiguity in conventional TDM-MIMO FMCW signal processing. The simulation results show that the accuracy and resolution of the algorithm are much higher than the conventional algorithms, such as 3D-FFT, and 3D-MUSIC.
A Runtime Security Monitoring Architecture for Embedded Hypervisors

Technical advances in automation, embedded hardware and virtualization nurtured the rapid development of safety-critical embedded systems. Avionic and Automotive are two of the domains that have seen significant development in recent years. However, the consideration of security of these embedded controllers often lags behind other functional improvements. This paper presents a brief threat landscape of embedded controllers and proposes a runtime security monitoring architecture aiming to protect these systems against threats that emerge during runtime. To facilitate rapid prototyping and encourage correct adaptation of security, similar to software development toolchains that guide development, the proposed architecture aims to provide a means for security monitoring that can be applicable to generic platforms.
Real-Time Monitoring and Control System of Water Parameters for Paiche Cultivation

The arapaima, also known as paiche or pirarucu is one of the largest freshwater fish in the Amazon, in which its cultivation has been growing progressively due to consumer demand. This research presents the design of a real-time monitoring and control system of water parameters for paiche farming, which employs mechatronics concepts using the VdI-2206 methodology. The study controls and monitors the water's temperature, dissolved oxygen, and pH. Sensors obtain the values for each parameter, process them by a PLC, and project them on an HMI interface. The environment is programmed based on the information provided by FONDEPES, which mentions that the water temperature for paiche cultivation must be between 26°C to 31°C, the pH level between 6 and 8 UI, and dissolved oxygen with a value greater than or equal to 4 mg/L. In addition, the proposed mechatronic system aims to be a user-friendly system, where the primary function is the monitoring and controlling water parameters for paiche cultivation, resulting in the correct functioning of the system, thus concluding the feasibility of implementation and the contribution to the development of new research.
Techniques for Torque Ripple Reduction in BLDC Motor for EV

Brushless DC Motor has wide-ranging applications in industries with high efficiency, low cost, high torque to mass ratio etc. Especially in Electric Vehicles (EV) it has gained its popularity where it mostly uses BLDC hub motor. But BLDC motor has got some drawbacks, especially during dynamic load conditions it experiences a slight vibration due to torque ripple. Torque ripple can be caused because of commutation torque ripple, mechanical defects, current ripples, imperfect EMF waveform, cogging torque ripple etc. This literally affects the smooth driving experience. This paper relies on the ways to mitigate the torque ripple which is the main incitement for less lifespan of BLDC motor than Induction Motor. It also focus on the mathematical modelling of Brushless DC motor and control techniques for torque ripple reduction. The advantage of this work is it uses simple and efficient technique to reduce the torque ripple.
Intelligent Classification of Spatial Targets Based on RCS Sequences

Using the radar cross section (RCS) sequence of spatial moving targets with different azimuth, elevation angles, and poses, classification of spatial targets are achieved according to their two-dimensional images by deep residual networks. The numerical results show that the two-dimensional radar images can contain sufficient information of the spatial targets and thus exploited by the deep residual networks to achieve good classification.
Sensorless HOSM Control based VGE Observer for a 7-Phase PMSM

This paper proposes a new sensorless control scheme based on the super twisting observer with an added varying gain exponent, and the higher order sliding mode controller for a non-sinusoidal seven phase Permanent Magnet Synchronous Machine (PMSM). This strategy is based on the splitting of the seven phase into mechanically coupled, but electrically independent machines. Only select harmonics of the machine are considered for both the position, speed estimation and control. To the best of our knowledge, the proposed observer and controller have not yet been applied to this machine type, they overcome the classical observers/controllers limitations linked to the perturbation and noise effects. Stability analysis of the proposed observer and controller is conducted and simulation results are shown at the end to highlight the feasibility of the proposed observer-controller scheme.
Hydrous minerals on Mars as seen by the CRISM and OMEGA imaging spectrometers: Updated global view

The surface of Mars has preserved the record of early environments in which its basaltic crust was altered by liquid water. These aqueous environments have survived in the form of hydrological morphologies and alteration minerals, including clays and hydrated salts. Because these minerals probe on Earth aqueous environments compatible with biotic activity, understanding their formation processes on Mars is of great exobiological relevance and also offers insight into Earth's now erased ancient water environments. Using remote sensing, we conducted a large‐scale investigation of the distribution, composition, age, and geomorphic settings of hydrous minerals on Mars, providing a sharpened global view of the early aqueous environments and their evolution with time. Aqueous alteration seems to have produced clays on a planetary scale but these are found to be restricted to the oldest observable terrains on Mars (∼4 Gyr). However, very diverse aqueous environments have also been found which suggest widespread, complex aqueous settings from the surface to kilometric depths, and spanning over 1 Gyr. By building a robust statistical sample of detections, the global trends inferred here attempt to provide a broad view of our current understanding of hydrous minerals on Mars and provide context for more localized, in‐depth analyses. Collectively, these trends suggest that at least transient conditions have existed on Mars which may have been favorable for pre‐biotic to biotic activity.
An Efficient Hardware Implementation of the Double Q-Learning Algorithm

Double Q-Learning (DQL) is an off-policy reinforcement learning algorithm providing better performance in a stochastic environment compared to the Q-Learning technique. This paper proposes an efficient FPGA based hardware architecture of the Double Q-Learning algorithm. The main originality of the proposed design is that allows to parallelize the actions Z. The proposed hardware architecture also performs an efficient technique to manage the data exchange based on two designed Q-Matrix LUTRAM asynchronous reading and synchronous writing memories. The implementation of the proposed design on a Xilinx Zynq Ultra scale+ MPSoC ZCU104 with 8 bits Q-Matrix data-width achieves a maximum frequency of 194 MHz while a dynamic power consumption of 45 mW. Compared to similar one previous work based on the Q-Learning architecture, the proposed implementation provides good trade-off in terms of algorithm performance versus required logic resources. Indeed, for environments with 8 states until 256 states, the proposed DQL architecture only requires (227 LUT, 75 FF, 66 LUTRAM) and (552 LUT, 75 FF 322 LUTRAM), respectively.
Introduction of Virtual Environment with Learning Personalized Content Recommendation for Realistic Avatar Creation in Online Platform using Machine Learning

Technology has developed quickly, resulting in the emergence of online platforms and virtual worlds that provide people with engaging experiences. There is a rising need for realistic avatar generation in these digital settings as virtual reality and augmented reality gain popularity. To provide personalized content recommendations, this study presents a revolutionary method for creating avatars on online platforms. The suggested technique tries to make avatars more realistic by customizing their appearance and behavior to each user. The system generates tailored suggestions for avatar customization based on user preferences, demographics, and previous interactions. Machine learning algorithms are used in this procedure to analyze huge datasets and spot trends in user preferences and behavior. The system also uses augmented reality and virtual reality technology to provide users with a more immersive experience. Users can interact with their avatars in a virtual setting by integrating these technologies, simulating their physical movements and interactions. There are numerous and extensive uses for this system. It applies to a variety of settings, including social networking, online gaming, and virtual meetings. The personalized content recommendation system for avatar creation offers users additional ways to express their uniqueness and take part in deeper interactions in the virtual world. In summary, this study offers a novel method for building avatars for online platforms that makes use of personalized content recommendations and machine learning approaches. The method increases the realism of avatars and gives consumers a more immersive and participatory experience by combining virtual reality and augmented reality. This system's successful implementation can transform online platforms and alter people interaction with virtual surroundings.
An Efficient Data Recognition and Classification in Cloud-IoT System using Optimized K-Means Algorithm and Hybrid CNN-SVM Deep Learning Model

In today’s world Internet of things is an emerging technology, which helps people to work smarter and potentially interconnects anything with real life object to sophisticated networked devices. The massive growth of IoT connected devices creates huge amount of data transfer at very high speed, which in turn cause difficulty in data storage, processing capacity, network management and especially data recognition. To overcome this problem, IoT devices are interconnected with CNN and cloud computing. Some of the IoT applications require data recognition for data classification. To attain better data recognition and classification a suitable CNN Deep learning technique is used in this research. Work has been compared with existing CNN variants and superior performance has been observed in each case.
Dual Holographic Reflective Metasurface System for Beam Conversion

In this article, a dual holographic reflective metasurface system is proposed for achieving flexible beam conversion. Holographic reflective metasurface A is designed to generate a multimode orbital angular momentum (OAM) vortex wave as the primary beamforming stage. Subsequently, holographic metasurface $\mathbf{B}$ is utilized to transform the multimode OAM vortex wave into two spherical waves with different modes, constituting the secondary beamforming stage. Through simulation, we have successfully demonstrated the attainability of the aforementioned beamforming capability at 10 GHz.
Perspectives on intervention robotics needs

Several countries have set up robotic accident response groups to be able to respond to the possibility of a major nuclear disaster. The goal is to send robots to manage the accident in a highly contaminating and irradiating situation instead of sending humans. Technological advances have made great strides since the creation of such groups after the Chernobyl (1986) and Fukushima (2011) accidents. These increasingly rapid advances could open up opportunities that users of such robots do not yet dare to imagine. The objective of this article is to place oneself in this perspective, taking stock of what was originally available when the groups were created, what is being used today, and what users would like in the future in order to mobilize the scientific and industrial communities on these issues.
Clothes Color Segmentation Based on Channel Attention Module

This paper studies a color segmentation network for mixed-color clothes based on BiSeNet V2. Aiming at the color category segmentation error in BiSeNet V2, the CAM_BiSeNet V2 network model is designed, and the channel attention module is added before the context embedding block in the semantic branch of BiSeNet V2. The selection of channels is integrated to achieve a better representation of the features in the channel dimension. The experimental results show that this algorithm can effectively improve the segmentation accuracy of the model, and the segmentation accuracy rate is increased by 10.61% (91.19% vs 80.58%)
The use of Image Processing for the Classification of Diabetic Retinopathy

Retinopathy represents one of the primary causes of blurred vision worldwide. To stop eyesight deterioration, prompt detection and therapy are essential. Deep learning as well as Artificial Intelligence have demonstrated significant promise in Diabetic Retinopathy detection and categorization. The existing research that has used Intelligence and machine learning based approaches for Diabetic Retinopathy detection are summarized in this publication. For the extraction and sorting of Diabetic Retinopathy pictures, a variety of deep learning algorithms, including convolutional neural nets and recurrent neural networks have been used. Researchers have demonstrated that this method performs better than conventional method of machine learning. Moreover, Automation solutions have already been created to help medical practitioners identify and diagnose Diabetic Retinopathy. Such techniques help identify individuals who really need immediate medical attention by analyzing retina photos and offering a classification scale for Retinopathy severity. The reliability, effectiveness, and affordability of Diabetic Retinopathy screening and diagnosis have such a tremendous deal of opportunity to be improved by Intelligence and Deep Learning techniques, which might also greatly enhance the results for patients. The promise of Intelligence Diabetic Retinopathy detection methods must be progressively explored and validated in actual areas of medicine The results from the DiarteDB0 information set were 97.6% accuracy in categorization, 97.96% compassion, 99.5% exactness, alongside 99.1% F1 score; the results from the Kaggle information set were 97.84% categorization accuracy, 98.45% compassion, 99.68% accurateness
An Ontological Approach for Understanding Animal Diseases and Their Symptoms

The accurate and timely diagnosis of animal diseases is essential for the health and well-being of animals, as well as for the prevention and control of disease outbreaks. One important aspect of disease diagnosis is the accurate representation and classification of diseases and their associated symptoms. The currently available ontologies explain either disease or symptom, but they do not provide a descriptive relationship between symptoms and their diseases. Furthermore, combining two separate ontologies is difficult due to a lack of domain knowledge and the different ontological structures used in different ontologies. As far as we know there isn’t any linked ontology knowledge base that can meet our needs. To address these challenges, we developed a novel animal disease and symptom ontology that aims to provide a descriptive representation of animal diseases and their symptom. Include synonyms for each symptom that motivate to disambiguate and identify the precise symptoms from the ontology. In the end, we were able to achieve an overall implementation accuracy of 83.8%. In addition to this, we make use of the structured methodology of the ontological knowledgebase, which provides researchers with the ability to broaden the ontology to include diseases and symptoms.
A Low-Profile Broadband Dual-Polarized Omnidirectional Antenna with Low Gain Variations for 5G Indoor Mobile Communications

A low-profile broadband dual-polarized omnidirectional antenna with low gain variations (GV) for 5G indoor mobile communications is presented. A vertical-polarized (VP) antenna element is composed of a pair of orthogonal conical plates and a top-loaded circular brass disc to broaden the impedance bandwidth. A horizontal-polarized (HP) element is consisted of four printed dipoles with rectangular etched slot and parasitic patches around the dipoles to broaden the operating bandwidth, and a 1-to-4 power divider feeding network on the bottom. The feeding network with the balun of “T”-shaped with terminated shorting-circuit via to improve the isolation between the VP and HP elements. Two pairs of extending strips printed along the ±45° direction are used to reduce the GV and broaden the impedance bandwidth. The antenna achieves a compact size of 110mm*110mm*40mm, and the height is only 0. 26λmin(λmin which is the free-space wavelength at the minimum operating frequency). The overlapped bandwidth of 76% (1.69-3.75GHz) with an excellent port isolation (> 28dB) is achieved. Satisfactory omnidirectional radiation characteristics are obtained for both the VP and the HP elements, the GV of the VP element is less than 2 dB, and the GV of the HP element is less than 2.5 dB.
Detecting the Originality of Extended Research Articles Using Similarity Techniques – A Proposal

— Research articles with respect to increased growth of research all round the world, personal interest of academicians, industrialists or other personals has paved way to huge knowledge repositories. The outcome of knowledge base provided by such a group is both advantageous and vice versa. In sense we mean that if the knowledge were used properly, it leads to innovation of new ideas or further enhancements, while on the other side when such knowledge is used repetitively in a reformulated fashion it would result in plagiarism of research articles pulling out the quality of research. This article proposes a method to detect the originality of research articles published as an extension of the previous works using similarity technique. We have proposed a method to summarize the scientific papers, so as to reduce the time involved in reading a document, there by presenting the originality of the research artciles. From the preliminary study carried out using the corpus collected, the study seems to be promising. We focus on to implement the proposed system and provide a comparison of the methods discussed in this paper analyzing the necessary parameters.