Miracle-dz/UniFAR-FTUs huggingface dataset:
A Polarization-Insensitive Dual-Transmission Band Frequency Selective Rasorber

A polarization-insensitive frequency selective rasorber (FSR) geometry with dual-transmission and dual-absorption band has been presented in this paper. Two passbands appear inside a wide absorption band, thereby resulting in multiple absorption regions surrounding the transmission peaks. The proposed FSR comprises of one absorptive layer and one transmissive layer, separated by an air spacer, and each of them is realized by patterning geometries in a periodic arrangement. The transmission bands are obtained at 5.30 GHz and 8.10 GHz with insertion losses of 0.79 dB and 0.46 dB, respectively. A wide absorption band, originally realized between 2.26 GHz and 8.79 GHz, gets divided into multiple absorption regions due to the presence of in-band transmission peaks and shows two discrete absorption bands from 2.01 GHz to 4.95 GHz and 5.81 GHz to 7.45 GHz. The structure achieves an overall fractional bandwidth of 118.20% with respect to its centre frequency. Polarization-insensitivity as well as angular stability for different polarizations (till 30 angle of incidence) are also observed in the structure. As compared to the existing dual-transmission band rasorber geometries, the proposed FSR offers a simpler design topology, lower footprint, and larger operating bandwidth. Proposed structure has been fabricated and measured to verify proposed design.
Design and Development of A SIW based Narrowband Bandpass Filter In LTCC Technology

A SIW based five-pole narrowband bandpass filter (BPF) has been designed and implemented in low-temperature co-fired ceramic (LTCC) technology. The problems associated with a narrowband BPF in LTCC technology are discussed in detail. An X-band filter with a fractional bandwidth of 230 MHz is implemented at 11.575 GHz. The filter is integrated with a back-to-back CPW-to-SIW transition for measurement purposes. The measured passband insertion loss of the transition integrated filter at the passband center frequency of 11.38 GHz is 3.194 dB. The filter provides an excellent out-of-band response, rejection being more than 30 dB below 11.13 GHz and above 11.63 GHz the passband.
Investigating The Challenges of Adopting Data Analytics In Zimbabwe’s Retail Sector

The retail sector is experiencing unprecedented volatility, uncertainty and ambiguity, and these culminated in the closure of some of its notable giants due to the global financial crisis and the novel corona virus pandemic. This paper uses Organisational Mindfulness (OM) and Resource Based View (RBV) to investigate the adoption of data analytics in developing countries using data from Zimbabwe’s retail sector. The results showed that while some industries use big data analytics (BDA), its adoption remains an insurmountable task for some Zimbabwean retailers due to infrastructural, resource and other technical challenges. It revealed that successful adoption of BDA in the retail sector can help change business operations, including the ability to match customer expectations, hone product lines and improve marketing campaigns.
Research on Microservice Coordination Technologies based on Deep Reinforcement Learning

In order to solve the problems of scaling, placement and traffic scheduling of a large number of microservices. Based on tensorflow and keras machine learning framework, this paper designs and implements a microservice coordination method based on DDPG (deep deterministic policy gradient) algorithm in DRL (deep reinforcement learning). Firstly, the network model and service traffic model are built, and the optimization objectives are defined, i.e. throughout and delay. Besides, a POMDP (partially observable Markov decision processes) and a traffic scheduling table are designed to build the service coordination framework and finish the ultimate algorithm. Secondly, the utility of our algorithm and other baseline-algorithms is numerically evaluated within different traffic pattern and real-world traffic traces. Thirdly, technology proposed in this paper and 5g core network are combined. Furthermore, the significance of this coordination technology on 5g core network in the trend of microservice is also discussed.
SIW Cavity Backed High-Gain Slot Antenna using Metamaterial for 5G mmWave Communications

This paper proposes a low-profile high-gain SIW-cavity backed slot antenna using metamaterial slab in the superstrate of the reference antenna for 5G mmWave communications. The proposed antenna operates at the frequency band of 27.7 - 29.2 GHz with realized gain greater than 9.8 dBi throughout the entire operating frequency band whereas the reference antenna gain is 4.6 dBi and the antenna profile is 0.2 λ0 at the frequency of 28 GHz. The superstrate slab is working as a low-impedance metamaterial and the front-to-back ratio is improved by 5.2 dB at 28 GHz due to the application of the metamaterial. The radiation efficiency of the proposed antenna is greater than 92 % throughout the entire operating frequency band of the antenna. This low-profile high-gain antenna can be used in mmWave communications.
Enhanced Gain Open-ended Waveguide Antenna using a Circular Substrate Disk

This work presents the gain enhancement technique of a conventional open-ended waveguide (OEWG) by using a circular dielectric substrate disk backed by a thin, perfect electric conductor (PEC) sheet along with PEC rings on the front side. This technique is applied on a commercially available WR-28 waveguide to enhance its gain from 5.62 dBi to 19.45 dBi at a frequency of 26 GHz. To validate this approach, the simulations and optimizations of the OEWG antenna are carried out using the full-wave simulator, Ansys HFSS.
The Analysis of Research Literature on the Role of AI in Industrial Sector

According to the advancement of technology, artificial intelligence (AI) allows the machine to perform some intelligent tasks in the business operation. With the emerging trend of AI in the business application, grouping the journal articles of artificial intelligence based on the industrial classification framework using clustering in K-Means algorithm and topic modeling with LDA technique are performed within this study. The finding shows the relationship between two methods together with the linkage of the business sectors including information sector management of companies and enterprise sector, manufacturing sector, finance and insurance sector, professional, scientific and technical services sector, transportation sector, healthcare and social assistance sector, and retail sector.
Enhancement of Quality of Service in Mobile Ad-Hoc Networks using Hybrid Load Balanced Simulator: Annealing Simulated

Wireless topologies can change rapidly and unexpectedly. Hybrid networks are connected by gateways, which act as entry points for all traffic. Some gateways may be subject to high traffic loads and therefore may be overloaded. To compare the performance of the proposed work, various parameters including end-to-end latency, power consumption, and throughput are considered. Load Balancing Hybrid Simulator (LBHS) is an algorithm used in this paper to distribute traffic load among multiple gateways to solve load balancing problems in MANET. It combines a Hybridge Annealing Simulation (HAS) network technology with Decentralized Random Search (DRS). A network that supports QoS in MANET. The Dominant Minimal Set (DMS) problem is solved using AS. The DSS heuristic algorithm provides an algorithm with a simple structure and a high degree of exploration. In addition to faster convergence than other algorithms, it also implements an axiomatic algorithm to ensure the diversity of agents and avoid falling into local optimum. The test results show that the proposed Hybridge Annealing Simulated Decentralized Random Search (HAS-DRS) procedure performs better than the dominated connection set (DCS).
Ant Colony Optimization based Medical Image Preservation and Segmentation

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work.
Detection of Skin Diseases via Deep Learning using SVM Method

Dermatological issues are one of the most preventable diseases in the world. Although it is widespread, studying it is challenging because of the many layers of complexity introduced by the presence of colour, concealment, and hair. Diagnosing skin problems early is essential for effective therapy. The method for identifying and treating skin injury is based on the specialist's level of competence and experience. There needs to be pinpoint accuracy in the analysis. Success rates for clinical diagnostic and clinical therapeutic frameworks are improving with time as a result of cutting-edge developments in medicine and data science. Skin disease diagnosis has benefited from the application of AI calculations and the utilisation of the large quantity of information available in hospitals and clinics. For this study, we collated a large number of previous studies that analysed skin illnesses via the lens of AI-based classification strategies. In their previous studies, the specialists employed numerous frameworks, instruments, and calculations. A small number of frameworks have been developed that are capable of correctly identifying skin diseases with varying degrees of suggestive precision. Multiple models have used image processing and component extraction methods to
Analysis and Simulation of FMCW mmWave Radar Multi-Target Detection in Traffic Scene

In order to verify mmWave radar detection effects of the target's radar cross section (RCS), this paper designs the multi-target detection task in the traffic scene to infer the principle of linear FMCM millimeter ultrasonic measurement of range, speed and angle, and points out the significance of the RCS analysis to radar detection performance. Additionally, the mmWave radar detection performance is examined. At last, the mmWave radar multi-target detection is tested in the simulated traffic scene, which helps verify the influence of RCS on the radar detection performance.
Ionospheric disturbances produced by chemical releases and the resultant effects on short‐wave ionospheric propagation

[1] As an effective means to actively modify the ionosphere, chemical releases can produce artificial ionospheric holes as a consequence of ionization reduction, which can have a great impact on radio wave propagation. To investigate the morphology control of ionospheric holes by various chemical releases and the resultant effects on radio wave propagation, a quantitative numerical model is developed on the basis of the approximate solutions of the diffusion equation of single-point release in uniform atmosphere. While single-point release produces ellipsoidal ionospheric holes, multipoint release can produce other types of ionospheric holes (such as parabola-like tubular ones), which is strongly dependent on changes in the release species, release altitude, and mass of released neutral gas. Releases of both H2O and SF6 can produce ionospheric holes with a similar spatial extent, but the latter tends to result in clearer boundaries and more pronounced electron density reductions. In addition, either an increase in released amount or releases at higher altitudes can lead to a broader hole. To evaluate the effects of an ionospheric hole on radio wave propagation, three-dimensional ray tracing simulations are performed. The ellipsoidal ionospheric holes can act as a lens focusing and bending radio waves, leading to multiple wave reflections inside the holes. In contrast, in the paraboloid tubular ionospheric holes, the rays can penetrate the disturbed region or reflect back, showing a strong dependence on radio frequency. It is well demonstrated that chemical releases can efficiently give rise to artificial ionospheric disturbances and thus modify ionospheric propagation of radio waves.
Efficient Path Plannning for Mobile Sink using Salp Swarm Technique in IoT-Het Networks

For this reason, mobile sinks (MS) have already seen extensive application due to their ability to provide network-wide energy consumption parity and offer implicit load balancing. These nodes form what is known as a cluster topology, and the process of constructing one is known as clustering. An effective route finding system is provided in this study that does more than just solve the aforementioned problem; it also pinpoints where the MS must make pit stops for the transmission of data to be completely trustworthy. Specifically, the Salp Swarm Optimal solution for IoT networks is used, whose primary goal is to reduce the overall grouping time required by the suggested system. The target sites that make up the MS's route are decided using the circumferential visit approach. The results of the performance study demonstrate that the suggested strategy extends the life of the network by decreasing the required number of cluster updates and the time it takes to complete each update.
Orthogonal Schemes for Handwritten Digits Recognizing from Image Data

Identification of numbers has gained excitement recently. Despite the fact that several learning focused categorization approaches are proposed for mnist dataset validation, the precision and processing time may still be improved. Dealing with a disease as an early union is rather common. Swarm approaches like Swarm Optimization seriously evaluate this unfavorable element (PSO). A novel approach using neural network models with convolutions is intended to address the limitations of traditional Soc (CNN). Clo is created by modifying the artificial neural network with the use of luck and analogous learnt optimized particle swarms (CNN-SOLPSO). This adaption is provided for the steadily growing population of the over. This projected enhancer shows increased efficacy when compared to other unconventional methods and expects the best characteristics from that wellbeing assessment. The Holdout library of transcribed digits is used to construct and evaluate the computation contained in the proposed model. the severely deformed, unpredictable, and manually produced pictures of digits that help compensate its Imagenet dataset database. The major objective of this effort is to contribute to an appropriate approach to digital by focusing on greater precision and better computations. Using Bas 2018b, it is possible to choose parameters for Training unshakable quality and drop capacity, Validate refinement and loss measurements, and Identify velocities with defect rate and completion moment.
Non-static brane probes, topological charges and calibrations

In this thesis we consider probe branes in 10- and 11-dimensional supergravity backgrounds. Firstly, we consider probing a class of 11-dimensional backgrounds with giant gravitons. These backgrounds arise from lifting solutions of 4-dimensional U(1)(^4) and 7-dimensional U(1)(^2) gauged supergravities. We find that giant gravitons degenerate to massless particles exist in arbitrary lifted backgrounds, and furthermore both these objects are degenerate to massive charged particles probing the associated lower-dimensional gauged supergravity solutions. We then move on to consider superalgebras for M2- and M5-brane probes in general 11-dimensional supersymmetric backgrounds. We derive the form of the topological charges which appear in the super translation part of the algebra. These charges are given by the integral (over the spatial world-volume of the brane) of certain closed forms constructed from Killing spinors and background fields. The super- translation algebra allows us to derive BPS bounds on the energy/momentum of probe branes in these general supersymmetric backgrounds. These bounds can be interpreted as generalized calibration bounds for these branes. We then use a similar procedure in type IIB supergravity to construct a calibration bound for a giant graviton in AdS(^5) x S(^5). As a by-product of this construction, we find a number of differential and algebraic relations satisfied by p-forms constructed from Killing spinors in type IIB supergravity. These relations are valid for the most general supersymmetric backgrounds. We then show that the calibration bound which we have constructed is saturated by a large class of general giant gravitons in AdS(^5) x S(^5), which are defined via holomorphic surfaces in C(^1)'(^2) x C(^3). Moreover, dual giant gravitons also saturate the calibration bound. We find that both these branes minimize "energy minus momentum" in their homology class.
Multibeam Pattern Reconfigurable Antenna Using SP3T Switching Network and RRS

This article discusses a new idea of radiation pattern and multibeam reconfiguration. Two different types of antennas have used along with two sets of reconfigurable reflecting surfaces (RRS). All these arranged so, it may provide the radiation pattern reconfiguration from broadside to backside and end-fire to back-fire direction. Two sets of traditional printed dipole antennas are used to provide end-fire to back-fire radiations. At the center, a slot-based antenna with two sets of RRS on top and bottom has been designed to provide broadside to backside radiation. To make all these possible, a reconfigurable single-pole three throw (SP3T) switching network has been provided. So, the proposed design is capable to provide four single beams as well as two bi-directional beams.
CSRR and Stub Loaded Miniaturised Tri-band Patch Antenna for 5G Base Station Application

In this paper, a complementary split ring resonator (CSRR) and stub-loaded miniaturized tri-band microstrip patch antenna is proposed for sub-6 GHz 5G base-station application. First, a CSRR loading is used on a square microstrip patch antenna to achieve dual-band response and antenna miniaturization simultaneously. Next, the tri-band response is achieved by adding two differently sized stubs. The proposed tri-band antenna works at 3.59 GHz, 3.85 GHz and 4.44 GHz, sub-6 GHz operating frequencies having peak gains of 4.8 dBi, 5.5 dBi and 6.7 dBi respectively. In order to reduce the cross-polarization levels at the operating frequencies, a rectangular slot is etched from the ground plane. The proposed antenna exhibits co-polarization to the cross-polarization separation of 20 dB, 11 dB and 17 dB at 3.59 GHz, 3.85 GHz and 4.44 GHz respectively. A prototype of the antenna is fabricated and measured results are compared with the simulated ones.
Forecasting Air Quality Index in Thailand Using Ensemble Method

Air pollution is one of the most serious problems in many regions of the world. Thailand also has had to face this trouble unavoidably, especially in the northern region of Thailand, the area that has been highly contaminated by air pollution for so many years. In this paper, an ensemble method was introduced to forecast the level of air quality index (AQI) in the northern part of Thailand. The ensemble method, in this study, is a technique gaining the results from majority vote of outputs of three classification models—k-nearest neighbors, random forest, and support vector machine. The proposed model compared the voted accuracy with the accuracies of existing classification models. It made use of the 2018 - 2021 data from seven stations in four provinces of Northern Thailand. In the end, the proposed model yielded 99.68% - 99.84% accuracy rate on average higher than most of the performance of the other comparative models.
Implementation of PMSG Based Wind Energy Conversion System with Solar based Battery storage

This paper illustrates the Wind Energy Conversion System (WECs) study and its designing. For this power generation process, overall system requires a Wind Turbine generator (WTG) and here a PMSG based Wind Turbine is usedamong different types of wind turbine generators. DC TO DC converter topologies (boost and bidirectional converters) connection is required in the Wind Energy Conversion systems as there are changes in the wind i.e., with variable wind speed and the load. The other way of generating electricity is throughthe solar array charging the battery, and the energy from the battery comes into picture when there is less wind or probably no wind i.e., at zero wind speed. The paper deals with powergeneration using both the Wind Turbine (WECS) and also a battery getting charged through the Solar Array. The major aim of the proposed work is to coordinate control techniques among system components like Wind Turbine Generator, DC TO DC converter topologies, battery controller etc. under various wind speeds. The MPPT technique is also employed to obtain maximum power and maintaining constant output DC voltage. The propounded work has been simulated and modeled with the help of MATLAB - SIMULINK software.
Optimization of Simulation Parameters of Input Shaper Based on Genetic Algorithm

In the motion control system, input shaper is often used to suppress the residual oscillation of high-precision positioning system, but the selection of input shaper parameters is difficult. In view of the difficulty of selecting input shaper parameters, this paper proposes to use genetic algorithm to select the parameters of input shaper. Firstly, the input shaper is designed in the light of the three-loop mathematical model of motor; Secondly, because the genetic algorithm directly optimizes the output of the motor and the algorithm can reduce the influence of the deviation of generator parameters on the parameter selection of the input shaper to a certain extent, the parameters of the input shaper are selected by the genetic algorithm to optimize the output of the motor; In the finish, a comparative experiment between a system with input shaper and a system with simple PID control is carried out. The results show that the optimized system can obtain higher robustness and smaller tracking deviation.