Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient VHO Algorithm to Enhance QoS in Internet of Vehicles with the Integration of 5G

In a de veloping country such as India, the intellection of smart city and the boom for a wide range of vehicles, Internet of Vehicles (IoV) has gained a lot of consideration by furnishing numerous benefits, including traffic congestion control, smart parking, vehicle emergency and monitoring levels of pollution. Furthermore, IoV provides support for vehicles over internet aid communication. In order to have a better communication between Vehicle-to-Everything (V2X), an advanced network infrastructure is required. The currently available networks like 3rd generation (3G), 4th generation (4G) or long term evolution (LTE) are not adequate for these kinds of communications; There comes the 5th Generation (5G) cellular network into the picture. The 5G offers real-time crowd sourcing, higher data rates, low latency for transmission and sensing as a complementary base for information. In addition to the leading edge network infrastructure, the mobility of vehicles urges to have a perfect handover (HO) mechanism among heterogeneous networks. This paper discuss about the integration IoV with 5G and the importance of vertical handover (VHO) mechanism using an Artificial Intelligence algorithm and analyze its performance based on few of the parameters such as data transfer rate, transmission delay, mean throughput, packet delivery ratio (PDR) and Quality of Service (QoS).
Ku‐band low phase noise oscillator using the high order mode loaded with L‐slot SIW resonator

This article introduces a novel high Q‐factor substrate integrated waveguide (SIW) cavity resonator and its application of Ku‐band low phase noise oscillator. Q‐factor of the proposed resonator is improved by using the L‐slot and the higher order mode. The L‐slot is placed on edge of TE102 mode field. Therefore, the L‐slot has little influence on resonant frequency but can obviously enhance roll‐off characteristic of S11. And the high order modes have the bigger Q value than fundamental mode. The negative resistance oscillator based on the proposed SIW cavity resonator is easily designed and tested. The experimental results of Ku‐band oscillator show outstanding low phase noise of −130.43 dBc/Hz at 1 MHz offset frequency from 15.55 GHz carrier frequency. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2325–2328, 2016
Personalized Path Recommendation with Specified Way-points Based on Trajectory Representations

With the development of the smart city, personalized path recommendation has already attracted the attention of researchers. However, it has not been considered that some users need to make personalized recommendations of a route for a given pair of OD (Origin-Destination) and some specified consecutive way-points. To our knowledge, this problem is studied for the first time. Essentially, this problem can be taken as inferring a high sampling rate fine trajectory from a low sampling rate rough trajectory composed of OD and way-points. The biggest challenge is that the user may just have some knowledge about the location rather than the precise location of a waypoint because a place may have many GPS points. This paper proposes a PSR (Personalized Selective Route) model based on trajectory learning for a given OD and a number of way-points. It integrates multi-source information to learn more comprehensive personalized preferences and introduces the Seq2Seq model with Multi-Head self-attention mechanism to automatically adjust the weights to capture more accurate temporal and spatial correlations. The experiments on the real traffic trajectory data show PSR model is robust to low sampling rate and noise. Compared with the best baseline, the accuracy of Top-l under the Euclidean distance of PSR is improved by 32.57%, and the accuracy of Top-3 is improved by 63.87%.
An Appreciation of Missile Structures

The invitation for this lecture requested “a review of missile structures from the viewpoint of an aircraft designer. ” This is inevitable because most of the author’s experience has been associated with aircraft design. There are however many people actively concerned with missiles who have had considerable experience designing aircraft and would be better qualified to make a more realistic approach to the subject. The difficulty for an outsider making a review of missiles is justifying the “need to know” and then, having found out, or deduced interesting facts, he may be made aware that to publish them would contravene some security ruling.
A Proficient Vertical Handover Decision Making Algorithm in Internet of Vehicles with 5G

As known, usage of mobile devices are enormously increasing day by day and these devices are being used in different applications where the user satisfaction, seamless connectivity, Quality of Service(QoS) and service among heterogeneous networks are very important features. These mobile devices have been introduced to include features such as advanced wireless technology support, seamless mobile networking, increased processing speed and improved multimedia services in recent days. These services allow the service provider to satisfy customers with enhanced service quality. Recently, the vehicle ecosystem emerged in the Internet of Vehicles (IoV); it involves the computational processing of moving vehicles to perform dynamic operations with wireless feature. To offer reliable services, Heterogeneous Wireless Networks (HWNs) such as 5th Generation (5G) cellular network is considered to permit users with global devices to access the required service at anyplace and anywhere.
Collaboration of Heterogeneous Medical Information Systems in the Republic of Serbia

Preservation of health data and monitoring of patients' treatment history is the basis for adequate treatment and prediction of potential diseases or conditions. Medical information systems (MISes) make it easier for healthcare professionals to search for and review all existing medical patient data for adequate treatment and provide the ability to warn of potential illnesses depending on previous treatments. However, MIS is often organized locally, at the healthcare institution or city level, and it is not possible to directly access data located in other healthcare facilities. Therefore, the collaboration of healthcare institutions' MISes is inevitable. This paper discusses the problem realization of heterogeneous MIS collaboration on the example of MIS MEDIS.NET collaboration with other healthcare institutions' MISes in the Republic of Serbia, which is reflected in the exchange of medical data for a particular patient. The described concept can be applied to the collaboration of other types of information systems, i.e., creating a distributed centralized system.
Superpixel-Based Relaxed Collaborative Representation With Band Weighting for Hyperspectral Image Classification

Representation learning methods, such as sparse representation (SR) and collaborative representation (CR), have been widely used in hyperspectral image classification. However, they merely considered the similarities between features. Due to the plentiful spatial and spectral information in hyperspectral images, the differences between features also need to be considered. Relaxed CR (RCR) is used in face recognition to accommodate the difference and similarity of features simultaneously. In this article, a novel method of RCR with band weighting based on superpixel segmentation is proposed for hyperspectral image classification. The $\boldsymbol {l}_{ \boldsymbol {2}}$ norm on band coefficients and global average coefficients is exploited to ensure the similarity, and the variance determines the specific coefficient-related weight of each band. The training set is selected from each superpixel, which is considered as a subgraph rather than independent pixels. It is favorable for concentrating on the difference between similar bands since the samples in each superpixel are of high similarity. Furthermore, extended multiattribute profile (EMAP) features, Gabor features, and local binary pattern (LBP) features are employed to increase the diversity of features; thus, a method of multifeatures’ RCR based on superpixels is proposed. Three typical data are used to validate the related algorithms. The experiments demonstrate that the proposed algorithms can effectively improve classification accuracy compared to state-of-the-art classifiers.
Simulation of Household Appliances with Energy Disaggrigation using Deep Learning Technique

Energy disaggregation is one of the major concerns in the modern power management in domestic utilities. Main aim is to read the individual load appliance readings from the whole data. There are so many techniques to the field, deep learning being promising. This paper state about simulation of household appliances for data aggregation and energy disaggregation of individual appliances using deep learning technique. For data collection we have used data of individual standalone house for summer season. Deep learning technique such as complex tree and linear modules are studied in this paper with the incorporation of complex technique for better efficiency. The performance efficiency of both the modules are tested and evaluated in this paper. To make it cost-effective the system is simulated in MATLAB/SIMULINK for the different trial cases.
Machine Learning Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

The massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity in HetNets. This leads to the pressing need to balance loads between cells, especially with the random distribution of users in numerous mobility directions. This paper provides a survey on the intelligent load balancing models that have been developed in HetNets, including those based on the machine learning (ML) technology. The survey provides a guideline and a roadmap for developing cost-effective, flexible and intelligent load balancing models in future HetNets. An overview of the generic problem of load balancing is also presented. The concept of load balancing is first introduced, and its purpose, functionality and evaluation criteria are then explained. Besides, a basic load balancing model and its operational procedure are described. A comprehensive literature review is then conducted, including techniques and solutions of addressing the load balancing problem. The key performance indicators (KPIs) used in the evaluation of load balancing models in HetNets are presented, along with the concurrent optimisation of coverage (CCO) and mobility robustness optimisation (MRO) relationship of load balancing. A comprehensive literature review of ML-driven load balancing solutions is specifically accomplished to show the historical development of load balancing models. Finally, the current challenges in implementing these models are explained as well as the future operational aspects of load balancing.
Optimized Artifical Neural Network For EEG-Based Emotion Recognition

The Emotion provides a great impact in cognitive activities of the humans, such as communication and interaction. The emotion recognition is the hot research topic as the emotion plays a significant role in artificial human computer interaction. The EEG is considered as the best modality considered for the emotion recognition, as it directly receives the signal from the human brain. Hence, in this research a novel optimization for the efficient emotion recognition based on the EEG signals is proposed. The classification of the emotion process is carried out by the Optimized Artificial Neural Network, in which the Brain storm Optimization (BSO) is employed for tuning the hyper parameter. Further, the process, such as artifacts removal, feature extraction, electrode selection enhances the accuracy of the classifier. The experimental results show that the proposed EEG based emotion recognition process attains maximum accuracy, sensitivity and specificity of 94.23% , 93.83% and 95.60% respectively.
A Comparative Analysis of SMS Spam Detection employing Machine Learning Methods

In recent times, the increment of mobile phone usage has resulted in a huge number of spam messages. Spammers continuously apply more and more new tricks that cause managing or preventing spam messages a challenging task. The aim of this study is to detect spam message to prevent different cybercrimes as spam messages have become a security threat nowadays. In this paper, studies on SMS spam problems to perform a better accuracy using several different techniques such as Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Random Forest, Logistic Regression and some more are performed. The result indicated that Support Vector Machine achieved the highest accuracy of 99%, indicating it might be useful as an effective machine learning system for future research.
Study on Water Quality and Wastewater Treatment using IOT

Human health, social and economic progress, and ecological sustainability all depend on water quality. This review paper gives a concept of how to choose datasets, parameters, algorithm functions, and the correctness of the outcomes. A significant element of the solution is to reduce pollution and enhance wastewater management. Usually, contaminants are removed from used water by a wastewater treatment system. It filters out hazardous and dangerous waste from water before returning it to a body of water. This paper provides review of water quality and wastewater treatment using IOT, from 2018 to 2021. Early warning algorithm, Vector Distance Algorithm (VDA), Proportional-Integral-Derivative (PID) algorithm, and many devices were used to monitor water quality. Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) autoencoders, and Facebook Prophet are three unique measurable and AI models (FPs), and other machine learning algorithms are used to classification of wastewater treatment. This review can aid in the decision-making and implementation of technology-based water quality and wastewater treatment in the future.
Probability of Bridge Failure Due to Pier Scour

Methods have been presented for computing the probability of bridge failure due to pier scour either using a maximum scour depth model or a time-dependent model. The probability of failure provides the basis for a tool which can be used in decision-making for both design of pier foundations or assessment of existing foundations. In the design of a bridge pier, the engineer can use this method to evaluate various design alternatives as a function of parameters such as the size, shape, and depth of the pier. A particular level of risk over the life of the structure that is socially acceptable could be set by policy. If the computed probability of failure due to scour at an existing bridge is currently high or is approaching an unacceptably high level, engineers would be alerted to a need for inspection and possible control of the scour hole around the piers. Without an estimate of the probability of failure, these decisions would be based only on the engineer's judgment. The computed failure probabilities do not eliminate the need for judgment, but provide valuable supplemental information for decision-making.
Rainformer: Features Extraction Balanced Network for Radar-Based Precipitation Nowcasting

Precipitation nowcasting is one of the fundamental challenges in natural hazard research. High-intensity rainfall, especially the rainstorm, will lead to the enormous loss of people’s property. Existing methods usually utilize convolution operation to extract rainfall features and increase the network depth to expand the receptive field to obtain fake global features. Although this scheme is simple, only local rainfall features can be extracted leading to insensitivity to high-intensity rainfall. This letter proposes a novel precipitation nowcasting framework named Rainformer, in which, two practical components are proposed: the global features extraction unit and the gate fusion unit (GFU). The former provides robust global features learning ability depending on the window-based multi-head self-attention (W-MSA) mechanism, while the latter provides a balanced fusion of local and global features. Rainformer has a simple yet efficient architecture and significantly improves the accuracy of rainfall prediction, especially on high-intensity rainfall. It offers a potential solution for real-world applications. The experimental results show that Rainformer outperforms seven state of the arts methods on the benchmark database and provides more insights into the high-intensity rainfall prediction task.
Free-space Laser Communications for Small Moving Platforms

The Japanese National Institute of Information and Communications Technology (NICT) is currently working towards the development of a series of miniaturized space laser-communication terminals with the aim to meet the requirements of a variety of different platforms, from drones to satellites, and to be applied in a variety of different scenarios, from fixed to moving platforms, operating in a wide range of distances and conditions, and without the need of extensive customization. This paper describes NICT's current efforts in this topic, and it introduces the prototypes that are being developed for the verification tests, which will happen in 2022.
The Role of Learning Analytics in Higher Education: A Strategy towards Sustainability

The impact of COVID-19 has been vast and enormous on every sector globally. But a tremendous change in the plan of action has been seen in the healthcare facilities and Education Sector. With the drastic shift from full-time offline modes of teaching to an online or hybrid model of teaching and learning, learning analytics has gained momentum. Learning Analytics can be broadly defined as a field focused on analyzing educational data to understand and improve learning processes. Precision in the education sector and its effectiveness in the learning process have been the areas of major academic investigations in the last decade of scholarly research work. This research study explores the available literature to make an empirical understanding of the development in this field of study. The primary objective is to explore the various constructs that lead to precise learning analytics, leading to the precision in higher education of the country. In alignment with the significant change in the functioning of the higher education institutions due to COVID-19, the study also focuses on the impact of COVID-19 in the development of learning analytics to promote a judicious learning environment for individuals and institutions. The study incorporates qualitative descriptive methodology for the analysis of the research objectives. The findings from this study corroborate the existing literature on learning analytics. It also aims to add a further scope of work in the same field. The study also outlines the significant drawback in promoting sustainable and precise learning in higher education.
An Automatic Ship Detection Method Adapting to Different Satellites SAR Images with Feature Alignment and Compensation Loss

Traditional deep learning Synthetic Aperture Radar (SAR) image object detection methods fail to provide effective detection results when faced with SAR image datasets with different joint probability distributions obtained from multiple imaging satellites. In this paper, an automatic SAR image object detection method based on domain adaptation is proposed to adapt to unlabeled target domain datasets acquired by different satellites. On the basis of introducing an adversarial domain adaptation learning strategy, we propose Adversarial Learning Attention (ALA) and Compensation Loss Module (CLM) on the baseline network. In ALA, considering the great difference in the scattering intensity of SAR images, the entropy vector can be used to distinguish the high-entropy and low-entropy regions among them and assign the corresponding weights, based on which the adversarial domain adaptation learning attention is proposed to achieve instance-level feature alignment and pixel-level feature alignment in source domain and target domain, respectively. In CLM, the domain alignment of pixel-level feature and instance-level feature of SAR image objects is first implemented, and then to make the feature alignment of both domains more accurate, better aggregation of proposals of different classes of prototype objects in the same domain is required, and further compensation loss is proposed to further restrict the prototype alignment of SAR object features in both domains. We conduct experiments on two SAR datasets obtained from different satellites whose results show the superiority of the proposed method over the state-of-the-art methods and the effectiveness of the proposed module in improving the detection accuracy.
Microwave Vibrometry: Noncontact Vibration and Deformation Measurement Using Radio Signals

Vibration and deformation monitoring is essential for condition monitoring and fault diagnosis in various fields, such as structural health monitoring (SHM) and equipment maintenance, in which effective vibration and deformation measurement is highly desired. The accelerometer is one of the most widely used vibration measurement instruments in a contact manner. However, the accelerometer has poor performance due to low-frequency response and low displacement measurement accuracy. What is more, for SHM of large structures, in which a great number of measuring points need to be arranged, the sensor networking is high-cost and time-consuming. Meanwhile, the added mass may affect the inherent characteristics of light structures.
Development of DC-DC Converters – A Review

DC-DC converter is an essential aspect of power electronics. Its voltage altering capabilities are used for power conversion applications. As the demand for different output voltage levels increases, so does the demand for new converter topologies, which can match the said demands. This piece of literature discusses various types of converters, such as cascaded and bidirectional converters. Also included is a discussion about the technological advancement of different types of converters and shows how the converters are different from each other schematically. The paper aims to depict the details of DC-DC converters' history and establish a foundation for the development of next-generation converters.
A N I NTRODUCTION OF U PCOMING R ADIO R ESOURCE M ANAGEMENT T ECHNIQUES FOR 5G N ETWORKS

Downlink (DL) and Uplink (UL) Decoupling (DUDe) and Machine Learning (ML). These technologies can significantly help towards more efficient resource allocation inside the next generation networks, offering increased spectral efficiency and data rates, better signal coverage, reduced latencies and many more. In this chapter, we will provide insights over the aforementioned technologies through firstly a literature review and later on by analyzing their architecture and their models. We will explain how these technologies can be taken advantage of in order to support the 5G networks and why they are core components of future networks, as it is expected that also 5G and Beyond networks will capitalize on them.
Constraint active search for experimental design

Many problems in engineering and design require balancing competing objectives under the presence of uncertainty. The standard approach in the literature characterizes the relationship between design decisions and their corresponding outcomes as a Pareto frontier, which is discovered through multiobjective optimization. In this position paper, we suggest that this approach is not ideal for reasoning about practical design decisions. Instead of multiobjective optimization, we propose soliciting desired minimum performance constraints on all objectives to deﬁne regions of satisfactory. We present work-in-progress which visualizes the design decisions that consistently satisfy user-deﬁned thresholds in an additive manufacturing problem.