Miracle-dz/UniFAR-FTUs huggingface dataset:
A Spatiotemporal ROI Mining Algorithm on AIS Trajectory Data

As one of the most important transportation in international logistics, marine transportation generate vast amount of data. At present, we have been able to collect ship navigation data from many approaches such as AIS data. And extracting accurate and valuable potential information from these data for further application is a new and important topic in related areas. In this paper, AIS data is selected as the experimental data and a Region of Interest mining algorithm based on historical AIS trajectory data is proposed. Unlike the clustering based approaches, this algorithm locates the ROI from location-time data in AIS history trajectories, which can detect those ROI where ships didn’t stay long, but visited frequently. Experiments and analysis on real AIS data shows that the proposed approach is effective and correct.
Femtocells Underlaid Macro-Cellular Networks

ABSTRACT This work presents the evaluation of the downlink (DL) performance of a dual-layer cellular networks by using energy efficiency (EE) metric, where femto base stations (FBSs), macro base stations (MBSs) and users (FUs) form independent spatial Poisson point processes (PPPs). The proposed network model is developed by considering number of antennas at each BS alongside a single antenna at each user with the use of the conventional spectrum re-utilization approach. Then, Coverage probability and EE expressions for the dual-layer cellular networks are exclusively derived analytically. It is also demonstrated that simulation results are almost inline with the analytical one in the PPP-based model. While coverage probability deteriorates with less margin in the lower FBS density region compared to the scheme presented in Xu and Qiu [“Energy efficiency optimization for MIMO broadcast channels,” IEEE Trans. Wireless Commun. , Vol. 12, no. 2, pp. 690–701, Feb. 2013. ] signalled not much turnaround of the network performance, EE in the lower and the upper FBS density regions are likely to remain between to Bits/Joule and to Bits/Joule, respectively. Proposed scheme tells us that it is firmly on course to match up with Vehicular Ad-hoc NETworks (VANET) applications without incurring high cost as EE, low latency, coverage probability and low power adaptability are back on good growth path.
Channel Estimation for mmWave Massive MIMO With Convolutional Blind Denoising Network

Channel estimation is one of the foremost challenges for realizing practical millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To circumvent this problem, deep convolutional neural networks (CNNs) have been recently employed to achieve impressive success. However, current deep CNNs based channel estimators are only suitable to a small range of signal-to-noise ratios (SNRs). Unlike the existing works, the modified convolutional blind denoising network (CBDNet) is proposed to improve the robustness for noisy channel by adopting noise level estimation subnetwork, non-blind denosing subnetwork, and asymmetric joint loss functions for blind channel estimation. Furthermore, the CBDNet can adjust the estimated noise level map to interactively reduce the noise in the channel matrix. Numerical results demonstrate that the proposed CBDNet-based channel estimator can outperform the traditional channel estimators, traditional compressive sensing techniques and deep CNNs in terms of the normalized mean squared error. In addition, the CBDNet can be used over a large range of SNRs, which hugely reduce the cost of offline training.
Technical Specifications of the Submarine Fiber Optic Channel Bandwidth/Capacity in Optical Fiber Transmission Systems

This work outlines technical specifications of the undersea fiber optic communication channel bandwidth, capacity with taken into account the maximum and minimum extended fiber cost in the presence of amplifiers stations. The number of amplifiers in the amplification stage are addressed based on the amplifier distance to strength the light signal in water depth after 5 km distance. The fiber channel capacity is estimated at different water depth and at the surface of the water. Minimum input signal power and required detectable received power are adjusted to ensure the high data rates in submarine cable systems under the best and worst conditions of the seawater pressure. The study emphasizes the high data rates transmission can be achieved at a distance of 10 km depth.
Investigation on Maximum Electromagnetic Torque of Permanent-Magnet Synchronous Machines

Many application fields need motors that could output large torque in a short span of time such as missile and aircraft, but larger torque needs larger volume that could influence the system performance. Therefore, promoting the motor’s maximum torque with small volume is the new challenge for motors. So, this paper presents a method for analyzing maximum electromechanical torque (with rate speed) of PMSM without the consideration of thermal effect produced by copper loss. This paper also discusses the factors that influence maximum torque of PMSM under the condition of $i_{\mathrm {d}}=0$ control mode. A function between motor torque and the inner parameters is first obtained under the constraint of electrical and magnetic saturation. For verifying the accuracy of the function and its results, the characteristics of the series motors with different inner parameters that are obtained in the function are testified by finite element method (FEM) software. Finally, a prototype motor is selected to be manufactured to verify the analysis result. Based on the torque experiments result, it could verify the accuracy of the function that describes the rule of PMSM maximum torque. Based on FEM software and prototype test verifying, the method could supply the theoretical support for PMSM maximum torque design.
UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model

Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model which takes both interpolation error and positioning error into account by making use of motion features of trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called UTSM based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers but also more tolerant of different sample frequencies and asynchronous sampling of trajectories.
Design of External Memory Error Detection and Correction and Automatic Write-back

— For space application, in order to improve the reliability of memory operation, the SEC-DED (40, 32) Hamming code is used for single event flip (SEU) fault protection of external memory for 8-bit width parity memory. Based on the (39,32) Hisao code, a parity bit is added to minimize the error correction probability of 3-bit error. By reusing the encoder and decoder, EDAC circuit area is reduced by 16.5%. By modifying Finite State Machine (FSM) of the memory controller, the faults detection and correction, and automatic write-back function of the corrected data are all complemented. Experimental results indicate that all 1 bit faults can be auto corrected and don’t affect the program results; all 2 bits faults can be detected, and the access memory operation can be terminated in time and take error detection trap. By adding fault tolerant design, read operation would have performance loss, but the SEU failure rate is lower by 6 orders of magnitude.
Adaptive Mobile Learning Framework Based On IRT Theory

Advances in remote systems and skilled online tools have made great enthusiasm for adaptable learning lately. Patterns show that diverse mobile learning is the next achievement of meaningful innovation. In any case, adaptable mobile learning progresses through its different perspectives and the links between them must be integrated. In this article, we will conduct a baseline survey of specific writing with mixed-use learning in the light of the fact that there is still a need for extensive research on disrupting classroom innovation, particularly on how the performance of various tasks affects the instructor function in the classroom as a media coach and learning facilitator. The easily accessible writing identified with the adaptable mobile adaptation unequivocally highlights that the robust adaptable learning system is missing and that few scientists have explained the existence of a permanent structure. To address this problem, a systematic framework is proposed in this paper to discover diversity that adapts to the individual characteristics of students as described in each student's learning preparation. This paper also presents the theory of material responsiveness and how it is generally applied in a test case for any online course for the time of flexible exams within the course [for the development of model information] of elements (evaluation questions), which are available in online vaults. Being a probability theory, the article depicts the variables to consider and how to detect probability that a student answers an appropriately defined element, known as the level of absorptive capacity, taking into account the prediction test and their previous answers.
Applying computer vision for detection of diseases in plants

Early detection and quantification of diseases in food plants are critical to agriculture industry and national food security. However, limitation in technology and cost has limited the success of applying Computer Vision in Plant Science. This research builds on the recent advance of Machine Learning, GPU and smartphones to tackle the problem of fast and low cost diagnosis of plant disease. In particular, we choose soybean as the subject for applying automatic disease detection. The reason is because soybean is an important crop for the state of Iowa and an important source of food for America. The plant is however, highly vulnerable to several type of diseases. This thesis consists of two sub-analyses of soybean diseases, which are: First, detection of a single disease in soybean, particularly Sudden Death Syndrome (SDS) with high detail (including location and severity). Second, detection of multiple diseases in soybean, using mobile phones which are resourceconstrained
Multiclass support vector machines with GenSVM

Binary support vector machines (SVM) have become a standard tool for supervised machine learning. Attractive features of the SVM are that its solution only depends on badly ﬁtting observations, it combats overﬁtting through regularization, can handle high dimensionality (thus many predictors), allows for nonlinear predictions, and is robust against outliers. Much less attention has been given to classiﬁcation problems with more than two classes. In the machine learning literature, such multiclass problems tend to be solved by repeatedly applying binary SVMs, for example, through one-versus-one (OvO) or one-versus-all (OvA). Although such approaches are generally fast, they can lead to regions that are inconclusive in their prediction. As an alternative, the present authors have proposed a single machine classiﬁer, called GenSVM (see, [1]). In this paper, we present its main properties and discuss examples of its implementations in the R and Python packages.
Applying genetic algorithm to university classroom arrangement problem

University classroom arrangement problem is considered as a sub-question of the university timetabling problem, which has been proved to be an NP-hard problem in combinatorial optimization field. In this paper, we firstly analyzed the relevant factors and set up a model of the problem. Then the genetic algorithm was applied to optimize the arrangement of classroom considering the moving distance of students between courses, which was caused by room changing. The experimental results have shown that the genetic algorithm could generate feasible solutions and optimize the classroom arrangement.
A Geometric Approach for Distributed Multi-Hop Target Localization in Cooperative Networks

This work addresses target localization problem in cooperative distributed sensor networks, in which all sensors are capable of measuring Received Signal Strength (RSS), but only some are appropriately equipped to measure Angle Of Arrival (AOA) of the received signal. A novel approach based on simple geometry and multi-hopping is proposed, which allows for natural conversion of the problem into a Generalized Trust Region Sub-Problem (GTRS). The proposed algorithm comprises three main steps, each of them with linear computational cost in the number of neighbors, making it suitable for real-time applications. Our simulation results validate the performance of the new algorithm, surpassing some significantly more complex ones, and almost achieving a lower bound set by an existing algorithm which uses some (unrealistic) assumptions in its favor.
LiKe: Lightweight Certificateless Key Agreement for Secure IoT Communications

Certificateless public-key cryptography (CL-PKC) schemes are particularly robust against the leakage of secret information stored on a trusted third party (TTP). These security features are particularly relevant for Internet of Things (IoT) domains, where the devices are typically preconfigured with secret keys, usually stored locally on the TTP for following maintenance tasks. Despite some contributions already proposed for the adoption of CL-PKC schemes in constrained IoT devices, current solutions generally require high message overhead, are computationally demanding, and place a high toll on the energy budget. To close this gap, we propose LiKe, a lightweight pairing-free certificateless key agreement protocol suitable for integration in the latest ZigBee 3.0 protocol stack and constrained IoT devices. LiKe is an authenticated key agreement protocol characterized by: 1) ephemeral cryptographic materials; 2) support for intermittent connectivity with the TTP; 3) lightweight rekeying operations; and 4) robustness against impersonation attacks, even when information stored on the TTP is leaked. LiKe has been thoroughly described, and its security properties have been proved via formal tools. Moreover, we have implemented and tested it on real IoT devices, in networks with up to 11 nodes—the source code has been released as an open source. Results are striking: on the OpenMote-b hardware platform, LiKe requires a total time of 3.259 s to establish session keys on each participating device, and at most 0.258% of the overall battery capacity, emerging as a lightweight and energy-friendly solution. Finally, comparisons with competing solutions do show the superior quality and viability of our proposal.
A Fast-Efficient parallel processing algorithm for straight line detection

This work presents a novel method for detecting straight lines in an image at a very high speed with optimum number of processors and their functionalities. The method can be used to extract straight lines directly from an image without noise removal and pre-processing. First the square image is converted to a binary edge image using a parallel edge detection mechanism. The parallel edge detection mechanism used in this work is capable of producing edge image within a short time. Then the binary square image is transferred to a system having large number of Processing Elements (PEs). A PE has only limited jobs such as pixel scanning, compare line length with nearby PEs and transmit data to the Main Control Unit (MCU). The MCU collects data from all PEs and evaluates straight lines. Even if the number of PEs is high, it is comparatively very much less than the parallel Hough Transform method and practically implementable using recent ULSI technologies.
Research on Spectrum Sensing Technology for Integrated Space-Ground Network

The demand for wideband wireless spectrum is increasing rapidly due to a rapidly expanding market of satellite communications and multimedia wireless services while the usable spectrum is becoming scarce due to current spectrum segmentation and static frequency allocation policies. Cognitive Radio (CR) can be an efficient technique to increase the spectrum utilization efficiency of heterogeneous wireless networks. Compressive sensing (CS) can overcome the traditional restriction that sampling rate must satisfy the Nyquist sampling theory, and it is also an important technology available for the integrated space-ground network. Aiming at the problem that the measurement processes of orthogonal matching pursuit (OMP) are easy to be disturbed by noise and the sparse information may not be available for practical applications. To overcome these problems, we have extended the idea of OMP to illustrate another recovery scheme called stochastic gradient orthogonal matching pursuit (SGOMP). It’s shown that the proposed algorithm shows robustness against noise. Moreover, with modified the early termination threshold (ETT), the complexity of the proposed algorithm can be reduced.
Validation of discovery learning-based to increase the ability of elementary students problem solving skills

This study aims to develop learning to produce mathematical learning device products based on discovery learning that are able to improve elementary students’ problem solving abilities that are valid, practical, and effective. The specific target to be achieved is to produce a product in the form of Teaching Materials for mathematics discovery learning to improve problem solving skills of elementary school students and RPP as a companion to the use of teaching materials. his is a development research with 4D models consisting of Define, Design, Develop, and Disseminate. Development is done up to the develop stage to produce valid learning materials. The revision process according to the validation’s assessment and suggestion, a valid mathinstructional material based on discovery learning in terms of content and construct is obtained. The validity of teaching materials is obtained through expert assessment and revision based on expert advice until the validity level of RPP and Teaching Materials designed is 3.45 and 3.42 with valid categories.
Potential of Semi-Closed Cycles for UAV Propulsion

Conventional Brayton cycles have demonstrated to be significantly less efficient than alternative propulsion systems (spark ignition, diesel, fuel cells, etc.) for low power output applications, such as for small size UAVs. The gas turbine performance could be enhanced through the introduction of heat exchangers, with the consequent increase of the overall engine weight. Semi-closed cycles have documented advantages of higher thermal efficiency and degree of compactness than traditional intercooled-recuperated open cycles. This paper discusses advantages and applicability of semi-closed cycles to a small gas turbine, designed for a medium altitude UAV mission. In particular, size and altitude effects have been accounted in the performance evaluation of two different semi-closed cycle arrangements designed for an output shaft power of 100 hp (74.57 kW). Resultant performance has been compared with equivalent simple recuperated and intercooled-recuperated open cycles. Furthermore, a final engine performance comparison has been made with data obtained from a similar analysis performed on a larger engine, with a power output of 300 hp (223.71 kW) and designed for an extremely high altitude UAV application. While promising results have been obtained for the larger case study, where semi-closed cycles have demonstrated superior performance and higher engine compactness than conventional solutions, similar trends have not been displayed for the smaller engines, as consequence of the strong size effects observed in the turbomachinery performance. For the 100 hp engine the semi-closed cycles are slightly outperformed by the open cycle engines.
Multivariate Statistical Methods for High-Dimensional Multiset Omics Data Analysis

ABSTRACT This chapter covers the state-of-the-art multivariate statistical methods designed for high dimensional multiset omics data analysis. Recent biotechnological developments have enabled large-scale measurement of various biomolecular data, such as genotypic and phenotypic data, dispersed over various omics domains. An emergent research direction is to analyze these data sources using an integrated approach to better model and understand the underlying biology of complex disease conditions. However, comprehensive analysis techniques that can handle both the size and complexity, and at the same time can account for the hierarchical structure of such data, are lacking. An overview of some of the developments in multivariate techniques for high-dimensional omics data analysis, highlighting two well-known multivariate methods, canonical correlation analysis (CCA) and redundancy analysis (RDA), is provided in this chapter. Penalized versions of CCA are widespread in the omics data analysis field, and there is recent work on multiset penalized RDA that is applicable to multiset omics data. How these methods meet the statistical challenges that come with high-dimensional multiset omics data analysis and help to further our understanding of the human condition in terms of health and disease are presented. Additionally, the current challenges to be resolved in the field of omics data analysis are discussed.
What Do Clinicians Think about Hip Precautions following Total Hip Replacement?

Introduction: Hip precautions are routinely recommended by occupational therapists to reduce the risk of dislocation following total hip replacement. The purpose of this study was to report the views and experiences of clinicians on the use of hip precautions with this client group. Method: Data were gathered from six National Health Service trusts, through 16 semi-structured telephone interviews. Participants included occupational therapists, physiotherapists, surgeons and nurses and data were analysed thematically. Results: Themes identified were: the rationale for, and disadvantages of, routine hip precautions and issues in the consistency of their application; the barriers and facilitators to relaxing hip precautions and the process and impact of changing practice; the contextual factors of service change and support for further research. Conclusion: There is variation in the views of clinicians regarding hip precautions due to differences in surgical opinion and lack of evidence as to whether precautions affect the risk of dislocation. Relaxing the routine provision of hip precautions is perceived to have an impact on patient recovery and satisfaction, the role and skill mix of therapy services, length of hospital stay and resource use. Trials are urgently required to investigate the effect of hip precautions on patient and surgical outcomes.
Face Recognition Door Lock

Smart Door Lock Major Qualifying Project The goals of this project were to build a modern, easy-to-use, smart door lock that allows for accessible unlocking and adds convenience, utility, and security to your home. It allows users to open their door remotely via the accompanying Smart Lock mobile app, or hands-free by using face recognition via a camera mounted on the door. The system was made up of three major components, including a cloud back-end, an on-board logical unit, and a mobile application.