Miracle-dz/UniFAR-FTUs huggingface dataset:
Wideband Printed TM01 to TE11 Mode Converters

Different structures for three categories of the TM01-TE11 mode converters; i.e., dielectric loaded-based, metallic septum-based, and mode converter with intermediate modes; are proposed and investigated for the aim of wider operational bandwidth. The principles of operation of all mode converters are explained in detail. Thanks to 3D printing capabilities, the design can be very flexible and the optimal results are achieved using more complicated structures in an easy and realizable fabrication process. The simulation and measurement results of the proposed TM01-TE11 mode converters, including reflection coefficients and radiation patterns, show great conversion efficiency and wideband performances with fractional bandwidths up to 20.8%.
Vehicle Activity Recognition using Mapped QTC Trajectories

The automated analysis of interacting objects or vehicles has many uses, including autonomous driving and security surveillance. In this paper we present a novel method for vehicle activity recognition using Deep Convolutional Neural Network (DCNN). We use Qualitative Trajectory Calculus (QTC) to represent the relative motion between pair of vehicles, and encode their interactions as a trajectory of QTC states. We then use one-hot vectors to map the trajectory into 2D matrix which conserves the essential position information of each QTC state in the sequence. Specifically, we project QTC sequences into a two dimensional image texture, and subsequently our method adapt layers trained on the ImageNet dataset and transfer this knowledge to the activity recognition task. We have evaluated our method using two different datasets, and shown that it out-performs state-of-the-art methods, achieving an error rate of no more than 1.16%. Our motivation originates from an interest in automated analysis of vehicle movement for the collision avoidance application, and we present a dataset of vehicle-obstacle interaction, collected from simulator-based experiments.
TIB AV-Portal: A reliable infrastructure for scientific audiovisual media

With the AV Portal 1 , the German National Library of Science and Technology (TIB) 2 in collaboration with the Hasso Plattner Institute (HPI)3 has developed a user-oriented platform for scientific films. This portal offers free access to high-quality computer visualisations, simulations, experiments and interviews as well as recordings of lectures and conferences from the fields of science and technology. The automatic video analysis of the TIB AV Portal includes not only structural analysis (scene recognition), but also text, audio and image analysis. Automatic indexing by the AV Portal describes videos at the segment level, enabling pinpoint searches to be made within videos. Films are allocated a Digital Object Identifier (DOI), which means they can be referenced clearly. Individual film segments are allocated a Media Fragment Identifier (MFID), which enables the video to be referenced down to the second and cited. The creator of the audiovisual media segment can choose between an Open Access licence and a declaration of consent, enabling them to decide how they wish to permit TIB to utilise the material. TIB recommends the “CC-Namensnennung – Deutschland 3.0” licence, which ensures that the creator is acknowledged and permits the comprehensive use of audiovisual media in research and teaching.
A Reliability-Oriented Fuzzy Stochastic Framework in Automated Distribution Grids to Allocate  $\mu$ -PMUs

This paper proposes a reliability-oriented stochastic aggregated integer linear framework for full observability of the automated distributed systems based on the <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-synchrophasor units. The <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-synchrophasor unit as a newly introduced high-tech device makes it possible for an accurate and high-speed measurement of the voltage and current waveforms in the distribution systems. This paper proposes a multi-stage strategy for the <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-synchrophasor unit placement together with the communication system requirements in the reconfigurable distribution systems, considering the zero-injection constraints in the model. To determine the optimal topology at the end of each phase, a reliability-based cost function is developed to optimize the customer interruption costs and power losses simultaneously. In order to model the uncertainties of forecast error in the active and reactive load demands as well as the failure rate and repair rate parameters, a stochastic framework based on the fuzzy cloud theory is employed. The proposed bi-level mixed integer linear programing approach is used to co-optimize the network switching scheme as well as the optimal <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-synchrophasor positions and communication infrastructure costs in the same framework. The simulation results on a practical test system verify the observability of the automated reconfigurable distribution system during the reconfiguration process.
Fault-Tolerant Control for Electric Vehicles With Independently Driven in-Wheel Motors Considering Individual Driver Steering Characteristics

This paper presents a fault-tolerant control (FTC) method for electric vehicles with independently driven in-wheel-motors (IWM) considering individual human driver steering characteristics. Both the human driver steering model and the vehicle lateral dynamic model are formed for the FTC strategy design while taking into account modeling inaccuracies. Based on the driver–vehicle system model, a dual-loop, sliding-mode controller is designed to deal with the IWM fault and accomplish vehicle motion control in a customized way with explicit respect to individual driver steering characteristics. The proposed control strategy contains two layers: the first layer calculates a reference heading angle aiming to reduce the tracking error induced by the IWM actuator fault; the second layer computes the additional yaw moment needed to track the reference heading angle and suppress the influence of the modeling errors. Such an individualized FTC control method can specifically assist the human driver in post-fault vehicle motion control while reducing the physical and mental workloads of the human driver. Simulation results of the proposed FTC method using MATLAB/Simulink-CarSim demonstrate that it can accomplish the expected post-fault vehicle motion control while providing appropriate control assistance to different drivers in an individualized and cooperative way.
Magia: Robust Automated Image Processing and Kinetic Modeling Toolbox for PET Neuroinformatics

Introduction Modelling of the radioactivity images produced by PET scanners into biologically meaningful quantities, such as binding potential, is a complex multi-stage process involving data retrieval, preprocessing, drawing reference regions, kinetic modelling, and post-processing of parametric images. The process is challenging to automatize mainly because of manual work related to input generation, thus prohibiting large-scale standardized analysis of brain PET data. To resolve this problem, we introduce the Magia pipeline that enables processing of brain PET data with minimal user intervention. We investigated the accuracy of Magia in the automatic brain-PET data processing with four tracers binding to different binding sites: [11C]raclopride, [11C]carfentanil, [11C]MADAM, and [11C]PiB. Materials and methods For each tracer, we processed 30 historical control subjects’ data with manual and automated methods. Five persons manually delineated the reference regions (cerebellar or occipital cortex depending on tracer) for each subject according to written and visual instructions. The automatic reference-region extraction was based on FreeSurfer parcellations. We first assessed inter-operator variance resulting from manual delineation of reference regions. Then we compared the differences between the manually and automatically produced reference regions and the subsequently obtained metrics. Results The manually delineated reference regions were remarkably different from each other. The differences translated into differences in outcome measures (binding potential or SUV-ratio), and the intra-class correlation coefficients were between 47 % and 96 % for the tracers. While the Magia-derived reference regions were topographically very different from the manually defined reference regions, Magia produced outcome measures highly consistent with average of the manually obtained estimates. For [11C]carfentanil and [11C]PiB there was no bias, while for [11C]raclopride and [11C]MADAM Magia produced 3-5 % higher binding potentials as a result of slightly lower time-integrals of reference region time-activity curves. Conclusion Even if Magia produces reference regions that are anatomically different from manually drawn reference regions, the resulting outcome measures are highly similar. Based on these results and considering the high inter-operator variance of the manual method, the high level of standardization and strong scalability of Magia, we conclude that Magia can be reliably used to process brain PET data.
Automatic Detection of Distal Humerus Features: First Steps

Identification of specific landmarks in tissues is fundamental for understanding the human anatomy in medical applications. Specifically, the assessment of bone features allows to detect several pathologies in orthopedics. The recognition has been formerly carried out via visual identification, providing insufficient accuracy. Automatic solutions are required to improve the precision and minimize diagnostic and surgical planning time. In this paper, we study distal humerus landmarks and a new algorithm to automatically detect them in a reasonable time. Our technique does not require a prior training, as a geometrical approach with a spatial decomposition is used to explore several regions of interest of the bone. Finally, a set of experiments are performed, showing promising results.
Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970

Output growth estimates for the regions of the UK are currently published at the annual frequency only and are released with a long delay. Regional economists and policymakers would benefit from having higher frequency and more timely estimates. In this paper we develop a mixed frequency Vector Autoregressive (MF-VAR) model and use it to produce estimates of quarterly regional output growth. Temporal and cross-sectional restrictions are imposed in the model to ensure that our quarterly regional estimates are consistent with the annual regional observations and the observed quarterly UK totals. We use a machine learning method based on the hierarchical Dirichlet-Laplace prior to ensure optimal shrinkage and parsimony in our over-parameterised MF-VAR. Thus,this paper presents a new, regional quarterly database of nominal and real Gross Value Added dating back to 1970. We describe how we update and evaluate these estimates on an ongoing, quarterly basis to publish online (at www.escoe.ac.uk/regionalnowcasting) more timely estimates of regional economic growth. We illustrate how the new quarterly data can be used to contribute to our historical understanding of business cycle dynamics and connectedness between regions.
Automatic Detection and Classification of Sewer Defects via Hierarchical Deep Learning

Video and image sources are frequently applied in the area of defect inspection in industrial community. For the recognition and classification of sewer defects, a significant number of videos and images of sewers are collected. These data are then checked by human and some traditional methods to recognize and classify the sewer defects, which is inefficient and error-prone. Previously developed features like SIFT are unable to comprehensively represent such defects. Therefore, feature representation is especially important for defect autoclassification. In this paper, we study the automatic extraction of feature representation for sewer defects via deep learning. Moreover, a complete automatic system for classifying sewer defects is proposed built on a two-level hierarchical deep convolutional neural network, which shows high performance with respect to classification accuracy. The proposed network is trained on a novel data set with over 40 000 sewer images. The system has been successfully applied in the practical production, confirming its robustness and feasibility to real-world applications. The source code and trained model are available at the project website.1 Note to Practitioners—Automatic defect inspection has become a fundamental research topic in engineering application field. Specifically, sewer defect detection is an important measure for maintenance, renewal, and rehabilitation activities of sewer infrastructure. In the current operation procedure, all the captured videos need to be inspected by experts frame by frame to recognize defects, yielding a significant low inspection rate with a significant amount of time. Previous work has attempted to employ traditional image processing methods for automated sewer defect classification. However, these methods get poor generalization capabilities since they use pre-engineered features. In most cases, sewerage inspection companies have to hire numerous professional inspectors to do this job, thereby consuming a lot of human and material resources. To address this problem, the authors propose an automatic detection and classification method for sewer defects based on hierarchical deep learning. Demonstrated by various experiments, the designed framework achieves a high defect classification accuracy, which can be easily integrated into an automatic sewer defect inspection system.1https://github.com/NUAAXQ/SewerDefectDetection
Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks

The communication overhead has become a significant bottleneck in data-parallel network with the increasing of model size and data samples. In this work, we propose a new algorithm LPC-SVRG with quantized gradients and its acceleration ALPC-SVRG to effectively reduce the communication complexity while maintaining the same convergence as the unquantized algorithms. Specifically, we formulate the heuristic gradient clipping technique within the quantization scheme and show that unbiased quantization methods in related works [3, 33, 38] are special cases of ours. We introduce double sampling in the accelerated algorithm ALPC-SVRG to fully combine the gradients of full-precision and low-precision, and then achieve acceleration with fewer communication overhead. Our analysis focuses on the nonsmooth composite problem, which makes our algorithms more general. The experiments on linear models and deep neural networks validate the effectiveness of our algorithms.
Control design using backstepping technique for a cart-inverted pendulum system

70 www.ijeas. org  Abstract— The cart-inverted pendulum system is one of the classical experimental systems that fully converges the complex properties of nonlinear control problems. It represents a class of real world systems such as two-wheeled mobile robots, pendubots, missile launchers and many more. The problems associated with it are always challenging topics in the field of control systems. This paper presents a novel technique to control this system stabilizing at a vertical upright position, its unstable equilibrium point. Simulation and experimental results will show a better performance of the proposed controller in comparison with Quadratic Optimal Regulator method under disturbance and change in mass.
Comparison between WDR & ASWDR Techniques using Image Compression with Different Wavelet

In this paper, two different Wavelet based Image Compression techniques are compared. The techniques involved in the comparison process are WDR and ASWDR. The above two techniques are implemented with different types of wavelet codecs. Wavelet difference reduction (WDR) has recently been proposed as a method for efficient embedded image coding. This method retains all of the important features like low complexity, region of interest, embeddedness, and progressive SNR. ASWDR adapts the scanning procedure used by WDR in order to predict locations of significant transform values at half thresholds. Here, there are two types of Wavelet transforms are applied on the images before compression. They are DD 2+2,2 Integer Wavelet transform and Daub 9/7 Wavelet transform. The quality of the reconstructed images is calculated by using three performance parameters PSNR, MSE and SNE values. The images yield high PSNR values and low MSE values. Keywords—Wavelet Image Compression, WDR, ASWDR
Convolutional Mixture of Experts Model: A Comparative Study on Automatic Macular Diagnosis in Retinal Optical Coherence Tomography Imaging

Background: Macular disorders, such as diabetic macular edema (DME) and age-related macular degeneration (AMD) are among the major ocular diseases. Having one of these diseases can lead to vision impairments or even permanent blindness in a not-so-long time span. So, the early diagnosis of these diseases are the main goals for researchers in the field. Methods: This study is designed in order to present a comparative analysis on the recent convolutional mixture of experts (CMoE) models for distinguishing normal macular OCT from DME and AMD. For this purpose, we considered three recent CMoE models called Mixture ensemble of convolutional neural networks (ME-CNN), Multi-scale Convolutional Mixture of Experts (MCME), and Wavelet-based Convolutional Mixture of Experts (WCME) models. For this research study, the models were evaluated on a database of three different macular OCT sets. Two first OCT sets were acquired by Heidelberg imaging systems consisting of 148 and 45 subjects respectively and set3 was constituted of 384 Bioptigen OCT acquisitions. To provide better performance insight into the CMoE ensembles, we extensively analyzed the models based on the 5-fold cross-validation method and various classification measures such as precision and average area under the ROC curve (AUC). Results: Experimental evaluations showed that the MCME and WCME outperformed the ME-CNN model and presented overall precisions of 98.14% and 96.06% for aligned OCTs respectively. For non-aligned retinal OCTs, these values were 93.95% and 95.56%. Conclusion: Based on the comparative analysis, although the MCME model outperformed the other CMoE models in the analysis of aligned retinal OCTs, the WCME offers a robust model for diagnosis of non-aligned retinal OCTs. This allows having a fast and robust computer-aided system in macular OCT imaging which does not rely on the routine computerized processes such as denoising, segmentation of retinal layers, and also retinal layers alignment.
SDR Implementation of a D2D Security Cryptographic Mechanism

Device-to-device (D2D) communication represents a promising technique to enable devices to communicate directly without the interaction of access points or base stations. The ad hoc and proximity nature of this communication introduce some very important security vulnerabilities. Key management, access control, privacy, secure routing, and transmission need dedicated signaling procedures and optimized implementation mechanisms that are appropriate for the mobile, low-energy, and low-processing power environment. This paper proposes a security mechanism for D2D communication involving the use of physically unclonable functions (PUF) for unique key generation, elliptic-curve cryptography (ECC) and Diffie–Hellman key exchange (DHKE) for key management, and Salsa20/20 as stream cyphering encryption method, suitable for confidentiality of the wireless transmissions. All these methods are implemented and tested on a software defined radio (SDR) communication platform consisting of a Zync-based system-on-chip (SoC), complemented by radio frequency (RF) daughter boards from analog devices–an integration using hardware and software co-design.
Locating the Nodes From Incomplete Euclidean Distance Matrix Using Bayesian Learning

Node localization in wireless sensor networks (WSNs) has received a considerable amount of attention. In this paper, using the natural low-rank properties of the Euclidean distance matrix (EDM), we formulate the node location finding problem from only a small fraction of random entries of the EDM as a low-rank matrix recovery problem. A Bayesian-learning-based method is utilized to recover the original EDM, based on which the relative positions of all the sensor nodes in WSNs are accurately estimated by applying classical multi-dimensional scaling (MDS). In addition, with the location knowledge of anchor nodes, we transform the relative positions into absolute positions. The simulation results illustrate that our proposed approach leads to superior performance over various other methods.
Improved Delay Performance in VANET by the Priority Assignment

Low delay performance in VANT need to an intensive study. In this article, we proposed a novel priority method to reduce transmission delay of one-hop in VANET. A priority was marked to each message based on static factors, dynamic factors and size of the message. Messages scheduling was implemented based on the priority of messages. The performance of proposed method was analysed in highway scenario on the average delay and waiting delay in queue. Simulation results are consistent with theoretical deriving. Therefore, by using the novel method, low delay and more reliable communication scenarios can be provided in Vehicular Ad-hoc Network.
Research on the Method of Fault Diagnosis Based on Multiple Classifiers Fusion

In traditional fault diagnosis method, a large number of experiments are needed to get the optimal performance classifier which diagnoses type of fault. Because of classifier algorithm limit, there is no one classifier can be applied to all kinds of fault diagnosis. In order to avoid the disadvantages caused by single classifier approach, decision level fusion method based on multiple classifiers fusion is introduced in the field of fault diagnosis. The fusion method with fuzzy comprehensive evaluation is put forward and the basic evaluation model is set up. The reasonable distribution of classifiers weight that affects diagnosis result directly is vital. Firstly, the evaluation function which measures member classifier’s diagnostic accuracy and correctness is constructed based on the theory of information entropy. Then, weights are distributed to each classifier with entropy coefficient according to the value of evaluation function. Experiments are carried out to demonstrate the effectiveness of the proposed method and results show that fault recognition rate after fusion is higher compared with the single classifier method.