Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusion image of subtracted and nonsubtracted rotational angiography for pretherapeutic evaluation of angioarchitectures of dural arteriovenous fistulas

Poster: "ECR 2013 / C-2570 / Fusion image of subtracted and nonsubtracted rotational angiography for pretherapeutic evaluation of angioarchitectures of dural arteriovenous fistulas" by: "H. Kiyosue1, S. Tanoue1, J. Kashiwagi2, H. Mori2; 1YUFU/JP, 2Oita/JP"
Optimization of wind thermal coordination dispatch using Flower Pollination Algorithm

In this paper the integration of thermal generators with wind units are considered and the solution of wind thermal dispatch problem using Flower Pollination Algorithm (FPA) is implemented. This algorithm is implemented to find the minimum production cost with valve effects of thermal units included. The approach is validated using two IEEE test systems consisting of six and thirteen units with a wind farm of 100MW capacity. The analysis is carried out neglecting the transmission losses. Simulation results predict the inclusion of wind power reduces the overall production cost.
Identification of Abnormal Electricity Consumption Behavior Based on Bi-LSTM Recurrent Neural Network

Abnormal electricity consumption (AEC) results in a huge safety hazard for the power grid. In particular, it is significant for the power grid marketing department to identify users’ AEC behavior. In view of the AEC of power grid users, a prediction model based on bidirectional long short-term memory network (Bi-LSTM) feature extraction network is proposed in this paper, which identifies the AEC behavior based on the historical electricity consumption data of users. The framework of TensorFlow was used to construct a model for feature extraction and result prediction. Some power consumption units in a prefecture-level city were selected for analysis. Experimental analysis shows that, compared with support vector machine (SVM), BP neural network and long short-term memory (LSTM), the proposed model is more accurate and robust in detecting AECs.
EchoFace: Acoustic Sensor-Based Media Attack Detection for Face Authentication

Face authentication systems have gained widespread popularity because of their user-friendly usage and increasing recognition accuracy. Unfortunately, the boom in mobile social networks has bought with it media-based facial forgery; a critical threat where an adversary forges or replays the victim’s photograph/video to fool the system. In this article, we propose EchoFace, an effective and robust liveness detection system to enhance face authentication in defending against media-based attacks, which works with today’s smartphones/smartwatches without any hardware modification. EchoFace uses active acoustic sensing to differentiate the uneven stereostructure of the face and the flat forged media. Our proposed scheme effectively extracts the desired reflection profiles from the target. Moreover, we propose effective similarity measurements of reflection profiles to distinguish live users from forged media, which works robustly under various environmental conditions. EchoFace only requires low cost and universally equipped acoustic sensors without human intervention for liveness detection, which can be easily deployed in a variety of application scenarios. We implement EchoFace on commercial smartphones, and experiment results show that EchoFace achieves an average detection accuracy higher than 96% and false alarm rate lower than 4% across various media attacks and different levels of background noise. This shows its great potential to enhance the security of widely deployed face authentication systems in real scenarios.
Design of Maximum Power Point Tracking for Dynamic Power Response of Tidal Undersea Kite Systems

The tethered-undersea-kite (TUSK) represents a new electric power generation concept for harvesting energy from tidal currents, with higher power density compared to traditional static tidal turbines. However, the power and rotating speed of the turbine fluctuate periodically depending on the kite's motion in the sea creating an additional challenge for the speed control of the generator. The mathematical model of the system's power generation is developed. Two alternative maximum-power-point-tracking (MPPT) algorithms suitable for this application are designed. The electrical torque of the generator is controlled directly or a more accurate closed-loop speed controller is used. The turbine inertia creates an error between the optimal-reference power and the actual generated power. This error is evaluated experimentally for both MPPT-algorithms when fluctuating mechanical torque typical for the TUSK-system is applied on the generator. Experimental results on a 35-kVA laboratory emulator are presented, where an accurate representation of the system dynamics and inertia are implemented.
A Nonlinear Extended State Observer for Sensorless IPMSM Drives With Optimized Gains

A nonlinear third-order extended state observer (TESO) is proposed for the rotor position and speed estimation for sensorless interior permanent magnet synchronous motor drives. The disturbance observation bandwidth of the proposed observer is expanded by deploying a gain optimization method. The measurement noise excited in the control loop is suppressed effectively by substituting the previous high gain configuration. Experimentally, both the steady-state and transient performance of the TESO are verified on testing rigs. In comparison with the performance of a conventional linear extended state observer, the TESO has a better capability of disturbance observation and noise suppression in a wide operating range.
Pre-Processing and Classification of Cough Sounds in Noisy Environment using SVM

This paper presents a methodology for automated analysis of cough sound signals using Support Vector Machines (SVM). Pre-Processing is necessary for suppression of noise from cough signals; and is the prime concern in accurate prediction of various respiratory disorders. Herein, a 'Fourth Order Butterworth High Pass Filter (FOBHPF) is therefore applied at the pre-processing stage followed by features extraction and classification using SVM. The performance evaluation during the simulations has carried out using Signal-to-Noise Ratio (SNR) parameter at output of the pre-processing stage and accuracy parameter at the end of classification stage.
3D Ear Biometrics: Acquisition and Recognition

Ear is one of the recent biometric traits used to recognize the human. The ear can be considered as a robust biometric feature with cosmetics, age, and facial expressions, which is unlikely with the other biometrics features. The ear has a predictable background and moreover, the ear can be used an effective supplementary biometric trait with various other biometrics. Furthermore, the distinctiveness property of the ear is highly motivated for security related operations. In this paper, a method for 3D ear acquisition is presented, which is used in creating one of the largest 2D and 3D ear database (IIT Indore) and the recognition performance on the acquired data using a 3D descriptor. We have used Signature of histograms of orientations (SHOT) and Binary SHOT (B-SHOT) descriptor as a 3D feature descriptor and matched the query and repository images and obtain a rank one authentication rate of 97.13% and 97.12%, respectively.
Efficient FPGA Implementation of Unified Point Operation for Twisted Edward Curve Cryptography

Combination of efficient point operation architecture and high intractability feature of cryptographic curve optimizes the performance of the Edward curve cryptography (EdCC) processor ensuring desired security. This research employs the most recently invented twisted Edward curve for performing point operation. A novel architecture of unified point operation has been designed over the 256-bit prime field which can execute Edward curve point doubling and point addition in one module instead of two divide modules. The proposed architecture has been implemented on Virtex-5 field-programmable gate array technology and achieved 5.48 $\mu$ s computational delay and only 10% of available slices for 256-bit point operation. Our design reduces almost 50% of the required clock cycles compared to other designs. Also, it assures a better security characteristic as to the conventional approach. To our best knowledge, the proposed architecture provides better performance in terms of throughput on FPGA than the other work which is effective for the design of the EdCC processor.
A 1-V, 8.6-nA Resistor-less PTAT Current Reference with Startup Circuit

A low voltage, low current, Proportional To Absolute Temperature (PTAT) current reference circuit is proposed. The conventional PTAT circuit is exploited to operate with a supply voltage of 1 V with suitable modifications in the circuit. Resistor has been replaced by an active circuit. All the devices are operating in weak inversion region. Zero static power startup circuit has been proposed to conform the proper initialization of the circuit. This PTAT current generator generates 8.6 nA of current at 1 V supply at room temperature. It is observed that the supply voltage sensitivity is as low as 7 percent per volt.
Designing Grasping Tools for Robotic Assembly Based on Shape Analysis of Parts

This paper aims to provide a method for automatically designing a set of grasping tools performing a sequence of robotic assembly tasks. First, the convex shape decomposition is applied to extract the grasped part of an object. From the shape information of the part, we determine the number of fingers of the grasping tool as well as the stroke and dimension of each finger. Next, the detailed shape of finger surface such as the slant angle and the curvature radius is determined by applying the plane clustering to the surface of the grasped part. We consider reducing the number of grasping tools used in a whole sequence of assembly by checking if a same grasping tool can be commonly used between two individual assembly tasks. Finally, the proposed method was verified through a series of robotic assembly experiments.
Design and Implementation of 1.5 kW Half Bridge Bidirectional DC-DC Converter based on Gallium Nitride devices

Use of wide band gap devices such as SiC MOSFET and GaN HEMT in a converter results in high power density and high efficiency as compared to state of the art Si switches. The objective of this work is to build a 350V DC (input), 1.5kW buck converter with switching frequency 100kHz. Commercially available GaN devices are compared based on performance in terms of package, conduction and switching losses. The module achieves more than 97% efficiency at all values of the current.
Two-Class Classification: Comparative Experiments for Chronic Kidney Disease

Over two million of population across worldwide is currently depending on dialysis treatment or a kidney transplant to survive from kidney disease. Therefore, it is imperative for health agencies such as hospitals or insurance companies to predict the probabilities of patients who suffers from chronic case of kidney diseases, hence requiring medical attentions. This study performs a comparative experiment on prediction of chronic kidney disease via a classification methodology. Two supervised classification algorithms are used to build the classification model, which are Two-Class Decision Forest and Two-Class Neural Networks. Experimental results showed that Neural Network performed better based on all features but Decision Forest produced optimal performance with high accuracy, and precision as compared to Neural Networks and other algorithms from the literature such as K-Nearest Neighbor, Support Vector Machine, and Rule Induction.
Ferroelectric Dual Material Gate All Around TFET Architecture for Enhanced Electrical Performance

This work examines the device behavior of ferroelectric dual material gate all around tunnel field effect transistor (FE-DMGAA-TFET). The ferroelectric material manifests the negative capacitance that increases the drain current by developing an internal positive feedback on the gate terminal. The enhanced electrical characteristics of FE-DMGAA-TFETs is observed in contrast to DMGAA-TFETs. The device performance is examined by varying the thickness of ferroelectric layer and ferroelectric material. It is observed that on increasing the thickness of ferroelectric layer, improved device characteristics are acquired. Moreover, the results depict that FE-DMGAA-TFETs is a better contender for future generation and low power applications.
Assessing Context-Aware Data Consistency

Data analysis is a demanding task that involves extracting deep insights hidden in data. Many businesses enforce data analysis irrespective of the domain, as it is crucial in minimizing developmental risks. Raw data cannot be used to perform any analysis as poor-quality data leads to erroneous decision-making. This makes data quality assessment a necessary function before data analysis. Data quality is a multi-dimensional factor that affects the analysis in multiple ways. Among all the dimensions, consistency is one of the most critical dimensions to assess. Context of data plays an important role in consistency assessment, as the records are inherently related within a dataset. Existing studies are computationally expensive and do not consider the context of data. In this paper, we propose a comprehensive context-aware data consistency assessment tool that uses machine learning to evaluate the consistency of data. Our model was developed on Apache Hadoop and Apache Spark to support big data, as well as to boost some computationally intensive algorithms.
Network Representation Learning Guided by Partial Community Structure

Network Representation Learning (NRL) is an effective way to analyze large scale networks (graphs). In general, it maps network nodes, edges, subgraphs, etc. onto independent vectors in a low dimension space, thus facilitating network analysis tasks. As community structure is one of the most prominent mesoscopic structure properties of real networks, it is necessary to preserve community structure of networks during NRL. In this paper, the concept of $k$ -step partial community structure is defined and two Partial Community structure Guided Network Embedding (PCGNE) methods, based on two popular NRL algorithms (DeepWalk and node2vec respectively), for node representation learning are proposed. The idea behind this is that it is easier and more cost-effective to find a higher quality 1-step partial community structure than a higher quality whole community structure for networks; the extracted partial community information is then used to guide random walks in DeepWalk or node2vec. As a result, the learned node representations could preserve community structure property of networks more effectively. The two proposed algorithms and six state-of-the-art NRL algorithms were examined through multi-label classification and (inner community) link prediction on eight synthesized networks: one where community structure property could be controlled, and one real world network. The results suggested that the two PCGNE methods could improve the performance of their own based algorithm significantly and were competitive for node representation learning. Especially, comparing against used baseline algorithms, PCGNE methods could capture overlapping community structure much better, and thus could achieve better performance for multi-label classification on networks that have more overlapping nodes and/or larger overlapping memberships.
Fuel Minimization of the Electric Engine Cooling System With Active Grille Shutter by Iterative Quadratic Programming

The electric engine cooling system with the active grille shutter requires intelligent and predictive control to reach its full benefits on fuel economy and thermal management. Conventional control methods regulate the coolant temperature to a fixed value but do not directly minimize the vehicle's fuel/energy consumption. By contrast, we design a fuel minimization controller through solving constraint nonlinear optimization problems, whose cost function is the total fuel consumption and constraints are the vehicle's physical limits. To achieve high computational efficiency and sufficient accuracy, the optimization problem is solved by iterative convex quadratic programming and quasilinearization. The advantages of the proposed control method on both fuel economy and engine thermal management are demonstrated by simulations.
Robust Motion Blur Kernel Estimation by Kernel Continuity Prior

The accurate kernel estimation is key to the blind motion deblurring. Many previous methods depend on the image regularization to recover strong edges in the observed image for kernel estimation. However, the estimated kernel will be degraded when recovered strong edges are less accurate, especially in images full of small-scale edges. Different from previous methods, we focus on the kernel regularization. Inspired by the fact that the blur kernel is highly related to the continuous camera motion trajectory during the image capturing, we propose to encourage the continuity of the kernel through a kernel prior. The proposed prior measures the continuity of each element in the kernel and generates a continuity map. By encouraging the sparsity of the map using $L_{0}$ norm, discontinuous kernel elements are suppressed. Since the model with the proposed prior is non-convex and non-linear, an approximation method is proposed to minimize the cost function efficiently. Numerous experimental results show that our method outperforms state-of-the-art methods on both the normal and challenging cases. Moreover, the proposed prior is able to further improve the performance of existing MAP-based methods.
Forecasting Air Passenger Data using various models

In this competitive world, transport services have become highly saturated. To operate an existing company or open a new company in the field of transport is very important and need to know the demand forecast. In the field of transport, service providers facing complex challenges due to the competitors. This paper explores the applications of a forecasting model to predict the number of passengers going to travel in the future. It also compares the efficiency of different models like Auto Regressive and Integrated Moving Average (ARIMA), HOLT WINTERS, Naïve, Snaïve, Drift and list their performances.
Estimation of Core Loss of High Frequency Nanocrystalline Transformer for Non-sinusoidal Excitation

This paper proposes a method to estimate the core loss of a high frequency transformer with non sinusoidal excitation. The voltage waveform obtained across a high frequency transformer, in a solid state transformer made using direct AC/AC converters is an example of a non-sinusoidal waveform. The correct estimation of core loss for such a non-sinusoidal excitation is difficult. The difficulty in estimation of core loss stems from the non applicability of the Steinmetz Equation to non-sinusoidal excitations. Manufacturers do not typically provide the core loss data for non-sinusoidal excitation. A number of improvements on the Steinmetz Equation have led to increased accuracy but in turn have increased the complexity of calculation. This paper proposes a simple method to estimate the core loss. The method combines experimental and modeling approach. The estimation of core loss is performed for a high frequency transformer made from nanocrystalline material, for non-sinusoidal excitations. The method presented in this paper reduces the dependence of the estimation on Steinmetz parameters, the material of the core involved and the type of excitation. The method involves measuring the core loss for square excitation and using the obtained data to model the transformer in MATLAB/Simulink. This model can then be used to estimate the core loss for non-sinusoidal excitation of the given transformer. The proposed method is implemented by estimating the core loss of a 10.5kVA high frequency transformer with non-sinusoidal excitation. The non-sinusoidal excitations are obtained from a direct AC-AC matrix converter.
Densification of 5G Heterogeneous Cellular Wireless Network Based on Millimeter-Wave Communication

The world is experiencing a massive advancement in the fifth generation (5G) of wireless networks communications. , which will bring about a noticeable change in the communication system as well as the emergence of new technology to keep pace with this system including Software Design Network (SDN). , Network Function Virtualization (NFV) and Massive Multi inputs Multi outputs (M-MIMO), which will rapidly and effectively improve the quality of services for consumers. The high frequency of tens of GHz used in this type of network, leading to up to tens of gigabits per second in the data rate. This paper dealt with the planning of 5G networks in urban and suburban area in Taiz city. The main problem that faces in the 5G wireless network that in suburban area where the mobility is high, the network implemented using overlapped cells to provide mobility from one cell to another and. However, overlapping increases interference and, consequently, may reduce network capacity. Also, in urban area, the main problem is due to dense deployment of multi-tier heterogeneous networks (Het-Nets) causes the inter-tier and intra-tier interferences. Areas are divided into small cells of different sizes according to population density and other natural factors to achieve network density by millimeter wave (mm- Wave) technology that uses a frequency of 60 GHz. , which is one of the most important characteristics of 5G networks resulting from an increase in the number of cells and (Het-Nets). So, it achieved a high data rate which reach to 8 Gbps at each cell. There are about 240 cells in urban area and 105 cells in the suburban area, so this made it able to provide services to more than 220,000 users in urban and more than 100,000 users in suburban in addition to the optimization of power, which has reached the value of power received less than -120 dBm.
An Adaptive and Lightweight Solution to Detect Mixed Rate IP Spoofed DDoS Attack in IoT Ecosystem

Internet of Things (IoT) is a fast-growing and promising technology segment that aims to build advanced developments in automation and control, communication technologies, intelligent sensors etc. Despite various gains, it suffers from several security breaches or device malfunction that provoke catastrophic crashes in the IoT ecosystem. Distributed denial of service (DDoS) attack is a serious peril to the availability of IoT devises and services. DDoS is a diverse attack and the detection of mixed (High/Low) rate IP Spoofed DDoS attack in the IoT ecosystem is an arduous job because of its surreptitious nature and mixed rate traffic pattern. In this paper, we propose an adaptive and lightweight solution which detects and mitigates the mixed rate IP spoofed DDoS attack in the IoT ecosystem. The approach is implemented on the Contiki OS and the experimental results show that the solution is adaptive and is able to identify the attack with 99.1% accuracy.
Dynamic real-time indoor environment mapping for Unmanned Autonomous Vehicle navigation

Unmanned aerial and ground vehicles today excel at autonomous navigation due to their reliance on GNSS sensors. GNSS sensors are an unviable option for navigation in indoor environments due to lack of direct line-of-sight with satellites and environmental awareness. This paper introduces a robust method for unmanned autonomous vehicles (UAVs and UGVs) that generates a knowledge base in the form of a pointset about an indoor environment using geometric primitives and range imaging sensors. The method uses depth data from range imaging sensors along with pose data from visual odometry sensors to generate a map of the environment while exploring in order to build a knowledge base. This knowledge base can be used to navigate in the mapped environment while avoiding obstacles and finding optimal route from start to goal. This paper discusses the efficacy of the proposed method, it also demonstrates results generated by implementing the method in a controlled indoor environment.