Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Automated Packaging of Transducer Arrays for 3D Ultrasound Computer Tomography

A semi-automated packaging process for transducer array manufacturing is presented. As an major innovation, a transducer disc is integrated in a sandwich structure between a printed circuit board (PCB) and an acoustic matching layer. Each of the transducer discs contains 18 lead-zirconium-titanate (PZT) fibres embedded in epoxy. To interconnect the transducer array components, adhesive bonding and automatic pick-and-place processes were used. A pre-series was evaluated by measuring the electro-mechanical impedance (EMI) before and after the assembly. Statistical analysis showed consistent behaviour of the series resonance fs and the electro-mechanical coupling keff before and after the packaging. This encouraged the manufacturing of 256 arrays. These arrays will now be integrated in an ultrasound computer tomography (USCT) system with 3D scanning aperture for breast cancer imaging. With this system, we intend to bridge the gap towards clinical use of full 3D USCT.
An Authenticated Keyword Searchable Conditional Proxy Re-encryption Scheme in Cloud Services

The internet and mobile platform together are playing a vital role in making people to keep and manage their data conveniently using cloud services. This made cloud servers to manage larger volume of data where data confidentiality has to be taken into consideration. The major issue is to overcome the tradeoff between satisfactory mobility and security and privacy in cloud environment. A long time the researches in this field focused on encryption and decryption techniques to protect data owners’ privacy and data confidentiality. These encryption and decryption techniques made another essential requirement to be focused such as searching on encrypted data. This leads to the evolution of techniques for searching on encrypted data. Further in another direction conditional proxy re-encryption scheme where cipher text satisfying certain keyword vector set determined by the owner can be re-encrypted by a proxy server started getting attention. However, the combination of both searching and hierarchical conditional proxy re-encryption has been attempted recently. In this paper the above scheme is analyzed and proved that the data owner authentication offered by the scheme is fails to satisfy the requirement of authentication property. Additionally an extended version of the scheme is proposed which ensures data owner authentication. The proposed scheme is analyzed and proved based on widely used BAN logic and AVISPA.
An Integrated GNSS/LiDAR-SLAM Pose Estimation Framework for Large-Scale Map Building in Partially GNSS-Denied Environments

This article presents an integrated global navigation satellite system/light detection and ranging (GNSS/LiDAR)-based simultaneous localization and mapping (SLAM) pose estimation framework to perform large-scale 3-D map building in partially GNSS-denied outdoor environments. The framework takes the advantage of the complementarity between GNSS positioning and LiDAR-SLAM to decompose the map building task according to the GNSS real-time kinematic (RTK) status. When mapping in GNSS-denied scenes, a 3-D LiDAR-SLAM algorithm is adopted to estimate poses and a correction algorithm is presented to correct drift errors. On the other hand, when mapping in open scenes, a GNSS-initialized LiDAR mapping algorithm (GL-mapping) is proposed to loosely couple GNSS positioning and LiDAR data registration. It can perform the orientation estimation without the use of either the high-cost inertial sensing device or the GNSS dual-antenna. Experiments are conducted in large-scale outdoor environments to demonstrate that the proposed framework can accomplish simultaneous pose estimation and map building with high precision in both open scenes and GNSS-denied scenes.
All solid photonic bandgap fibers for high-power fiber lasers

Fiber lasers have been increasingly playing a significant role in marking, cutting, welding, and other material processing industrial applications. Further power scaling of fiber lasers needs to overcome transverse mode instability and a range of nonlinear effects. Further improvements in effective mode area scaling and higher-order-mode suppression are essential. All-solid photonic bandgap fiber provides a potentially powerful solution due to its strong higher-order-mode suppression in large cores. In this talk, we will review our work in this area.
EERA: An Energy-Efficient Resource Allocation Strategy for Mobile Cloud Workflows

Mobile cloud computing (MCC) which can invoke cloud services and offload tasks from the mobile device to the cloud has become an appropriate computing paradigm to provide many useful and complex workflow applications. However, offloading tasks needs extra communication time and energy, which leads to a conflict between saving the energy and improving the QoS. Thus, how to allocate these different resources (mobile or cloud) efficiently during the workflow execution process to optimize the system energy consumption under the deadline constraint is still a huge challenge in MCC. To address such an issue, this article proposes an energy-efficient resource allocation strategy for workflow applications in MCC. Firstly, we formulate the resource allocation problem into an optimization problem with the goal of minimizing the UtilityCost which represents the trade-off between energy consumption and the total execution time. Then, we use an energy-efficient resource allocation algorithm shortened as EERA to solve the problem. The algorithm firstly finds all the Partial critical paths of the workflow graph to segment the whole optimization problem into several local problems, and then uses the discrete particle swarm optimization algorithm to find the local optimal solution for every local problem, finally the local optimal solution is integrated to obtain the global one. In addition, we have adapted several representative algorithms for evaluation and comparison purpose. Comprehensive experimental results show that our EERA based strategy achieves the best overall performance on three key measurements including the energy consumption of the device, the execution time of the workflow and the UtilityCost.
Using Convolutional Neural Networks for Segmentation of Iris Images

In this paper, we consider the problem of iris image segmentation. In particular, we study three convolutional neural network architectures, namely, U-Net, LinkNet, and FC-DenseNet. As a part of the study, optimal parameters for training neural networks are determined, and augmentation is used to improve the segmentation accuracy. All experiments in this paper were carried out using the open MMU Iris Database. As this dataset is not provided with the ground-truth segmentation, we performed the manual segmentation of iris images to evaluate the segmentation quality. The results obtained showed that all three neural network architectures provide high segmentation accuracy, while the best results were obtained using U-Net.
Classification of Ethereum Vulnerabilities and their Propagations

Blockchain technology is having an ever-increasing impact on distributed applications domain, since the adoption of Blockchain 2.0 led to the spread of smart contracts. In such a context, Ethereum is the framework with the highest diffusion in terms of smart contract’s development, with a consequent rise of exploitation of code vulnerabilities, some of which causing bad financial losses. For this reason, this paper focuses on the issues of Ethereum smart contracts implementation (made with the Turing-complete language Solidity), providing a comprehensive systematization of such vulnerabilities basing on a slice of the Common Weakness Enumeration (CWE). Moreover, some relevant propagation cases among different vulnerabilities and CWE groups, observed in exploited contracts, are highlighted.
Automatic Monitoring and Control System in Aeroponic Plant Agriculture

This paper describes the implementation of an automatic control system for aeroponics. This system contains a lattepanda as the main processing unit and an adjustable interface to suit the needs of the cultivated plant. It gets input from temperature sensors, light sensors and humidity sensors and displays the output on the LCD screen module. The system results are simulated with the help of simulation software. And then the evaluation of the experimental results of the control system applied to aeroponics is based on environmental factors. The proposed lecture grows on an aeroponic device. The green plant was chosen to build an automatic control system in aeroponics. The results show that the system can be more efficient in saving labour and increasing the economic value of the product.
Ensemble Learning Classification for Medical Diagnosis

According to a report by the World Health Organization(WHO) of diseases responsible for deaths, heart disease tops the chart. It is revealed that diagnosing heart disease in an earlier stage is an important issue and researchers across the world have investigated to develop intelligent support systems that could help a physician in better medical discernment beforehand. Machine Learning can be used to develop a medical diagnosis system for predicting heart disease which not only promises a more accurate diagnosis but also reduces the cost of diagnosis. In this paper, we are automating the task of predicting heart disease by using statistical methods/machine learning algorithms. We are exploiting some of the most important algorithms used these days like Logistic Regression, Support Vector Machine (SVM), Decision trees and also Ensemble Learning algorithms (Bagging, Boosting and Random Forests). We conduct these experiments on Cleveland Heart Dataset available on UCI KDD Archive. The dataset distribution judged by our research fits well with decision trees and gives better results with ensemble learning methods, we justify this claim by comparing ensemble results with other Machine Learning approaches. Random forest classifier works best for this data set distribution, the random-nature of this ensemble approach fits the dataset well providing good test accuracies of upto 96.26%
Ship Tracking in High-Resolution Range-Compressed Airborne Radar Data Acquired During Linear and Circular Flight Tracks

Ship tracking is a key maritime surveillance requirement. Research on ship tracking using airborne radar platforms is not widely available in the open literature. In this paper, a concept of ship tracking using range-compressed (RC) airborne radar data is proposed. The proposed tracking algorithm is suitable for dense multitarget scenarios. Tracking is performed in the range-Doppler domain where the moving ships may appear out of the clutter region, improving their detectability. A powerful track management system is developed using SQLite database for handling gaps in the target detections and also the false detections. Some simulations and real experimental results from DLR's F-SAR system are provided to prove the concept.
GIS Insulation Defect Diagnosis Method Based on Improved MFCC and PCA-SVM Model

In order to improve the accuracy of GIS partial discharge pattern recognition, this paper proposes an improved MFCC and PCA-SVM voiceprint recognition model based on ultrasonic signals. Firstly, pre-process the ultrasonic signal according to the characteristics of GIS partial discharge; secondly, calculate the standard MFCC parameters, and calculate the first and second difference accordingly, and combine the dynamic and static parameters as the voiceprint characteristic quantity; finally establish the PCA-SVM classifier performs dimensionality reduction and recognition on the feature quantity. The test was conducted on a 110kV GIS, and the GIS partial discharge ultrasonic signals of four insulation defects of metal particles, floating potential, creeping discharge and corona discharge were collected for testing. The test results show that the MFCC feature vector after adding the first-order and second-order difference features can accurately and effectively reflect the ultrasonic signal characteristics under different insulation defects of the GIS, and the PCA-SVM algorithm has a high recognition and diagnosis success rate and a high recognition speed. The test proves that the diagnosis model proposed in this paper can effectively improve the efficiency of GIS defect recognition.
Fault Diagnosis Approach for Pneumatic Control Valves Based on Modified Expert System

This paper studies the fault diagnosis method of pneumatic control valve. Firstly, the faults characteristics of pneumatic control valves are analyzed according to the operating principle and status of pneumatic control valves; secondly, the expert experience of the fault diagnosis of pneumatic control valves is summarized, which is verified according to the operating mechanism; thirdly, a fault diagnosis approach for pneumatic control valves based on modified expert system is proposed, by combining particle swarm optimization (PSO) algorithm with expert rules. Finally, the availability and advantages of the proposed approach is verified by the designed valve experimental system platform. The results show that compared with the basic expert system-based method, the modified method improves the accuracy and reduces the false negative rate effectively.
Pairwise Location-Aware Publish/Subscribe for Geo-Textual Data Streams

The continued proliferation of location-based social media and ridesharing services brings up the omnipresence of geo-textual data, which is often characterized by high arrival rates, diversified and duplicated content. A host of existing studies focus on efficient processing of continuous queries over geo-textual data streams by developing effective location-aware publish/subscribe systems. However, these systems often suffer from two limitations: duplicated feedings and low-quality feeding information. To address the limitations, we propose to develop a novel location-aware publish/subscribe system that feeds subscribers with geo-textual object pairs rather than individual objects. We believe that delivering object pairs to subscribers is capable of improving the information feeding quality and subscriber satisfaction. To address the problem, we propose a two-phase subscription matching scheme. The first step is online geo-textual object join. We apply a time-based sliding window that filters out out-dated geo-textual objects over the data stream. Based on the sliding window, we develop an efficient hierarchical geo-textual object online join algorithm that merges duplicated geo-textual objects. The second step is object pair matching. For each object pair, we take it as input and run a dedicated geo-textual object pair matching algorithm to find a subset of subscriptions that matches the input object pair. Our empirical study shows that our proposal is able to achieving high efficiency and effectiveness in comparison with baseline.
Alpsoc Ant Lion * : Particle Swarm Optimized Hybrid K-Medoid Clustering

K-medoids clustering aims at partitioning a numerous data points into different K medoids based on the similarity distance between object pair. Cluster efficiency varies with respect to medoid initialization and may results in local optima traps. Various evolutionary swarm based approaches are adopted to obtain enhanced performance. Suitable combination of K-medoids with optimization technique does not operate well as expected while considering computational time. No such techniques are developed to solve entire clustering drawbacks. Assurance is not provided to any method in attaining success by solving the overall issues. Hence in this, an improved K-medoids integrated with Ant lion optimization and Particle swarm optimization algorithm commonly referred as ALPSOC is proposed to obtain optimized cluster centroid in which the computational complexity is preserved with better improvements in performance. Further the intra-cluster distance, F-measure, Rand Index, Adjusted Rand Index, Entropy and Normalized Mutual Information is evaluated for different datasets adopting the presented approach. The proposed algorithm is simulated on different datasets and is compared with different existing techniques based on above performance metric. From the observed results, it is shown that the proposed method functions better in all cases maintaining to solve clustering limitations.
An Effective Semantic Security Metric for Industrial Cyber-Physical Systems

the emergence of Industrial Cyber-Physical Systems (ICPS) in today’s business world is still steadily progressing to new dimensions. Although they bring many new advantages to business processes and enable automation and a wider range of service capability, they also propose a variety of new challenges. One major challenge, which is introduced by such System-of-Systems (SoS), lies in the security aspect. As security may not have had that significant role in traditional embedded system engineering, a generic way to measure the level of security within an ICPS would provide a significant benefit for system engineers and involved stakeholders. Even though many security metrics and frameworks exist, most of them insufficiently consider an SoS context and the challenges of such environments. Therefore, we aim to define a security metric for ICPS, which measures the level of security during the system design, tests, and integration as well as at runtime. For this, we try to focus on a semantic point of view, which on one hand has not been considered in security metric definitions yet, and on the other hand allows us to handle the complexity of SoS architectures. Furthermore, our approach allows combining the critical characteristics of an ICPS, like uncertainty, required reliability, multi-criticality and safety aspects.
A New Hybrid Forecasting Architecture of Wind Power Based on A Newly Developed Temporal Convolutional Networks

Wind energy planning, dispatching and control in the generation and conversion of wind power generation industry are becoming more and more important for today’s electrical power system, which needs accurate and stable wind speed prediction A new nonlinear hybrid model, SSA-TCN is proposed to improve the accuracy of wind power prediction. Singular Spectral Analysis (SSA) is introduced into the proposed method to decompose the original wind power sequence into four parts: trend, primary detail components, secondary detail components and noise. Moreover, a new Temporal Convolutional Networks (TCN) network is adopted in this paper instead of the current most advanced LSTM network. The output of TCN model has a longer actual memory range and is better able to cope with the gradient disappearance. In order to verify the advancement of SSA-TCN, the experimental part shows that the performance comparison between SSA-TCN, TCN and LSTM. The result shows that SSA-TCN has the best performance of all.
Hybrid Beamforming for Multi-User Dual-Function MIMO Radar-Communication System

This paper investigates a hybrid beamforming design for multi-user dual-function radar-communication (DFRC) system. The hybrid transmit/receive beamformers are designed by maximizing the sum-rate under constraints of power as well as similarity between the designed beamformer and the reference one that has good beampattern property. To solve the nonconvex optimization problem, we devise a two-stage method. To be more specific, in the first stage, we obtain the fully digital beamformer based on the weighted mean-square error minimization (WMMSE) method, in the second stage, we iteratively optimize the digital and analog beamformers to approximate the achieved fully digital beamformer. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.
Design and Experimental Validation of Automated Millimeter-Wave phased Array Antenna-in-Package (AiP) Experimental Platform

Antenna-in-package (AiP) technology has been a main design scheme of millimeter-wave (mmWave) phased array antennas for 5G communications. The beamforming pattern of mmWave phased array AiP is steered by controlling the amplitude and phase excitations of AiP elements. It would be helpful to evaluate the control accuracy of complex excitations and to reliably calibrate mmWave AiP. However, these tasks are not trivial and time consuming in practice, especially when the number of AiP elements is large. To address this problem, this article presents an efficient and automated mmWave phased array experimental platform. To reduce measurement time, a control software is developed to automatically control the AiP and measurement instruments simultaneously. This experimental platform has flexible gain and phase control of AiP element excitations, and customized measurements in an automated and efficient way can be realized. The effectiveness of the experimental platform is demonstrated by several measurement campaigns, where control accuracy, array calibration, and beam steering for mmWave phased array AiP are extensively investigated.
Information Need and Expectation Model for Measuring The Use of Institutional Repositories

This paper aims at the influence investigation of information needs, and user expectancy on the use of IRs among the university lecturers in academic libraries in Indonesia. A questionnaire survey was administered to 350 lecturers from six universities in Indonesia. Statistical analysis was conducted to validate the relationship of observed variables by using Partial Least Square of Structural Equation Modelling (PLS-SEM). The study confirms the five research hypotheses are supported while the remain of two hypotheses are not supported. The research findings show that the research model substantially contributes to explain the use of institutional repositories by 64 %(0.644).
The Transition Between Reactive and Radiative Regimes for Leaky Modes in Planar Waveguides Based on Homogenized Partially Reflecting Surfaces

The gradual transition from a reactive to a radiative regime is studied for leaky modes supported by multilayered planar open waveguides. The so-called leaky cutoff condition, i.e., the frequency for which the leaky phase constant <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> equals the leaky attenuation constant <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, originally introduced for microstrip lines and other printed structures, is investigated here with the aim of providing detailed information on the relative amount of reactive and radiative attenuation for leaky modes excited by finite sources and propagating as cylindrical waves along general planar waveguides. Analytical results are derived on the basis of a lossy parallel-plate-waveguide model and are validated through full-wave numerical simulations of 2-D leaky-wave structures based on grounded slabs covered with lossless or lossy partially reflecting surfaces (including, e.g., graphene layers) that can be treated as homogenized sheets. An analysis of the complex wave impedance of the considered leaky modes is also provided, in order to assess the frequency ranges where a good input matching can be expected for practical sources. In this regard, an <italic>ad hoc</italic> impedance matching network is designed and full-wave validated for a specific case to show that is indeed possible to achieve a good impedance matching below the cutoff in practical designs.