Miracle-dz/UniFAR-FTUs huggingface dataset:
Metrological validation of a photogrammetry-based technique

Photogrammetry is a technique that allows one to extract three-dimensional measurements and information about an object from two-dimensional digital images. This techniques finds many applications, and it is particular useful for the documentation of artifacts in the field of cultural heritage. To this purpose, the validation of the dimensional accuracy of the 3D models obtained through photogrammetry is necessary and must be investigated. This paper presents the validation of a photogrammetry-based technique, using a Coordinate Measuring Machine (CMM). The validation employs a reference object manufactured by using a 3D printing machine and designed to be employed in multi-spectral characterizations. The reference is a dodecahedron with the twelve faces coated with several pictorial materials, which are representative of a wide historical period. Thus, the reference can be used also for the integration of multi-spectral imaging data that are not detectable in the visible spectrum. The image acquisition process is fully automated. The quality of such acquisitions is quite good, as it is proved by the presented experimental validation. Some acquisitions both with visible light and with UV light are presented in the paper as well.
A DDoS Attack Detection on Cloud Framework Using Improved Features Based Machine Learning Approach

A Denial of Service (DDoS) attack consumes the network bandwidth and computing resources of a targeted system, preventing the target system from being DDoS attacked by unauthorized users. This research work proposed a modified feature selection-based neural network for efficient DDoS attack detection. The proposed method is divided into three stages: feature selection, training, and testing. For the implementation of the proposed work using MATLAB 2020 software, MATLAB is a well-known academic as well as an industrial research software package for this work. In R2020 MATLAB, the proposed method is designed and simulated. There are different types of DDoS attacks present on the cloud internet. This research work uses the Canadian Institute of Cyber Security (CICIDS2017) data set to perform the proposed methodology. This data set was selected for classification because it consists of 80 parameters. Other than the benign traffic, as per the tools used, the flow records are labeled as 'Slowloris', 'Slowhttptest', 'Hulk', and 'Begian'. The proposed method shows good results in terms of accuracy, precision, selectivity, sensitivity, and confusion matrix (C.M.). The presented method shows an accuracy of 99% and the other parameters are discussed in the simulation and result section.
Failure Mechanism Investigation of Die-Attach Solder Based on the Birth and Death Technology

To guarantee reliability of the insulated gate bipolar transistors (IGBTs), the failure mechanism investigation attracts much popularity. As a major failure location, die-attach solder gains considerable attention. To detect the cracks of the die-attach solder, generally, it is implemented to roughly dig holes, which may lead to inaccurate analysis. To address this, a method based on the birth and death technology is introduced in this digest, in which two kinds of failure processes, i.e, fails from edge and fails from center, are deliberately considered. Additionally, various factors including thermal resistance (Rth), the junction temperature (Tj) and the case temperature (Tc) are extracted to analyze the different failure stages of die-attach solder. Notably, a novel method is adopted to accurately indicate failure. Moreover, the Tc will show different performance in two failure processes. With this, it can be used to distinguish failure mechanism.
Fault Diagnosis of Reactor Based on Vibration Signal Information Entropy

By collecting and studying the time-domain characteristics of the vibration signal on the surface of shunt reactor, it is found that the vibration signal in each period fluctuates violently when the reactor has mechanical failure. The moving average sequence information entropy of vibration signal is extracted as the feature vector, and a One-Class Support Vector Machine (OCSVM) mechanical fault diagnosis model is constructed to realize the health state evaluation of shunt reactor with 99.2% accuracy. Furthermore, a fast fault detection method is proposed. This method only uses four random sampling points, which reduces the difficulty of field operation on the premise of ensuring the average fault diagnosis rate of 98.5%. Therefore, the information entropy feature of moving average sequence is an important feature of fault diagnosis of reactor mechanical equipment, which has strong practical engineering significance for reactor health diagnosis.
Higher Order Extended State Observer in Presence of Fast Varying Mismatched Uncertainty

Extended State Observer (ESO) is the technique well suited for estimating the states of the plant and matched uncertainty. Generalized Extended State Observer (GESO) is preferred for slow varying mismatched uncertainty systems. It is noticed that, in case of fast varying mismatched uncertainty, capacity of estimating a states and uncertainty degrades. To overcome this problem an integration of higher order ESO (HESO) and GESO is carried out in this paper and termed as Higher Order Generalized Extended State Observer (HGESO). In this paper a comparative analysis of GESO and HGESO is carried out by considering a steady state second order plant on which fast varying mismatched uncertainty is continuously acted. Simulation results states that a higher order ESO (HGESO) is able to track the states and uncertainty more effectively with minimum control effort.
Pavement Macrotexture Evaluation for Tire Test Benches using Surface Profile Measurements

This paper presents a new approach to evaluating the characteristics of structured metal strips used to simulate road surfaces during the testing of car tires. The new computation procedure yields more realistic and reliable results than the methods proposed in the ISO 13473-1. The proposed virtual volumetric patch method (VVPM) takes advantage of the fact that the instrument yields measurement data on a regular grid, corresponding to a patch of the surface. This permits the implementation of the primary computations as tensors, yielding a simple and numerically efficient solution. Tensor polynomials are used to eliminate low frequency distortions of the strip, resulting from inner tensions, which would otherwise negatively influence the computed characteristics. The VVPM estimates the equivalent volume of water that could be embedded in the road surface, prior to the surface becoming submerged. This also yields an estimate for the true texture depth. The new approach, with the gridded nature of the data, also enables the computation of texture randomness and coherence wavelengths. Yielding additional valuable information about the structure of the surface. Real-world measurement data were acquired from a test bench; one such data set is used throughout the paper to demonstrate the effectiveness of the proposed processing.
Molecularly Imprinted Polymers and Inkjet-Printer technology to develop Optical-Chemical Sensors

In this work, we present a chemical extrinsic optical fiber sensor, based on a novel micro-pattern of silver nanoparticles, produced by InkJet printing on a polyethylene terephthalate (PET) substrate, covered by a molecularly imprinted polymer (MIP) film. The optical-chemical sensor chip is monitored via two plastic optical fibers (POFs) connected with a light source and a spectrometer. In particular, the pattern is formed by parallel lines arranged in orthogonal position with respect to the direction of the input light. As a proof of concept, we have deposited on the sensor surface an MIP receptor layer specific for furfural (2-FAL). The preliminary experimental results have shown a limit of detection (LOD) relative to 2-FAL equal to 0.05 ppm, demonstrating the goodness and feasibility in biochemical applications of the proposed sensing approach.
Companion Mobility to Assist in Future Human Location Prediction

Location prediction plays an important role in modeling human mobility. Existing studies focused on developing a prediction model which is based solely on the past mobility of only the person of interest (POI), rather than including information on the mobility of her/his companions. In fact, people frequently move in a group, and thus, using mobility data of a person’s companions can enhance accuracy when predicting that person’s future locations. Motivated by this, we propose a two-phase framework for predicting an individual’s future locations that fully benefits from spatio-temporal contexts embedded in that person’s and his/her companions’ mobility. The framework first determines the POI’s companions, then predicts future locations based on mobility information for both the POI and selected companions. Two companion selection methods are proposed in this work. The first method uses spatial closeness (SC) to determine the companions of the POI by measuring the similarity of the individuals’ geographic distributions. The second method builds person ID embedding (PIE) vectors, and cosine similarity is used to select the POI’s companions. To mitigate the curse of dimensionality, the framework also uses a stacked autoencoder in which the encoder compresses a high-dimensional input feature (e.g., location, time, and person ID) into a low-dimensional latent vector. For the second phase of the framework, a bidirectional recurrent neural network (BRNN)-based multi-output model is proposed to predict a person’s future locations in the next several time slots. To train the BRNN model, weighted loss is used, which takes into account the importance of each future time slot to predict the POI’s locations accurately. Experiments are conducted on two large-scale Wi-Fi trace datasets, demonstrating that the proposed model can effectively predict human future locations.
A Framework for Network Security Verification of Automated Vehicles in the Agricultural Domain

The agricultural sector increasingly relies on automated vehicles. These machines are often based on a CANbus control network and equipped with different wireless interfaces to implement different functionalities, such as remote control through radio links, GPS-based localization, and Wi-Fi-based data exchange. Nevertheless, CANbus presents severe vulnerabilities that expose these vehicles to cyberattacks. In this context, it is crucial to develop efficient procedures for network security verification of automated agricultural vehicles. The present work proposes a framework for evaluating the network security of agricultural vehicles based on four main dimensions: CANbus Security and Network Segmentation, Remote control based on Radio-Links, Wireless Gateways, and GPS security. We presents a testbed we are developing to test the proposed procedures, also discussing the related methods and procedures.
Hierarchical Memory Diagnosis

High-quality memory diagnosis methodologies are critical enablers for scaled memory devices as they reduce time to market and provide valuable information regarding test escapes and customer returns. This paper presents an efficient Hierarchical Memory Diagnosis (HMD) approach that accurately diagnoses faults in the entire memory. Faults are diagnosed hierarchically; first, their location, then their nature (i.e., static or dynamic), and finally, their functional fault model. The HMD approach leads to a more accurate diagnostic, enabling the precise identification of yield loss causes.
Joint Speed and Bandwidth Optimized Strategy of UAV-Assisted Data Collection in Post-Disaster Areas

In natural disasters, due to the destruction of ground infrastructure, ground terminals (GTs) participating in rescue cannot communicate with the outside world, so the rapid establishment of wireless network is crucial for rescue operations. Due to its large coverage and rapid deployment, unmanned aerial vehicles (UAVs) are often used in emergency communications. In this paper, a hovering UAV and a cruising UAV are adopted cooperatively to collect data efficiently from GTs. Firstly, the locations of the GTs are modeled by a Thomas cluster process (TCP) which is more accurate. Secondly, based on the different distribution densities of GTs, the speed in sub-regions and bandwidth allocation of UAV are jointly optimized to maximize the number of GTs served by UAV, where the GTs need to upload the minimum amount of data within a given cruise cycle to ensure efficiency. The optimization problem we propose is a mixed integer non-linear problem (MINLP) and NP-hard. To solve this problem, we employ the improved particle swarm optimization (PSO) to find the optimal solution. Extensive simulations and experiments show that the number of served GTs can be effectively increased by jointly optimizing the speed and bandwidth of sub-regions compared with the constant speed and static channel resource allocation scheme.
CNN-based Data-Model Co-Design for Efficient Test-termination Prediction

Failure diagnosis is a software-based data-driven procedure. Collecting an excessive amount of fail data not only increases the overall test cost, but may also lead to degradation of diagnostic resolution. Test-termination prediction is thus proposed to dynamically determine which failing test pattern to terminate testing, producing an amount of test data that is sufficient for an accurate diagnosis analysis. In this work, we describe a novel data-model co-design method of using deep learning method for efficient test-termination prediction. In particular, images describing the failing test responses are constructed from failure-log files. A multi-layer convolutional neural network (CNN) embedding a residual block is then trained, based on the images and known diagnosis results. The learned CNN model is later deployed in a test flow to determine the optimal test-termination for an efficient and quality diagnosis. Experiments on actual failing chips and standard benchmarks demonstrate that the proposed method outperforms SOTA works. Our method creates opportunities to harness the power of deep learning for improving diagnostic efficiency and quality.
Simulation and Reduction of Speckle-induced Uncertainty in Laser Triangulation Sensors

Laser triangulation sensors are one of the most commonly used optical sensors in dimensional metrology and quality control. This paper presents a method for simulating the laser speckle-induced measurement uncertainty, representing a major performance limitation of these sensors, as well as a method for reducing the influence of speckle on the achievable accuracy. To investigate the relation between triangulation angle and the resulting speckle-induced uncertainty, simulations are performed for three sensor geometries, revealing that a larger angle results not only in a smaller measurement range but also in a reduced influence of laser speckles and a smaller uncertainty. The accuracy of the simulation method is validated by measurements conducted with an experimental setup, demonstrating good agreement between the measured and simulated uncertainty values. To reduce the impact of speckles on the measurement accuracy, a diffuser mechanism is integrated into the optical path, reducing the resulting speckle-induced measurement uncertainty by up to 63%.
Robust Stability and Ellipsoidal Quality Estimates of Interval Multidimensional Control Systems*1

The algebraic method of researches of robust stability of continuous and discrete interval dynamical systems is considered, as well as the method of ellipsoidal quality estimates of multidimensional control systems. This paper presents the original results obtained for the research of the stability of continuous and discrete linear interval dynamical systems, called the Algebraic method of robust stability. Ellipsoidal quality estimates based on majorant and minorant estimates calculated using of singular values of corresponding criterion matrices of these properties are proposed for investigation of qualitative properties of multivariate control systems. The computational apparatus for determining ellipsoidal estimates is based on singular value decomposition matrix using standard SVD-procedures. Basic concepts of ellipsoidal estimation method are given.
Automated Testing to Detect Status Data Loss in Android Applications

When user installs an Android app, it is expected that the data should be persistent at all states, which requires saving the instance data for all the activities. An app can be paused or sent to the background due to other interruptions or user intervention. It is difficult for the programmer to test this issue for all the activities. This results the issue of data loss, the information/details entered by user in the app are not saved when there is any interruption. This results in degrading the user experience because user should enter the information each time there is an interruption. This research proposes a tool, DroidDLP, a Data Loss Preventor in Android, which will detect the information loss from a given android application. We have tested 395 applications and found 12 applications with the issue of data loss. This approach is proved highly accurate and reliable to find the apps with this defect, which can be used by android developers to avoid such errors.
Current Optimization for Low-Frequency Ride-Through in Speed-Sensorless Induction Motor Drives

To cope with the speed observability for speed-sensorless induction motor drives, the low-frequency ride-through methods are presented to ensure the speed observability at low stator frequencies during steady-state operations. However, the current will increase since the low-frequency ride-through method is realized by correcting rotor flux reference or magnetizing current reference. To obtain optimal current and higher efficiency, this paper proposes a current optimization method for low-frequency ride-through under different load conditions. Besides the observability, the minimum current is adopted as an additional optimization objective to derive the locations of boundary points. Furthermore, the synchronous speed loop controller is developed. Finally, the effectiveness of proposed method is verified on an ARM-based 2.2 kW IM experimental setup.
Developing Formal Models for Measuring Fault Effects Using Functional EDA Tools

State-of-the-art EDA tools largely employ functional circuit models that are inadequate for verifying and emulating design properties related to fault effect and tolerance. In this paper, we derive fully synthesizable fault effect propagation models for formally reasoning about fault-related design behaviors under different types of faults. We associate each signal bit with a binary fault label to reflect its fault attribute. We further derive fine-granularity precise propagation policies and specify these policies as formal models for fault effect analysis using functional EDA tools. Experimental results using IWLS benchmarks have demonstrated that our formal models can be used to measure fault propagation effects and accelerate fault verification through hardware emulation. Our work makes a step towards property driven EDA flows that allow fault tolerance and dependability to be verified alongside functional correctness.
An Iris preprocessing and segmentation procedures for Iris Recognition: the darkest crescent algorithm based on the RGB space

In this study, a new method of Image preprocessing and segmentation based on the RGB space is presented. The method consists of preprocessing the images by cleaning and removing unnecessary areas then applying a double search for the darkest areas to find the iris and the pupil. For experimental results, the study is implemented on Iris@PBMLT dataset which is a new private IRIS dataset. The results are compared with those that are obtained by the classical integro-differential operator method. The obtained results showed that the proposed method has acceptable accuracy.
Vertically aligned carbon nanotube arrays as thermal interface material for vibrational structure of piezoelectric transformer

The application and characterization of thermal interface material (TIM) for vibrational structures is investigated in this paper. The vibrating feature during the operation requires unique solution for its thermal management, since the connection between the device and heat dissipater should be able to conduct heat efficiently and impose minimum constraint onto the vibration simultaneously. As a typical vibrational device, piezoelectric transformers (PTs) are discussed in this paper. The PTs have urgent demands for thermal dissipation since their power conversion efficiency decrease rapidly with the rising temperature. A novel method by applying vertically aligned carbon nanotube (VACNT) arrays to the interface between PT and heat dissipater is presented to enhance the performance of piezoelectric transformers. VACNT arrays are one of the excellent TIMs. It can directly establish thermal contact between two surfaces by van der Waals’ forces. In addition, the unique anisotropic character of CNT arrays provides enough flexibility to accommodate the vibration during the operation. Different configurations of TIMs are compared with each other in this work, including CNT arrays, tape of polypropylene (PP) membrane and without heat transfer structure (HTS). The results indicate that the temperature rise is lowest and the efficiency is highest at the same power density while CNT arrays served as the TIM. Almost no significant fretting and wearing damage occurred on PT electrode surface with CNT arrays TIM even after working continuously for 120 days. Meanwhile, the thermo-physical properties of CNT arrays at contact interface are measured by optical transient thermo-reflectance technique.
An Integrated Transformer for LLC Resonant Converter Applications of Low Output Voltages and High Currents

Typically, the LLC resonant converter uses separate magnetic components for the resonant inductance and the transformer. While the literature describes methods to integrate both into one single component, these methods usually require litz wire for both the primary and secondary winding. For applications with a low-voltage, high-current output, these methods, however, cannot be employed as the secondary winding is conventionally constructed as a single-turn, which is hardly possible with litz wire. For these applications, this paper proposes an integrated planar transformer where single-turn copper sheets are employed on the secondary side. The integrated stray inductance is achieved through an additional ferrite leg on the secondary side. By using interleaving of the primary and secondary windings, proximity losses are mitigated to a large extent. This paper discusses realization options and design implications for the transformer. Finally, a prototype is developed achieving a maximum efficiency of about 96 %.