Miracle-dz/UniFAR-FTUs huggingface dataset:
Particle Swarm Optimization in Multi-Antenna SAR-based Localization for UHF-RFID Tags

This paper presents the application of the Particle Swarm Optimization (PSO) in synthetic aperture radar (SAR) methods exploiting multiple antennas for UHF-RFID tag localization. To perform 3D tag positioning, a robot equipped with two reader antennas is moving in the indoor scenario. The applicability of the PSO approach in SAR-based localization is discussed through a numerical analysis and an experimental campaign. A centimeter order localization error is achieved for 3D tag localization with a reduced computational cost with respect to classical SAR-based methods.
Efficient deviation detection between a process model and event logs

Business processes described by formal or semi-formal models are realized via information systems. Event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade. By comparing an existing process model with event logs, we can detect inconsistencies called deviations, verify and extend the business process model, and accordingly improve the business process. In this paper, some abnormal activities in business processes are formally defined based on Petrinets. An efficient approach to detect deviations between the process model and event logs is proposed. Then, business process models are revised when abnormal activities exist. A clinical process in a healthcare information system is used as a case study to illustrate our work. Experimental results show the effectiveness and efficiency of the proposed approach.
L1 Adaptive Control of Quadcopters

The issue of developing a control system for compensating external disturbances acting on the quadcopter is considered. The proposed design procedure consists in introducing a decoupling compensator into the system and subsequent synthesis of an adaptive regulator that provides compensation of external disturbances. It is shown that by virtue of choosing the value of the adaptation gain one can reduce the control system error to any arbitrary small value. The proposed control architecture can be used for developing fault-tolerant control systems of multirotor copters.
Clinical benefit of INCB7839, a potent and selective ADAM inhibitor, in combination with trastuzumab in patients with metastatic HER2+ breast cancer.

3025 Background: In HER2+ breast cancer, the HER2 protein is cleaved by the metalloproteinase ADAM10 resulting in release of the HER2 extracellular domain (ECD) into the serum while the truncated HER2 receptor (p95) remains in the membrane as a constitutively active kinase. Studies have shown that the presence of p95 is associated with poor clinical outcome, including resistance to trastuzumab-based therapy. Therefore, inhibition of HER2 cleavage by the ADAM inhibitor INCB7839 may enhance the clinical efficacy of trastuzumab in HER2+ breast cancer patients. Methods: This study was initiated as a single arm modified dose escalation open label trial of INCB7839 + trastuzumab in women with HER2+ metastatic breast cancer, naive to chemotherapy. Three doses of INCB7839 were studied (100 mg, 200 mg, 300 mg BID) with an expansion arm at the 300 mg dose. Trastuzumab was administered on a Q3 week schedule. Pharmacokinetics, plasma HER2 ECD levels and p95 expression in tumor tissue were assessed in addition to clin.
Collaborative mobile industrial manipulator: A review of system architecture and applications

This paper provides a comprehensive review of the development of Collaborative Mobile Industrial Manipulator (CMIM), which is currently in high demand. Such a review is necessary to have an overall understanding about CMIM advanced technology. This is the first review to combine the system architecture and application which is necessary in order to gain a full understanding of the system. The classical framework of CMIM is firstly discussed, including hardware and software. Subsystems that are typically involved in hardware such as mobile platform, manipulator, end-effector and sensors are presented. With regards to software, planner, controller, perception, interaction and so on are also descripted. Following this, the common applications (logistics, manufacturing and assembly) in industry are surveyed. Finally, the trends are predicted and issues are indicated as references for CMIM researchers. Specifically, more research is needed in the areas of interaction, fully autonomous control, coordination and standards. Besides, experiments in real environment would be performed more and novel collaborative robotic systems would be proposed in future. Additionally, some advanced technology in other areas would also be applied into the system. In all, the system would become more intelligent, collaborative and autonomous.
Reconfigurable fractional microwave signal processor based on a microcomb

We propose and demonstrate reconfigurable fractional microwave signal processing based on an integrated Kerr optical microcomb. We achieve two forms of microwave signal processing functions-a fractional Hilbert transform as well as a fractional differentiator. For the Hilbert transform we demonstrate a phase shift of 45 degrees - half that of a full Hilbert transform, while for the differentiator we achieve square-root differentiation. For both, we achieve high resolution over a broad bandwidth of 17 GHz with a phase deviation of less than $5^{\mathrm {o}}$ within the achieved passband. This performance in both the frequency and time domains demonstrates the versatility and power of micro-combs as a basis for high performance microwave signal processing.
Benefit assessment of installing innovative conductors in overhead lines

The ongoing evolution of the electricity system to implement climate action policies translates into a continuous increase in the penetration of renewables and the need to strengthen the transmission grid to optimize the use of these sources and contain operating costs. The development of innovative overhead line conductors with better performance than those of traditional lines would bring benefits in terms of increased energy efficiency in transmission and distribution networks without requiring the replacement of existing towers. The project CALAJOULE, co-financed by the Italian Ministry of Economic Development in the framework of the “Ricerca di Sistema” programme, aims at proposing innovative solutions for overhead line conductors for the containment of Joule power losses. This paper presents the main characteristics of the innovative conductors along with the expected benefits deriving from their use in place of the traditional ones.
Classification of an Embedded System Instruction EMI Using a Deep Convolutional Neural Network

This paper proposes a method for classifying combinations of instructions EMI of an embedded system by using a convolutional neural network. First, measuring the EMI emitted by 16 different combinations of instructions from an embedded system, then transform the EMI data to spectrogram and build a massive volume dataset to train the convolutional neural network. By analyzing the confusion matrix, we classify those similar combinations of instructions as the same groups. After several interactions, the 16 combinations of instructions can be classified into 5 groups. In the final result, the convolutional neural network model has 98% training accuracy and 83% prediction accuracy.
Evaluation of EMG Signal Time Domain Features for Hand Gesture Distinction

This paper illustrates a collection of wide used time domain features of EMG signal, investigates their ability to distinguish hand gestures from one subject performing six different gestures with two sensor channels. The extracted set of features is presented as a fusion between the two sensor channels and a visual evaluation of their usability for performed hand gesture distinction is described. Results have shown that extracting Integrated EMG, Variance, Mean absolute values type one and type two, Average amplitude change and zero crossing time domain features are more significant for hand gesture recognition based on surface EMG signal analysis then extracting temporal moments coefficients, Wilson amplitude and myopulse percentage rate. However, the necessity of a feature selection step to eliminate information redundancy before hand gesture classification is concluded.
Validation of Vibrating Intrinsic Reverberation Chamber using Computational Electromagnetics

Our study demonstrates a validation of the computational electromagnetics modeling and a simulation of a vibrating intrinsic reverberation chamber (VIRC). The VIRC generates a random electromagnetic environment by shaking the flexible conductive walls to vary the boundary conditions. The finite-difference time-domain method was applied to model a VIRC and simulate its behavior. The VIRC's behavior was modeled using rough surfaces and multiple independent cavity models. We performed two statistical analyses for the numerical validation. First, the field distribution in the VIRC was evaluated using the chi-squared goodness-of-fit (GOF) test. Second, the spatial field uniformity was evaluated using the standard deviation of the maximum and the average of each orthogonal electric field magnitude within the defined working volume. These statistical analysis results indicated that a random electromagnetic environment in the VIRC could be simulated by applying multiple independent cavity models with three orthogonal rough surfaces.
Fast Fail-Over Technique for Distributed Controller Architecture in Software-Defined Networks

Recent studies have proposed different approaches to mitigate the risk of overload and failure in Software-Defined Networks (SDNs). Some of these approaches have proven effective but only in specific use cases, making it potentially difficult to generalize their application. Furthermore, network failure detection and recovery by the SDN control plane requires sophisticated software logic running on multiple controllers that are non-intrusive to the network environment. While this allows more flexibility to respond to failure events, it also implies that each controller application must include its recovery logic, which increases code complexity. In this paper, we propose a fast fail-over technique for solving the problem of a controller failure or target availability in the network. We argue that inter-domain controller synchronization can result in high network overhead and should be minimized to per-need base only. To this end, upon detecting a failure in the control plane, the proposed fast failure recovery technique leverages a load-shifting scheme to initialize alternate paths and proactively instantiate flow rules to reduce flow setup latency. To prevent packet loss during failure recovery, we utilize a forwarding information table that quickly replays inputs to the controller after failure recovery. Our extensive experiments show that the average latency incurred by the controller to controller communication is approximately twice that of per-need based synchronization. The experimental results also show that our proposed technique achieved a 50% reduction in service interruption period and 75% flow_mod reduction during a single link failure over the traditional SDN baseline approach.
Analysis of the SSLMS algorithm for complex-valued data

Terms for the steady-state mean-square error, the optimum step-size, and the corresponding minimum value of the tracking mean-square error of the sign–sign least mean square algorithm for complex-valued data are derived in the work. Moreover, convergence comparison of the least mean square (LMS) algorithm and the sign–sign LMS algorithm for complex-valued data is also presented. Finally, simulation results are presented to support our analytical findings.
Online Unsupervised Learning of the 3D Kinematic Structure of Arbitrary Rigid Bodies

This work addresses the problem of 3D kinematic structure learning of arbitrary articulated rigid bodies from RGB-D data sequences. Typically, this problem is addressed by offline methods that process a batch of frames, assuming that complete point trajectories are available. However, this approach is not feasible when considering scenarios that require continuity and fluidity, for instance, human-robot interaction. In contrast, we propose to tackle this problem in an online unsupervised fashion, by recursively maintaining the metric distance of the scene’s 3D structure, while achieving real-time performance. The influence of noise is mitigated by building a similarity measure based on a linear embedding representation and incorporating this representation into the original metric distance. The kinematic structure is then estimated based on a combination of implicit motion and spatial properties. The proposed approach achieves competitive performance both quantitatively and qualitatively in terms of estimation accuracy, even compared to offline methods.
A Phase-based Method for UHF D Gate Access Control

This paper presents a phase-based method for UHF-RFID tag classification in gate access control systems. The proposed system exploits only a single reader antenna and multiple phase samples collecting during the tag motion. Experimental results in real indoor scenario show the method capability to discriminate incoming, outgoing, and passing tag actions. The method can be applied at every identification point equipped with a COTS UHF-RFID system with a software/firmware implementation without requiring additional hardware.
Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment

Cardiotocograph (CTG) is a widely used tool for fetal surveillance during labor, which provides the joint recording of fetal heart rate (FHR) and uterine contraction data. Unfortunately, the CTG interpretation is difficult because it involves a visual analysis of highly complex signals. Recent clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms modulated by the autonomic nervous system. Certainly, this modulation reflects variations in the FHR, whose characteristics can involve significant information about the fetal condition. The main contribution of this work is to investigate these characteristics by a new approach combining two signal processing methods: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and time-varying autoregressive (TV-AR) modeling. The idea is to study the CEEMDAN intrinsic mode functions (IMFs) in both the time-domain and the spectral-domain in order to extract information that can help to assess the fetal condition. For this purpose, first, the FHR signal is decomposed, and then for each IMF, the TV-AR spectrum is computed in order to study their spectral dynamics over time. In this paper, we first explain the foundations of our proposed features. Then, we evaluate their performance in CTG classification by using three machine learning classifiers. The proposed approach has been evaluated on real CTG data extracted from the CTU-UHB database. Results show that by using only conventional FHR features, the classification performance achieved 78, 0%. Then, by including the proposed CEEMDAN spectral-based features, it increased to 81, 7%.
Demonstration of Thermally-Assisted Programming With High Speed and Improved Reliability for Junctionless Nanowire NOR Flash Memory

This study demonstrated the thermally-assisted (TA) programming of a NOR flash memory device, which was composed of a junctionless silicon nanowire with gate-all-around structure. Fast programming was achieved with the aid of Joule heat, which was generated in a homogeneously heavily doped SiNW by a voltage difference between the source and drain. Its programming speed is the fastest among various NOR flash memories ever reported. The mechanism for programming NOR flash memory inevitably degrades the quality of the tunneling oxide. But the proposed thermally-assisted programming provides simultaneous curing of the damaged tunneling oxide.
Assessment of Direct and Fluid-Mediated Cold Atmospheric Plasma Treatment Efficacy on Squamous Cell Carcinoma

Squamous cell carcinoma (SCC) is the second most common skin cancer among the white race. Plasma is an ionized gaseous state of matter containing chemically active species, such as ions, electrons, photons, reactive oxygen and nitrogen species, and UV light. Cold atmospheric plasma (CAP) has just recently been showing promising anti-cancer activities supported by the ability to induce cell death via apoptosis and cell cycle arrest leading to tumor cell destruction in vitro and in vivo. In this study, two different plasma treatment methods, which are direct plasma treatment and fluid-mediated plasma treatment, apply on SCC and keratinocytes cell lines to determine lethal dose. Also, apoptotic behaviors of two cell types are evaluated with TiterTACS™ apoptosis detection kit. For direct plasma treatment, 60 seconds exposure to CAP found as optimum time and, for fluid-mediated plasma treatment 15 minutes holding of 30 seconds CAP exposure N-Acetyl Cysteine (NAC) solution found as optimum treatment time. Results show that CAP can selectively inactivate SCC cell line through apoptosis while no damage or apoptotic behavior observing in keratinocyte cell line.
Prediction of Melting Profile of Mild Steel Weld Metals Using Regression Analysis

The microstructure of a weldment can be maintained by ensuring a steady state homogenous melting profile of the welding operation, which includes the deposition of optimum volumes of melted filler wire and substantial part of the heat affected zones of the parent metal to form the weld pool. The melting pattern of the entire welding process should be protected from atmospheric air, so as to enhance weldment quality. In this study, the melting profile of mild steel is investigated by looking at the parent metal angular distortion bead width and penetration volumes of deposited filler and the melting efficiency was determined. Predictive models were also developed to determine the above listed melting properties by applying the regression analysis. The result obtained showed that, there is almost a perfect fit between the calculated and predicted angular distortion, as well as between the calculated and predicted volume of filler wire melted. There is also a close correlation between the calculated and predicted melting efficiency. However, for the bead width, bead penetration and volume of weld metal deposited, there were variations of values and a heterogeneous correlation between the calculated, measured and predicted values. The effects of the process parameters on the obtained properties of the melting profile were investigated and optimum process parameters were determined.
An Augmented Reality Application for Personalised Diamond Shopping

This paper presents an Augmented Reality (AR) Application (App) developed for bespoke jewellery solutions. The App allows users to experience a visual representation of diamond rings through their mobile device. Customers could design and review bespoke jewellery without having to visit the jeweller’s shop and could communicate changes to the jeweller in real-time (saving time and money). The use of AR would allow customers to view their bespoke jewellery on their fingers to gain a better idea of how it would look once completed. Twenty seven participants evaluated the App by completing a questionnaire after using it. The application of Exploratory Factor Analysis resulted in four factors (compatibility, likeability, functionality and usability). Overall, the participants appreciated the AR App; regardless of their gender, age and experience.
Identity Authentication Using Portable Electroencephalography Signals in Resting States

Biometric identity authentication technology is widely used in the field of information security. As a new biological method, electroencephalography (EEG) is gradually applied in biometric recognition, because scientists believe that practicability and portability are the development direction of EEG identity authentication. This work investigates the feasibility of using resting state EEG signals recorded by single-channel portable device for identity authentication. Single-channel EEG classification are effectively improved by using mixed-method in three feature domains (i.e., time, frequency, and time-frequency), feature selection (Rayleigh quotient) and classifier design (ensemble classifier). We invited 46 subjects to participate in the EEG identity authentication experiment. Experimental results show that the open-eyed resting state is an ideal authentication method, and the average classification accuracy of the authentication algorithm can reach 95.48% in 2 seconds, validating that the new method of processing single-channel EEG signals is especially useful in extrapolating EEG identity authentication to realistic field contexts.
Electric Vehicles Insertion in Power Grids and Impacts on Power Quality: A Conventional and Inductive Charging Comparative Study

This article compares, in terms of power quality, two different types of electric vehicle charging systems: conventional and inductive. The inductive charging has no physical contact with the electric vehicle, so the electricity reaches the vehicle circuit through an inductive coupling. The results of this study shows, through Simulink/MATLAB® simulation, that the inductive charging injects more voltage and current harmonics than the conventional charging, for three scenarios considered of battery charging.
High Gas Void Fraction Flow Measurement and Imaging Using a THz-Based Device

Measuring in real-time two-phase flow composition of a mixed fluid having high gas void fraction (GVF) remains a challenging task in oil–gas fields. Such fluid is abundant in gas pipelines where pressure and temperature fluctuations lead to condensate gas. This may also be the case of crude oil produced from CO2 or steam-based enhanced oil recovery, where the injected gas is mixed with the produced oil. This article presents a new concept of high GVF measurement and flow regime determination using a terahertz-based imaging system. It explores the fact that the gas phase has very low absorption of THz waves, while it yields an absorption factor that is proportional to the amount of liquid. The recent availability of low-cost THz imaging systems that can generate two-dimensional images at more than 100 frames/s makes them well suitable for flow metering applications. Two different artificial intelligence algorithms, namely support vector machine (SVM) and artificial neural network (ANN), were assessed using an in-house multiphase flow loop. The corresponding results reveal that while ANN and SVM yield very accurate results, the SVM technique performed slightly better where a maximal error of 0.46% for GVF in the GVF range from 80% to 100% could be achieved. In addition, it could accurately determine all three type of flow regimes (i.e., annular, stratified, or slug flow). This suggests that the technique can be considered as a good candidate for next-generation flow metering and imaging of multiphase flows.
Applications of Artificial Intelligence and Machine Learning in the Area of SDN and NFV: A Survey

Artificial Intelligence (AI) and Machine Learning (ML) have gained a huge interest from academia and industry in solving very complex problems in several fields. In this paper, we present a short survey of the main application areas of AI/ML in SDN and NFV based networks. We classify the main advancements in the area in different categories based on their application track and identify the corresponding AI techniques utilized. In addition, identify and discuss the main challenges and future directions in the area. We stress that AI/ML can play a vital role in providing a way towards self-configured, self-adaptive and self-managed networks. However, the research is limited due the identified challenges in this area.