Miracle-dz/UniFAR-FTUs huggingface dataset:
Passivity Controller Based on Load-Side Damping Assignment for High Stiffness Controlled Series Elastic Actuators

This article offers a new insight to the achievable stiffness limitation of the impedance control of series elastic actuators (SEAs) by suggesting a comparative analysis of energy ports and passivity control framework at the energy port to enhance the maximum achievable rendered stiffness of SEAs. To this end, it is explored that SEAs have two different port definitions to assess energy and passivity of the system—spring port and load port; and conservatisms for passivity evaluation of two ports are investigated and compared utilizing frequency characteristics. The results reveal that the load port passivity exhibits less conservatism, which allows to render larger achievable stiffness in impedance-controlled SEAs. Moreover, key parameters that determine the passivity characteristic of the SEA impedance control are discovered based on the load port passivity analysis. A novel passivity control framework that incorporates the time-domain passivity observer and the passivity controller is designed utilizing the load port energy monitoring, which offers a less conservative assessment of the systems passivity. Throughout this novel port passivity analysis and the passivity control, it can be achieved to render maximum rendered stiffness of the SEA impedance control much higher than the limitation that has been perceived as the maximum value.
Charge-Based Model for the Drain-Current Variability in Organic Thin-Film Transistors Due to Carrier-Number and Correlated- Mobility Fluctuation

In this study, a consistent analytical charge-based model for the bias-dependent variability of the drain current of organic thin-film transistors is presented. The proposed model combines both charge-carrier-number-fluctuation effects and correlated-mobility-fluctuation effects to predict the drain-current variation and is verified using experimental data acquired from a statistical population of organic transistors with various channel dimensions, fabricated on flexible polymeric substrates in the coplanar or the staggered device architecture.
Extreme Multi-Label ICD Classification: Sensitivity to Hospital Service and Time

This work deals with clinical text mining for automatic classification of Electronic Health Records (EHRs) with respect to the International Classification of Diseases (ICD). ICD is the international standard for the identification of diseases and health conditions in EHRs and the foundation for reporting health statistics. Machine learning-based techniques have proven robust to infer classification models from EHRs. Since each EHR tends to involve multiple diseases, multi-label classification is required. The concern in this work is the versatility of the models inferred and their ability to generalise in two ways: as time goes ahead and across hospital services or health specialties. Indeed, in this work, we show the capabilities of a Bidirectional Recurrent Neural Network (RNN) with GRU units and ELMo embeddings on two corpora (a corpus comprising a set of EHRs within the Basque Health System, namely Osakidetza, and the well-known MIMIC-III corpus). To delve into and assess the versatility of the models, we focus on their resilience across hospital admissions taken over two different years and also across six distinct hospital services. In addition, we paid attention to the classification performance to estimate ICD codes of different granularity (e.g. with or without essential modifiers). Our best results are 39.55% and 47.28% F-Score for the Osakidetza and MIMIC-III datasets respectively, with the original main label-sets. Regarding the models evaluated per specialty, the most remarkable results are 57.00% and 72.74% F-Score, in the Cardiology and Nephrology medical services respectively.
Cell Pattern Dependency of Charge Failure Mechanisms During Short-Term Retention in 3-D NAND Flash Memories

In this letter, we analyzed charge failure mechanisms depending on cell patterns during short-term retention in 3-D NAND flash memories. In the previous study, we have separated complex retention characteristics into three charge failure components using a stretched exponential function. Vertical charge failure components with very small time-constants have almost no dependency between the solid pattern (S/P) and checker-board pattern (C/P). However, the lateral migration (LM) component affected by the lateral electric field (E-field) shows strong dependency on the cell patterns. When Program verify (PV) level decreases, the lateral E-field decreases and the pattern dependency disappears. Finally, we revealed that the difference in the short-term retention characteristics according to the cell patterns is due to LM component, and demonstrated that almost the same retention characteristics would be obtained at low PV levels regardless of the cell patterns.
FFT Optimizations and Performance Assessment Targeted towards Satellite and Airborne Radar Processing

Following the re-invention of the FFT algorithm by Cooley and Tukey in 1965, a lot of effort has been invested into optimization of this algorithm and all its variations. In this paper, we discuss its use and optimization for current and future radar applications, and give a brief survey on implementations that have claimed relatively high advantages in terms of performance over existing solutions. Correspondingly, we present an in-depth analysis of state-ofthe-art solutions and our own implementation that will allow the reader to evaluate the performance improvements on a fair basis. Therefore, we discuss the development of a highperformance Fast Fourier Transform (FFT) using an enhanced Radix-4 decimation in frequency (DIF) algorithm, compare it against the Fastest Fourier Transform in the West (FFTW) autotuned library as well as other solutions and frameworks.
Power Allocation Schemes for Uplink Massive MIMO System in the Presence of Imperfect CSI

In this paper, spectrum efficiency (SE) and energy efficiency (EE) performances of uplink massive multiple-input multiple-output (MIMO) systems with continuous-rate adaptive modulation and imperfect channel state information (CSI) are investigated over Rayleigh fading channels. Given a target BER, closed-form SE and EE expressions are derived. With these results, a constrained non-concave optimization problem of power allocation (PA) to maximize the SE is firstly formulated subject to the maximum transmit power per user. A near-optimal PA scheme with the concave-convex procedure (CCCP) method is then developed, and it can offer nearly optimal performance close to that of the optimal scheme with exhaustive search, but require lower complexity. Since this scheme needs more iterations, we propose a suboptimal scheme with reduced complexity and slight performance loss, which utilizes the property of a large number of receive antennas to approximate the objective function as a concave one, thereby reducing the number of iterations. Secondly, the PA problem for EE maximization under the same constraints is addressed. Based on the CCCP method and fractional programming theory, we propose a near-optimal PA scheme to solve this problem, and it has the EE performance very close to the optimal exhaustive search scheme. However, multi-layer iterations make this scheme have relatively higher complexity. For this reason, we propose a low-complexity suboptimal scheme with small EE loss by using variable transformation and a large number of antennas. Based on the above results, we simultaneously optimize both EE and SE. A multi-objective optimization problem (MOP) subject to the maximum power constraint and proportional data rate constraint is formulated. By means of the weighted sum method, the complicated MOP is transformed into a simple single-objective optimization problem. Then a PA scheme is proposed by using the Lagrange multiplier method to balance the EE and SE effectively. Simulation results validate the effectiveness of the proposed PA schemes in term of high SE and EE, and illustrate the fundamental tradeoff between EE and SE for different parameter settings.
Effect of Washing Cycles on Glued Conductive Joints Used on Stretchable Smart Textile Ribbons

The article presents a research focused on reliability testing of glued joints on conductive textile ribbons during the washing and drying cycles. For the experiment, the textile ribbons with electrically conductive silver coated copper threads were used. The SMD chip resistors were conductively connected onto the ribbons by UV curable non-conductive adhesive. The prepared samples were exposed to the 30 washing and drying cycles. The result shows that reliability of conductive joints after washing and drying is sufficient and these joints on ribbons can be recommended.
Formalizing Integration Patterns with Multimedia Data

Previous works on formalizing enterprise application integration (EAI) scenarios showed an emerging need for setting up formal foundations for integration patterns, the EAI building blocks, in order to facilitate model-driven development of enterprise applications and ensure their correctness. So far, the formalization requirements were focusing on more "conventional" integration scenarios, in which control-flow, transactional persistent data and time aspects were considered. However, none of these works took into consideration another arising EAI trend that covers social and multimedia computing. In this work we propose a Petri net-based formalism that addresses requirements arising from the multimedia domain. We also demonstrate how to realize some of the most frequently used multimedia patterns and discuss which implications our formal proposal may bring into the area of the multimedia EAI development.
Acoustic methods for detection of specific failure modes in capacitive MEMS microphones

Capacitive MEMS microphones are found today in the majority of mobile phones. Methods to detect failures easily and non-invasively during the manufacturing or usage of microphones, and to maintain microphones performance have become of critical importance to microphones and mobile phones manufacturers. This article provides four new methods, based solely on acoustic measurements to measure pull-in voltage and snapback voltage ranges, and to identify the states of electrostatic capture for single membrane capacitive MEMS microphones. 100% success rate in detecting membrane capture events is demonstrated for this type of device.
Optically Pumped Cold Cesium Beam Atomic Clock Based on Two-dimensional Magneto-optical Trap

We report the scheme and preliminary experiments of optically pumped cold cesium beam atomic clock based on two-dimensional magneto-optical trap. It is predicted that the Ramsey fringe linewidth of such atomic clock can reach 25Hz, while the output frequency stability is expected to be $\sigma_{\mathrm{y}}(\tau)=2\times 10^{-14}/\sqrt{\tau}$.
Mitigating SYN flooding Attack and ARP Spoofing in SDN Data Plane

As the number of network devices increases rapidly, it becomes more and more difficult to defend network attacks. Large-scaled attacks, such as SYN flooding, may lead to heavy burden to the switches as well as the controller in a software defined network (SDN). In this paper, we investigate the SYN flooding and Address Resolution Protocol (ARP) spoofing attacks in SDN, and then propose mechanisms to address these two attacks. We also present a new scheme to detect SYN flooding by using only a few forwarding rules. Moreover, we utilize the Programming Protocol-independent Packet Processors (P4) technique to mitigate the burden of the controller.
Preliminary Melanoma Detection Mobile Application using Support Vector Machine Classification

This paper proposes a mobile application that uses a mobile phone camera attached to an enhanced lens to capture images of any suspicious portrusions on the body (e.g. mole) and be able to predict whether it is melanoma using image processing and machine learning techniques. The images are preprocessed to remove the noise and segment the region of interest (ROI). Features that distinguish melanoma from normal tissues are extracted such as the texture, color, and geometrical shape. The proposed method uses Support Vector Machine (SVM) classification algorithm for training and prediction. The proposed method is implemented and tested on publicly available datasets. Experimantal results showed that the method was able to detect the melanoma cases with a prediction accuracy of 79%.
Implementation of QCM with Uniform Mass Sensitivity Distribution

Quartz crystal microbalance (QCM) has been widely used in various fields due to its high sensitivity, low system complexity, and the ability to output online in real time. However, characteristic of the Gaussian mass sensitivity distribution brings large error and limits QCM further use in practical applications and commercialization. Through theoretical calculation, it is found that the ring electrode QCM can obtain an approximately mass sensitivity distribution by carefully selecting the inner, outer diameters and electrode mass loading factor. A plating experiment was performed to verify the uniform mass sensitivity distribution of 10 MHz ring electrode QCMs with inner and outer diameters of 2 mm and 5 mm, and the experimental results are consistent with the theoretical analysis results. Due to the effects of material density and other factors, the electrode mass loading factor of the gold QCMs for achieving a uniform mass sensitivity distribution is different that of silver QCMs. The special designed QCMs are low-cost and conducive to improving the repeatability.
Filters navigation and positioning based on mining vehicle motion model

The navigational positioning accuracy of a seabed mining vehicle not only directly affects the efficiency of aggregation, but also affects the reliability and stability of mining operations. In order to determine the noise reduction filtering algorithm, Which is most suitable for state estimation and position estimation models in mining vehicle sea trials, an Adaptive Kalman Filter (AKF) based on linear self-navigation position estimation is first proposed under an ideal Gaussian noise model and compared With conventional Kalman filter (KF) and Innovation Kalman filter (IKF) algorithms. Secondly, based on the underwater projection observatory, a nonlinear position estimation model based on distance and angle is proposed, introducing Gaussian noise. Particle Filter (PF) and improved particle filtering algorithms such as the Unscented Kalman Particle Filter(UPF), the Extended kalman Filter(EPF) are used for state estimation. The simulation results show that under the nonlinear position estimation model, UPF not only solves the problem of conventional Particle Filter (PF) divergence, but also significantly improves the accuracy of position estimation compared to self-navigation position estimation. The UPF algorithm based on an underwater projection observatory is best suited for navigational positioning of polymetallic nodule seabed mining vehicles.
Laser Phase Noise Measurement by Using Offset Optical Phase Locked Loop

Phase noise is a key parameter to evaluate the short-term stability of a single-frequency laser. It is of great important for many applications, such as Lidar, coherent optical communication, optical sensing, and optical atomic clock, just to name a few. In this paper, we propose a method to characterize the phase noise of a single-frequency, which based on an offset phase locked loop (OPLL). The phase of the laser under test and a low noise reference laser are locked via an offset OPLL. The phase noise of the laser under test can be extracted by the beat-note of the two lasers. Experimentally, we achieve the phase noise of a narrow-linewidth laser, which agree well with the results of a commercial optical noise analyzer.
An Evaluation of Network Service Monitoring Method Using User Traffic Information

In many cases, telecommunications carriers maintain their communication networks by monitoring the normality of equipment, which they assume represents normality of services. Since telecommunications carriers provide a network service to users, they want to directly manage and operate the service itself. However, a service monitoring method cannot be provided on a large scale due to the problems of system size and high cost. In recent years, with the spread of telemetry, individual users' traffic information can be obtained from routers at low cost, which means the service usage status of individual users can be monitored. On the other hand, it is difficult to know whether a service has failed or is simply not being used when no traffic is observed. To overcome this problem, we propose and evaluate a service normality check method by using time series prediction in this paper.
A Broadband Switched-Transformer Digital Power Amplifier for Deep Back-Off Efficiency Enhancement

This article presents a switched-transformer-based polar Doherty digital power amplifier (DPA) for deep back-off efficiency enhancement. A single-transformer-footprint parallel-combining transformer (PCT) power combiner is introduced for 1.3–3.5-GHz wide frequency coverage, dynamic load modulation at 0-/6-/12-/18-dB power back-off (PBO) levels, and an ultra-compact die size. Implemented in 40-nm CMOS, the DPA is powered by a 1.1-V supply and only occupies 0.7 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 1.15 mm<sup>2</sup> chip area. It achieves 21.4-dBm peak power with the power added efficiencies (PAEs) of 31.3%, 27.7%, 16.6%, and 7.7% for 0-/6-/12-/18-dB PBOs at 1.5 GHz. With 20-MHz 64QAM long term evolution (LTE) signal, the DPA achieves <inline-formula> <tex-math notation="LaTeX">${P}_{{\text {avg}}}$ </tex-math></inline-formula> of 15.2 dBm and the average PAE of 25.3% with −32.5-dB error vector magnitude (EVM) at 1.5 GHz. Moreover, for 20-MHz 64 QAM WLAN signal, <inline-formula> <tex-math notation="LaTeX">${P}_{{\text {avg}}}$ </tex-math></inline-formula> of 14.7 dBm with the average PAE of 20.1% is obtained with − 25-dB EVM at 2.4 GHz.
Chip-scale Active Optical Clock Scheme

In this paper, we propose a chip-scale cesium four-level active optical clock based on a microfabricated atomic vapor cell. This millimeter-scale vapor cell is coated on both ends of the wall, and the coated walls directly construct the optical resonator. The microfabricated atomic vapor cell is also used to stabilize the frequency of the pump laser, to further reduce the influence of light shift caused by the frequency-stabilized pump laser on the linewidth of active optical frequency standard signal. We make a theoretical analysis and design an experimental scheme. The chip-scale active optical clock with small size and high performance has wide application in precision measurement and detection, and other fields, and it will aid to promote the definition of time unit second based on the optical clock.
Effect of Packet Loss and Delay on V2X Data Fusion

Sensing data fusion is one of the most important technologies in autonomous driving. Its performance depends on advance communication technology. Cellular-Vehicle to Everything (C-V2X) initially defined as LTE V2X in 3GPP Release 14 is a solution for vehicle communication that includes Vehicle-to-Infrastructure (V2I), Vehicle-to-person (V2P), and Vehicle-to-Vehicle (V2V). Although 4G LTE and 5G provides high-speed transmission, packet loss and delay are still inevitable. Packet loss and delay affect the safety of autonomous driving, especially for the judgment of emergency. In this paper, we compare the accuracy of data fusion under different rate of packet loss and broadcast frequency on the simulated platform CALAR. And we propose a skill to improve accuracy. Experiments show that the proposed skill significantly alleviates the effect of communication packet loss and delay on the accuracy of V2X data fusion.
Scheduling of Scientific Workflows in Multi-Fog Environments Using Markov Models and a Hybrid Salp Swarm Algorithm

Security attacks are a nightmare to many computing environments such as fog computing, and these attacks should be addressed. Fog computing environments are vulnerable to various kinds of DDoS attacks, which can keep fog resources busy. Typically in such attacks, fog environments often have less available resources, which can negatively impact the scheduling of Internet of Things (IoT) submitted workflows. However, most of the existing scheduling schemes do not consider DDoS attacks’ effect in the scheduling process, increasing the deadline missed workflows and offloaded tasks on the cloud. For dealing with these issues, a hybrid optimization algorithm is proposed, comprising both Particle Swarm Optimization (PSO) and Salp Swarm algorithm (SSA), to solve the workflow scheduling problem in multiple fog computing environments. Two discrete-time Markov chain models are proposed for each fog computing environment to address DDoS attacks’ effects on them. Our first Markov model computes the average available network bandwidth for each fog. The second Markov model finds the average number of available virtual machines (VMs) for each fog; the models address different levels of DDoS attacks. Extensive simulations show that by predicting the effects of DDoS attacks on fog environments, the proposed approach can effectively mitigate the number of offloaded tasks on cloud data centers and can reduce the number of the deadline missed workflows.
Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

Narrowband Internet of Things (NB-IoT) is a type of low-power wide-area (LPWA) technology standardized by the 3rd-Generation Partnership Project (3GPP) and based on long-term evolution (LTE) functionalities. NB-IoT has attracted significant interest from the research community due to its support for massive machine-type communication (mMTC) and various IoT use cases that have stringent specifications in terms of connectivity, energy efficiency, reachability, reliability, and latency. However, as the capacity requirements for different IoT use cases continue to grow, the various functionalities of the LTE evolved packet core (EPC) system may become overladen and inevitably suboptimal. Several research efforts are ongoing to meet these challenges; consequently, we present an overview of these efforts, mainly focusing on the Open System Interconnection (OSI) layer of the NB-IoT framework. We present an optimized architecture of the LTE EPC functionalities, as well as further discussion about the 3GPP NB-IoT standardization and its releases. Furthermore, the possible 5G architectural design for NB-IoT integration, the enabling technologies required for 5G NB-IoT, the 5G NR coexistence with NB-IoT, and the potential architectural deployment schemes of NB-IoT with cellular networks are introduced. In this article, a description of cloud-assisted relay with backscatter communication, a comprehensive review of the technical performance properties and channel communication characteristics from the perspective of the physical (PHY) and medium-access control (MAC) layer of NB-IoT, with a focus on 5G, are presented. The different limitations associated with simulating these systems are also discussed. The enabling market for NB-IoT, the benefits for a few use cases, and possible critical challenges related to their deployment are also included. Finally, present challenges and open research directions on the PHY and MAC properties, as well as the strengths, weaknesses, opportunities, and threats (SWOT) analysis of NB-IoT, are presented to foster the prospective research activities.