Miracle-dz/UniFAR-FTUs huggingface dataset:
Technology for forming of barrier ribs on a plasma display panel (PDP) by applying a photosensitive glass paste

Technologies for processing inorganic compounds into high-definition products are used in many areas including thin-film transistors and liquid crystal displays. These technologies are based on the technology of depositing thin films of I μm or less in thickness using vacuum processes such as sputtering and vapor deposition. The present review compiles the results of research made on photosensitive paste technology for processing inorganic materials into high-definition thick films of several micrometers to less than 200 micrometers in thickness. The technology for processing inorganic materials into films of several micrometers to less than 200 micrometers in thickness is used for producing the electrodes and barrier ribs of plasma display panel (PDP), and is expected to widely spread into such areas as IC packages in the future.
Towards Breast Cancer Response Prediction using Artificial Intelligence and Radiomics

With breast cancer being one of the recurring diseases affecting women around the globe, the World Health Organization disclosed that more than 620,000 women died from breast cancer in the world in 2018 alone, which represents approximately 15% of all female cancer deaths. Thus, breast cancer diagnosis presents one of the main challenges that need to get timely treatments. In this context, multiple image modalities, namely mammography, echography and magnetic resonance Imaging (MRI) are used for breast tumor diagnosis. One of the main treatments of this pathology is chemotherapy. However, several secondary effects (hair loss, osteoporosis, vomiting, etc.) can occur due this treatment, and cancer can not respond to it. This paper aims to suggest a novel method to predict breast tumor response to treatment, using three main steps: 1. Tumor segmentation from MR images ; 2. Extraction of features from segmented tumors in order to generate a complete and exploitable database ; 3. The use of deep and machine learning architectures to compute tumor-response prediction models. Experimental results are applied using a public QIN Breast DCE-MRI dataset of breast cancer patients.
A Robust Hybrid Position/Force Control Considering Motor Torque Saturation

This paper proposes a robust hybrid position/force control (HPFC) system for multi-degree of freedom manipulators (MDoFMs) with torque constraints on each joint. General HPFC systems can control the interactive contact forces and positions of manipulators in various environments. In HPFC systems, to improve the control performance for MDoFMs, motor torque saturation should be considered. Thus, a robust HPFC system that considers torque constraints by predictive functional control (PFC) is proposed in this paper. The proposed method simultaneously handles the response characteristics and torque constraints of actuators by using PFC as joint space position controllers. Additionally, the robustness of actuators against external forces is enhanced, and the model parameter errors are compensated by the disturbance observer technique. Moreover, implicit force control law and inverse kinematics are introduced for the workspace position/force control to implement the joint space position controllers. Consequently, the stability and robustness of the actuators are considered, and suitable position/force control can be performed even if the torque is saturated. The validity of the proposed method is tested with three planar manipulator joints with a uniaxial force sensor.
Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.
Designing Fast and Smooth Trajectories in Collaborative Workstations

Collaborative robotics is one of the main drivers of the fourth industrial revolution. However, its introduction implies new challenges related to the mechanical risk they may represent and the psychological stress that they can induce on people. In order to reduce these risks, countermeasures like velocity limits and smooth trajectories have been implemented. Nevertheless, from these solutions excessively slow motions which cannot be integrated in fast work cycles may derive. In this letter, we propose a novel trajectory planning methodology which aim is to reduce these risks and minimize the execution time. We rely on available literature to identify a family of psychologically safe paths and apply on it a novel reparametrization algorithm capable of minimizing the execution time while limiting the mechanical risk represented by the robot. A numerical implementation of the method is discussed and experimental results reported as prove of the effectiveness of the idea.
Self-Weighted Supervised Discriminative Feature Selection via Redundancy Minimization

Feature selection plays a key role in many machine learning problems. Especially as an important data preprocessing method, robust and pragmatic feature selection methods can be applied to extract meaningful features and eliminate redundant ones. As we all known, many feature selection methods select features by using some certain feature evaluation criteria to obtain the corresponding score for every feature, such that we can select high score features. Unfortunately, correlated features usually connect with each other, which may result in large correlations between top ranked features, such that the redundancy among the selected features is brought about. To solve this problem, we introduce the redundancy matrix A in the AGRM (a novel auto-weighted feature selection framework via global redundancy minimization) framework. Meanwhile, we introduce the adaptive redundancy matrix S and treat the redundancy matrix S as an optimizing variable, rather than setting the redundant matrix S as a prior. In addition, we propose a robust algorithm to efficiently address the constrained optimization problem. Finally, extensive experiments on six datasets show the superiority of our proposed method.
Energy Efficient Positioning of Flying Base Stations via Coulomb’s law

In this paper, we propose an algorithm for an initial positioning of the unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs). We target to maximize the FlyBSs deployment efficiency in terms of the number of users satisfied with an experienced data rate and the energy consumed by the FlyBSs to fly to their initial positions. The required data rates of the users, energy consumption of the FlyBSs, and mutual interference among all base stations and users are represented as electrical forces and the initial positions of the FlyBSs are determined via Coulomb’s law. In comparison to the state of the art algorithms, the efficiency of the FlyBS deployment is improved at least 2.5 times and, at the same time, the users’ satisfaction with the experienced data rate is increased between 6 to 10 %.
A 4-tap 3.5 μm 1.2 Mpixel Indirect Time-of-Flight CMOS Image Sensor with Peak Current Mitigation and Multi-User Interference Cancellation

The evolution of 3D depth-sensing technology enables various applications in mobile devices, from conventional photography enhancement (e.g., autofocus and bokeh effect) to new applications such as augmented reality and 3D scanning. Usually, 3D depth sensors operate with RGB color sensors for image fusion; the spatial resolution of depth sensors should be compatible to that of RGB sensors for better image fusion quality, which requires mega-pixel depth sensors. Among different depth-sensing techniques, only indirect time-of-flight (ToF) sensors can generate higher resolution depth maps with smaller system cost and power [1,2]. Recent indirect ToF sensors have achieved continuous improvements in depth performance [1–4]. However, one of the issues with indirect ToF sensors is peak current during exposure time [1,2]. To solve the issue, current spreading can be adopted by driving each column group in a different time point; the resulting column fixed-pattern phase noise (FPPN) is compensated by time-interleaving two opposite-directional delay chains [2]. However, this approach suffers from demodulation contrast (DC) degradation around the edge of the pixel array, by combining two different phase signals at the pixel level [2]. Another issue for indirect ToF sensors is multi-user interference [5]. When multiple ToF cameras with the same modulation frequency shine light onto the same object, the reflected light signals interfere each other, which may distort depth information. In this work, a mega-pixel indirect ToF sensor is reported with the solutions for the two issues: 1) peak current mitigation with minimal side-effect, and 2) multi-user interference cancellation.
High K+ supply avoids Na+ toxicity and improves photosynthesis by allowing favorable K+:Na+ ratios through the inhibition of Na+ uptake and transport to the shoots of Jatropha curcas plants.

This study assessed the relationships between external K+ supply and K+ : Na+ ratios associated with Na+ toxicity in Jatropha curcas. Plants were exposed to increasing external K+ concentrations (6.25, 12.5, 25, 37.5, and 50 mM), combined with 50 mM NaCl in a nutrient solution. Photosynthesis progressively increased as the external K+ : Na+ ratios increased up to 0.75. The increase of photosynthesis and plant dry matter correlated positively with K+ : Na+ in xylem and leaves. The transport rates of K+ and Na+ from roots to xylem and leaves were inversely correlated. These ions presented an antagonistic pattern of accumulation in all organs. Maximum rates of photosynthesis and plant growth occurred with leaf K+ : Na+ ratios that ranged from 1.0 to 2.0, indicating that this parameter in leaves might be a good indicator for a favorable K+ homeostasis under salinity conditions. The higher K+ affinity and selectivity compared with Na+ in all organs associated with higher xylem flux and transport to shoots are essential for maintaining adequate K+ : Na+ ratios at the whole-plant level. These characteristics, combined with adequate K+ concentrations, allow J. curcas to sustain high rates of photosynthesis and growth even under toxic NaCl levels.
Multi-task Offloading and Computational Resources Management in a Mobile Edge Computing Environment

In Mobile Cloud Computing, Smart Mobile Devices (SMDs) and Cloud Computing are combined to create a new infrastructure that allows data processing and storage outside the device. The Internet of Things refers to the billions of physical devices that are connected to the Internet. With the rapid development of these, it is clear that the requirements are largely based on the need for autonomous devices to facilitate the services required by applications that require rapid response time and flexible mobility. In this article, we study the management of computational resources and the trade-off between the consumed energy by an SMD and the processing time of its tasks. For this, we define a system model, a problem formulation and offer heuristic solutions for offloading tasks in order to jointly optimize the allocation of computing resources under limited energy and sensitive latency. In addition, we use the residual energy of the SMD battery and the sensitive latency of its tasks in defining the weighting factor of energy consumption and processing time.
A 2D Rapidly-Convergent Iterative Digital Predistortion Method Using Direct Learning Structure

This paper presents a two-dimensional (2D) digital predistortion (DPD) algorithm, which models the power amplifier (PA), inverses the identified model and calculates the predistortion coefficients with an iterative approach. The method is applied to compensate for the nonlinear distortion of in-band and cross-band modulation in concurrent dual-band transmitters. Experimental measurements were carried using a dual-band OFDM signal with center frequencies of -40MHz for the lower band and 40MHz for the upper band. The predistortion results demonstrate that the algorithm converges after the third iteration, and the adjacent channel power ratio (ACPR) and the normalized mean square error (NMSE) are reduced 19dB and 22dB respectively.
An Iterative Approach to Syndrome-based Deep Learning Decoding

Recently, Machine Learning has been considered for use in various communications subsystems, providing an alternative paradigm for addressing core problems in the physical (PHY) and medium access control (MAC) layers. Several methods and neural network topologies have been proposed for channel decoding targeting short and moderate length codes. In this paper, we consider the Syndrome-based Deep Learning Decoder and propose a modification in order to enable iterative operation. The proposed modifications perform iterations of the original decoder, which are unrolled to build a larger network. The unrolled iterations are subsequently trained jointly. Moreover, the proposed iterative functionality is incorporated within the learning process by accounting for the error of each iteration in the loss function. By sharing the weights between the iterations, we show that it is possible to reduce the storage requirements of the original decoder by a factor of 2.6x for BCH(63,45), while approaching maximum-likelihood decoding performance. Finally, simulation results are provided for several code lengths and rates, which demonstrate the error-correcting capability of the proposed iterative decoder.
Non-ideal Continuous Class-E PA Modes for Maximum Efficiency and Maximum Linearity

This paper presents the analysis of a class-E PA using an equivalent circuit model based on a look-up table generated by numerical simulation. The proposed methodology allows the direct determination of the drain voltage and current waveforms considering the transistor's triode region, finite switching time, and short-channel and thermal effects on saturation current. The revealed non-ideal space of high-efficient solutions is used to determine the class-E modes for maximum efficiency and maximum linearity. To support the theoretical results, a particular topology is implemented using a 25W-GaN transistor. The experimental results of the manufactured class-E are presented. Over a bandwidth of 2.37-2.41 GHz, the demonstrator has presented efficiency better than 75% with constant 42 dBm of continuous wave power.
Vulnerabilities, Threats, and Impacts of False Data Injection Attacks in Smart Grids: An Overview

The wide range of vulnerabilities and impacts of False data injection attack (FDIA) makes it the most challenging cyber-physical security threat in smart grids. This paper provides a detailed analysis of the various vulnerabilities, threats, and impacts of FDIA in smart grids. Our work considers the different layers and systems in the smart grids ranging from the local to the wide area monitoring and control applications. This work supplies a good insight of FDIA which inspires the research and development of defense mechanisms.
Preliminary Experiment for Mentally Supporting Rehabilitation Robot based on Emotion Estimation

These days, many rehabilitation robots that support patients physically or mentally are being proposed. The robots that offer physical support are used as treatment to improve the physical condition such as weakness or lack of muscles by estimating and changing dynamic control depending on the muscle status. These robots are successfully supporting the patient. However, control of the robot based on the patient’s emotion such as depression and stress is more difficult than to support the patient physically. One of the problems comes from the lack of the clear relation between accurate detection of emotion such as depression and stress, and successful control of the robot. To solve this problem, we firstly try to measure the relation between the control of the rehabilitation robot and the dynamic change in the patient’s emotion. We propose a method to control the rehabilitation robot based on the user’s mental status. We evaluated the emotions of the person operating the rehabilitation robot using an index to estimate their mental state from heart rate variability (HRV). HRV is used to measure the valence, an index of pleasure. We conducted an experiment in which the participants were directed to control the rehabilitation robot with three different levels of pushing force required to operate the robot. From the results, we confirmed that the emotion of the participants had significantly changed depending on the load applied to the robot.
33.4 An 8A 998A/inch3 90.2% Peak Efficiency 48V-to-1V DC-DC Converter Adopting On-Chip Switch and GaN Hybrid Power Conversion

With the advances in artificial intelligence, devices are becoming more intelligent and power hungry. The 48V-to-1V converter, which offers a promising solution to the high-power density data center and automotive applications, is quickly gaining the interest of researchers [1 –4]. The prior state-of-the arts, however, mainly employ Gallium Nitride (GaN) or other discrete switches for 48V-to-1V design, which increases costs and hurts power density. In this work, a 12-level series-capacitor converter adopting on-chip switch and GaN hybrid power conversion is proposed. By exploiting the benefits of low-voltage on-chip transistors, this work reduces component count, increases switching frequency and improves power density.
Spatio-temporal Clustering-based Parking Area Division of Dockless Shared Bicycles

With the rise of Dockless Shared Bicycles in cities, the rational division of docks for Shared bikes has become an important issue in urban road planning. In the initial stage of Dockless Shared Bicycles, randomly parked bicycles bring great pressure to urban road traffic because there is no fixed parking area. The appearance of an electronic fence has improved the user's shared bicycles specification to a certain extent. However, due to the variability of urban road planning, it is very necessary to optimize the parking area of bicycles regularly. In this paper, the cycling data of Shared bicycles are studied by using the Spatio-temporal clustering algorithm, the flow and parking rules of dockless Shared bicycles are explored to seek the optimal division of bicycles areas.
Growth and characterization of epitaxial films of tungsten-doped vanadium oxides on sapphire (110) by reactive magnetron sputtering

Epitaxial films of W-doped vanadium oxides including W–V2O3, W–VO2 and W–V2O5 were deposited on sapphire (110) by reactive magnetron sputtering of a V–W (1.6 at. % W) alloy target in a range of oxygen partial flows. The deposited films were characterized by x-ray diffraction (XRD), Rutherford backscattering spectrometry, atomic force microscopy and resistance-temperature measurement. The W-to-V ratio in the oxide films showed values higher than that in the target, and the ratio increased with increasing oxygen flows. With precise control of the oxygen partial, flow, not only the W–V2O3, W–VO2 and W–V2O5, but also a series of epitaxial films with lattice parameters varying between those of W–V2O3 and W–VO2 were formed. It was found from an XRD pole figure study that the W–VO2 exhibited a triple-domain structure with the three domains correlated by 120° in the in-plane orientation. The W–VO2 epitaxial films clearly showed a semiconductor-to-metal phase transition with a reduced Tc by 23 °C/at. % W, whereas .
Spatial evolution of the electron energy distribution function in a low-pressure capacitively coupled plasma containing argon and krypton

The spatial evolution of the electron energy distribution function (EEDF) in the axial direction of a capacitively coupled plasma with two parallel plate electrodes is investigated using an optical emission line-ratio method for Ar/Kr discharges. When the rf power is increased from 25 to 400 W at a pressure of 400 mTorr, we observe a transition from convex EEDFs to concave ones and a sharp increase in electron density, due to an α–γ mode transition, which is believed to be caused by the high-energy electrons originating in the high-voltage sheath. We also investigate the spatial evolution of the EEDF when the pressure is increased from 45 to 500 mTorr at a power of 100 W. The EEDF is uniform at pressures below 180 mTorr and becomes non-uniform at higher pressures, owing to the decrease in the energy relaxation length of the high-energy electrons.
24.2 A 14nm-FinFET 1Mb Embedded 1T1R RRAM with a 0.022µ m2 Cell Size Using Self-Adaptive Delayed Termination and Multi-Cell Reference

High-density embedded nonvolatile memory (eNVM) at advanced technology nodes is still in great demand for SOC chips used in consumer electronics, self-driving cars, industrial control, and IoT edge devices. Although embedded NOR Flash is still main stream, its process complexity and high integration cost make it difficult to scale beyond 28nm. RRAM has become a promising alternative owing to its excellent scalability, low power, and compatibility with logic processes [1 –6]. However, several challenges still exist that restrict RRAM from practical eNVM applications, as shown in Fig. 24.2. 1. 1) a high supply voltage is needed, because of high operation voltages and a considerable voltage drop on write path. 2) The IR drop on write path varies significantly with the distance of a cell from the peripheral circuits. 3) Prior self-write termination (SWT), with its abrupt cutoff, fails to form dense conductive filaments (CFs). While delayed termination helps to alleviate this issue, its delay time is not optimized for fast and slow cells [3]. 4) Previous read reference schemes that use a small number of HRS/LRS cells may collapse when encountering tail bits, resulting in a poor reference [6]. In this work, we demonstrate a 1Mb 14nm-FinFET RRAM with a $0.022 \mu m^{2}$ cell size. The cell array is in a deep N-well with adequate P-substrate biasing to alleviate the difficulty of high voltage transfer. Placing the bit line (BL) and source line (SL) drivers on the top and bottom of the array reduces the IR-drop variation for near and far cells. Self-adaptive delayed termination (SADT) adjusts the termination time according to cell characteristics (fast or slow), helping to generate robust CFs. A read reference with multiple dummy 1T1R cells avoids the reference collapse issue. The test chip supports reliable read operation at a minimum supply voltage of 0.5V from $-40^{\circ}C$ to $125^{\circ}C$.