Miracle-dz/UniFAR-FTUs huggingface dataset:
Class-J SiGe  $X$ -Band Power Amplifier Using a Ladder Filter-Based AM–PM Distortion Reduction Technique

In this paper, a class-J power amplifier for operation in the X-band realized in SiGe bipolar technology is presented. The proposed design combines the high efficiency of class-J operation with solutions to mitigate AM-PM distortion down to a level compatible with high spectral efficiency modulation schemes like 64-QAM. The design of a transformer-based output matching network and the trade-off between efficiency and pure class-J operation related to a non-ideal second harmonic termination are discussed. The main sources of AM-PM distortion are identified in the base-emitter and collector-based bipolar junction transistor capacitance variation with power level and solutions that make the design less sensitive to these impairments are proposed. Linear MIM capacitors are used at the power amplifier (PA) core output to realize the proper class-J second harmonic termination, while an input matching network based on a Bessel ladder filter is adopted to make the signal phase at the PA core input less sensitive to base-emitter capacitance variation. A class-J PA previously designed for maximum power-added efficiency (PAE) is used as a reference throughout the paper to highlight the trade-off’s entailed by AM-PM distortion reduction. The designed PA features a peak gain of 15 dB at 10 GHz and 8-to-12 GHz −3 dB bandwidth. The saturated power is 22 dBm with a 33% PAE, while the output $p_{1\,\text {dB}}$ is 19 dBm. The AM-PM distortion at $p_{1\,\text {dB}}$ is 5 degrees.
Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to a growing interaction between the earth science and statistical domains. With this in mind, we reviewed the literature on statistical machine learning methods commonly applied to remote sensing data. We focus particularly on applications related to the United Nations World Bank Sustainable Development Goals, including agriculture (food security), forests (life on land), and water (water quality). We provide a review of useful statistical machine learning methods, how they work in a remote sensing context, and examples of their application to these types of data in the literature. Rather than prescribing particular methods for specific applications, we provide guidance, examples, and case studies from the literature for the remote sensing practitioner and applied statistician. In the supplementary material, we also describe the necessary steps pre and post analysis for remote sensing data; the pre-processing and evaluation steps.
Multi-insight visualization of multi-omics data via ensemble dimension reduction and tensor factorization

MOTIVATION Visualization of high-dimensional data is an important step in exploratory data analysis and knowledge discovery. However, it is challenging, because the interpretation is highly subjective. If we see dimensionality reduction (DR) techniques as the main tool for data visualization, they are like multiple cameras that look into the data from different perspectives or angles. We can hardly prescribe one single perspective for all datasets and problems. One snapshot of data cannot reveal all the relevant aspects of the data in higher dimensions. The reason is that each of these methods has its own specific strategy, normally based on well-established mathematical theories to obtain a low-dimensional projection of the data, which sometimes is totally different from the others. Therefore, relying only on one single projection can be risky, because it can close our eyes to important parts of the full knowledge space. RESULTS We propose the first framework for multi-insight data visualization of multi-omics data. This approach, contrary to single-insight approaches, is able to uncover the majority of data features through multiple insights. The main idea behind the methodology is to combine several DR methods via tensor factorization and group the solutions into an optimal number of clusters (or insights). The experimental evaluation with low-dimensional synthetic data, simulated multi-omics data related to ovarian cancer, as well as real multi-omics data related to breast cancer show the competitive advantage over state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION https://folk.uio.no/hadift/MIV/ [user/pass via hadift@medisin. uio.no]. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Robust Coordination Control of AFS and ARS for Autonomous vehicle Path Tracking and Stability

In this paper, considering uncertain external disturbance due to the frequent variation of running conditions, a novel robust coordination control strategy of active front steering (AFS) system and active rear steering (ARS) system is proposed to simultaneously suppress lateral path tracking deviation while maintaining autonomous vehicle stability under dynamic driving situations at handling limits. Firstly, reference path model, vehicle dynamics and kinematics model with uncertain external disturbance are established. Then, a robust H∞ coordination control scheme is developed based on linear matrix inequality (LMI) by coordinating AFS and ARS. Finally, an emergency double lane change maneuver is carried out via Matlab/Simulink-CarSim co-simulation. The results show that, under uncertain external disturbance, the proposed robust coordination control strategy can provide sufficient path tracking capability as well as stability for autonomous vehicle at or near the physical limits of tyre friction.
MiR‐409‐3p and MiR‐1896 co‐operatively participate in IL‐17‐induced inflammatory cytokine production in astrocytes and pathogenesis of EAE mice via targeting SOCS3/STAT3 signaling

Th17 cells and interleukin‐17 (IL‐17) have been found to play an important role in the pathology of multiple sclerosis (MS) and its animal model, experimental autoimmune encephalomyelitis (EAE). Response to IL‐17, reactive astrocytes accompany with immune cells infiltration and axonal damage in MS/EAE. However, the role and the regulatory mechanism of IL‐17‐activated astrocytes in inflammation and in the EAE process still remain largely unknown. Here, we elucidated that miR‐409‐3p and miR‐1896, as co‐upregulated microRNAs in activated astrocytes and in EAE mice, targeted suppressor of cytokine signaling proteins 3 (SOCS3). Overexpression of miR‐409‐3p or miR‐1896 significantly reduced SOCS3 expression and increased phosphorylation of STAT3 as well as induced the inflammatory cytokines production (IL‐1β, IL‐6, IP‐10, MCP‐1, and KC), CD4+T cells migration and demyelination, in turn aggravating EAE development. Importantly, the effects of co‐overexpression of miR‐409‐3p and miR‐1896 in vitro or in vivo are strongly co‐operative. In contrast, simultaneously silenced miR‐409‐3p and miR‐1896 co‐operatively ameliorates inflammation and demyelination in the central nervous system of EAE mice. Collectively, our findings highlight that miR‐409‐3p and miR‐1896 co‐ordinately promote the production of inflammatory cytokines in reactive astrocytes through the SOCS3/STAT3 pathway and enhance reactive astrocyte‐directed chemotaxis of CD4+T cells, leading to aggravate pathogenesis in EAE mice. Co‐inhibition of miR‐409‐3p and miR‐1896 may be a therapeutic target for treating MS and neuroinflammation.
Evolutionary Approach to Straight Line Approximation for Image Matching in Dance-Posture Recognition

The proposed system aims at automatic identification of an unknown dance posture referring to the 34 primitive postures of ballet, simultaneously measuring the proximity of an unknown dance posture to a known primitive. A simple and novel seven stage algorithm achieves the desired objective. Skin color segmentation is performed on the dance postures, the output of which is dilated and edge is detected. From the boundaries of the postures, connected components are identified and the boundary is piecewise linearly approximated using modified artificial bee colony algorithm. Here, lies the novelty of our work. From the approximated boundary, features are extracted in terms of internal angles. This whole procedure is repeated for all the training images as well as testing image. The classification of the training image containing ballet posture is done using Euclidean distance matching.
The Offset-Compensated Nonlocal Filtering of Interferometric Phase

The nonlocal approach, proposed originally for additive white Gaussian noise image filtering, has rapidly gained popularity in many applicative fields and for a large variety of tasks. It has proven especially successful for the restoration of Synthetic Aperture Radar (SAR) images: single-look and multi-look amplitude images, multi-temporal stacks, polarimetric data. Recently, powerful nonlocal filters have been proposed also for Interferometric SAR (InSAR) data, with excellent results. Nonetheless, a severe decay of performance has been observed in regions characterized by a uniform phase gradient, which are relatively common in InSAR images, as they correspond to constant slope terrains. This inconvenience is ultimately due to the rare patch effect, the lack of suitable predictors for the target patch. In this paper, to address this problem, we propose the use of offset-compensated similarity measures in nonlocal filtering. With this approach, the set of candidate predictors is augmented by including patches that differ from the target only for a constant phase offset, which is automatically estimated and compensated. We develop offset-compensated Versions of both basic nonlocal means and InSAR-Block-Matching 3D (BM3D), a state-of-the-art InSAR Phase filter. Experiments on simulated images and real-world TanDEM-X SAR interferometric pairs prove the effectiveness of the proposed method.
Shielding Effectiveness of Randomly Distributed Conductive Elements: Experimental Analysis and Simplified Model

The shielding effectiveness of cementitious composites loaded by conductive fibers exhibits a particular frequency behavior that differs a lot from the case of powder fillers. The effects is ascribed to the random distribution of the conductive fibers inside the mortar that form a sort of grid with random side. Furthermore, contact among each fibers is not perfect and characterized by both a resistive and a capacitive nature. Some measurement data obtained by the nested reverberation chamber method are shown, and a simplified model is presented to predict such frequency behavior.
A Design of Programmable Parser Generation System Based on Dynamic Programming

Concerning the problem that the parsers of current network forwarding devices were difficult to deal with the emerging network requirements, this paper proposed a Programmable Parser Generation System based on dynamic programming (PPGS). First of all, by configuring program control module, user could customize protocol parse graph, achieving the addition and deletion of emerging protocols rapidly. In addition, table structure based on TCAM and corresponding dynamic programming algorithm made short headers aggregated, thus covering multiple packet headers in single parse entry and reducing parse delay. Finally, the parsing logic was achieved by configuring parse tables into programmable hardware. We implemented experimental verification based on NetFPGA-I0G platform. Compared with the existing schemes, PPGS can achieve 187Gbps parse speed and reduce parse cycle effectively with the similar resource occupation.
Residual Learning and LSTM Networks for Wearable Human Activity Recognition Problem

Human activity recognition (HAR) has become an important research area in pervasive computing because of its extensive applications in solving real-life, human-centric problems. Due to the spatial complexity and temporal divergence of human behavior data, conventional machine learning methods can't handle this problem effectively. As one of the deep learning algorithms to solve HAR problem, Long Short-Term Memory (LSTM) is an excellent learning methods in handling time series problems, which can sufficiently use the historical data. But for traditional LSTM algorithms, when we add more depths to the network, the gradient vanishing arises. To this end, a deep long short-term memory (LSTM) network with residual connection is proposed. Generally, the proposed network shows improvements on both the temporal (using LSTM) and the spatial (residual connections stacked deeply) dimensions. Experiments with the Opportunity Dataset show that, comparing with baseline LSTM, our algorithm can recognize the human activities with an F1 score of 0.908, increased by 1.4%.
DGS and SRR Based FSS Microstrip Antenna for 5G Communication

This paper gives a compact small size FSS patch antenna with DGS and SRR for 5G communication application. The proposed antenna employs RT/duroid 5800 for patch and Fr-4 for frequency selective surfaces. The antenna provides higher gain and required radiation pattern using DGS, SRR and FSS techniques. This compact size antenna will be used as an intended radiator for 5G portable devices from 27.4 to 29.55 GHz frequency band.
Design of Adaptive Sliding Mode Controller for Four-Mecanum Wheel Mobile Robot

In this paper, an adaptive sliding mode controller is designed for the omnidirectional mobile robot with four Mecanum wheels to track the trajectory. The Mecanum wheel mobile robot has been widely used in factories, hospitals, families and other workspaces because of its omnidirectional ability. Consider the dynamic model of the omnidirectional mobile robot based on Mecanum wheel with uncertainties and external disturbances, a neural network adaptive sliding mode control (NNASMC) strategy is proposed, which combines the techniques of Radial Basis Function (RBF) neural network adaptive control method and sliding mode control method. Finally, some simulation experiments are conducted in the Matlab environment. Simulation results demonstrate the effectiveness of the proposed control method.
The Factors Affecting Joint R&D in the Service Industry: Focusing on Organizational Innovation and Defense Mechanisms

Today, technological innovation activities are very important for the survival and competitive advantage of firms, and then many firms are doing joint R&D. In particular, the size and proportion of the service industry has been increasing steadily in recent years, and the interest in technological innovation is also increasing. However, since the previous studies have dealt with the manufacturing industry, this study focuses on the factors affecting joint R&D in the service industry. For the analysis, we use the data from Korean Innovation Survey (KIS) in 2016 conducted by Science and Technology Policy Institute (STEPI). Among the data of the service industry, only those firms that have performed actual R&D activities are selected. As a result of the analysis, it is found that the firms that performed organizational innovation positively influence joint R&D for technological innovation activities. In addition, firms with defense mechanisms such as securing intellectual property and confidentiality within the firm have positive effects on joint R&D. It is very important for firms to acquire new knowledge while protecting their knowledge, because the service industry is a knowledge-intensive industry. The results of this study will do much for setting up firms' strategies for joint R&D.
Design of Fractional-Order Backstepping Sliding Mode Controller for the Quadrotor Unmanned Aerial Vehicles

This paper proposes a fractional-order controller based on backstepping control method and sliding mode control technique for four-rotor unmanned aerial vehicle (UAV). As it is known that the quadrotor UAV is a complex, second-order system which is also nonlinear, coupled and even under-actuated. With the help of Newton-Euler formula, the dynamics can be clearly expressed in mathematical form. Then the backstepping control method is used in controller design firstly. Secondly, the sliding mode control technique with fractional-order operator is added to improve the robustness and anti-interference ability of system. Next, the fractional-order theory is used to eliminate the chatter brought by the sliding mode control. Finally, simulation results are applied to demonstrate the validity of the controller.
Single-Parameter Optimization Method for Extracting Coupling Matrix from Lossy Filters

This paper presents a novel single-parameter optimization method for extracting coupling matrix from either measured or electromagnetic simulated S-parameters of a narrow band coaxial-resonator filter with losses. Having had the polynomials of the S-parameters of a filter by the Cauchy method with removing phase shift, the rational polynomials can be obtained. Once the rational polynomials having been determined, a single-parameter optimization method is proposed to obtain $\varepsilon$ and the coupling matrix with an assigned topology, which can be extracted using well established techniques. The measured S-parameters compared with the S-parameters obtained from the coupling matrix, and the attenuation factor $K$ is easily determined. The loss effects will be removed after obtaining the value of the attenuation factor K. The approach is useful and can be used in computer-aided tuning of microwave filters. Example is presented to illustrate the validity of the proposed method.
Design of a Soft Strain/Curvature Sensor for Smart Wearable Device

In order to meet the needs of the smart wearable devices for strain and curvature detection, a flexible strain/curvature sensor with simple structure and convenient manufacture technology is designed by using silicon rubber and gallium-indium-tin alloy. The basic structure of the sensor is proposed and the working principle of the soft sensor is demonstrated. Moreover, the mathematical model of this strain/curvature sensor is defined to analysis the corresponding relationship between strain/curvature and the rate of resistance change. Finally, the production process of the experimental prototype system is given and the discussion of the proposed structure is illustrated.
Forest Fire Monitoring System Based on UAV Team, Remote Sensing, and Image Processing

This work presents the fire monitoring and detecting system for tactical forest fire-fighting operations based on a team of unmanned aerial vehicles, remote sensing, and image processing. The idea of such a system and its general parameters and possibilities are described. Functions and missions of the system, as well as its architecture, are considered. The image processing and remote sensing algorithms are presented, a way for data integration into a real-time DSS is proposed. The results of experimental research of the prototype system are presented. The combination of multi-UAV-based automatic monitoring, remote sensing and image processing techniques provides required credibility and efficiency of the fire detection.
Hybrid Task Scheduling for Mobile Devices in Edge and Cloud Environments

Mobile cloud computing enables mobile devices to augment their computational capabilities with centralized resources. However, due to latency issues, mobile cloud computing is unsuitable in many situations. Lately, mobile edge computing appeared as a more decentralized paradigm, which utilizes nearby resources. It benefits from the continuously increasing amount and the enhancing performance of end-user devices. Compared to mobile cloud computing, nearby resources are limited, but reachable with a significantly shorter latency. We argue that a combination of both approaches can drastically improve the performance of mobile applications. In this paper, we introduce a hybrid scheduling approach. Beyond centralized scheduling on remote edge and cloud resources, we introduce an ad-hoc scheduling mechanism on nearby edge devices. Our context-aware scheduler considers both options along with the characteristics of the task to decide between an execution in the cloud or in the edge. We use mechanisms for edge device discovery, connection management, and task allocation as well as a context-aware utility function. We integrate our approach into an existing distributed computing system and evaluate the hybrid scheduler within a real-world testbed.
An Adaptive Feature Extraction Algorithm for Classification of Seismocardiographic Signals

This paper proposes a novel adaptive feature extraction algorithm for seismocardiographic (SCG) signals. The proposed algorithm divides the SCG signal into a number of bins. , where the length of each bin is determined based on the signal change within that bin. For example. , when the signal variation is steeper. , the bins are shorter and vice versa. The proposed algorithm was used to extract features of the SCG signals recorded from 7 healthy individuals (Age: 29.4±4.5 years) during different lung volume phases. The output of the feature extraction algorithm was fed into a support vector machines classifier to classify SCG events into two classes of high and low lung volume (HLV and LLV). The classification results were compared with currently available non-adaptive feature extraction methods for different number of bins. Results showed that the proposed algorithm led to a classification accuracy of ~90%. The proposed algorithm outperformed the non-adaptive algorithm. , especially as the number of bins was reduced. For example, for 16 bins, F1 score for the adaptive and non-adaptive methods were 0.91±0.05 and 0.63±0.08. , respectively.
PV Modules Based Grid Connected System Using Matlab Simulation

The Photovoltaic (PV) system can supply electric energy to the load by direct conversion of solar energy through photovoltaic (PV) effect. The process in which solar energy is converted into electrical energy is known as photovoltaic effect. It is depend on the different circumstance by different equations of current and voltage based on mathematical modeling. It gives current and voltage of PV module by different no. of cells and variation in irradiation and temperature. The PV cell output voltage boosts-up by the boost converter and the AC grid by DC/AC converter (inverter) control the power. This module can be further connected to the utility grid connected system for commercial and industrial application. It is provides precise modeling of P-V characteristics of solar system.
Output-Constrained Point- to- Point Tracking of Underactuated Surface Vessel with Uncertainties

This paper considers the point-to-point tracking control problem of an underactuated surface vessel (USV) with output constraints and uncertainties. The controller design is based on backstepping algorithm. Barrier Lyapunov function (BLF) is used to address the output constraints. In addition, a radial basis function neural network (RBFNN) is constructed to provide an estimation and compensation of the unknown disturbances. It is proved that under the proposed control, the constraints will never be violated, and all closed-loop signals are bounded. Simulation results and comparisons illustrate the effectiveness and advantages of the proposed controller.