Miracle-dz/UniFAR-FTUs huggingface dataset:
A C-band GaAs Doherty Power Amplifier MMIC with Compact Size and 1-GHz Bandwidth

This paper presents a fully integrated C-band Doherty power amplifier (DPA) in 0.25-um GaAs pHEMT process. The inductive component number and inductance values in the output network of the DPA are minimized, which reduces the chip size significantly. The input matching is optimized in the large signal region for both main power amplifier (PA) and auxiliary PA to achieve higher efficiency and output power. The performance of DPA is enhanced by using an uneven input splitter to deliver more power to main PA. The fabricated DPA exhibits a bandwidth of 1 GHz, with the chip area of only 1.8 mm × 2.2 mm. In the frequency range of 4.6 GHz to 5.6 GHz, the DPA realizes a saturation output power of 29.8 dBm – 31 dBm, a peak power-added efficiency (PAE) of 41% – 46%, and a PAE at 6 dB power back-off of 24% – 27%. The small signal gain is higher than 11 dB, in the large bandwidth from 4.5 GHz to 6.2 GHz.
Vehicle-Based Bi-Objective Crowdsourcing

Mobile crowdsourcing is an emerging complex problem solving paradigm that makes use of pervasive mobile devices equipped with multi-functional sensors. Recently, vehicles have also been increasingly adopted for mobile crowdsourcing, as the vehicles, as well as drivers, can provide diverse sensing capability and predictable mobility. Existing mobile crowdsourcing algorithms mostly recruit workers to complete one kind of sensing tasks, i.e., location-based query tasks or automatic sensing tasks. In this paper, we investigate the possibility of recruiting a set of vehicles to simultaneously complete these two categories of tasks, so as to maximize the sensing utility of each participant. We first model the worker recruitment for vehicle-based crowdsourcing as a bi-objective optimization problem with respect to the sensing capability and predictable mobility of vehicles. The recruitment problem is proven to be NP-hard, and we design two heuristic algorithms based on the bi-objective greedy strategy and the multi-objective genetic algorithm to find the solutions. The experimental results with a real-world traffic trace data set show that the proposed algorithms outperform some existing algorithms in finding solutions that maximize both objectives.
Demo Abstract: Battery-Free 802.15.4 Receiver

We present the architecture for an 802.15. 4 receiver that enables battery-free operation. To reach micro-power consumption, the architecture diverges from that of commodity receivers in the following ways: First, similar to backscatter transmitters, it offloads the power-hungry local oscillator to an external device. Second, we avoid the energy cost of demodulating a phase-modulated signal by treating 802.15. 4 as a frequency-modulated one, allowing us to receive with a simple passive detector and an energy-efficient thresholding circuit. We demonstrate an off-the-shelf prototype of our receiver receives 802.15. 4 from a distance of 470 cm with the carrier generator 30 cm away. This range is sufficient to integrate with deployed wireless sensor networks (WSNs). We demonstrate this integration by pairing our receiver with a 802.15. 4 backscatter transmitter and integrating it with unmodified commodity sensor nodes running the TSCH protocol.
Self-Triggered Model Predictive Control for Continue Linear Constrained System: Robustness and Stability

M0638. In the paper, a dual-mode control strategy based on a self-triggered model predictive control (SMPC) and a state-feedback law is present for a continue linear time-invariant (LTI) system with state and input constrains. Compared with nominal model predictive control (NMPC), robustness is guaranteed to reject unknown disturbances for the system. At each triggering instant, the self-triggered mechanism is designed to compute the inter-execution time and reduce the frequency of solving the optimization problem and the communication. Then, the stability of the dual-mode strategy is analyzed to ensure the states practically stable. The theoretical results are verified by numerical simulation and the effects of parameters are discussed in detail.
Different Reformulation, ZD Tracking Control and Analysis of One-Link Rigid Robot System with Motor Dynamics

As machinery transmission devices, motors are commonly utilized in robot systems and may significantly affect the dynamic characteristics and stability of robot systems. In view of the fundamental role of the one-link rigid robot system, this paper investigates the tracking control of such a system with motor dynamics. Based on Zhang dynamics (ZD), a new kind of ZD controller is developed and investigated for achieving the tracking-control purpose. Besides, the characteristics and differences of the ZD method compared with other conventional methods are presented. Moreover, theoretical analysis and result are also presented to guarantee the global and exponential convergence performance of the ZD controller.
NesJ: A Java to NesC Translation Engine with Runtime Validation for Wireless Sensor Networks

Embedded system application development for Wireless Sensor Networks (WSN) can be challenging, due to the low-power and lean-resource nature of the embedded system hardware, and most embedded system SDKs lack support for Object Oriented Programming (OOP). NesC is the embedded programming language used to develop applications for TinyOS, a widely-used operating system for WSNs, and it suffers from its steep learning curve and complexity of debugging and validation. In this paper, we present NesJ, a translation engine that converts applications written in Java, a widely-used OOP language, to NesC with built-in support for runtime validation. (i) We describe the rules used to guide the translation process. (ii) We present the design of the translation engine. (iii) Finally, we demonstrate our approach with case studies.
The Load Aggregation Strategy of Central Air-conditioning for Smoothing Wind Power Fluctuation

Compared with the household air-conditioning, central air-conditioning has larger rated capacity and more concentrated operating period. These characteristics of the central air-conditioning make it a high quality resource that can be participated in the centralized load regulation. Firstly, the equivalent thermal parameters model of central air-conditioning and the thermal comfort model of human body are established, and then the aggregation strategy of central air-conditioning loads is proposed to minimize the operation fees of load aggregator under the constraints of the minimum aggregated load requirement, adjustment time period and users' comfort index. The particle swarm optimization algorithm is adopted to solve the optimal problem in this paper. Finally, the aggregated central air-conditioning loads are used to smooth the wind power fluctuation in the wind farm, and the optimal dispatch costs are compared with the investment cost of energy storage equipment needed to compensate the power fluctuation. The analysis results show that the load aggregation strategy of central air-conditioning is more economical than the configuration of traditional energy storage equipment.
Formation Coordinated Control for UUVs Based on Position-Velocity Communication Topology

In this paper, in terms of the system structure of a single unmanned underwater vehicle (UUV), combined with the autonomous and coordinated requirements of UUVs in the formation, the multi-UUVs formation distributed control system is constructed, based on coordinated layer. Using the UUV non-linear coupling model as the control object, the multi-UUVs formation coordinated control system is constructed, based on precise feedback linearization. Aiming at the limitation of hydro-acoustics communication bandwidth and the delay of communication system, a UUV formation coordination control method is proposed, based on double independent communication topologies. Under the conditions of double-layer communication topology, each UUV receives the position and velocity status information of other members at the same time. And using the consistency theory verifies the stability of the multi-UUVs formation. Lastly, the stability of multi-UUVs formation in traditional topology and double-layer topology is simulated and verified, respectively.
Real-Time Manufacturing Machine and System Performance Monitoring Using Internet of Things

This paper introduces a framework to assess the performance of manufacturing systems using hybrid simulation in real time. Continuous and discrete variables of different machines are monitored to analyze performance using a virtual environment running synchronous to plant floor equipment as a reference. Data are extracted from machines using industrial Internet of Things solutions. Productivity and reliability of a physical system are compared in real time with data from a hybrid simulation. The simulation uses discrete-event systems to estimate performance metrics at a system level, and continuous dynamics at a machine level to monitor input and output variables. Simulation outputs are used as a reference to detect abnormal conditions based on deviations of real outputs in different stages of the process. This monitoring method is implemented in a fully automated manufacturing system testbed with robots and CNC machines. Machines are integrated on an Ethernet/IP control network using a programmable logic controller to coordinate actions and transfer data. Results demonstrated the capacity to perform real-time monitoring and capture performance errors within confidence intervals. Note to Practitioners—Estimating expected performance of a manufacturing system processing different parts across multiple machines is a complex problem due to the lack of closed-form equations. Existing solutions focus on monitoring stochastic variables such as production or failure rate, or machine dynamics in separate environments often running asynchronous to the real system. This paper addresses the problem of monitoring and assessing the performance of complex manufacturing systems in real time. The proposed framework uses a real-time hybrid simulation of manufacturing at a machine and system level. The hybrid approach is based on a discrete and continuous model of manufacturing equipment integrated to run synchronously with the real plant floor operation. Data from both the virtual and real environments are merged to assess performance. Deviations from expected values represent an error that can trigger a warning signal to production, maintenance, and/or manufacturing personnel at the plant regarding health and productivity of plant operations.
Simulation of Student Skills: The Novel Technique Based on Quantization of Cognitive Skills Outcomes

To pass a job interview or written examination, students must have a specific set of Cognitive Skills (CS). The effects of frustration (aggression, giving up, loss of self-confidence, and depression) negatively affect these particular skills. Traditionally, a psychologist is invited for a job interview to determine these skills. Thus, there is no existing work which can predict CS outcome of students influenced by the effects of frustration. In the current attempt, we propose a technique that simulates the relationship between CS and frustration effects. Therefore, by quantization, we defined a specific range $(0.1\ \leq CS\leq 10)$ for CS and then divided it into 34 periodic CS outcomes with a period of 0.3. Furthermore, Bayesian inference method is used to calculate the posterior probabilities of each outcome of CS under the influence of frustration effects. During the experiments, the proposed technique tested on test dataset that have prior probabilities of CS and frustration effects. The results show that the proposed technique successfully simulated the statistical associations between CS and frustration effects. Finally, we concluded our work by the comparison with other CS predictions techniques.
An Adaptive Quasi-Sliding Mode Control Without Chattering

The main problem hindering the engineering application of the conventional sliding mode control (CSMC) is the well-known chattering phenomenon. This work investigates how to design a chattering-free adaptive sliding mode controller for a class of nth-order nonlinear uncertain systems. To remove the undesired chattering of the CSMC method, a novel continuous sigmoid function has been introduced to generate the quasi-sliding mode process instead of using the discontinuous sign function. At the same time, an effective adaptive control gain is designed by using the same sigmoid function. Compared with the existing works on the adaptive sliding mode control design, the adaptive mechanism proposed in this paper can effectively relax the restrictions on the prior information of the system uncertainties. In particular, the designed parameters and the adaptive control gain can characterize the admissible boundary of the high-frequency gain uncertainty of the control input in real time. We refer this approach as an adaptive quasi-sliding mode control (AQSMC). The stability analysis of the AQSMC method has also been provided with the help of Lyapunov theory. Finally, some numerical simulation results are presented to demonstrate its effectiveness and superiority.
Gastroenteropancreatic Neuroendocrine Tumors

Neuroendocrine tumors (NETs) are heterogeneous malignancies arising from the diffuse neuroendocrine system. They frequently originate in the gastroenteropancreatic (GEP) tract and the bronchopulmonary tree, and their incidence has steadily increased in the last 3 decades. Fundamental biologic and genomic differences underlie the clinical heterogeneity of NETs, and distinct molecular features characterize NETs of different grades and different primary sites. Although surgery remains the cornerstone of treatment for localized tumors, systemic treatment options for patients with metastatic NETs have expanded considerably. Somatostatin analogs have demonstrated both antisecretory and antitumor efficacy. Peptide receptor radionuclide therapy with lutetium‐177 dotatate (177Lu‐DOTATATE) has been approved for advanced GEP‐NETs. The antitumor activity of everolimus has been demonstrated across a wide spectrum of NETs, and the antiangiogenic agent sunitinib has been approved for pancreatic NETs (pNETs). Chemotherapy with temozolomide and capecitabine has recently demonstrated an unprecedented prolongation of progression‐free survival in a randomized trial of pNETs. Multiple retrospective series have reported the efficacy of liver‐directed therapies both for palliating symptoms of hormone excess and for controlling tumor growth. Telotristat, an oral inhibitor of tryptophan hydroxylase, has been shown to reduce diarrhea in patients with carcinoid syndrome. Defining the therapeutic algorithm and identifying biomarkers predictive of response to treatments are among the main priorities for the next decade of research in the NET field.
Conception mécatronique - Vers un processus continu de conception mécatronique intégrée

Cet article presente les bonnes pratiques a mettre en œuvre pour concevoir des produits mecatroniques. La demarche de conception d'un systeme mecatronique peut etre abordee comme une declinaison de l'ingenierie des systemes avec ses specificites. La mecatronique necessite de replacer la conception du produit dans son cycle de vie global, et de mettre en œuvre simultanement une collaboration interdisciplinaire, utilisant une demarche commune d'integration. La specificite de la conception de produits mecatroniques reside dans les differents niveaux d'integration : fonctionnelle, multidomaine et physique, pour obtenir un produit avec une plus haute valeur ajoutee.
The Application Prospect Analysis of Technology Based on WeChat Official Accounts

Through determining the choice of the social media(WeChat) adhere to the criteria of the WCI, the WeChat Official Accounts which contain professional technical background and knowledge transmission target can be filtered as the research objects in the paper. The purpose of this paper is to finish the topic analysis of the information in the WeChat Official Accounts via the method of text mining, along with the support of regarding the push notifications in the WeChat Official Accounts as the data set. Combined with the judgement of social cognition based on diverse topics, the conclusion of application prospect analysis of technology will be verified with the trial of the topic model and sentiment analysis. Finally, a case study of application prospect analysis of graphene Technology serves as the role of the empirical research in this paper.
On Image Denoising Method Based on Sparse Representation

An important way for people to obtain information is vision. However, during the collection and transmission of digital images, there is often some noise that is not good for people to get information quickly and correctly. Therefore, before the image processing analysis, the image denoising pretreatment is particularly important. However, the traditional denoising method will lose the details of the image and the result of denoising is unsatisfactory. In recent years, with the continuous improvement of sparse representation theory, it has also made great progress in image processing and has been well applied in image denoising, which has become one of the most effective methods in current image denoising methods. Using sparse coding decomposes the image on sparse domain, and the handling of the sparse coefficient by setting threshold. And based on a sparse representation of an overly-complete dictionary, an image is represented by a linear combination of atoms in the dictionary. Its adaptability also brings better denoising effect.
PanGFR-HM: A Dynamic Web Resource for Pan-Genomic and Functional Profiling of Human Microbiome With Comparative Features

The conglomerate of microorganisms inhabiting various body-sites of human, known as the human microbiome, is one of the key determinants of human health and disease. Comprehensive pan-genomic and functional analysis approach for human microbiome components can enrich our understanding about impact of microbiome on human health. By utilizing this approach we developed PanGFR-HM (http://www.bioinfo.iicb.res.in/pangfr-hm/) – a novel dynamic web-resource that integrates genomic and functional characteristics of 1293 complete microbial genomes available from Human Microbiome Project. The resource allows users to explore genomic/functional diversity and genome-based phylogenetic relationships between human associated microbial genomes, not provided by any other resource. The key features implemented here include pan-genome and functional analysis of organisms based on taxonomy or body-site, and comparative analysis between groups of organisms. The first feature can also identify probable gene-loss events and significantly over/under represented KEGG/COG categories within pan-genome. The unique second feature can perform comparative genomic, functional and pathways analysis between 4 groups of microbes. The dynamic nature of this resource enables users to define parameters for orthologous clustering and to select any set of organisms for analysis. As an application for comparative feature of PanGFR-HM, we performed a comparative analysis with 67 Lactobacillus genomes isolated from human gut, oral cavity and urogenital tract, and therefore characterized the body-site specific genes, enzymes and pathways. Altogether, PanGFR-HM, being unique in its content and functionality, is expected to provide a platform for microbiome-based comparative functional and evolutionary genomics.
Lightweight Support Vector Clustering Algorithm for Community Detection in Complex Networks

The community structure is one of the most attractive properties of a complex network. This structure has been fundamental to advancements in various scientific branches. Numerous tools that involve community detection algorithms have been used in recent studies. In this paper, we propose a lightweight support vector clustering method. It surpasses traditional support vector approaches in terms of accuracy and complexity on account of its innovative design of distance calculations and the utilization of stable equilibrium points in the community assignment process. Extensive experiments are undertaken in computer-generated networks as well as real-world datasets. The results illustrate the competitive performance of the proposed algorithm compared to its community detection counterparts.
Critical Discount Factor Values in Discounted Supergames

This paper examines the subgame-perfect equilibria in symmetric 2×2 supergames. We solve the smallest discount factor value for which the players obtain all the feasible and individually rational payoffs as equilibrium payoffs. We show that the critical discount factor values are not that high in many games and they generally depend on how large the payoff set is compared to the set of feasible payoffs. We analyze how the stage-game payoffs affect the required level of patience and organize the games into groups based on similar behavior. We study how the different strategies affect the set of equilibria by comparing pure, mixed and correlated strategies. This helps us understand better how discounting affects the set of equilibria and we can identify the games where extreme patience is required and the type of payoffs that are difficult to obtain. We also observe discontinuities in the critical values, which means that small changes in the stage-game payoffs may affect dramatically the equilibrium payoffs.
Revisiting the Role of Thiopurines in Inflammatory Bowel Disease Through Pharmacogenomics and Use of Novel Methods for Therapeutic Drug Monitoring

Azathioprine and 6-mercaptopurine, often referred to as thiopurine compounds, are commonly used in the management of inflammatory bowel disease. However, patients receiving these drugs are prone to developing adverse drug reactions or therapeutic resistance. Achieving predefined levels of two major thiopurine metabolites, 6-thioguanine nucleotides and 6-methylmercaptopurine, is a long-standing clinical practice in ensuring therapeutic efficacy; however, their correlation with treatment response is sometimes unclear. Various genetic markers have also been used to aid the identification of patients who are thiopurine-sensitive or refractory. The recent discovery of novel Asian-specific DNA variants, namely those in the NUDT15 gene, and their link to thiopurine toxicity, have led clinicians and scientists to revisit the utility of Caucasian biomarkers for Asian individuals with inflammatory bowel disease. In this review, we explore the limitations associated with the current methods used for therapeutic monitoring of thiopurine metabolites and how the recent discovery of ethnicity-specific genetic markers can complement thiopurine metabolites measurement in formulating a strategy for more accurate prediction of thiopurine response. We also discuss the challenges in thiopurine therapy, alongside the current strategies used in patients with reduced thiopurine response. The review is concluded with suggestions for future work aiming at using a more comprehensive approach to optimize the efficacy of thiopurine compounds in inflammatory bowel disease.
Finite-time consensus control for second-order multi-agent systems with output constraint

This paper discusses a leaderless finite-time consensus problem for second-order multi-agent systems with output constraint under uncertain time-varying control coefficients and unknown nonlinear perturbations. Based on the Barrier Lyapunov function and the technique of adding one power integrators, the finite-time consensus controller is clearly constructed to guarantee finite-time consensus for second-order multi-agent systems while the output constraint is not violated in a range. The effectiveness of the design method is finally validated by a numerical example.