Miracle-dz/UniFAR-FTUs huggingface dataset:
A Linear High-Efficiency Millimeter-Wave CMOS Doherty Radiator Leveraging Multi-Feed On-Antenna Active Load Modulation

This paper presents a Doherty radiator architecture that explores multi-feed antennas to achieve an on-antenna Doherty load modulation network and demonstrates high-speed high-efficiency transmission of wideband modulated signals. On the passive circuits, we exploit the multi-feed antenna concept to realize compact and high-efficiency on-antenna active load modulation for close-to-ideal Doherty operation, on-antenna power combining, and millimeter-wave (mm-Wave) signal radiation. Moreover, we analyze the far-field transmission of the proposed Doherty radiator and demonstrate its wide field of view (FoV). On the active circuits, we employ a gigahertz-bandwidth adaptive biasing at the Doherty auxiliary power amplifier (PA) path to enhance the main/auxiliary Doherty cooperation and appropriate turning-on/-off of the auxiliary path. A proof-of-concept Doherty radiator implemented in a 45-nm CMOS silicon on insulator (SOI) process over 62–68 GHz exhibits a consistent 1.45<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>–1.53<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> PAE enhancement at 6-dB power backoff (PBO) over an idealistic class-B PA with the same PAE at <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {1\,dB}}$ </tex-math></inline-formula>. The measured continuous-wave performance at 65 GHz demonstrates 19.4/19.2-dBm <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {SAT}}/P_{\mathrm {1\,dB}}$ </tex-math></inline-formula> and achieves 27.5%/20.1% PAE at peak/6-dB PBO, respectively. For single-carrier 1 Gsym/s 64- quadratic-amplitude modulation (QAM), the Doherty radiator shows average output power of 14.2 dBm with an average 20.2% PAE and −26.7-dB error vector magnitude (EVM) without digital pre-distortion. Consistent EVMs are observed over the entire antenna FoV, demonstrating spatially undistorted transmission and constant Doherty PBO efficiency enhancement.
An overview of intelligent surveillance systems development

In the efforts to fight against crime, city halls and governments install video surveillance system that works 24h/day like thousands of eyes in airports, ports, major roadways, government buildings and important facilities. However, such kinds of surveillance systems were strongly depended on hardware architecture and budget for installation. In this condition intelligent surveillance system (ISS) has growing attention due to the increasing demand on security and safety in areas with limited human intervention.
Automatic Generation of Peak-Power Traffic for Networks-on-Chip

Early estimation of the peak power consumption of a system under development is crucial in assessing the design’s thermal profile and reliability, and in benchmarking the chip-level power management features. In this paper, we present a high-level systematic methodology for generating the appropriate traffic patterns that trigger the peak power consumption in a network-on-chip (NoC), irrespective of the latter’s structural and functional properties. The generation of peak-power traffic is performed by solving a novel optimization problem based on integer linear programming, which models the traffic that can realistically flow in the network, thus avoiding any fake and pessimistic scenarios. This formulation can handle arbitrary network configurations and routing algorithms, including heterogeneous network topologies with multiple link widths and voltage/clock domains. The proposed technique maximizes both the network utilization and the data switching activity, thereby causing, on average, <inline-formula> <tex-math notation="LaTeX">${4 \times }$ </tex-math></inline-formula> higher power consumption than synthetic traffic patterns with random behavior. Most importantly, the proposed method reveals the realistic ceiling of the NoC’s peak power consumption, by reporting significantly lower peak power (<inline-formula> <tex-math notation="LaTeX">${3\times }$ </tex-math></inline-formula> less), as compared to fake worst-case scenarios that can never, in fact, occur during the NoC’s normal operation.
CROTPN Based Collision-Free and Deadlock-Free Path Planning of AGVs in Logistic Center

In recent years, automated guided vehicle (AGV) is becoming increasingly important for logistic center, which usually has tens of thousands of express packages to sort and transport every day. In order to enhance the efficiency of sorting, multiple AGVs have been used to transport as many express packages as possible in a given time. However, there may exist collisions and deadlocks when two or more AGVs are trying to pass through a shared area at the same time. In this paper, we study collision-free and deadlock-free path planning of multiple AGVs with the objective of minimizing the makespan, that is, the maximum time of the AGVs to finish a round of delivery. To solve the problem, we first propose a candidate path generation algorithm to generate several paths for each AGV, from which a combination of the paths is selected based on optimal path combination selection (OPCS) algorithm, such that the collisions and deadlocks can be minimized. Then, we construct a colored resource-oriented timed Petri net (CROTPN) model for dynamic changing AGV routes after slicing the scheduling period into timeslots. Through Slot-Control policy, the collision and deadlock can be effectively avoided. Our experiment is simulated based on CPN tools. Experiment results show that our method can achieve a high sorting throughput using the least AVGs.
Improved Pedestrian Detection under Mutual Interference by FMCW Radar Communications

The widespread of automotive radars leads to increase of mutual interference, which in turn degrades road safety. The effect of mutual interference with a focus on detection of pedestrians is investigated. It is shown that detection of pedestrians degrades in the presence of mutual interference. A joint radar communication solution is proposed that increases pedestrian detection probability with negligible impact in the ranging error.
A Two-Stage Game Model for Combined Heat and Power Trading Market

The proliferation of technologies such as combined heat and power systems and smart meters has accelerated the multienergy trading. A two-stage game model is built in this paper to analyze the optimal strategy of each player and their interactions. Specifically, in consumer game model, we present an autonomous and distributed micro energy management system among users in which power and heat are traded at the same time. In supplier game model, integrated energy suppliers receive energy demand information and share the market according to the game result. In addition, we present a detailed proof of the existence and uniqueness of the Nash equilibrium, which lays the foundation for researchers to build an effective model using game theory. Further, a distributed algorithm specifically designed for this model is proposed. The players can retain a certain level of anonymity without revealing the details of their energy consumption or generation schedules to other players. Lastly, simulation results confirm the feasibility of two-stage game model and the effectiveness of the proposed distributed algorithm.
Stable PID Control for Mobile Robots

In order to guarantee asymptotic stability for mobile robot, proportional-integral-derivative (PID) controllers are needed. In this paper, we use an uncoupled linear PID controller with explicit conditions to control a mobile robot, in which semiglobal asymptotic stability of the linear PID controller was proven. Explicit conditions are given for choice the control gains of the linear PID controller, and assure the stability of the closed-loop system. Experimental studies for differential wheels robot are addressed. The theoretical analysis presented in this work provides a tool for the design and implementation experimentally of controllers on real mobile robots.
Stabilization of Discrete-Time Singular Markov Jump Systems With Repeated Scalar Nonlinearities

This paper focuses on the state feedback stabilization problem for a class of discrete-time singular Markov jump systems with repeated scalar nonlinearities. First, on the basis of the implicit function theorem and the diagonally dominant Lyapunov approach, a sufficient condition is obtained, which ensures the regularity, causality, uniqueness of solution in the neighbourhood of the origin, and stochastic stability for the system under consideration. Moreover, by employing some lemmas and matrix inequalities, the sufficient condition is changed into a set of linear matrix inequalities. Then, the procedures of designing the state feedback controller are given. Eventually, three examples are presented to show the validness of the proposed approach.
Experimental Demonstration of BLE Transmitter Positioning Based on AOA Estimation

The introduction of the Bluetooth Low Energy (BLE) standard significantly streamlines the development of the Internet of Things (IoT) applications. These applications often require the sensor positioning to link the measurements with its location. Therefore, it is essential to conduct studies on BLE transmitter positioning methods. Power fingerprinting transmitter positioning approaches are commonly used in BLE network. However, these methods have their own limitations in terms of practical use and ease of implementation. The purpose is to develop a simple BLE transmitter positioning of high accuracy. In this paper, a BLE transmitter positioning method is proposed based on Angle of Arrival (AOA) estimation. Multiple Signal Classification (MUSIC) algorithm is used for angle estimation due to the high angular resolution and sensitivity. Several experiments have been conducted in an indoor environment and the results compared with simulation results. Experimental results show that our proposed BLE transmitter positioning method achieves a promising positioning accuracy.
Separation of a Functional Film from a SiC Substrate by Ion Cut without Bonding

A new method that separates a film of SiC with a GaN epi layer from the main body of a substrate is presented in this paper. The method uses proton implantation to generate an ion damaged layer underneath the surface of the SiC substrate. A GaN epi functional layer is deposited on the SiC substrate after the ion implantation. Without bonding the wafer to any other wafer, a stress inducing layer is applied on the GaN layer to separate the GaN layer and the SiC film from the main body of substrate at previously introduced ion damage layer.
Interactive Query Reformulation for Source-Code Search With Word Relations

Searching source code is a common activity in many software engineering tasks. To some extent, the quality of the query determines the accuracy of query results. In practice, it is difficult for developers to provide a high-quality query, especially for the novice who just takes over the software project with a short time. What is more, existing code search techniques using queries expressed in natural language offer little support to help developers determine whether the search results are relevant or not. When a query preforms poorly, it has to be reformulated. In this paper, we present a novel approach, INQRES, to interactively reformulate the search query considering the relations between words in the source code to optimize the query quality. INQRES analyzes the keyword relations in the source code and builds AND and OR relations in an interactive way for developer to select suitable words for query reformulation. To evaluate the effectiveness of INQRES, we perform an empirical study on the jEdit project. Empirical results show that INQRES can effectively reformulate the search query, and the quality of the reformulated query of INQRES is better than that of the state-of-art technique, i.e., QReformu.
Semantic Content Representation to Meet the Challenges of Video Analysis in Multimedia Entertainment

This paper proposes an ontological approach for enabling semantic-aware video content annotation and retrieval in order to facilitate content video analysis. Semantic Web ontologies express key entities and relationships which describe videos in a formal machine-processable representation. Ontology-based knowledge representation systems can be used for content analysis, concept recognition, reasoning processes and for enabling user-friendly and intelligent multimedia content retrieval, thus meeting the challenges of video analysis.
Fast and Flexible Large Graph Embedding Based on Anchors

Dimensionality reduction is one of the most fundamental topic in machine learning. A range of methods focus on dimensionality reduction have been proposed in various areas. Among the unsupervised dimensionality reduction methods, graph-based dimensionality reduction has begun to draw more and more attention due to its effectiveness. However, most existing graph-based methods have high computation complexity, which is not applicable to large-scale problems. To solve this problem, an unsupervised graph-based dimensionality reduction method called fast and flexible large graph embedding (FFLGE) based on anchors is proposed. FFLGE uses an anchor-based strategy to construct an anchor-based graph and design similarity matrix and then perform the dimensionality reduction efficiently. The computational complexity of the proposed FFLGE reduces to <inline-formula><tex-math notation="LaTeX">$O(ndm)$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of samples, <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> is the number of dimensions and <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> is the number of anchors. Furthermore, it is interesting to note that locality preserving projection and principal component analysis are two special cases of FFLGE. In the end, the experiments based on several publicly large-scale datasets proves the effectiveness and efficiency of the method proposed.
Development of Myoelectric Prosthetic Hand based on Arduino IDE and Visual C# for Trans-radial Amputee in Indonesia

This paper presents a preliminary study on design and development of prosthetic hand controlled by electromyography (EMG) sensor for patients who suffered from trans-radial amputee. The system is made up of an embedded Arduino microcontroller equipped with Visual C# programming as graphical user interface (GUI). The prototype of the prosthetic hand has five grip modes i.e. hand open and close, hook finger movement, peace finger movement, pointing finger movement, and active thumb. The mechanism of prosthetic hand is designed with selected mode grip pattern, where each motion is triggered from muscle contraction that is acquired using EMG sensors. In addition, the input of EMG signal can be monitored from the graphs in GUI based on Visual C# programming. Each finger in the prosthetic hand prototype is driven by a linear actuator. The objective of this study is to design and develop a prototype of myoelectric hand based on EMG sensor and create a prosthetic hand grip pattern mode program.
Performance Analysis of an Organic Rankine Cycle with Different Working Fluids for Heat Recovery from an Internal Combustion Engine

Internal Combustion Engine (ICE)waste heat resources mainly include jacket cooling water heat and exhaust gas heat. However, jacket cooling water heat is usually ignored due to its low temperature in the bottoming cycle which uses Organic Rankine Cycle (ORC)to recover waste heat from an ICE. This paper establishes an optimization model of an ICE-ORC bottoming cycle to maximize the ORC net power output for different ICE rated power outputs with different working fluids. Moreover, three different ICE-ORC bottoming cycles are considered: a simple cycle with the use of only exhaust gas as the ORC thermal source, a cycle with the use of exhaust gas as the ORC thermal source and jacket cooling water preheating working fluid, and a cycle with a specific jacket cooling water circuit which includes a splitter and an exhaust gas heat exchanger. The results demonstrate that whether it is necessary to recover jacket cooling water waste heat depends on the property parameters of working fluid, each ICE-ORC bottoming cycle has a suitable working fluid to obtain maximum ORC power output respective, and the maximum ICE-ORC bottoming cycle efficiency could be increased by 16% using pentane as working fluid compared with single ICE generator set.
Impact of Nonideal LED Modulation on RSS-based VLP Performance

Current (theoretical) Visible Light Positioning (VLP) systems assume the transmission of ideal waveforms. Existing VLP LED transmitters, however, transmit far from perfect signals, causing a plunge in the positioning accuracy. The impact of transmitter nonidealities, such as overshoot and rise time, remains unclear. The lack of quality (industry-ready) LED drivers presents a major hurdle in large-scale VLP adoption. The extent to which transmitter nonidealities are tolerable, is valuable information for LED driver designers. This paper provides specifications for the latter. It charts the potentially detrimental impact of transmitter nonidealities on the positioning performance. The influence of both overshoot and rise/fall time is studied.
Parameterization of a Convolutional Autoencoder for Reconstruction of Small Images

A convolutional autoencoder (CAE) is formed by combining a convolutional neural network and an autoencoder, to take both their advantages in reconstructing the output from a compact, latent representation of the input. However, to our best knowledge, there is no exact recommendation for parameterizing a CAE, such as deciding the number of neurons in the hidden bottleneck layer of a CAE to avoid “underfitting” and “overfitting” of the network. Hence, a framework for deriving an optimum set of CAE parameters for the reconstruction of input images based on the standard MNIST data set is presented in this paper. The robustness of the parameters on a different image size's data set, like the SVHN, is then verified. Our results show that for small ($28\times 28$) and ($32\ \times 32$) pixels' input images, having 2560 neurons at the hidden bottleneck layer and 32 convolutional feature maps can result in optimum reconstruction performance for the CAEs. In addition, the quantitative Mean-Square-Error and the qualitative (2D visualization of the neurons' activation, the histogram statistics and estimated source entropy at the hidden layers) analysis methodology provided by this work can provide a good framework for deciding the parameter values of the CAEs to provide good representations of the input image.
Simulink Model for PWM-Supplied Laminated Magnetic Cores Including Hysteresis, Eddy-Current, and Excess Losses

A new implementation of an iron-loss model for laminated magnetic cores in the MATLAB/Simulink environment is proposed in this paper. The model is based on numerically solving a one-dimensional diffusion problem for the eddy currents in the core lamination and applying an accurate hysteresis model as the constitutive law. An excess loss model is also considered. The model is identified merely based on the catalog data provided by the core material manufacturer. The implementation is validated with analytical and finite-element models and experimentally in the case of a toroidal inductor supplied from a GaN FET full-bridge inverter with 5–500 kHz switching frequencies and a deadtime of 300 ns. Despite the simple identification, a good correspondence is observed between the simulated and measured iron losses, the average difference being 3.3% over the wide switching frequency range. It is shown that accounting for the skin effect in the laminations is significant, in order to correctly model the iron losses at different switching frequencies. Some differences between the measured and simulated results at high switching frequencies are also discussed. The model is concluded to be applicable for designing and analyzing laminated magnetic cores in combination with power-electronics circuits. The Simulink models are openly available.
An optimized control access mechanism based on micro-service architecture

With the development of cloud technology and the continuous evolution of software architecture, micro-service has become a very popular software development architecture mode, which is more suitable for the situation of business expanding and rapid function iteration. This paper discusses the basic concepts and main features of micro-service architecture, and analyzes the shortcomings of the traditional architecture, so as to obtain the basic requirements of access control in micro-service environment. By studying the traditional access control technology, this paper analyzes its limitations and shortcomings in the micro-service environment, and proposes an access control optimization model based on RBAC.
Integrative Clustering and Supervised Feature Selection for Clinical Applications

Widely accessible biomedical data provides new opportunities to discover knowledge for quality healthcare. However, optimal selection of the most informative, representative and non-redundant features from the huge volume of datasets are to be better explored. To address this challenge, in this paper we propose an integrative clustering and supervised feature selection approach. In our framework, the unsupervised clustering contributes to reducing redundancy by exploring the correlation among features, while supervised learning selects informative and representative features by examining the relation between features and outputs. Our approach was evaluated on a high-dimensional biomedical image feature set and a clinical dataset for survival prediction. Experimental results demonstrated that the selected features had the higher discriminative ability and lower redundancy, and our feature selection outperformed two state-of-the-art methods in terms of feature significance and feature correlation. Furthermore, experiments revealed that redundancy reduction helped handle the overfitting problem.