Miracle-dz/UniFAR-FTUs huggingface dataset:
The P4->NetFPGA Workflow for Line-Rate Packet Processing

P4 has emerged as the de facto standard language for describing how network packets should be processed, and is becoming widely used by network owners, systems developers, researchers and in the classroom. The goal of the work presented here is to make it easier for engineers, researchers and students to learn how to program using P4, and to build prototypes running on real hardware. Our target is the NetFPGA SUME platform, a 4x10 Gb/s PCIe card designed for use in universities for teaching and research. Until now, NetFPGA users have needed to learn an HDL such as Verilog or VHDL, making it off limits to many software developers and students. Therefore, we developed the P4->NetFPGA workflow, allowing developers to describe how packets are to be processed in the high-level P4 language, then compile their P4 programs to run at line rate on the NetFPGA SUME board. The P4->NetFPGA workflow is built upon the Xilinx P4-SDNet compiler and the NetFPGA SUME open source code base. In this paper, we provide an overview of the P4 programming language and describe the P4->NetFPGA workflow. We also describe how the workflow is being used by the P4 community to build research prototypes, and to teach how network systems are built by providing students with hands-on experience working with real hardware.
On-Chip Detection of Trace Gases Using Photonic Matched Filters

This theoretical modeling and simulation the design and projected performance of an on-chip detector of trace gases using photonic matched filters, with operation in the 3–5-μm mid-infrared range. The thermo-optically tuned multi-Sagnac loop Mach–Zehnder interferometer (MZI), based on the germanium-on-silicon nitride technological platform, has been adopted to detect the signature of gases with regularly spaced and narrow absorption lines. The MZI arms are comprised of an N-cascaded connection of Sagnac loop reflectors, each pair separated by a waveguide whose length is designed according to the line spacing of the gas under detection. Detection of one or two gas absorption bands using one filter band is achieved at the Drop port of an MZI filter having on each arm the apodized Hamming Bragg waveguide grating. We performed parametric analysis on the GON platform to find the design rules for the waveguide gas cell and the Hamming or multi-Sagnac filters targeted at CH4 or CO2 or N2O or SO2 or CO. The analysis predicted good sensor performance.
Delay Optimization of 4-Bit ALU Designed in FS-GDI Technique

Arithmetic Logic Unit (ALU) is an essential building block in many applications such as microprocessors, DSP, and image processing, while power efficiency is a general concern in VLSI design. This paper presents Delay time optimization of 4-bit ALU designed using full-swing gate diffusion input (GDI) technique. Simulations carried out in Cadence virtuoso using 65nm TSMC processes with a supply voltage of 1.2 volts and a frequency of 125 MHz, Simulation results revealed improvement in Delay time and overall Energy of the optimized ALU design.
Vision-Based Online Learning Kinematic Control for Soft Robots Using Local Gaussian Process Regression

Soft robots, owing to their elastomeric material, ensure safe interaction with their surroundings. These robot compliance properties inevitably impose a tradeoff against precise motion control, as to which conventional model-based methods were proposed to approximate the robot kinematics. However, too many parameters, regarding robot deformation and external disturbance, are difficult to obtain, even if possible, which could be very nonlinear. Sensors self-contained in the robot are required to compensate modeling uncertainties and external disturbances. Camera (eye) integrated at the robot end-effector (hand) is a common setting. To this end, we propose an eye-in-hand visual servo that incorporates with learning-based controller to accomplish more precise robotic tasks. Local Gaussian process regression is used to initialize and refine the inverse mappings online, without prior knowledge of robot and camera parameters. Experimental validation is also conducted to demonstrate the hyperelastic robot can compensate an external variable loading during trajectory tracking.
Experimental study on the selection of access point for localization in indoor environments

Nowadays, the popular adoption of smart phones has enabled the development of several location-based applications such as indoor localization for shopping centers, airports, hospitals, among others. Typically, these services are based on Wireless Local Area Networks (WLANs), where the selection of access points (APs) plays an important role for the localization accuracy. In this paper, we evaluate different criteria of AP selection as well as the number of required APs, the impact of AP power transmission and the usage of different frequency bands. The results are obtained by means of experimental evaluations that provide more realistic comparisons. Finally, we provide novel guidelines for the design of improved indoor positioning systems.
Link Quality Estimation Method for Wireless Sensor Networks Based on Stacked Autoencoder

In wireless sensor networks, effective link quality estimation is the basis of topology management and routing control. Effective link quality estimation can guarantee the transmission of data, as well as improve the throughput rate, and hence, extend the life of the entire network. For this reason, a stacked autoencoder-based link quality estimator (LQE-SAE) is proposed. Specifically, the zero-filling method is developed to process the original missing link information. Then, the SAE model is used to extract the asymmetric characteristics of the uplink and downlink from the received signal strength indicator, link quality indicator, and signal-to-noise ratio, respectively. These characteristics are fused by SAE to construct the link features’ vectors, which are given as inputs to the support vector classifier (SVC), for which the link quality grade is taken as its label. The experimental results in different scenarios show that the LQE-SAE has better accuracy than link quality estimators based on the SVC, ELM, and WNN.
Research on Topic Recognition of Network Sensitive Information Based on SW-LDA Model

The mining of network sensitive information is of great significance for understanding the social stability of the network. Obtaining the network public opinion of sensitive information is helpful to master Internet users’ attitudes toward important social events. The related artificial intelligence technology can achieve the topics from the network texts. At present, the current topic recognition model has a low recognition rate for sensitive information and usually generates some inaccurate topic keywords. In this paper, a topic recognition method of the network sensitive information based on a sensitive word weighted-latent Dirichlet allocation (LDA) model is proposed. First, the basic sensitive word vocabulary is constructed by manual collection, and the embedding representation of the word is obtained through the training of a large amount of network corpus based onWord2vec. The semantic similarity between the word embedding is calculated to extend the basic sensitive word vocabulary. Second, the extended sensitive word vocabulary is embedded in the LDA model. On the one hand, it can improve the semantic understanding and the recognition ability of LDA for the sensitive topic words and promote the quality of the generated topic words. On the other hand, it can also improve the relevance of the topic keywords and the related topics and find more fine-grained keywords. The experimental results show that the sensitive word weighted-LDA model can effectively improve the topic recognition quantity and quality of sensitive information. This paper is helpful to the development of artificial intelligence. The generated corpus in this paper is meaningful to the research of text classification, clustering and information retrieval, and so on.
Smart Charging of Electric Vehicles According to Electricity Price

The growing popularity of private vehicles’ electrification will have a negative impact on the electric power system, especially on the distribution networks, if electric vehicles (EVs) charging is not managed properly. In this paper, a new technique for smart charging of EVs is proposed and tested with simulation. A fuzzy logic controller is used to control and manage the EV charging process to maximize electric utility and EV owner benefits. The electric utility’s benefit is to mitigate the EV charging impacts on the distribution network by shifting EV charging to the off-peak period, while EV owners’ benefit is to charge the EV at low cost. The controller regulates and controls the EV charging power depending on electricity price signal provided by the electric utility and EV battery state of charge (SoC). This controller needs basic communication with the electric utility to receive the electricity price signal every 1 hour. The objective of the controller is to charge EVs at low cost while keeping the normal operating conditions of the distribution network. MATLAB/SIMULINK is used to perform simulations and test the effectiveness of the proposed smart charging method. The results demonstrated that the proposed smart charging method reduced the impacts of EVs charging on the distribution network compared with uncontrolled charging.
Laterally-Dependent Velocity Estimation Bias in Plane-Wave Doppler Ultrasound

Doppler ultrasound has become a standard method used to diagnose, grade and monitor vascular diseases and their progression. Ultrafast plane-wave Doppler enables rapid simultaneous B-mode, color and spectral Doppler measurements across large fields of view. However, plane-wave imaging can lead to a substantial laterally-dependent bias in the velocity estimation. Here, we investigate this bias quantitatively both by numeral simulations and phantom experiments. We demonstrate that this velocity bias is caused by asymmetrical geometric spectral broadening and derive a correction algorithm.
A High Power GaN MMIC 36.5 Watt X Band Power Amplifier

This work presents the design of a high power amplifier circuit for the X band application having frequency of operation from 9.75 GHz to 11 GHz. Operating frequency range has been selected according to the input return loss (|S(1, 1)|) and output return loss (|S(2, 2)|) value and it is always larger than 12 dB for this work. In addition, insertion loss |S(1, 2)| is always larger than 29.41 dB for worst case condition. In case of X band application this architecture can be widely used for the bandwidth of 1.25 GHz and it achieves 36.5 watt output power with input 1dB compression point (P1dB) of 29 dBm. Also, it shows the power added efficiency (PAE) of 35% and this architecture has been implemented with one driver amplifier (DA), four high power amplifiers (HPAs) and six Wilkinson power divider and or combiner circuits. Power combiners and dividers circuit’s loss have been adjusted with the gain path of the one DA and HPA circuit for improving the linearity of the overall PA circuit. Proper impedance matching has been done for the input-output and intermediate stage of the power amplifier circuit with the help of smith-chart utility in advanced design system (ADS) tool in Keysight. Maximum power gain in dB of the power amplifier circuit is 17. According to our knowledge this work shows a new state of the art performances for this power amplifier. OIP3 and IIP3 of the power amplifier for the input power of 20 dBm are 55.063 dBm and 39.197 dBm, respectively. All design components of the PA circuit have been carried out in UMS 0.25 µm GaN HEMT process.
Finding N Users with Most Happiness Influences on Online Social Networks

In the real world, emotions guide people’s thoughts and influence their life behaviors, and emotions can be affected by interpersonal interactions. Following the increased popularity of the internet, more and more people are using online social networks, and recent research has found that online users have real-world emotional contagion behaviors. However, most previous studies were based mainly on text analysis and few have analyzed single emotions. Therefore, in this paper, we take Facebook as the study object, and we analyze the influences of interactive behaviors, such as the number of ‘likes’ a post receives, the number of times a post is shared, the number of followers, and the number of comments on a post, on the user’s state of happiness. We thus establish which kinds of online users possess the most influencing state of happiness. The experimental results show that the number of times a post is shared is the best indicator of online users possessing the most influencing state of happiness.
DISPARITY MAP ESTIMATION FROM CROSS-MODAL STEREO

Mono-modal stereo matching problem has been studied for decades. The introduction of cross-modal stereo systems in industrial scene increases the interest in cross-modal stereo matching. The existing algorithms mostly consider mono-modal setting so they do not translate well in cross-modal setting. Recent development for cross-modal stereo considers small local matching and focus mainly on joint enhancement. Therefore, we propose a guided filter-based stereo matching algorithm. It works by integrating guided filter equation in a basic cost function for cost volume generation. The cost volume can be further improved via smoothness constraints and confidence filtering. We show that the proposed algorithm is able to estimate disparity correctly within acceptable interval in many important regions and outperforms many existing algorithms. We believe that the proposed algorithm will become the new benchmark for cross-modal stereo matching problem.
A 22.8-to-32.4 GHz Injection-locked Frequency Tripler with Source Degeneration

In this paper, injection-locked frequency tripler with wide locking range and high fundamental tone rejection is presented. Undesired fundamental harmonic due to the injected signal is notched out with the source degeneration. Prototype circuit is designed in 45-nm CMOS technology and achieves 28.4 dBc of harmonic rejection ratio. The tripler exhibits 34.8% of locking range with 4 dBm of input signal power. With 1.1 V of supply voltage, the core circuit consumes 5.2 mA and occupies only 265μm × 160μm of chip-area.
Linewidth and RIN Measurement of a New Single Mode Semiconductor Laser

In this paper, we present a detailed measurement of the spectral linewidth and RIN of a new single mode semiconductor laser. The laser with active section cavity length of 600μm exhibits a minimum intrinsic linewidth of Voigt fitting around 760 kHz at 85 mA drive current and the minimum RIN is below -160dB/Hz at room temperature.
Graph-Projected Signal Processing

In the past few years, Graph Signal Processing (GSP) has attracted a lot of interest for its aim at extending Fourier analysis to arbitrary discrete topologies described by graphs. Since it is essentially built upon analogies between classical temporal Fourier transforms and ring graphs spectrum, these extensions do not necessarily yield expected convolution and translation operators when adapted on regular multidimensional domains such as 2D grid graphs. In this paper we are interested in alternate definitions of Fourier transforms on graphs, obtained by projecting vertices to regular metric spaces on which the Fourier transform is already well defined. We compare our method with classical graph Fourier transform and demonstrate its interest for designing accurate convolutional neural networks on graph signals.
Small-Signal Modelling and Design Validation of PV-Controllers With INC-MPPT Using CHIL

The maximum energy that can be harvested from a photovoltaic (PV) system at any instant depends on the effectiveness and response time of the maximum power point tracking (MPPT) algorithm used and related controllers. To facilitate proper controller design, a precise mathematical model of the system is required. This paper presents a comprehensive small signal model capable of describing the dynamics of the power stage and controllers. The power stage consists of a PV system and a dc–dc boost converter including the parasitic elements operating in inverse-buck mode. The MPPT and PV voltage controller constitute the control system. The steady state and transient responses of the system are evaluated by controller-hardware-in-the-loop approach where the power stage is simulated in a real time digital simulator and the control operations are performed in a digital signal processor. The frequency response is experimentally determined using a Gain-Phase analyzer. This unique approach allows control system designers to test and validate a control system design before implementing it with a laboratory scale hardware or any real-life application. This method adds an extra layer of design authentication on top of conventional offline simulations.
Automated Neural Network Accelerator Generation Framework for Multiple Neural Network Applications

Neural networks are widely used in various applications, but general neural network accelerators support only one application at a time. Therefore, information for each application, such as synaptic weights and bias data, must be loaded quickly to use multiple neural network applications. Field-programmable gate array (FPGA)-based implementation has huge performance overhead owing to low data transmission bandwidth. In order to solve this problem, this paper presents an automated FPGA-based multi-neural network accelerator generation framework that can quickly support several applications by storing neural network application data in an on-chip memory inside the FPGA. To do this, we first design a shared custom hardware accelerator that can support rapid changes in multiple target neural network applications. Then, we introduce an automated multi-neural network accelerator generation framework that performs training, weight quantization, and neural accelerator synthesis.
Model Predictive Control of Distributed Generations With Feed-Forward Output Currents

In this paper, a voltage and frequency control scheme based on model predictive control is proposed for inverter-based distributed generations (DGs). Currents injected into an off-grid system (e.g., a passive network with loads or an islanded microgrid) at the point of common coupling of the DG are considered as disturbances and used as feed-forward signals. These signals enhance the transient performance of the DG control system for a wide range of switched loads as well as for switching and operating the DG in an islanded microgrid. The stability and robustness of the proposed control scheme are analyzed and discussed. The effectiveness of the scheme is demonstrated by extensive time-domain simulations using PSCAD/EMTDC for various loads (such as balanced/imbalanced, and nonlinear and dynamic loads), fault conditions, and DG operation after switching in an islanded microgrid. Comparison of the obtained results with those of three previously developed schemes shows superiority of the proposed scheme.
Weighted Pyramid Network for Foreign Object Debris Detection on Airfield Pavement

In this paper, a novel network architecture Weighted Pyramid Network (WPN) is proposed to detect foreign object debris (FOD) on airfield pavement. WPN is a region-based algorithm and it produces region proposals from multi-scale layers with different ratio. Fed to fully-connected layer, these multi-scale proposals are exacted to the same size by undergoing a RoIAlign layer. Moreover, Finetune Loss is designed in WPN to concentrate the model on hard examples and this practice improves the efficiency of training a lot. In the end, compared to some popular object detection network, WPN is proved to achieve better result for FOD detection on airfield pavement.
Automated Botnet Traffic Detection via Machine Learning

Connected machines become more vulnerable to malware infections which potentially cause them to be controlled as part of a botnet for cybercrime activities. Prompt detection of infected machines is required for protecting local networks and infrastructure as well as reducing the impact of botnets. In this paper, we propose the use of machine learning techniques involving multi-layer perceptrons and decision trees on network traffic analysis for the detection of botnet traffic. We enhance components of an existing detection framework with these techniques to automate its processes and improve performance at the same time. Our experiments indicate that the modifications successfully improved the overall performance of botnet traffic detection in both supervised and semi-supervised manners.
Traffic Load Reduction of Multi-owner, Multikeywords and Multi-user Searches Using Parallel Searching and Cache Trapdoors

With the development of cloud computing, sensitive information of outsourced data is at the risk of unauthorized accesses and the cost of implementation. Several approaches have been provided to enable searching the encrypted data to protect data privacy but can't handle the problems of traffic load and searching time cost. To combat this issue, this paper presents a cache algorithm for query in the user part to reduce communication cost between the user and cloud provider. Also we propose a parallel searching algorithm to reduce the computation, time of searching and traffic overload in cloud server.
Tri-band Pass Filter Using Optimized Psi-shaped Resonators

In this paper, a tri-band pass filter (BPF) at 0.75 GHz, 2.1 GHz, and 3.5 GHz is designed using a modified tri-mode Psi shape resonator whose parameters are optimized using particle swarm optimization algorithm (PSO). The Psi shape resonator input impedance has three resonance frequencies with two transmission zeroes which improve the isolation between passbands. A mathematical analysis is performed to calculate the center resonance frequencies of the proposed design for the tri-band pass filter (BPF). The implemented design is simulated, fabricated, and measured. All measured results are consistent with the analysis and the simulations.
Analysis of Grass Area Aerial Image Set

In the recent years, the emergence of drone industry has expanded its utilities purpose in the commercial world. When coupled with computer vision, it can provide useful analysis of the aerial image set taken from the drone, and have since benefited across various industries such as agriculture and land surveillance. For a land surveillance mission, not only does it have the possibility of producing results with higher accuracy, it will also reduce the number of manpower and time required if similar tasks were carried out manually. Singapore is known as a City in a Garden due to the huge amount of greeneries spread across the country. Many of these greeneries consists of large empty grass patch which need maintenance work by hired contractors and visual checks by agency officers every few weeks. With a few thousands site large and small, the amount of manpower required is tremendous. In this paper we propose a solution that can resolve the manpower issue and offer visual analysis through computer vision methods.