Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovery and Reconstruction of Multicast Tree in Software-Defined Network: High Speed and Low Cost

Multicast is an efficient tool to transfer the same data from a publisher to multiple subscribers, and its performance relies critically on the resiliency. Compared with traditional networks, the centralized control and flexible programmability of the Software Defined Network (SDN) is more beneficial for solving the failure recovery problem. This paper presents a design of an OpenFlow controller which can recover and reconstruct a multicast tree rapidly, while reducing the Ternary Content Addressable Memory (TCAM) consumption simultaneously. We choose the protection mechanism, and calculate all the backup paths after the topology information is mastered by the controller. When a failure occurs, affected switches can enable the use of backup paths to continue forwarding multicast packets. As duplicate packets may exist after the failure recovery, the reconstruction of a tree is required by modifying only a small fraction of forwarding rules. The performance evaluations demonstrate that our proposed method recovers a multicast tree with a high speed and low cost. On one hand, the maximum failure recovery time of two protection-based methods including our proposed method is less than 11 ms, while that of two restoration-based methods is 44 ms and 30 ms, respectively. On the other hand, the amount of backup forwarding entries required by another protection-based method exceed our proposed method as long as 3 or more multicast groups exist in the network. Moreover, when our proposed method is adopted, the amount of backup forwarding entries per switch is less than 30, which can be ignored when compared with the capacity of SDN switches (a switch can support a few thousand forwarding rules).
FRVO: A Filter Enhanced Interaction Model for Pedestrian Path Prediction in Crowded Scenarios

The paper presents a filter enhanced interaction model to predict pedestrian trajectory in crowded scenarios, which is essential for robots navigating in pedestrian-rich environments. An adaptive interaction model based on reciprocal velocity obstacle is employed to simulate interaction among pedestrians. It allows an agent to be aware of other agents’ aggressiveness and environment crowdedness. Besides, we implement a filter-based online learning framework with adaptive noise covariance to continuously refine the interaction model’s inner state, which addresses the uncertainty of pedestrian path planning. We highlight the path optimality and energy efficiency of our method by simulation. Experiments with real-world dataset demonstrate the generality to unknown environments of our method and at least 25% improvement on path prediction error compared with prior models.
A High Performance CMOS Voltage-Controlled Oscillator for Micro-resonant Infrared Detector Closed-loop/Self-excited ASIC

A closed-loop self-excitation/detection application specific integrated-circuit (ASIC) based on the phase-locked loop (PLL) used for micro-resonant infrared detector is designed to improve the measurement accuracy, miniaturization and integration. A switched capacitor array is proposed and used in the VCO to meet the requirements of the system. The VCO was implemented by $0.18 \mu \mathrm{m}$ CMOS technology, the chip area is 1.1*0.9 mm2. The proposed VCO can be operated at 2.603. 38 GHz, the measured phase noise are -125.9 dBc/Hz@1MHz at 2.60 GHz and -123.3 dBc/Hz@1MHz at 3.38GHz respectively. The VCO draws 3.06 mW power consumption from a 1.8 V supply.
Towards Explainable Multi-Label Classification

Multi-label classification is a very active research area and many real-world applications need efficient multi-label learning. During recent years, explaining machine learning predictions is also a very hot topic. A lot of approaches have been proposed for explaining multi-class classifier predictions. However, almost nothing has been proposed for multi-label and ensemble approaches. This paper brings two main contributions. It first proposes a natural framework consisting in reasoning with base classifier explanations in order to provide explanations for the multi-label predictions. The second contribution focuses on binary relevance, a widely used approach in multi-label classification, and distinguishes two kinds of explanations: common explanations shared by all base classifiers predicting positive labels and joint explanations combining explanations from each base classifier predicting a positive label. The paper proposes an efficient approach for deriving such explanations. Experimental studies show positive results that can be achieved on many multi-label datasets.
Multipath Transmission Mechanism with P4 Switches in Software Defined Networks

This study investigated programmed control of transmitting video and audio streaming data with P4 switches in software defined networks. When a packet is transmitted to a P4 switch, the switch checks the match actions and invokes a random function to determine which output port the packet should be sent to. Thus, the switches in the network can route the streaming data via different path according to the random function so as to provide multipath transmission. Simulation results showed that the proposed multipath transmission mechanism can improve transmission efficiency, reliability, and network throughput.
A Handover Algorithm for Video Sharing over Vehicular Networks

Vehicular networks over 5G are a promising solution that provides the requirements for an extensive amount of applications, such as Video Streaming. However, the higher number of cells deployed in 5G scenarios causes a high number of disconnections and handovers, which may compromise the quality of the user experience. In this paper, we introduce a predictive QoE- and mobility-aware handover algorithm for vehicular networks called HoVe. HoVe takes into consideration user location (current and predicted), QoE, and radio resources to provide a robust handover decision and high QoE for video-based applications in 5G VANETs. Simulation results show the efficiency of HoVe, delivering video with 18% better QoE when compared to state-of-the-art algorithms in vehicular scenarios.
Distributed Message Processing System Based for Internet of Things

With the landing of 5g, the amount of data generated in the Internet of things environment has increased dramatically. The main problems faced by the IoT are numerous access nodes, large data volume, complex connecion management, and strong data sharing requirements. In response to these problems, we propose a distributed message processing system for collecting and processing data in the IoT environment. We analyze the data transmission characteristics and requirements of different applications in the Internet of Things in detail, and proposes a new message-oriented data processing model. With this model, data processing becomes more convenient and efficient. The overall structure of the data. processing model is divided into two parts, a data collection module and a data aggregation module. The data collection module is composed of a communication interface, a registration interface and a data storage interface. The data aggregation module is composed of a data summary interface and a context interface. Two major data modules work together to process the entire process of data scenes to be used. The model can be used in mines, smart homes, it will improve the reliability of data transmission Greatly.
Understanding Traffic Load in Software Defined WiFi Networks for Healthcare

Software Defined WiFi network (SD-WiFi) is a new paradigm that brings programmability into WiFi networks by introducing a centralized network control. SD-WiFi provides access to numerous internet applications such as Video, FTP, HTTP etc. The growth of wireless devices has led to enormous peaks in traffic load especially in health care sector. Understanding load and its relationship to QoS parameters at physical and MAC layers in SD-WiFi still remains an issue. This paper presents a suitable wireless load definition and analyzes the SD-WiFi performance by varying the load conditions. Extensive simulations have been performed to study the performance metrics such as packet loss and latency, through an OpenFlow enabled access point (AP), by changing packet payload and packet generation times. This study can serve as a basis to optimize SD-WiFi and configure parameters for real time applications.
Design of Small 5.5 GHz Band-Notched UWB monopole Antenna

A compact ultra-wideband monopole antenna is proposed. The antenna is simply composed of a fork-shaped monopole, a pair of rectangular stubs extended from the ground and a rectangular strip connected with the fork-shaped monopole. The overall size of the antenna is as small as $19 \times 22.5\times 1.6\ \mathrm{mm}$. Experimental results show the proposed antenna covers 3.75-10.6 GHz with 10 dB return loss, rejecting the 5.12–5.9 GHz band for WLAN.
Hyperspectral Band Selection for Lithologic Discrimination and Geological Mapping

Classification techniques applied to hyperspectral images are very useful for lithologic discrimination and geological mapping. Classifiers are often applied either to all spectral channels or only to absorption spectral channels. However, it is difficult to obtain different lithology information using specific absorption regions from the narrow bandwidth and contiguous spectral channels due to spectral variability among rocks. In this article, we propose a band selection (BS) method for hyperspectral lithologic discrimination, in which the lithological superpixels are first gathered. A spectral bands selection criterion is learned by measuring the homogeneity and the variation of the lithological superpixels, and lithologic discriminating bands are identified by an efficient clustering algorithm based on affinity propagation. In this article, two geologic test sites, i.e., the Airborne Visible/Infrared Imaging Spectrometer data of the Cuprite, Nevada, USA, including 11 lithologic units (9 types of rocks) and the Hyperion data of Junggar, China, with 5 lithologic units, are chosen for validation. The performance of the proposed BS method is compared with those of using all the bands, specific absorption spectral channels, and two literature BS techniques. Experimental results show that the proposed method improves mapping accuracy by selecting fewer bands with higher lithologic discrimination capability than the other considered methods.
Intelligent Vehicle Trajectory Tracking Algorithm Based on MPC Using Laguerre Functions

A trajectory tracking control algorithm is designed for the front wheel steering of the intelligent vehicle. The state space equation of vehicle trajectory tracking is established on the strength of the vehicle kinematics model. The model predictive control algorithm using Laguerre functions is used for realizing the optimal control of intelligent vehicle trajectory tracking. The orderd number of parameters in the algorithm is less than the ordered number of parameters in the conventional method(LQR), and a limited parameters number are focus on achieving a long control horizon. In order to check on the effect of the algorithm, it is simulated on MATLAB and compared with the traditional approach. The results show that the algorithm has superior performance and can be effectively and accurately applied to the trajectory tracking control of the intelligent vehicle.
Application Level Performance Measurements of Multi-Connectivity Options in Cellular Networks for Vehicular Scenarios

Intelligent Transportation Systems (ITS) and vehicle passengers request for high-speed and reliable data access from vehicle to infrastructure networks. For example, passengers like to consume multimedia services and new use cases arise from ITS and connected vehicular services that require high-speed and reliable connectivity solutions. To achieve these requirements multi-connectivity is a viable solution to increase throughput and reliability. Such options exist on various layers. On the network access layer multiple antennas are deployed for transmitting and receiving data in parallel data streams; on the transport layer different network access opportunities are utilized by multi-path protocols such as multi-path TCP. At the present study, we evaluate performance metrics of these options in a real-world vehicular scenario. We quantify throughput, delay, and reliability improvements of using multiple antennas and multi-path TCP and also indicate issues of multi-path TCP. Further, we demonstrate performance improvements by the change of the congestion control protocol.
Droop Control of Proportional Resonant controller Fed Hybrid Distrubuted Generator in Low voltage AC Microgrid

Power flow control is vital in microgrids whether it is AC or DC microgrid. Many Droop control techniques are used to efficiently control the power injected by DGs (distributed generators) in a decentralized manner. Residential low rating microgrids are eventually increasing which are hybrid in nature and need to control the power injected to a grid in grid-connected and to meet power balance in Islanded modes. A modified current based power point tracking is proposed in this paper to improve transient behavior of DC side parameters thus improves DC bus voltage regulation and Proportional resonant control is introduced which is responsible to control the active and reactive power flow simultaneously from inputs given by droop controller. The same reference is used to extend the droop control strategy in a grid-connected case also with necessary modifications.
PIDPlus Application under Wireless Measurement Environment

The use of wireless devices can greatly reduce project period and cables, and improve the flexibility of system design or redesign. However, wireless technology accompanied with uncertain delays is not reliable as wired transmission. Evident overshoot and oscillation or even instability appear in PID control with wireless measurements. According to the feature of PID control with wireless measurements, an enhanced PID controller, PIDPlus, is applied to get better control performance. In PIDPlus, the integral component stops working when there are no new measurements as expected and the derivative component is revised to reduce possible spikes when new values are transmitted suddenly. The result of simulation and experiment prove the effectiveness of the enhanced PID controller.
Inferring IoT Device Types from Network Behavior Using Unsupervised Clustering

The Internet-of-Things (IoT) is increasingly becoming a major challenge for network administrators to monitor and manage connected devices and sensors, ranging from smart-lights to smoke-alarms and security-cameras. In addition to new device offerings, manufacturers tend to automatically perform firmware upgrade from their cloud servers to change functionalities of existing devices that are operational in the field. This makes it difficult to re-train device classification models in order to capture legitimate changes dynamically. In this paper, we develop a modular device classification architecture that allows us to dynamically accommodate legitimate changes in network IoT assets, either addition of a new device type or upgrades of existing types, without replacing the entire set of models. Our contributions are twofold: (1) We identify key traffic attributes that can be obtained from flow-level network telemetry to characterize individual IoT devices. We develop an unsupervised one-class clustering method for each device to detect its normal network behavior. (2) We tune individual device-specific clustering models and use them to classify IoT devices in real-time. We enhance our classification by developing methods for automatic conflict resolution and noise filtering. We evaluate the efficacy of our scheme by applying it to traffic traces of ten real IoT devices, and demonstrate its ability to achieve overall accuracy of more than 94%.
Low Cost Frequency Doubler Using a Modified Dual-Mode Hybrid Ring Coupler

This paper presents a low cost frequency doubler with high performance. A modified dual-mode hybrid ring coupler is employed in the doubler design. The operation principle of the ring coupler is analyzed. A 12-/24 GHz doubler is designed and fabricated based on the proposed coupler. Measurement results show the doubler achieves low conversion loss of less than 9 dB, and fundamental/third-harmonic rejection of more than 28 dBc, within the input frequency range of 11 to 12 GHz.
An Incentive Caching Mechanism in Wireless Networks Based on Stackelberg Game

In this paper, an incentive caching mechanism between multiple mobile network operators (MNOs) and multiple video service providers (VSPs) is proposed to alleviate the traffic burden on the cellular backhaul link. MNOs provide storage space to VSPs and VSPs compete to decide the caching demand amount to improve users' experience. The caching resource allocation problem is formulated as a Stackelberg game, in which MNOs act as leaders and VSPs act as followers. The existence of Nash equilibrium (NE) in followers non-cooperative game is proved. The optimal strategies of MNOs and VSPs are obtained by using distributed iteration algorithm to solve the game problem. Simulation results show that the proposed mechanism could obtain higher revenue than other schemes.
A Divide and Conquer Algorithm for Dominance Testing in Acyclic CP-Nets

The Conditional Preference Network (CP-net) represents user's conditional ceteris paribus (all else being equal) preference statements in a graphical manner. In general, an acyclic CP-net induces a strict partial order over the outcomes. The task of comparing two outcomes (dominance testing) is generally PSPACE-complete, which is a limitation for this intuitive model, especially when representing and solving preference-based constrained optimization problems. In order to overcome this limitation in practice, we propose a divide and conquer algorithm that compares two outcomes according to dominance testing. The algorithm divides the original CP-net into sub CP-nets, and recursively calls itself for each of the sub CP-nets until it reaches to a termination criterion. In the termination criterion, the answer of the dominance query is returned. With a theoretical analysis of the time performance, we demonstrate that the proposed algorithm outperforms the existing methods.
Integration and Characterization of Transparent Thin Film Transistors with Carbon Nanotubes as Aligned Channel and Electrodes

Transparent top-gate aligned-channel all-carbon-nanotube thin film transistors (all-CNT-TFTs) are proposed and integrated on a polyethylene naphthalate substrate, with 99.9%-purity semiconducting carbon nanotubes (SCNTs) as channel and metallic carbon nanotubes (MCNTs) as source/drain/gate electrodes. Fabricated by Langmuir-Blodgett (LB) method, an aligned-channel all-CNT-TFT with an on/off current ratio of 3.89×104, as well as a transparency of 86% has been achieved. The ultrathin films formed by LB method were uniform on the whole sample region, which can provide stable performance of the transistors. The aligned SCNT channel and MCNT source/drain electrodes can form clear tube-tube contact, which is useful for accurate mechanism analysis.
Temperature Compensation of Laser Triangular Displacement Sensor

The measurement precision of the laser triangulation displacement sensor will be decreased with temperature, its application areas have been restricted for a long time. Three main factors affecting measurement accuracy were analyzed and two algorithms were proposed in this paper to compensate for the temperature error. Experiments of the three aspects such as measurement distance, chip temperature and ambient temperature are shown. In addition, a temperature compensation model based on multiple linear regression was proposed. Considering that various influencing factors are not simple coupling, a neural network technology that can effectively solve nonlinear problems was used and a three-layer neural network model was also built, which can generate temperature compensation values. At the same time, the results of the two algorithms were compared. Results show that the neural network model reduces the displacement temperature drift from the original $-80 \mu \mathrm{m}\sim +50 \mu \mathrm{m}$ to within $\pm 20 \mu\mathrm{m}$ in the range of 0 $\sim 40^{\circ}\mathrm{C}$, which significantly improves the temperature adaptability of the laser displacement sensor and is successfully applied in the aerospace field.
Design of a New Rotary Micropositioning Stage Driven by Piezoelectric Actuator

This paper presents the design of a new rotary micropositioning stage, which is actuated by a linear-type piezoelectric stack actuator. This structure realizes a large enough output angle by introducing lever displacement amplifiers. This work also includes innovative designs for accepting the force and displacement of PZT. Moreover, it avoids undesired displacement by adopting symmetrical structure design. The proposed design also exhibits a compact structure, which provides convenience in practical application. Finite-element analysis is conducted with simulation study to verify the performance of the presented rotary micropositioning stage.
Selection and Structural Design of Reactive Power Compensators for a 200 MW Floating Offshore Wind Farm

In this paper, the reactive power compensator of a 200 MW offshore wind farm was discussed. The proper capacity and control method of the reactive power compensator were chosen to compensate for the reactive power generated by the power transmission cable and to meet the grid code. In order to select the type of reactive power compensator, an economic analysis was carried out taking into account the capital expenditure and operating expenses of the reactive power compensator, and the transient analysis was performed using PSCAD/EMTDC. The combination of a fixed shunt reactor and a static VAR compensator was chosen in consideration of the grid code, economy, dynamic control characteristics and fault-ride-through capability. These results can be used to effectively select reactive power compensation devices for offshore wind farms.
Lost in Disclosure: On the Inference of Password Composition Policies

Large-scale password data breaches are becoming increasingly commonplace, which has enabled researchers to produce a substantial body of password security research utilising real-world password datasets, which often contain numbers of records in the tens or even hundreds of millions. While much study has been conducted on how password composition policies—sets of rules that a user must abide by when creating a password—influence the distribution of user-chosen passwords on a system, much less research has been done on inferring the password composition policy that a given set of user-chosen passwords was created under. In this paper, we state the problem with the naive approach to this challenge, and suggest a simple approach that produces more reliable results. We also present pol-infer, a tool that implements this approach, and demonstrates its use in inferring password composition policies.