Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Power Amplifier Characteristic Based on Orthogonal Polynomials Method for FBMC Systems

In this paper, an analytical expression of nonlinear power amplifier characteristic based on orthogonal polynomials method for filter bank multicarrier modulation (FBMC) systems is proposed. FBMC has been widely deployed for broadband wireless communications. Nevertheless, one of the drawbacks of the FBMC systems is a large peak-to-average power ratio (PAPR) in its time domain signal. This leads to a nonlinear distortion when the signal is passed through a high power amplifier (HPA) of a transmitter. In this paper, an analytical computation of the HPA characteristic based on the orthogonal polynomial method for baseband uniform, exponential, and Rayleigh input distribution with odd and even polynomial coefficient order is proposed. The HPA modelling error comparison between the proposed orthogonal method and the conventional polynomial method is investigated at a different IBO. Simulation results show that the proposed orthogonal polynomial method is superior to the conventional polynomial method. Moreover, the HPA modelling error of the proposed orthogonal polynomial method for uniform input distribution yield better result compared with the exponential and the Rayleigh input distribution case.
A Novel Semi-Supervised Learning Approach for Network Intrusion Detection on Cloud-Based Robotic System

Although the cloud-based robotic system has provided the services in various industries, its data safety is continuously threatened, and the network intrusion detection system (NIDS) is considered as a necessary component to ensure its security. In recent years, many machine learning (ML) techniques have been applied for building a more intelligent NIDS. Most NIDSs based on the ML method and artificial intelligence techniques are either supervised or unsupervised. However, the supervised learning for NIDS depends much on the labeled data. This weakness makes it harder to detect the latest attack patterns. Meanwhile, the unsupervised learning for NIDS often fails to give the satisfactory results. Therefore, this paper proposed a novel fuzziness-based semi-supervised learning approach via ensemble learning for network intrusion detection on the cloud-based robotic system, which can address the above issues. First, due to the good generalization ability of ensemble learning, we construct an ensemble system trained by the labeled data. Moreover, for better utilizing the unlabeled data, a fuzziness-based method is adopted for data analysis. In this way, the noisy and redundant examples in the data set are removed. Finally, we use the same ensemble approach to combine both supervised and unsupervised parts. To verify the effectiveness and robustness of the NIDS, the proposed approach is tested on the NSL-KDD data set, which is a commonly used traffic data set. The experimental results show that the proposed approach achieves the accuracy 84.54% and 71.29% on the, respectively, “KDDTest+” and “KDDTest-21” data sets. When compared with the state-of-the-art method, the proposed method also delivers a promising result.
Are all cities with similar urban form or not? Redefining cities with ubiquitous points of interest and evaluating them with indicators at city and block levels in China

ABSTRACT Urban forms reflect spatial structures of cities, which have been consciously and dramatically changing in China. Fast urbanisation may lead to similar urban forms due to similar habits and strategies of city planning. However, whether urban forms in China are identical or significantly different has not been empirically investigated. In this paper, urban forms are investigated based on two spatial units: city and block. The boundaries of natural cities in terms of the density of human settlements and activities are delineated with the concept of ‘redefined city’ using points of interests (POIs), and blocks are determined by road networks. Urban forms are characterised by city-block two-level spatial morphologies. Further, redefined cities are classified into four hierarchies to examine the effects of different city development stages on urban forms. The spatial morphology is explained by urbanisation variables to understand the effects. Results show that the urban forms are spatially clustered from the perspective of city-block two-level morphologies. Urban forms tend to be similar within the same hierarchies, but significantly varied among different hierarchies, which is closely related to the development stages. Additionally, the spatial dimensional indicators of urbanisation could explain 41% of the spatial morphology of redefined cities.
Reactive Power Compensation Method for Distribution Network from Electric Vehicles

As Plug-in hybrid electric vehicles (PHEVs), dips usually occur on the bus because multiple PHEVs are connected to the same bus simultaneously. In view of the flaws of the existing traditional compensators and their extra cost, the grid -side reactive power compensation by the chargers of PHEVs during charging is expected to address this issue. To provide such compensation during charging, it is necessary that PHEVs incorporate bidirectional chargers. Among different types of bidirectional chargers, the DC voltage controller is unable to compensate voltage. Alternatively, the chargers with active power controllers regulate active power as well as reactive power independently, which is effective and convenient. In this paper, we propose a PR control strategy for the PHEV s chargers to tune reactive power online and to increase power factor. The simulation examples are given to verify the effectiveness of the proposed strategy.
An Enhanced Cuckoo Search Algorithm for Solving Optimization Problems

In this paper, we present an enhanced cuckoo search (ECS) algorithm based on Gaussian diffusion random walks and greedy selection approach. Despite wide applications of cuckoo search (CS) algorithm, it suffers from low convergence speed and lacks balance between local and global search. To overcome these limitations, ECS algorithm is proposed. It employs Gaussian diffusion random walks instead of Levy flights random walks to enhance the local search. In addition, the greedy selection approach is employed to ensure that ECS reaches the optimum solution. Twenty one IEEE benchmark functions are used to evaluate the performance of the proposed ECS algorithm against CS and a recent adaptive cuckoo search (ACS) algorithms. The ECS shows excellent performance in reaching optimum values with a high convergence speed compared to CS and ACS algorithms. In addition, with several experiments, the effects of population size and the abandon probability $\pmb{P}_{\pmb{a}}$ are carried out for the three algorithms and experiments shown that ECS is more robust than CS and ACS algorithms. Furthermore, superior performance of ECS algorithm against four other competitive algorithms has also been shown.
Log Likelihood Ratio Based Relay Selection Scheme for Amplify and Forward Relaying with Three State Markov Channel

This paper presents log likelihood ratio (LLR) based relay selection scheme for a cooperative amplify and forward relaying system. To evaluate the performance of the aforementioned system model, a three state Markov chain based fading environment has been presented to toggle among Rayleigh, Rician, and Nakagami-m fading environment. A simulation is carried out while assuming that there is no possibility of direct transmission from the source and destination terminal. Simulation results on the basis of Bit Error Rate (BER), Instantaneous Channel Capacity, and Outage probability have been presented and compared for different cases. In each case, the best performance of the proposed algorithm is obtained with a Binary Phase Shift Keying (BPSK) modulation scheme.
Synthesis and characterization of 3D CoMoO4/rGO aerogel for supercapacitor electrodes

The 3D CoMoO4/rGO aerogel was synthesized using hydrothermal method and used as electrode for high- performance supercapacitors. Microstructure analysis showed that the CoMoO4/rGO hybrid composite is composed of one- dimensional CoMoO4 nanorods grown on reduced graphene oxide nanosheets, which exhibited higher specific capacitance than pure rGO, CoMoO4 nanorods with good rate capability. The composite retained 84.7 % of its original specific capacitance after 1000 charge–discharge cycles at a current density of 1 A g-1, which demonstrated that the long-term cycling stability of CoMoO4/rGO aerogel is outstanding. Conductive graphene improves electron conductivity and enhances the Faradaic pseudo-capacitance of CoMoO4. The remarkable electrochemical performance makes the 3D CoMoO4/rGO aerogel one of the most competitive electrode materials for electrochemical energy storage, which provides a potential way to fabricate the all-solid-state and wearable energy storage devices.
Blind Filtering at Third Parties: An Efficient Privacy-Preserving Framework for Location-Based Services

Location-based service (LBS) has gained increasing popularity recently, but protecting users’ privacy in LBS remains challenging. Depending on whether a trusted third party (TTP) is used, existing solutions can be classified into: TTP-based and TTP-free. The former relies on a TTP for user privacy protection, which creates a single-point-failure and is thus impractical in reality. The latter does not require any TTP, but usually introduces redundant point-of-interest (POI) records in query result and thus incurs significant computation and communication costs on the user side, making them unsuitable for resource-constrained mobile devices. In this paper, we propose a novel framework to protect user privacy while ensuring efficiency. Our framework also uses redundant POI records to protect privacy against LBS provider but employs a semi-trusted third party, called proxy, to filter out redundant POI records. To protect privacy against proxy, we design a novel filtering protocol, Blind filter, to allow the proxy to filter out redundant encrypted POI records in a blind way. In comparison with existing solutions, our framework is not only resilient to dual identity attack, but also incurs lower communication and computation overhead. Comprehensive analysis and experiments show that our framework is secure and highly efficient in mobile environments.
Meshless Interpolating Moving Least Square Mixed Collocation Method for Elasticity Problem

In this paper, a new interpolating moving least square (IMLS) method is proposed which has the properties of moving least square (MLS) and the radial basis function (RBF). In the approach, the polynomial and the RBF are combined. The coefficients of the polynomial are obtained by MLS approximation and the coefficients of RBF are obtained through the condition that the interpolation function obtained passes through all scattered nodes in an influence domain. Thus shape functions preserve the Kronecker delta function property which makes the implementation of essential boundary conditions much easier than the meshless methods based on the MLS approximation. In addition, the diffuse derivatives instead of full derivative are applied for the derivatives of shape functions. Thus, the computational efficiency is improved. Examples on elasticity problems show that the present method has the same accuracy and convergence rate as MLS.
Door and Doorway Etiquette for Virtual Humans

We introduce a framework for simulating a variety of nontrivial, socially motivated behaviors that underlie the orderly passage of pedestrians through doorways, especially the common courtesy of opening and holding doors open for others, an important etiquette that has been overlooked in the literature on autonomous multi-human animation. Emulating such social activity requires serious attention to the interplay of visual perception, navigation in constrained doorway environments, manipulation of a variety of door types, and high-level decision making based on social considerations. To tackle this complex human simulation problem, we take an artificial life approach to modeling autonomous pedestrians, proposing a layered architecture comprising mental, behavioral, and motor layers. The behavioral layer couples two stages: (1) a decentralized, agent-based strategy for dynamically determining the well-mannered ordering of pedestrians around doorways, and (2) a state-based model that directs and coordinates a pedestrian's interactions with the door. The mental layer is a Bayesian network decision model that dynamically selects appropriate door-holding behaviors by considering both internal and external social factors pertinent to pedestrians interacting with one another in and around doorways. Our framework addresses the various door types in common use and supports a variety of doorway etiquette scenarios with efficient, real-time performance.
In-Vehicle CAN Network MessageTampering and Forgery Detection Architecture Based on Data Information Redundancy

Controller Area Network (CAN) is one of the most important in-vehicle networks. The data are mainly sensor signals or control commands. There is correlation between the data transmitted in different CAN frames and different data fields for they come from collaborative components of one vehicle. The redundancy information between the data could be calculated out with the vehicle model and control logic relations, and used to detect forgery data. A tampering and forgery detection method for CAN based on the idea is presented, and illustrated with a powertrain CAN model on a CANoe-based experimental bench.
A Computing Perspective of Quantum Cryptography [Energy and Security]

In the continuous evolution of the computer age, the next big step in security is being achieved by embracing quantum cryptography. As the unit of information shifts from the currently used bit to the quantum bit (qubit), a new realm of untapped features is offered that can be used to ensure the confidentiality of the information being shared among participants. This system confidentiality is ensured by the use of a quantum channel for exchanging secret keys, which are then used to encrypt the data being shared. The system is a combination of cryptography and quantum computing that is aided by the laws of physics.
A New Master Control Framework for the Next Generation Service Robots Based on ROS

The next generation service robots will become more intelligent and have stronger kinematic and dynamic abilities. This requires a stable, flexible, and extensible master control framework. A new framework, which takes those potential abilities into consideration, is proposed based on ROS in this work. The framework is comprised of five modules. The task manager module contains a novel mechanism for task management and action learning. Other four modules integrate several toolkits of ROS and extend their functions. The new framework has been used to control Sawyer robot to finish some tasks.
Integrating Diversity into Neural-Network-Based Face Deidentification

The extensive use of video surveillance and mass visual media distribution in modern video sharing, social media and cloud services have ignited concerns about the privacy of people identifiable in the scenes. A popular approach for visual privacy protection is face deidentification technique, which aims at concealing the identity of individuals in the video or image while still preserving certain facial attributes after deidentification. Prior research has successfully proposed κ-Same solutions, which provide k-anonymity privacy protection. However, they always cause every $k$ original faces to share the same de-identified faces, making the diversity within the set of the original faces lost. To address this challenge, this paper presents an deidentification procedure, which ensures anonymity by synthesizing surrogate faces for deidentification using generative neural networks (GNNs) while integrates diversity into de-identified faces. We demonstrate the feasibility of the proposed method with extensive experiments which show that our alteration method is indeed effective.
Distributed Disturbance Rejection for Dynamic Networked Systems

In this paper, we consider the distributed synchronization problem of nonlinear dynamic networked systems with heterogeneous disturbances. For the rejection of disturbances, we propose dynamic controllers which do not rely on the disturbance information. By graph theory and Lyapunov methods, a sufficient condition for the synchronization of nonlinear networked systems is established and it relies on the knowledge of the smallest nonzero eigenvalues of graph Laplacians. In order to implement the proposed disturbance rejection protocols in a fully distributed fashion, adaptive control laws are proposed for the adjustment of coupling strengths of each subsystem. We show that connected network topologies are sufficient for synchronizing the networked systems with the proposed adaptive control laws. A simulation example is given to illustrate the disturbance rejection performance.
Design and Analysis of Microchannel for the Thermal Management of Multi-stacked LTCC Laminates

In micro-system packaging, the high-density integration of various chips and devices on the substrate imposes high demand on the heat dissipation capability of the substrate. LTCC substrates have been widely used in the packaging of radio frequency/microwave integrated circuits due to their satisfactory heat resistance, thermal conductivity, convenient and flexible wiring, and low coefficient of thermal expansion [1]. For high integration and miniaturization as required by modules like high-power T/R components, under the current process conditions, it is difficult to fulfil the requirements with just one single substrate, so two or more stacked LTCC substrate laminates in one package must be considered. However, the thermal management of stacked laminates is a challenge to be overcome. In this paper, ANSYS software is used to physically model the structure of a microchannel network, namely a dual-layer spiral and serpentine microchannels. The heat-dissipating capability of the connected microchannel substrate mainly depends on the temperature rise of the lower substrate which containing the fluid outlet. The water mass flow rate at the inlet is controlled at 45ml/min can meet the heat dissipation requirements of 15W/cm2 at the heat source of the upper and lower substrates, which can expand the applications of LTCC-based microwave power module, and can provide the technology support for miniaturized equipment.
Visualization of Electromagnetic Field Distribution with Augmented Reality

Since electromagnetic fields are invisible, methods to visualize spatial distribution have been proposed. Conventional methods tend to display planar distributions and measurement systems become larger. In this study, we developed a method to realize a three - dimensional display of electromagnetic field intensity measured utilizing the screen of a tablet terminal by combining simple devices. By using the AR marker to calculate the relative coordinates of the viewpoint of the observer, it became possible to visualize the three-dimensional distribution of the electromagnetic field with the natural action of holding the tablet terminal over the measured object.
Consecutive Feature Network for Object Detection

Feature Pyramid Network (FPN) is one of the best object detection algorithms in the current object detection field, which uses convolutional neural network (CNN) to detect different scaled objects in an image. However, FPN's feature fusion method ignores the influence of the consecutive feature, which hinders the information flow. In this paper, we proposed an end-to-end image detection model called CFN (Consecutive Feature Network) to overcome this problem and speed up the detection process. Under the premise of equal accuracy, the novel feature fusion method we propose can detect faster than other methods. In the feature fusion module, features from consecutive layers with different scales are merged instead of compartmental layers, which will be fed to the classification and regression subnet to predict the final detection results. On the PASCAL VOC 2007 test, without any data augmentation training skills, our proposed network can achieve 77.1 mAP (mean average precision) at the speed of 3.9 FPS (frame per second) on a single Nvidia 1080Ti GPU. Code will be made publicly available.
Robust Adaptive Control of MIMO Nonlinear Systems with Output Constraint and Minimum Adjusting Parameters

In this paper, the problem of robust adaptive control is discussed for a class of multi-input multi-output (MIMO) nonlinear systems with unmodeled dynamics and output constraint. Based on modified dynamic surface control (DSC) and the approximation capability of neural networks, the adaptive vector virtual control and control law are designed in recursive each step. A dynamic signal is used to handle unmodeled dynamics in the system. By introducing the barrier Lyapunov function (BLF), the output constraint is not violated in the designed adaptive control system. The stability analysis shows that all the signals in the closed-loop control system are semi-globally uniformly ultimately bounded (SGUUB). Simulation results with the 2-DOF robotic system is provided to demonstrate the effectiveness of the proposed control scheme.
Parent Selection Strategies in Niching Genetic Algorithms

In this work, we analyze the effect of three parent selection strategies in a niching genetic algorithm: binary tournament, most similar and most dissimilar parent selection. These strategies were implemented in a steady-state genetic algorithm (GA) that uses the Restricted Tournament Selection (RTS) method for niching formation. The implemented strategies were compared to the random parent selection used by the standard RTS method and tested on the 20 functions of the CEC 2013 benchmark set of the Competition on Niching Methods for Multi-modal Optimization. The results indicate the most similar parent selection as the best strategy, mainly concerning high dimensional functions. Moreover, the proposed GA-RTS algorithm with the most similar parent selection scheme proved to be competitive with other state-of-the-art niching algorithms.
On Controlling Autonomous Unmanned Aerial Vehicle Formation with Coarse Information

This brief study the gradient-based control for multi-agents formation using graph theory, such as the UAV formation. The existing works are often with the assumption of accurate measurement of agents' neighbors, while this brief use coarsely quantified controller. For this, Filippov solution, tools of analyzing the non-smooth dynamical system, will be introduced. We prove that using coarse distance information, the convergence of formation is also global, except for the initial position is collinear. Finally, the experimental simulation results are presented to validate the correctness of the theoretical analysis.
A Risk Assessment Method for Void in SnAg Solder Bump Influence on Reliability of Flip Chip Packaging

The demand of solder bump packaging keeps growing in recent years because of its advantage of volume I/O counts, competitive electrical performance and smaller packaging dimension. However, considering reliability risk, void in solder bump is one of critical defects during solder bump and flip chip process. Different criteria for solder void comes from different OSAT (Outsourced Semiconductor Assembly and Test), design house and IDM (Integrate Design Manufacture) based on their own product characters and application condition. There should be a common procedure to apply risk assessment to convince all parts to align the solder void criteria. This study adopted a practical risk assessment procedure on solder bump void. Different sizes of solder bump void are demonstrated and used for reliability test, to assess the risk of potential failure, hence this test procedure can ensure that some certain kinds of bump voids are safe in flip chip process and product application. Also it is helpful to be a reference to align solder bump void criteria.