Miracle-dz/UniFAR-FTUs huggingface dataset:
PM10 Concentration Forecasting Based on CEEMDAN, SE and LSTM Neural Network

To improve the predicting accuracy of PM10 concentration prediction, this paper presents a combined prediction model of PM10 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE) and long short-term memory (LSTM). The PM10 concentration time series is decomposed into a series of restructured subsequences with obvious complexity differences by CEEMDAN-SE firstly. Then, by adding meteorological parameters to each different restrict-ured subsequence, the LSTM prediction model is built. By adding the prediction results, the final results are got. Meanwhile, the data of four monitoring stations in Tangshan city is used to implement simulation experiment. Experiment results confirm that the proposed prediction model compares with other prediction models to show high prediction precision, and good universality.
Using FGSM Targeted Attack to Improve the Transferability of Adversarial Example

At present, many people pay attention to the safety problems of artificial intelligence, and the emergence of adversarial examples is one of these problems. The adversarial examples can be used to attack a neural network classification model to make its classification wrong. It is an important method to improve the attack effect of adversarial examples by improving the transferability of adversarial examples and enabling them to attack multiple different neural network classification models at the same time. When we use FGSM algorithm to attack a model, first, we set ∊ a medium magnitude value, and then use targeted attack, which can improve the transferability of the adversarial examples generated by this algorithm. We can use FGSM algorithm to carry out white-box attack on the neural network model. The first step is to set a fixed value. Then, because FGSM algorithm is non-targeted attack, when adding a termination condition of iterative attack, we can use FGSM algorithm to carry out targeted attack, so that the generated adversarial examples can be identified as specific tags by the neural network model. We found that the transferability of the adversarial examples generated in this way is improved and these adversarial examples can attack many different neural network models.
Preliminary Study of the Voice-controlled Electric Heat Radiator

This study presented a voice-controlled electric heat radiator which was named as “little sun”. The switch of the radiator was controlled by the input voice into the microphone. A voice recognition module connected with Arduino microcontroller which assigned the switches to turn on or off by the instructions of the user's voice. The results presented that the little sun works. In 82 tests, 80 voice commands was successfully recognized at the first command, and 2 voice commands was successfully recognized after repeating the first commands in the condition of signal-to-voice ratios (SNR) in the range of 10 to 30 dB. Therefore, a low cost, easy to use and high performance voice controlled radiator was implemented to improve the convenience and comfortability of human life.
Surface Exploration with a Robot-Trailer System for Autonomous Subsurface Scanning

Autonomous surface exploration with mobile robots entails the problem of simultaneous trajectory planning and path tracking, while also on-board robot localization and kinematics control are essential. When it comes to articulated robotic setups, planning and control is more challenging since the towed component that typically bears a tool or a sensing mechanism should closely follow a specific trajectory in order to accurately complete its task. The paper at hand presents a holistic surface exploration method with an articulated mobile robot that tows a two-wheeled trailer. A variation of Boustrophedon global path planing module has been developed considering robot's embodiment for full field coverage, integrated with a real-time Model Predictive Controller (MPC) to ensure trailer's path following. Articulated robot's state estimation is provided with stereo-based visual odometry. The system has been evaluated both in simulation and in realistic environment, proving its ability to perform dense surface exploration that in our particular case, eventually enables a Ground Penetrating Radar (GPR) mounted on the trailer to autonomously scan the entire target area.
Dimension Dependent Silicon Photonics Rib Waveguide Characterisation

In this paper, SOI rib waveguides with the variation of core dimensions have been demonstrated. The analysis of a silicon rib waveguide is performed by simulating the SOI waveguide containing a forward biased PN junction by applying the electro-optic carrier - injection effect via electric voltage changes. The dimensions of the waveguide/ring resonator have been altered. The dimensions of the waveguide/ring resonator include the width of the core waveguide (W), the height of the core waveguide (Hc), the rib thickness (Hr) and the height of buried oxide (Ho). Thus, in this research, the dimensions of the waveguide/ring resonator will be varied at distinct value with a specific interval in order to investigate the performance of the micro-ring resonator. This is because as the dimensions of the waveguide vary, its resonance wavelength will be change and thus shifting of resonance will occur. The simulation generates refractive index of the waveguide with respect to the applied voltage and refractive index, loss, gain and dispersion of the waveguide with respect to wavelength.
Three-Dimensional Constellation Modulated Visible Light Communications with Pixelated Addressable Micro-LED Array

In this paper, we improved the VPPM (Variable- Pulse-Position-Modulation) modulation mode referred to the PHY II based on the research about the short distance visible light communication IEEE 802.15. 7 standard, and put forward a compound digital baseband modulation method named VPPM-DPAM (Variable-Pulse-Position-Modulation-Discrete Pulse-Amplitude-Modulation) which is suitable for indoor white Micro-LED array by introducing the DPAM based on the dispersion of light intensity, the modulated method we presented not only improves the communication speed of the system but also provides an accurate dimming mechanism for indoor adjustable luminance lighting which realizes the integration of communication lighting. What's more, we further discussed the constellation design in three-dimensional optimization of VPPM-DPAM which focused on the nonconvex optimization problem, found that the communication performance and lighting performance of the system have a constraint relation which was verified in both simulation and experiment. Finally, a visual light communication system based on DPAM-VPPM with compatible communication and lighting performance is obtained with certain anti-noise capability.
Privacy Preserving Medium Access Control Protocol for wireless Body Area Sensor Networks

Wireless Sensor Networks (WSNs) play vital role in several fields and particularly add more in the health domain. Several applications in the health domain utilize the WSNs to diagnose the disease and cure the patients. However, there is threat and risk involved for exploiting the privacy of the patient and deteriorating the performance of operating tools and systems. There are few existing approaches handling the attacks to maintain the privacy issue of wireless body area sensor networks (WBASNs). Most of the threats are expected on the Medium Access Control (MAC) sub-layer that affect the performance of WBANs. In this paper, we introduce privacy preserving Medium Access Control (PP-MAC) to detect the selfish attack. The algorithm aims to save energy because selfish attack leads to additional energy consumption. In this type of attack, the illegitimate node has edge over the legitimate nodes at the MAC sub-layer. As a result, the illegitimate node consumes the resources negatively. The effectiveness of our proposed algorithm is tested by using OMNET++ and compared with other known existing MAC protocols designed for WBANs. Based on the simulation results, our proposed protocol performs better than other protocols in detecting the rate of selfish attacks, saving the energy, and performance throughput.
Design and Analysis of Effective Vivaldi Antennas for Brain Tumor Detections: Ku Frequency Band

Our goal is to design different Vivaldi antennas with simple, small, and low cost. They can be used in Ku frequency band, which can be used for brain tumor detection in the presence of abnormal tissues or tumor. The whole work is divided by two parts, antenna design part and brain tumor detection part. The antenna is being designed to work properly across the given Ku frequency 13.5 GHz to 14.71GHz. For tumor detection, a multilayer brain model (skin, fat, bone and white matter) is designed using the CST microwave studio. Basically, the antenna is placed between the skull and white matter of brain model to radiate into brain tissues. We analyzed the return loss and 3dB point to ensure the presence of brain tumor. The measured specific absorption rate shows that it is within the patient safety range.
Recent Advances in Underfill for New Package Architectures

Various types of advanced packages are available, including Fan-Out Wafer Level Package (FO-WLP), Flip-Chip Chip-Scale Package (FC-CSP) and Flip-Chip Ball Grid Arrays (FC-BGA) packages. These advanced packages are migrating to multi-chip package architectures such as 2.3D, 2.5D technology [1]–[6]. These advanced ball grid arrays (BGAs) incorporate multiple dies on a substrate or an interposer. This paper examines the motivation for the move toward heterogeneous integration of multi-chip architecture by a comparison of die size and die yield. It also reports the results of the 2.5D package trend analysis. By comparing a silicon interposer with a redistribution layer (RDL) interposer, further simulations were performed to investigate the thermomechanical stress behavior of an interposer package against the warpage of the package and tensile stress of underfill and micro-bumps [7]. For a multi-die interposer package, underfill seals below and between the die areas. Between the dies, the underfill was sealed vertically like a wall. The stress distribution of the underfill between the die and the interposer, the underfill between the interposer and the substrate, and the vertical underfill wall is discussed. Low Coefficient-of-Thermal-Expansion (CTE)/high modulus underfill was compared to high CTE/low modulus underfill in the same interposer package.
A Collaborative Simulation Architecture for Influence Evaluation of Communication Network Faults on Power Grid System

With the rapid development of the smart grid, the power grid simulation integrated with its communication system becomes very important for power grid evaluation and deployment. In this paper, a collaborative simulation architecture for power grid system with the consideration of its communication system, in which the DIgSILENT, the QualNet, and an association controller are connected by two investigated interfaces, i.e., a data interaction interface (DII) and a dynamic control interface (DCI), is proposed for the influence evaluation of communication network faults on the power grid system. DII supports the information exchanging between the DIgSILENT and the QualNet, and the DCI is in charge of dynamical fault configuration of communication network, control message transmission from association controller to both DIgSILENT and QualNet, and state information feedback from DIgSILENT and QualNet to association controller. Experimental results show that the proposed interfaces can effectively implement data exchange between DIgSILENT, QualNet and association controller with an acceptable interface delay. And the influence of communication network faults on power grid system can be obtained obviously.
Artificial intelligence-empowered resource management for future wireless communications: A survey

How to explore and exploit the full potential of artificial intelligence (AI) technologies in future wireless communications such as beyond 5G (B5G) and 6G is an extremely hot inter-disciplinary research topic around the world. On the one hand, AI empowers intelligent resource management for wireless communications through powerful learning and automatic adaptation capabilities. On the other hand, embracing AI in wireless communication resource management calls for new network architecture and system models as well as standardized interfaces/protocols/data formats to facilitate the large-scale deployment of AI in future B5G/6G networks. This paper reviews the state-of-art AI-empowered resource management from the framework perspective down to the methodology perspective, not only considering the radio resource (e.g., spectrum) management but also other types of resources such as computing and caching. We also discuss the challenges and opportunities for AI-based resource management to widely deploy AI in future wireless communication networks.
Design of Ultra-wideband Cavity-backed and Slot-tuned Bowtie Antenna for Ground Penetrating Radar Applications

A slot-tuned and cavity-backed bow-tie antenna is proposed to facilitate application of Ground penetrating radar (GPR). Resistive loaded method is employed to broaden the impedance bandwidth and improve the waveform fidelity. To improve the effects of resistive loading, a slot-tuned structure is applied. Compared slot-tuned and regular antenna is studied about frequency characteristic. The numerical results show that the proposed antenna has a bandwidth from 100 MHz to 800 MHz with VSWR less than 2, and the time-domain waveform has a low-time ring. It shows that the antenna can be well suitable for subsurface detecting application.
Electromagnetic Transient Model and Parameters Identification of PMSG-Based Wind Farm

In order to study the equivalent electromagnetic transient characteristics of PMSG-based wind farm, the structure, control strategy and coordinates transformation method of PMSG-based wind turbine generator are analyzed in this paper. To this end, a detailed electromagnetic transient model of PMSG-based wind turbine generator is established. The steady state equivalent circuit, phasor graph and initializing method of state variable are developed. Then, the process of parameters identification using genetic algorithm is introduced. Since in the transient process, the change of rotor speed is very small, and the input mechanical torque of the wind turbine can be approximately considered constant. Therefore, the parameters of the mechanical torque need not to be identified, which greatly reduces the number of parameters involved in the identification and improves the convergence speed of the algorithm and the stability of the identification results. Finally, a simulation example is given to illustrate the effectiveness of the proposed electromagnetic transient model and the parameters identification method.
JCache: Journaling-Aware Flash Caching

Virtual machines (VMs) have become the predominant paradigm for deploying applications, and flash-based caches are widely deployed for VMs to improve their performance. However, due to the semantics of journaling in the VMs’ file system are transparent to flash caches, traditional flash caching with journaling induces inefficient logging I/O traffic to the shared storage server and duplicated caching in flash, which not only underutilizes flash caches but also aggravates their wearout. This paper presents JCache, a journaling-aware flash caching solution to address these problems. First, JCache proposes the virtual journal device design to receive and deliver the journaling semantics in the VMs to the cache manager so as to optimize the VM’s journaling. Second, JCache proposes a cache-only logging mechanism, which transparently uses the persistent flash caches as the journal area and does cache-only logging to safely eliminate the logging I/O traffic to the shared storage server. Third, JCache proposes the logical caching mechanism, which eliminates the duplicated flash caching induced by logging I/Os and in-place updates to mitigate the wearout of flash caches. Evaluations show that JCache improves the application performance by up to $11.4\times $ and reduces the flash cache writes by up to 42% compared to traditional flash caching solutions.
A Probabilistic Cascading Failure Model for Dynamic Operating Conditions

Failure propagation in power systems, and the possibility of becoming a cascading event, depend significantly on power system operating conditions. To make informed operating decisions that aim at preventing cascading failures, it is crucial to know the most probable failures based on operating conditions that are close to real-time conditions. In this paper, this need is addressed by developing a cascading failure model that is adaptive to different operating conditions and can quantify the impact of failed grid components on other components. With a three-step approach, the developed model enables predicting potential sequence of failures in a cascading failure, given system operating conditions. First, the interactions between system components under various operating conditions are quantified using the data collected offline, from a simulation-based failure model. Next, given measured line power flows, the most probable interactions corresponding to the system operating conditions are identified. Finally, these interactions are used to predict potential sequence of failures with a propagation tree model. The performance of the developed model under a specific operating condition is evaluated on both IEEE 30-bus and Illinois 200-bus systems, using various evaluation metrics such as Jaccard coefficient, $F_{1}$ score, Precision@ $K$ , and Kendall’s tau.
Live Demonstration: Face Recognition on an Ultra-Low Power Event-Driven Convolutional Neural Network ASIC

We demonstrate an event-driven Deep Learning (DL) hardware software ecosystem. The user-friendly software tools port models from Keras (popular machine learning libraries), automaticaly convert DL models to Spiking equivalents, i.e. Spiking Convolutional Neural Networks (SCNNs) and run spiking simulations of the converted models on the hardware emulator for testing and prototyping. More importantly, the software ports the converted models onto a novel, ultra-low power, real-time, event-driven ASIC SCNN Chip: DynapCNN. An interactive demonstration of a real-time face recognition system built using the above pipeline is shown as an example.
VChain: Efficient Blockchain based Vehicular Communication Protocol

Smart transportation systems have focused more on solving real-world problems for traffic and vehicles. The concept of communication between vehicles to Road Side Unit (RSU) requires precise attention to preserve traffic security with better performance output in vehicular communications. The blockchain is a decentralized computation and information sharing platform. This paper proposed a blockchain based architecture using one-way hash function to store and access traffic related information on the blockchain network by vehicle. To improve performance in the computation of block validation, proposed approach maintains a list at every block manager(BM) through which BM checks whether receiver BM have validated the block of sender BM earlier or not. The proposed protocol resists attacks like modication, impersonation, replay, device injection, false reputation and appending attack. Moreover, performance results are comparatively better than what reported by previous studies.
Influences of Control Parameters on Harmonic Characteristics of Current-controlled Inverters

Since a large number of inverters with LCL filters are widely used, harmonic disturbances of power systems are easily amplified by harmonic resonance. In order to study the influence of control parameters on the harmonic characteristics of current-type inverters, the Norton equivalent model of the current-controlled inverter with LCL filter is studied, where the influences of control parameters on two transfer functions, i.e., Ni and Yeq, are investigated. It shows that the performance of harmonic suppressing of inverters could be improved by choosing appropriate parameters of proportional resonance controller of the inverter. The correctness of theoretical analysis is verified by the simulations in PSCAD/EMTDC.
Task-driven Deep Learning Network for Dynamic Cerebral Perfusion Computed Tomography Protocol Determination

Dynamic cerebral perfusion computed tomography (DCPCT) imaging has the ability to detect ischemic stroke via hemodynamic maps. However, due to multiple acquisitions protocol, DCPCT scanning imposes high radiation doses on patients and might increase their potential cancer risks. The DCPCT protocol that decreases DCPCT samples by increasing sampling intervals can greatly reduce radiation dose, but this may introduce bias in the hemodynamic maps estimation, affecting the diagnosis. To address this issue, in this study, we present a deep learning network to determine the DCPCT protocol to realize the dose-reduction task, i.e., decreasing DCPCT samples, and the diagnosis-quality task, i.e., improve hemodynamic maps accuracy. Specifically, one interpolation convolutional neural network is fully designed to estimate the DCPCT images at the sampling interval, termed as dynamic cerebral perfusion interpolation network (DCPIN). The present network treats the DCPCT measurements as a "video" to characterize the maximum temporal coherence of spatial structure among phases, and interpolates a frame at any arbitrary time step between any two frames. First, a flow computation network is used to estimate the bi-directional optical flow between two input DCPCT frames by linearly fusing to approximate the required intermediate optical flow. Second, another flow interpolation network is designed to refine the flow approximations and predict soft visibility maps. Finally, the estimated flow approximations and visibility maps are merged together to jointly predict the intermediate DCPCT frame. Experimental results on patient data clearly demonstrate that the present DCPIN can achieve promising reconstruction performance, i.e., high-quality DCPCT images and high-accuracy hemodynamic maps.
Demo Abstract: Cost-Effective Crowdsensing: Spectrum Monitoring with Sitara

We demonstrate the spectrum-monitoring capabilities of Sitara, a portable, compact, narrow-band software-defined radio (SDR). We developed Sitara as a platform targeting large-scale participant experiments in crowdsensing and general spectrum measurement. As such, Sitara fills a niche between fully-featured off-the-shelf solutions like the USRP products and the more cost-effective but tethered options in the RTL-SDR family of devices. Sitara is capable of transmitting and receiving common waveforms or capturing raw IQ samples for further processing. A mobile application interfaces with the Sitara over Bluetooth 5 for control and collection of measurement data during experiments. Sitara operates in standalone mode, controlled locally by a user or as a remotely-managed node, controlled by our cloud-based framework. Here we demonstrate operation of the Sitara in both these functions in a spectrum monitoring scenario.