Miracle-dz/UniFAR-FTUs huggingface dataset:
Identify IoT Devices through Web Interface Characteristics

With the continuous expansion of the business scale of the Internet of Things, the concept of the Internet of Everything has been deeply rooted in the hearts of the people. At the same time, various security issues have been exposed, which has greatly affected the trust of enterprises and users. Discovering and identifying IoT devices as an important part of IoT security should be taken seriously. In this paper, we propose a new method for classifying devices on the Internet of Things, which uses the data characteristics of the main page of the device's web interface to classify the vendor name, even the product name and other information of the device. We randomly selected IP address ranges on the Internet for scanning, and obtained a training set containing 80,000 entries and covering more than 1,000 devices from 74 vendors. Using the random forest algorithm and proceeding 10-fold cross-validation, our method classified IoT devices with an accuracy rate of over 97.5%.
LDPC-Coded Spectrally Efficient FDM System with Iterative Decoder

In this paper, low-density parity-check (LDPC) codes are applied in spectrally efficient frequency-division multiplexing (SEFDM) with iterative decoder to eliminate the influence from inter-carrier interference (ICI). SEFDM has higher spectral efficiency than orthogonal frequency-division multiplexing (OFDM) by compressing the subcarrier spacing. This self-induced ICI is eliminated by LDPC codes and iterative decoder. The SEFDM scheme analyzed in this paper is not limited by subcarrier number and modulation order because the ICI cancellation scheme has relatively low computational complexity. With LDPC codes, the iterative-SEFDM scheme supports QPSK, 16QAM, 64QAM and 256QAM modulation formats. To the best of our knowledge, the SEFDM scheme with high-order 256QAM modulation is first demonstrated. With bandwidth compression and effective ICI cancellation, LDPC-coded iterative-SEFDM can achieve better bit error rate performance than its counterpart LDPC-coded OFDM at the same spectral efficiency.
Characterization of Additively Formed Copper Layer by Blue Laser-Sintered Copper Nanoparticles

We investigated the sintering of copper nanoparticles by blue diode laser in atmosphere to apply copper nanoparticles as preformed insert-materials for die bonding of power devices. Copper paste consisting of copper nanoparticles, ethylene glycol, and polyvinylpyrrolidone was formulated. The surface morphology and oxidation state of the sintered copper were evaluated. We found that the higher the laser power, the more the necking between copper particles, and that blue laser sintering successively suppressed oxidation of copper.
EMI Reduction Method for Over-Coupled WPT System using Series-None Topology

This paper proposed EMI reduction method for over-coupled wireless power transfer (WPT) system using seriesnone (SN) topology. The series-none (SN) topology does not has the compensation capacitor for minimizing reactive power of receiver side. The proposed topology has higher input impedance than conventional series-series (SS) topology, resulting in the odd harmonic components of current for SN topology has lower than that of SS topology. In addition, when the over-coupling phenomena occurs, there is not much difference in efficiency of transferring power compared to SS topology. The proposed method is verified through the 3D EM tool and circuit simulation.
Development of PD Detection Technique of Overhead distribution lines using UHF and ultrasonic

In order to improve the accuracy and efficiency of partial discharge (PD) detection on overhead distribution lines, this paper presents a method of PD detection on overhead distribution lines based on ultrasonic and ultrahigh frequency (UHF). By analyzing the propagation characteristics of ultrasonic and UHF signals, an inversion formula of PD intensity is established, which can accurately eliminate the influence of detection distance on the accuracy of PD detection. According to the difference of ultrasonic velocity and UHF signal velocity, a method of calculating PD defect distance based on statistics was proposed to provide a new reference for the defect location of covered conductor lines and improve the inspection efficiency. Then, the algorithm design scheme based on the above method is introduced in detail. Finally, a test platform was built in the laboratory to test and analyze the common defect model of overhead line, and the effectiveness of the method proposed in this paper was verified.
T3S: Effective Representation Learning for Trajectory Similarity Computation

Advances of the sensor and GPS techniques have motivated the proliferation of trajectory data in a wide spectrum of applications. Trajectory similarity computation is one of the most fundamental problems in trajectory analytics. Considering that the high complexity of similarity computation is usually a bottleneck for large-scale trajectory data analysis, there are many research efforts for reducing the complexity such as the approximate algorithms. However, most of them are proposed for only one or two specific similarity measures, and thus cannot support different similarity measures well. In this paper, we propose a deep learning based model, namely T3S, which embeds each trajectory (i.e., a sequence of points) into a vector (point) in a d-dimensional space, and hence can significantly accelerate the similarity computation between the trajectories. By applying recurrent and attention neural networks, T3S can capture various unique characteristics of the trajectories such as the ordering of the points, spatial and structural information. Furthermore, our learning based T3S can easily handle any trajectory similarity measures by adjusting its parameters through the training. Extensive experiments on two real-life datasets demonstrate the effectiveness and efficiency of T3S. T3S outperforms state-of-the-art deep learning based methods under four popular trajectories similarity measures.
Comparison of Two SLAM Algorithms Provided by ROS (Robot Operating System)

Navigation of the mobile Robot is one of the salient aspects of robotics. For a robot to navigate successfully in an environment, a robot needs a map of that environment. Simulation Localization and Mapping (SLAM) is a versatile mapping method that can be used for mapping. This paper investigates two of the SLAM algorithms provided on an open-source framework called the Robotic Operating System (ROS) with other software (RVIZ and Gazebo). This paper aims to compare the result of the two SLAM algorithms (Hector SLAM, GMapping) in terms of map accuracy and the average time taken for the Waffle Pi (robot model) to reach its various destinations in an unknown indoor environment. The distinctiveness of this paper lies in the evaluation of map qualities with respect to the time taken by the Waffle Pi robot to navigate through the Gazebo world environments where dynamic obstacles were introduced.
Middleware Challenges and Platform for IoT-A Survey

Internet of things is a network that provides ability to control devices and, manage and remotely monitor them. It basically is a platform which creates new serviceable information from enormous streams of real time data. The sensors like RFID, IR, GPS and laser scanners etc. are installed by IoT for everything that we use in our daily life and making connection of them with the internet using certain protocols for interchanging information and communication for obtaining various applications like intelligent recognition, tracking, location, management and monitoring. In internet to things the decisions are being made without any human interaction. With the technical support from IoT, smart agriculture, smart home, smart university and smart city projects have gain momentum. The intelligence of this network of everyday things created by IoT is governs by the software, called middleware. The middleware is one of the enabling technologies of integrating and collecting data from devices interconnected through internet and make decisions based on it by allowing them to communicate among themselves. Middleware is emerges as the software layer between application and communication layer; it creates abstraction such as hiding hardware details. In this paper, we surveyed the main challenges faced by the middleware that needs to be addressed and survey of IoT application protocols and the survey of most popular middleware for internet of things.
HST+: An Efficient Index for Embedding Arbitrary Metric Spaces

Metric embeddings have been widely used in approximate algorithms to guarantee the effectiveness of geometric problems. Among the metric embedding techniques, Hierarchically Separated Tree (HST) is one of the most prevalent data structures, which maps the points of the original metric space into a tree-based metric space. A few selected applications of the HST include clustering, task assignment, trip planning, privacy preservation, information routing in wireless sensor networks, etc. Despite the popularity in ensuring the effectiveness, the HST-based solutions can be inefficient in large-scale datasets, since the state-of-the-art construction method has high time and space complexity (O(n3) and O(n2) in the worst-case). Moreover, existing studies overlook the insertion of new points in real applications (deletions can be trivially supported), which can cause the reconstruction of the whole HST. To address these limitations, we focus on designing an efficient index for embedding arbitrary metric spaces by tree metric spaces. Specifically, for construction, we design a dynamic programming-based method, which significantly reduces the time and space complexity to O(n2) and O(n) respectively. For insertion of new points, we propose a new data structure, called Hierarchically Separated Forest (HSF), i.e., a collection of HSTs. An HSF can efficiently support insertion of new points with a tight theoretical guarantee (O(log n)). Finally, extensive experiments demonstrate the superior performance of our proposed algorithms with respect to the effectiveness and the running time. For instance, compared with the state-of-the-art algorithms, our construction method is up to 29.8× faster and our insertion method is up to 491× faster.
A Novel Defocused Image Segmentation Method Based on PCNN and LBP

The defocus blur concept adds an artistic effect and enables an enhancement in the visualization of image scenery. Moreover, some specialized computer vision fields, such as object recognition or scene restoration enhancement, might need to perform segmentation to separate the blurred and non-blurred regions in partially blurred images. This study proposes a sharpness measure comprised of a Local Binary Pattern (LBP) descriptor and Pulse Coupled Neural Network (PCNN) component used to implement a robust approach for segmenting in-focus regions from out of focus sections in the scene. The proposed approach is very robust in the sense that the parameters of the model can be modified to accommodate different settings. The presented metric exploits the fact that, in general, local patches of the image in blurry regions have less prominent LBP descriptors than non-blurry regions. The proposed approach combines this sharpness measure with the PCNN algorithm; the images are segmented along with clear regions and edges of segmented objects. The proposed approach has been tested on a dataset comprised of 1000 defocused images with eight state-of-the-art methods. Based on a set of evaluation metrics, i.e., precision, recall, and F1-Measure, the results show that the proposed algorithm outperforms previous works in terms of prominent accuracy and efficiency improvement. The proposed approach also uses other evaluation parameters, i.e., Accuracy, Matthews Correlation Coefficient (MCC), Dice Similarity Coefficient (DSC), and Specificity, to assess better the results obtained by our proposal. Moreover, we adopted a fuzzy logic ranking scheme inspired by the Evaluation Based on Distance from Average Solution (EDAS) technique to interpret the defocus segmentation integrity. The experimental outputs illustrate that the proposed approach outperforms the referenced methods by optimizing the segmentation quality and reducing the computational complexity.
Modeling of magnetic sensor based on BAW magnetoelectric coupling micro-heterostructure

Sensors actuated by bulk acoustic wave (BAW) have attracted lots of attention due to their high sensitivity, GHz-level high frequency, and small size. Different from the previous studies, the performance optimization of magnetoelectric (ME) micro-heterostructure has been adequately considered in this work. We constructed a micro-level model of the magnetic sensor based on BAW ME coupling 2~5 layers micro-heterostructure. Its properties working under both direct current bias and high-frequency alternating magnetic field were systemically analyzed. The results show that the sensitivity and linearity of the sensor can be optimized by adjusting the layer number of the device and the bias magnetic field applied to the ME heterostructure. Eventually, the highest sensitivity of 3.24 V/Oe . cm has been achieved on the magnetic sensor with three-layer structure at the first resonant frequency of 2.2 GHz, and linearity can be strictly controlled within 2% with measuring range of 75~150 Oe. These results has demonstrated that the micro-model of the BAW ME coupling micro-heterostructure, and realized a method to improve its sensitivity and linearity by optimizing the device structure and test conditions. This achievement will further guide the structural design and performance optimization of ME coupling devices.
Multi-Deep Features Fusion Algorithm for Clothing Image Recognition

In order to improve recognition accuracy of clothing image and fully exploit the multi-category features extracted from global area, main part, to part areas. This paper utilizes the target detection technology and deep residual network (ResNet) to extract comprehensive clothing features, aims at focusing on clothing itself in the process of extraction procedure, and proposes multi-deep features fusion algorithm for clothing image recognition. First, the YOLOv3 model extracts the global area, main part of clothing and part areas of the image, which forms three category area images, so as to weaken the influence of background and other interference factors. After that, the three category images were sent respectively to improved ResNet for feature extraction, which has been trained beforehand. The ResNet model is improved through optimizing the convolution layer in the residual block, and adjusting the batch normalized layer and the order of the activation function layer, also adjusting the network convolution kernel structure. Finally, the multi-category fusion features were obtained by combining the overall features of the clothing image from the global area, the main part, to the part areas. The experimental results show that the proposed algorithm eliminates the influence of interference factors, makes the recognition process focus on clothing itself, greatly improves the accuracy of the clothing image recognition, and better than the traditional deep residual network based methods.
Broadband Transmission Properties of cBN-Si Interfaces

We analyze the real and imaginary parts of cBN in a broad wavelength range both 0.5-1.0 µm and 1.36-10 µm respectively, and use both Fresnel theory and Meep software package to calculate the transmittance and reflectance. The theoretical and numerical results show that the transmision and reflection on the interfaces mentioned above have an abnormal phenomenon, and the physical mechanism is analyzed. Between 0.5 μm and 1 μm, the cBN film is transparent with the transmissivity equal to the order of 1.0. When the light wavelength is in the range of 4-9 μm, the structures made of cBN and Si, such as cBN film, cBN-Si double layer film and multiple layer film, have the same optical characteristic with the highest reflectivity and lower absorptivity and zero transmissivity. These findings can be used in the structure design of the optoelectronic devices, such as the optical bandpass filter, the optical bandstop filter, the optical attenuator, the photodetector, and the solar cell.
Frequency Reconfigurable Triangular Microstrip Antenna for Wireless Communication

Frequency reconfigurable triangular microstrip antenna (MSA) for wireless communication is being presented in this paper. The proposed antenna is derived by removing a rectangular slice of width 1mm just below the top of triangular antenna. The upper part of the antenna acts as a parasitic patch for the main radiator. A PIN diode is mounted across the slot to provide necessary electrical connectivity in between main radiating patch and the parasitic patch. A U-slot is introduced on the triangular main radiator, to increase the electrical length of the flowing currents, which helps in antenna miniaturization. By applying appropriate DC bias to switch on/off the PIN diode, the operating frequency of the antenna can be electronically switched at two different bands. The simple triangular antenna (without U-slot) can be operated at either 2.42 GHz or at 2.52 GHz. The same sized antenna loaded with a U-slot, can be operated at relatively lower frequencies i.e. 2.19 GHz or 2.28 GHz in the two wireless communication band. The proposed antennas can be effectively used in electronically switchable duplex wireless systems.
Planar Gradient-Meander Line Microwave Inductor and Designing a 10-MHz–67-GHz Stopband Inductor for Ultrawideband Applications

This article presents a new planar gradient-meander line (PGML) inductor, analyzes its characteristics, and provides a simplified expression for its high-frequency inductance approximation. We further introduce the procedure of PGML inductance that explains the high-frequency application principle of the inductance. Inductors of different sizes on the print circuit board (PCB) and monolithic microwave integrated circuit (MMIC) substrate are fabricated with the same length as the meander line inductors control groups. The samples for broadband microwaves have an excellent inhibitory effect. Full-wave electromagnetic simulations show that the first design on PCB has a more than 18-dB suppression from 10 MHz to 10 GHz, more than 37-dB suppression from 10 to 67 GHz, and a circuit area of <inline-formula> <tex-math notation="LaTeX">$26\,\,\text {mm} \times {22}$ </tex-math></inline-formula> mm (occupied by maximum inductor). The inductance is about 0.4 nH @ 400 MHz, and the maximum Q value is 175 @400 MHz. The GaAs substrate design has a more than 10-dB suppression from 36 to 67 GHz, with a circuit area of <inline-formula> <tex-math notation="LaTeX">$1.5\,\,\text {mm} \times {0.7}$ </tex-math></inline-formula> mm. The inductance is 5.5 nH @ 3 GHz, and the maximum value is 8.4 @ 3 GHz.
Linear stiffness tuning of MEMS triangular capacitors

Based on the proposed MEMS triangular capacitors previously, we performed a further investigation on the stiffness tuning characteristics of a spring-softening triangular capacitor including the fringe effect and effective stiffness nonlinearity and the effects on the performance of a MEMS accelerometer. Both the numerical and experimental results demonstrate a linear yet reduced stiffness tuning ability of the triangular capacitor as a result of fringe effect. With applying a very large tuning voltage (e.g. 15 V), the linearity of the stiffness tuning is significantly degraded. With the stiffness tuning of the triangular capacitor, the sensitivity of the MEMS accelerometer is improved by 20%, and the corresponding Allan bias instability is slight decreased as expected.
THz Hybrid Graphene &Metal Patches Metasurface for RCS Reduction and Digital Coding

Metasurface that shows excellent ability in controlling electromagnetic (EM) waves has attracted tremendous attention. Based on the polarization conversion and the Pancharatnam–Berry (PB) phase, a hybrid metasurface that consists of graphene and metal patches (MGMPs) is presented to realize reduction of the radar cross section (RCS) and the beam reconfiguration of terahertz waves. The unit cells of MGMPs are designed for the linearly polarized (LP), the left circularly polarized (LCP) and the right circularly polarized (RCP) waves. The MGMPs can realize the polarization conversion and produce the phase difference by using coding elements “0” and “1” in combination. The MGMPs are generated using random code, which lead to the redistribution of electromagnetic waves energy and achieve RCS reduction in a wide frequency band range and incident angle. In addition, polarization conversion ratio (PCR) of higher than 90% is achieved. The simulation results show that the MGMPs can achieve the RCS reduction in a wide frequency range from 0.35 THz to 1 THz (96.3%) at the normal incidence for THz waves. The super working bandwidth further indicates that the MGMPs have huge application potential in the field of RCS reduction.
Open Field Smart Planting System of Family Farm

In view of the current majority of family farm planting, especially open field planting, the information technology level is lower. Based on the Internet of Things(IoT) technology, an open field smart planting system is designed. STM32 microprocessor is used as the main control chip to design terminal nodes. Each node has the ability to sense the air temperature and humidity, soil humidity, light intensity and other planting environment parameters, and has on-off control function for irrigation and plastic film device. The number of terminal nodes can be increased or decreased to meet the needs of open field smart planting. The terminal node connects the NB-IoT base station through NB-IoT module, and then transmits the sensing data to the cloud server. We can monitor the plants, the planting environment and the working states of the device by the 5G wireless cameral. The user terminal is a smartphone. User can log in to the open field smart planting system through mobile APP, and then interact with the terminal device, monitor the sensing data in real time, watch videos of plant growth status and planting environment, and control the irrigation and plastic film devices remotely. It can also be set to automatic control mode to control the devices automatically. The system has the advantages of simple architecture, convenient deployment and use. The rented cloud server is used for data storage and data processing. Data is transmitted through services provided by operators. It is safe and reliable with low operation and maintenance cost, and is suitable for open field planting of family farm.
A literature review on cloud based smart transport system

since the inception of human civilization, the invention of fire and wheel has played a vital role in its gradual evolution through several decades. Precisely, after human beings have learned to cook tasty food using fire, they also faced the issue of immediate paucity of food in their vicinity. As a result, they had to travel from one location to another location in search of food, shelter and for a better lifestyle. This eternal search for a better lifestyle is still carried forward in this present generation. With gradual scientific developments, its applications have made this search more easier and convenient for us. Mainly due to the attraction of urban and comfortable lifestyles, this flow of masses directs from rural areas to urban areas, which has created huge pressure over the lifestyle and particularly the transportation system of urban areas. The economic development of any city is dependent on its business friendly environment. Researchers have applied several advanced technologies to implement the smart transportation system within and out a city to roll out urban lifestyle in full gear. In this paper authors have studied those existing research works to find scope for further contribution in the area of smart transportation system in a more integrated manner.