Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of compact C-band one into eight-way equal Wilkinson Power Divider

Wilkinson power divider (WPD) is a widely adopted technique for splitting the microwave signal into two equal or unequal parts and maintaining good isolation between output ports. In order to increase the bandwidth, multi-stage WPD is used. This work aims to design and analyze the performance of a three-stage 1-into-8 Wilkinson equal power divider suitable for frequency band 4–8 GHz. The design has been realized on commercially printed circuit board (PCB) of RO4350B 10-mil substrate. The design was optimized for 1 into 2 two-stage WPD cell, and seven such cells have been utilized according to layout requirement. The overall size of the fabricated circuit is 81.28x25. 4mm. Physical results show average insertion loss at 4 GHz is better than 2.1 dB and at 8 GHz better than 3.95 dB. Return loss is better than 15 dB at both input and output ports. Isolation between adjacent output ports is better than 18.5 dB.
Tumor Segmentation and Detection using Convolutional Neural Network

Tumor is an old age disease, higher life expectancy rate, and population boom are contributing to the higher number of patients. Rare and aggressive tumors can have a lot of swelling associated with them. Some tumors may grow at a slower rate and may not be visible and grow over a long period of time. It becomes difficult to perform manual segmentation on MR images in a given period of time and highly dependent on the expertise and experience of the operator. In this paper, authors have proposed to detect tumor using Convolutional Neural Network (CNN) and U-Net based deep convolutional networks. The implementation is on Pytorch an open source machine, learning library developed by the Facebook artificial intelligence research group. Pytorch is basically the combination of two languages, python, and torch. Authors have used three major libraries named torch.nn, torch.autograd and torch.vision which provided automatic differentiation for all the operations on tensor. It basically returns the 2D tensor representing the identity matrix. Experimentation performed is to achieve a higher degree of accuracy in tumor segmentation. The results have been compared with existing literature and found satisfactory up to a certain level.
Commensurate Evaluation of Support Vector Machine and Recurrent Neural Network MPPT Algorithm for a PV system under different weather conditions

The expeditious broadening of Photovoltaic (PV) energy has attracted the private and government precinct world-wide due to the reduction of costs and being cleaner source of energy. However, most of the maximum power point tracking (MPPT) controller are inefficient under rapid change of environmental conditions. Under partial shading conditions (PSC) MPPT controllers fail to track global maximum power point (GMPP). Therefore, it is essential to propose MPPT controller that will be able to locate GMPP. In this study, the two powerful machine learning and deep learning MPPT algorithms are used to force the PV system to operate at higher efficiency under sudden change in solar irradiance and temperature. Support Vector Machine (SVM) and Recurrent Neural Network (RNN) performances are validated and proved using MATLAB SIMULINK simulation software.
Design of a Compact Broadband Antenna for Base Stations based on the Characteristic Mode Analysis

In this paper, the characteristic modes of a pair of crossed bow-tie radiators are analyzed. By using spline-contoured patches and off-the-center feeding structures, multiple modes are created and excited to broaden the antenna bandwidth. Finally, a compact broadband antenna is achieved for base stations operating over 2G/2G/LTE and recently released L and S bands.
Recent Advance on the Stability of Switched Systems based on Formal Methods

A switching system is a typical hybrid system, which comprises of a family of subsystems and switching rules describing the switching relationship between subsystems. Most of the physical world is the physical process of the interaction between continuous dynamic systems and discrete dynamic systems, so the research on switching systems is very important. In recent years, the automaton theory in the computer field has been applied to switching systems, and formal methods have been used to study the stability of switching systems. This paper provides a survey of the formal methods of discrete-time linear switching systems, and describes the latest developments between switching systems and automata, provides possible future research directions.
Planar Wideband mm-Wave Antennas for mm-Wave 5G applications

This paper introduces several wideband planar mm-wave antennas developed in our group for the upcoming mm-wave 5G wireless system, including wideband high gain antennas for the E-band back-haul application and multiple-radiation-mode wideband antennas for the QLink-pan applications. It shows different to the classical substrate integrated waveguide slotted antenna which only supports narrow-band applications when a high radiation gain is desired, a wideband high gain substrate integrated waveguide antenna can be realized by loading parasitic elements for realizing the substrate integrated magneto-electric dipole or multiple radiation-modes. The introduced antennas have high radiation efficiency and low costs. They are promising candidates for the mm-wave wireless system.
Stack ResNet For Short-term Accident Risk Prediction Leveraging Cross-domain Data

With an increasing number of private cars, traffic accident prediction plays an important role in urban management. Existing methods tend to utilize classic learning techniques with historical observations on accident records. Therefore, they fail to consider cross-domain data into consideration and ignore the spatiotemporal dependencies. Recently, as more heterogeneous urban data are available, it is promising to predict citywide accidents in a more fine-grained way. However, it is challenging due to the inherent sparsity of traffic accident records and the complexity of multiple influenced factors. Besides, real-time data cannot be collected completely for short-term forecasting. In our paper, we propose a multi-view spatiotemporal deep learning framework to fuse cross-domain urban data. Specifically, we first assign heterogeneous data into the corresponding grid, then extract the features related to accidents into tensors or vectors. Moreover, we propose a ResNet based multi-task learning framework with speed inference model to realize the prediction of near future’s accident risk distribution.
Improving Destination Prediction via Ensemble of Trajectory Movement Separation and Adaptive Clustering

Destination prediction has attracted more and more attention due to its broad applications in location-based services such as traffic navigation, hotel recommendation and personalized advertising. It aims to predict future destination locations according to historical trajectory records as well as some extra information. Significant progress has been made based on Markov-chain models and neural networks. In this paper, we propose a novel Separated Trajectory Movement and Adaptive Clustering (STMAC) framework, which leverages the potential pattern of trajectory movement trend to separate historical trajectories into different categories, and then employs adaptive clustering for each category to discover fine-grained local clusters as candidate destination locations. In particular, STMAC adopts the weighted centroid of top- $k$ discovered candidate clusters to predict a more accurate and more appropriate destination location. We conduct experiments on two real-world datasets to evaluate STMAC and other methods. Experimental results demonstrate that STMAC achieves significant improvement over competitors in terms of prediction error and accuracy. Extensive experiments on parameter analysis further show the effectiveness of our model.
Two types of dual-band bandpass filtering Wilkinson power divider with microstrip composite resonators

A dual-band bandpass filtering Wilkinson power divider is newly introduced in this paper. A 2-degree bandpass filter topology with dual-band performance replaces two $\lambda/4$ transmission lines in Wilkinson power divider design. There are two different design methods for proposed power divider application: Type I and Type II. Design equations and circuit realization are summarized in detail. Considering better isolation performance and compact circuit size, Type II is preferred in this paper. In experiment, a Type II Wilkinson power divider with central frequencies of 2.4 GHz and 5.2 GHz is designed and fabricated using microstrip lines and open-/short-stubs. There is good agreement between the simulated and measured results.
Skin Anomaly Detection Using Classification Algorithms

This paper proposes a solution for the most easily detectable type of cancer, skin cancer. Due to the ozone layer thinning, it was observed an increase in the number of skin lesion anomalies. Those anomalies are produced by the increasing UV radiation intensity. Good protection against sun is not always facile, mainly in the exposed regions of the skin like the face and the lower regions of the arms. Most types of cancers can be easily extracted via surgery if they are detected in one of the incipient development stages. Using a high resolution image, a classification algorithm can detect certain patterns in the nevi color, shape, veins or irregularities inside. Analyzing those aspects, a person can track their nevi development easily and intervene if it necessary. The originality of this paper consist in designing several convolutional neural networks, using the Python programming language, the Keras API and the Tensorflow framework alongside with proper dataset selection. The convolutional neural networks were trained using an open source dataset, with the purpose of formulating a diagnosis for new patients. The convolutional neural networks are provided with 7 classes of images, representing 7 types of skin lesions as input, and will output a diagnosis. The result of the research was a classifier with a convenient classification accuracy based on the dataset, and a medium accuracy based on new data.
Dual-Beam Analog Beamforming for mmWave Communications

Conventional initial beam access schemes for millimeter wave systems suffer from high computational complexity and prolonged access times. Hence this paper proposes an efficient beam access algorithm based upon simultaneous dual-beam codebook transmission and beam coding. The spatial search space is formulated as an optimization problem and a novel direct-pattern search is proposed here, i.e., the algorithm adapts the beam search by conquering the observed cluster profile. Simulation results show that the algorithm significantly overcomes major access methods.
Effects of interface trap charges on the electrical characteristics of back-gated 2D Negative Capacitance FET

In this work, a fully analytical compact drain current model of back-gated two-dimensional (2D) negative capacitance (NC) FET including interface trap charges has been developed by solving Poisson's, drift-diffusion and 1-D Landau-Khalatnikov equations, and it is validated against the experimental data. In addition, the impact of interface trap charges on the electrical characteristics of the back-gated 2D NC-FET is investigated systematically based on the model. It is found that the subtresdhold swing (SS) and on-off current ration (Ion/Ioff) are seriously degraded because of the presence of interface traps at 2D channel/oxide interface and 2D NC-FET with a big tf is more immune to the degradation induced by the interface traps.
Smart Grid (SG) and Data Center (DC) Integration: A New Conceptual Framework

As SG is heading towards “Mega Grid”, there is a pressing need to interface SG to fast-processing engines called “Data Centers” (DCs), possessing massive computational power and extreme storage capabilities. Considering the above, this paper elaborates alarming issues of SG that forced attention towards its computational needs due to system complexity, nonlinearity, computational cost, and computational burden. Moreover, in the light of alarming SG problems, we surveyed promising features of DCs, such as massive computational power, fast processing engines, extreme storage capabilities, on-demand services, and cloud interaction. Furthermore, this paper presents bi-directional services integration between DCs and SG to justify their mutual interaction. Based on the above claim, we discussed a computational model that validated the mutual services of DCs and SG. Finally, a computer-based simulation study is conducted for SG power and control stability enhancement, while DC received a reliable power supply and increased revenue through the Service Level Agreement (SLA).
Robotic cooperation and communication control system

In recent years, robots and the Internet of Things have become popular, and robots and the Internet of Things have begun to slowly enter the relevant applications of smart homes. In order to meet various requirements in home applications, various home-based robots and Internet of Things appliances have emerged into daily life. These family robots are generally independent individual goods. The networked robots can control or view the robot's data status at the most. The robots can only operate independently and cannot communicate, because the Internet of Things means comprehensive. Sexual integration, this study uses iRobot Create2 and YASKAWA DX100 as the basis for the development of intelligent robot control and communication research, and testing feasibility and development.
CoHOG: A Light-Weight, Compute-Efficient, and Training-Free Visual Place Recognition Technique for Changing Environments

This letter presents a novel, compute-efficient and training-free approach based on Histogram-of-Oriented-Gradients (HOG) descriptor for achieving state-of-the-art performance-per-compute-unit in Visual Place Recognition (VPR). The inspiration for this approach (namely CoHOG) is based on the convolutional scanning and regions-based feature extraction employed by Convolutional Neural Networks (CNNs). By using image entropy to extract regions-of-interest (ROI) and regional-convolutional descriptor matching, our technique performs successful place recognition in changing environments. We use viewpoint- and appearance-variant public VPR datasets to report this matching performance, at lower RAM commitment, zero training requirements and 20 times lesser feature encoding time compared to state-of-the-art neural networks. We also discuss the image retrieval time of CoHOG and the effect of CoHOG's parametric variation on its place matching performance and encoding time.
Maximal Correlation Regression

In this paper, we propose a novel regression analysis approach, called maximal correlation regression, by exploiting the ideas from the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation. We show that in supervised learning problems, the optimal weights in maximal correlation regression can be expressed analytically with the relationships to the HGR maximal correlation functions, which reveals theoretical insights for our approach. In addition, we apply the maximal correlation regression to deep learning, in which efficient training algorithms are proposed for learning the weights in hidden layers. Furthermore, we illustrate that the maximal correlation regression is deeply connected to several existing approaches in information theory and machine learning, including the universal feature selection problem, linear discriminant analysis, and the softmax regression. Finally, experiments on real datasets demonstrate that our approach can obtain performance comparable to the widely used softmax regression based-method.
Optically Powered Battery-Free Portable Microsystem for WSN and IoT Applications

This paper presents an ultra low power all-optical portable battery-free embedded microsystem that can be powered and controlled by a smart mobile phone. The microsystem contains an ultra-low voltage microcontroller as the processing unit; and, it receives power via a mini-solar cell. The optically powered microcontroller reads the local sensor data and transmits it to the mobile phone via modulating the light of an LED. A custom optical communication protocol that is based on Pulse Period Modulation is designed for the system. An image processing based mobile application utilizing the built-in flash of the phone is also programmed for controlling the microsystem. The mobile phone application using the built-in rear-facing camera analyzes the modulated optical signal from the LED on the microsystem and extracts the transmitted data. Experimental results of various local temperature measurements are also given.
Low Power Design for DVFS Capable Software

Today's central processing unit (CPU) technology makes it possible for a processor to adjust its voltage and clock frequency dynamically depending on its current CPU load, memory load, or operating system requests. Higher energy saving is possible if the application's power profile can be estimated or determined fast and accurate. If the application's power profile is completely known prior to execution, then maximum/optimum power saving is possible using off-line dynamic voltage and frequency scaling (DVFS) algorithms. The question is, how to determine full power-profile of an application prior to execution and how much power saving is possible. In this paper, we discuss the methodology to determine full power profile of an application and calculate potential energy savings using off-line DVFS algorithms for randomly generated and real-life cases.
A Fully Automatic Water Feature Extraction Method with Adaptive Training Samples and One-Class Classification Algorithms

Automatic water feature delineation from satellite imagery is a challenging task due to the variability of sensor parameters, acquisition conditions, and geographical heterogeneity of scenes. To mitigate above challenges, we proposed a fully automatic method for surface water feature dilatation from the moderate resolution satellite imagery through the integration of adaptive and automatic training sample collection by a modulated spectral reflectance curve and one-class classification algorithm. The specific modulation codes that are partially able to represent water features have been chosen to collect positive training samples for the target class. In addition, one-class classification algorithm has applied to classify multispectral imagery, and it utilized the collected datasets as the positive-labeled training samples for the target class. The novelty of the proposed approach is that it's applicable to several moderate resolution sensors having SWIR, NIR, Red, and Green spectral bands. We evaluated the robustness of the proposed method on different multispectral earth observation sensors (Landsat 8OLI, LISS-III IRS-P6, and Sentinel 2AMSI) having heterogeneous acquisition environments. We achieved the overall accuracy on test sites ranges from 93.90 % to 97.27 %, and kappa statistics is ranges from 0.7622 to 0.9453.
A Predictive Model of Academic Failure or Success for Institutional and Trace Data

In recent years, Educational Data Mining (EDM) has contributed significantly to the prevention of various challenges in many sectors including the academia. This paper brings a comparative study of EDM impact, using institutional data and trace data generated by a virtual learning environment to predict academic failure or success. For this, we created a model of educational data mining using logistic regression as a classifier and cross validation as a test method. Logistic Regression is widely used as a classifier (or predictor) in educational data mining. The developed model was applied to a public data set of 32,593 students, distributed among seven courses. The results show that the application of the proposed model in the trace data favors excellent predictions, on average 42% better, in relation to using just the institutional data. The results also revealed that it is better to separate the trace data from the institutional data in the model application, since the gain in the prediction is negligible and the computational time spent is around four times greater if the both data sets are combined. In addition to contributing to the findings, we also believe that our methodology can be re-applied by researchers, educators and education managers.
Enterprise Architecture Towards Transdisciplinary Knowledge Unification

This paper proposes an approach to unify different knowledge using Enterprise Architecture. The approach concerns on the theory and engineering of knowledge unification. We also propose a design pattern for describing and reuse the transdisciplinary knowledge unification. A case study is also shown for the nursing narrative to create a unified knowledge. Moreover, the types of transdisciplinary knowledge unification are clarified.
Analysis of Dynamic Characteristics of Electrodynamic Suspension Train Based on Halbach Permanent Magnet Array

A permanent magnet electrodynamic suspension train is designed and its structure and working principle are described. Structural parameters related to electromagnetic force are analyzed. The stability and oscillation characteristics are studied of the suspension system and guiding system near the equilibrium point, and factors are discussed that affecting the oscillation frequency. The response of the system under the external disturbance is simulated, and then a viewpoint is put forward that the dynamic characteristic of the electric suspension system can be measured by that of the equilibrium point. In addition, the dynamic response of the electrodynamic suspension system to the vehicle-rail resonance is studied, and it is of great significance to the design of the bridge.
Retrieval Topic Recurrent Memory Network for Remote Sensing Image Captioning

Remote sensing image (RSI) captioning aims to generate sentences to describe the content of RSIs. Generally, five sentences are used to describe the RSI in caption datasets. Every sentence can just focus on part of images’ contents due to the different attention parts of annotation persons. One annotated sentence may be ambiguous compared with other four sentences. However, previous methods, treating five sentences separately, may generate an ambiguous sentence. In order to consider five sentences together, a collection of words, which named topic words contained common information among five sentences, is jointly incorporated into a captioning model to generate a determinate sentence that covers common contents in RSIs. Instead of employing a naive recurrent neural network, a memory network in which topic words can be naturally included as memory cells is introduced to generate sentences. A novel retrieval topic recurrent memory network is proposed to utilize the topic words. First, a topic repository is built to record the topic words in training datasets. Then, the retrieval strategy is exploited to obtain the topic words for a test image from topic repository. Finally, the retrieved topic words are incorporated into a recurrent memory network to guide the sentence generation. In addition to getting topics through retrieval, the topic words of test images can also be edited manually. The proposed method sheds light on controllability of caption generation. Experiments are conducted on two caption datasets to evaluate the proposed method.