Miracle-dz/UniFAR-FTUs huggingface dataset:
RegCNN: A Deep Multi-output Regression Method for Wastewater Treatment

Wastewater treatment is a pivotal approach dealing with water pollution problem. During the actual production process of wastewater treatment, predicting input water's output quality acts as a critical step. Existing methods rely mainly on biotechnology and demand a long time cost, which cannot be applied in practice. Therefore, we propose a novel approach to address this problem, which utilizes the recent advance in deep neural networks, and achieves both high accuracy and low running cost simultaneously. Only less than 3% test samples have prediction errors that exceed limits even in the worst case, and only less than 40 seconds are needed when training our model on a real-world dataset of thousands of samples. To our best knowledge, we are the first to apply convolutional neural network to predict output water quality indicators accurately and rapidly. Furthermore, our proposed model supports dynamic update to cope with the variations of wastewater treatment plant configurations.
An Active Power Decoupling Control Method of Single-Phase Cascaded H-Bridge Rectifier

Single-phase cascaded H-bridge rectifier(CHBR) of power electronic traction transformer(PETT) has disadvantage of secondary ripple power. To solve this problem, a CHBR with the active power decoupling(APD) topology is used, and a comprehensive control strategy is proposed, which includes dc-side capacitor voltage control and the APD control strategy based on real-time calculating conduction duty cycle. Firstly, the causes of secondary ripple power and the working mechanism of the APD circuit are analyzed. Then, based on the theoretical analysis, the control strategy of the APD of the CHBR is established to realize rectifier voltage control. Based on real-time calculating conduction duty cycle of each APD arm, the secondary ripple power during rectifier conversion can be suppressed, and the circuit works in the current intermittent mode. So that,the system reliability and power density can be improved. Finally, simulation and experiment results are carried out to verify the of correctness and effectiveness of the proposed method.
Ku Band 12-way Radial Power Combiner Design for Satellite Communication

A Ku Band 12-way radial power combiner is designed and simulated for satellite communication systems desiring high transmitting power. This 12-way radial combiner is consisted by three parts, namely coaxial to waveguide, waveguide and waveguide to coaxial. To achieve 12-way power combination still maintaining low loss and good I/O matching, input coaxial with high characteristic impedance is proposed to cooperate with parasitic capacitance between the center probe and ground. This special configuration would form a resonating circuit in matching. The design results show it can achieve insertion loss less than 0.18 dB and return loss more than 15 dB at 13.5-16.5 GHz. The overall configuration is compact in size ($113\times 113\times 17.9 \text{mm}^{3}$) and the detailed dimensions are also provided for the convenience of combiner design and fabrication.
A Physics-based Model of RRAM Probabilistic Switching for Generating Stable and Accurate Stochastic Bit-streams

A physics-based probabilistic switching model for RRAM stochastic number generator (SNG) is developed to obtain stable and accurate stochastic bit-streams (SBS) for stochastic computing. By taking account of the physical origin of intrinsic variations, the switching probability can be described under various operation schemes. By modeling the cumulative effect between continuous cycles, the probability shift (PS) which seriously affects the accuracy of SBS can be quantified. By using the model, an optimized Recover scheme is proposed to mitigate the PS, and obtain a wide range of stable switching probabilities. The model can be used to design the operation scheme of SNG and choose appropriate bitstream length to achieve target system performance according to the requirement of application. Furthermore, the model is successfully implemented to evaluate the performance of RRAM-based image processing system, which is a cost- and energy-efficient solution for edge detection.
Design and Implementation of Sensorless DC Voltage Regulation for Shunt Active Power Filter Based Single Phase P-Q Theory

In this study, the sensorless DC voltage regulation approach for a shunt active power filter (SAPF) has been suggested. The proposed approach has been combined with the single-phase p-q theory to enhance power quality of the grid. The sensorless DC voltage regulation is based on the calculation of the operating losses of the SAPF throughout each operation period. The validity of the proposed approach is evaluated by simulation study realized in Matlab/Simulink, and confirmed on the power system prepared in the laboratory. The results of the arranged simulation and the implementation studies demonstrate that the sensorless DC voltage regulation is as effective as the proportional-integral used classically in the SAPF applications. Due to reduced number of sensors, the developed combination is not complicated, and its cost is less than classical PI control.
Microwave Power Transmission in Fully Enclosed Space Using Reconfigurable Reflecting Elements

This paper presents a microwave power transmission scheme in fully enclosed space using reconfigurable reflecting elements (i.e., elements terminated with tunable reactive loads). The scheme is modeled by a multi-port network, based on which the expression of power transmission efficiency in terms of scattering parameters is obtained. The maximum achievable power transmission efficiency and the optimal reactive loads of reflecting elements can be found by solving a simple optimization problem. The model is verified by simulation and experimental results, which demonstrate that the scheme can improve power transmission efficiency significantly in a fully enclosed environment.
Use of Adaptive Antenna Arrays to Reduce Interference in the E-UTRA Network

A computer model for investigating the interference environment in the 4G LTE (E-UTRA) network has been made. The results of modulation of uplink (UL) noise over the use of sectoral antennas of base stations are presented. The technology of power control is used to reduce noise. Recommendations on the use of adaptive antenna array technology to reduce interference in the E-UTRA network are provided.
Wideband Substrate-Integrated-Waveguide-Fed Magneto-Electric Dipole Array Antenna

A wideband magneto-electric (ME) dipole array antenna fed by substrate integrated waveguide (SIW) is proposed in this paper. By combining the bowtie-shaped slot on the top surface of the SIW as a magneto dipole, and a shorted patch as an electric dipole, the ME-dipole element is realized. Then a $1\times 8$ array operating at 28GHz is formed with eight ME-dipole elements and a wideband SIW feed network. Simulation results show that the array antenna can achieve an impedance bandwidth of 31.4% (24.2-33GHz) and a peak gain of 14dB. Moreover, stable radiation patterns, low cross-polarization levels and low front-to-back ratio are obtained over the wide band, which makes the proposed antenna a good candidate for the 5G millimeter-wave wireless communication system.
Text Sentiment Analysis Based on ResGCNN

Sentiment analysis of text is a significant task in Natural Language Processing (NLP), and Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are two commonly used deep learning models of NLP. RNN’s variant Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) can solve the long-term dependence of traditional RNN. Their variant Bi-directional LSTM (BiLSTM), Bi-directional GRU (BiGRU) solve the problem that unidirectional LSTM and GRU can only access the above but not the below. However, the ability of the cyclic neural network to extract key features is not strong. CNN can extract local features of text vectors effectively, but ignores the meaning of text contexts. TextCNN can extract text location features by using convolution cores of multiple different windows. As the number of layers increases, deep learning can obtain more complex features, but it will cause the problem of gradient disappearance, and errors cannot be effectively back-propagated, and the residual networks can solve it. This paper proposes a ResGCNN network combining residual network, multilayer BiGRU, and TextCNN. The excellent classification performance is obtained on multiple English and Chinese data sets.
Disturbance Rejection Control Strategy of Hybrid Battery/Super Capacitors Power System Based on a Single Converter

This paper proposes a robust control strategy for a hybrid battery/super capacitors power system with a single converter. In such systems, it is required to smooth the battery current considering the load power transient and the current oscillations of the DC bus delivered by the load in the context of a hybrid electric vehicle. Furthermore, constraints of all the system components, i.e. maximum current of the battery and super capacitors, state of the charge of the super capacitors and battery temperature should be taken into account. To deal with these demands, a novel cascaded voltage control loop is proposed. For the outer voltage loop, an anti-windup proportional integral controller is used to regulate the super capacitor voltage at its nominal value. On the other hand, for the inner voltage loop, an anti-windup super twisting controller is proposed to ensure the convergence of the DC bus voltage to its reference value while attenuating the external perturbation. Indeed, the specific feature of such controller is that it works like a low-pass filter which leads to attenuate the current oscillations in the DC bus.
Non-ebeam AlGaN/GaN HEMTs with $f_{\max}$ of 206 GHz for Mass Production

We report 100-nm gate AlGaN/GaN high electron mobility transistors (HEMTs) without electron-beam (e-beam) lithography featuring current gain cut-off frequency ($f_{\mathrm{T}}$) and maximum oscillation frequency ($f_{\max}$) of 66 GHz and 206 GHz, respectively. Fabricated in commercial fab, the gate width of ∼80 nm using i-line stepper and sidewall technology on 6-inch wafer indicates the possibility application in rapid mass production compared to slow and expensive e-beam method.
Energy-Aware Collaborative Computation Offloading Over Mobile Edge Computation Empowered Fiber-Wireless Access Networks

With the emergence of smart mobile devices (SMDs) and mobile applications, the cloud-mobile edge computing (MEC) collaborative computation offloading (CMCCO) scheme, i.e., offloading the computation-intensive task from the local SMD to either the MEC server, or the remote mobile cloud computing server (MCC), is widely identified as a promising candidate under the conflict between SMDs’ limited computing ability and computing-intensive application requiring higher energy consumption. Meanwhile, the existing CMCCO scenario over integrated cloud-MEC Fiber Wireless broadband access networks (CM-FiWi) architecture, by generally fixing computing ability and transmitting power, still achieves higher computation offloading overhead in terms of task’s aggregate response time and SMD’s energy consumption. In light of this, the energy-aware collaborative computation offloading (EA-CCO) paradigm with very diverse types of computation tasks over CM-FiWi broadband access network is provided in this paper. An iterative searching algorithm for collaborative computation offloading scheme (ISA-CCO) is proposed as a solution to obtain minimized task offloading overhead, which jointly takes scaling computing ability, variable transmit power, and residual battery rate into considerations. Extensive numerical results demonstrate that the proposed solution outperforms the traditional paradigms, e.g., optimal enumeration collaborative computation offloading scheme (OECCO), approximation collaborative computation offloading algorithm (ACCO), and game theoretic collaborative computation offloading scheme (GT-CCO). More specially, the proposed ISA-CCO scheme obviously achieves lower overall task offloading overhead than those fixed transmit power and computational frequency scaling.
Application of Grey Correlation Analysis in Effective Utilization of Similarity-based Remaining Useful Life Prediction Methods

Similarity-based remaining useful life (RUL) prediction methods are useful tools in prognostics, which are capable of making a long-term RUL prediction in a high accuracy by comparing signals from the test instance and references. In order to utilize the similarity-based methods effectively in practice, a uniform grey similarity measure was proposed based on grey correlation analysis method after data preprocessing. First, a grey time series was generated to represent the degradation of the test instance based on the monitoring data to ensure its size are same as the references both in length and time dimension. Second, a uniform grey similarity measure was developed improve the accuracy. It can not only measure the local similarity but also the whole degradation trend of the time series. Finally, the RUL of the current degradation process can be predicted using a weighted average method. The board-level package degradation data under random vibration loadings was used to evaluate the performance of this method and the results show that the proposed method is more practical with a better prediction performance in comparison with the existing methods.
Filtering Balun Based on Multi-Mode Rectangular Cavity Integrated With PCB Feeding Structure

A filtering balun based on multi-mode rectangular cavity is proposed in this paper for the first time. The printed circuit board (PCB) microstrip lines cooperated with coupling slots are utilized as feeding structures. The $TE_{101}$ mode is employed to construct the passband while the $TE_{011}$ and $TM_{110}$ modes are used to generate two out-of-band zeros. Besides, by analyzing the electromagnetic field distributions, the balun characteristic at the two outputs are achieved due to the antisymmetrical arrangement of the output feeding lines. For demonstration, a second order cavity filtering balun is designed and measured. Both good filtering response and balun characteristic are observed in the measurement, which validate the proposed method.
A Reconfigurable Compact RF Module for 5G

This paper focuses on the design and fabrication of a dual-channel reconfigurable RF transceiver covering frequency bands of 3.5GHz and 4.9GHz for 5G massive MIMO systems. The module contains the motherboard and RF front-end board. Both of their size meet the commercial FMC230 standard, i.e. $69\text{mm}^{\ast}78.8\text{mm}$. The two are connected by coaxial structure in a stacking manner. By plugging in different RF board, the module can realize communication of multiple different frequency bands. On the basis of the simulation design, the RF transceiver module is fabricated and tested. The test results show that the RF transceiver has satisfactory EVM and meets the design requirements.
Collaborative Online Edge Caching With Bayesian Clustering in Wireless Networks

In this article, we study the edge caching problem by considering the heterogeneous context with unknown users’ preferences. The cache provider (CP) can personalize the users’ storage based on available data to maximize the overall cache hit rate, accounting for the dynamic natures of both mobile edge cache scenarios and the users’ preferences. Toward this end, we introduce an online Bayesian clustering caching algorithm for the CP to autonomously learn the users’ interactive cache hit data in a collaborative way while maintaining sustainable scalability. Specifically, a Bayesian generative framework called the Dirichlet multinomial mixture (DMM) model is used to describe the uncertainty about the latent number of users’ clusters, each of which consists of the users with the same preference. Then, a dynamic clustering policy is proposed to obtain both the underlying mapping of users to clusters and the preferences of each cluster by using a collapsed Gibbs sampling algorithm. Subsequently, cache decisions are made according to the generated mappings by extending the traditional cache bandit algorithm to a new bandit mechanism with clusters of arms, capable of expediting the learning process between the exploitation and exploration. We theoretically characterize the value of dynamic Bayesian clustering for the long-term edge caching scenario with respect to the regret incurred by the noncluster schemes. Finally, using a real-world data set, our numerical results show that the proposed scheme outperforms the caching algorithms without clustering in the uncertain network scenario.
Temperature Field Simulation and Ampacity Calculation of Multi-loop Cable Tunnel with Ventilation

The number of cable loops in the tunnel is the main factor that causes the cable temperature rise and limits the cable ampacity. In order to improve the cable ampacity of multi-loop cable tunnel, the electromagnetic - thermal - flow coupling calculation model of cable tunnel was established based on ANSYS finite element simulation software. The influence of tunnel ventilation speed and ventilation distance on cable temperature distribution was studied, and the ampacity was calculated according to temperature distribution. The results show that the temperature of the cable conductor decreases by less than 1°C for each 2m/s increase of wind speed when the speed is more than 6m/s. The larger the wind speed, the smaller the temperature difference of cable conductor between the air inlet and outlet. The longer the ventilation distance, the larger the temperature difference of cable conductor between the air inlet and outlet. The ampacity of 110kV, 220kV and 500kV cables is respectively 1645A, 2031A and 2238A when the wind speed is 6m/s. The ampacity of 110kV, 220kV and 500kV cables is 1141A, 1517A and 1678A respectively when the ventilation length is 4m.
ARG-Based Malignancy Evaluation for Pulmonary Nodule Detection

Malignancy evaluation is an important stage in lung cancer diagnosis. However, due to the complexity of medical image data, processing the images costs a long time. Aimed for this problem, we propose a method based on the Attributed Relational Graph (ARG) in this paper to measure the degree of pulmonary nodule malignancy, with And-Or Graph (AOG) and Markov Blanket (MB) used for dimensionality reduction. Our method is tested on real images of Shanghai Chest Hospital and Shenyang Shengjing Hospital with classification accuracy 70.85%, and tested on the IDRI-LIDC database with accuracy 75.01%. The method can help to identify and analyze the benign and malignant nodules correctly and quickly.
Minimizing Active/Reactive Power Losses in Electricity Networks Based on Optimal Location of Battery Energy Storage System

The development in battery energy storage (BESS) technology and rapid rise in power demand have urged together adopting the storage system in conventional power plants in order to cope with the additional loads at an optimal cost. This paper presents an optimization strategy to address effective interfacing of BESSs to the grid with the sole objective of minimizing active power losses; however, voltage profile e nhancement i s targeted a swell a s a s ub t ask. The methodology is tested based on real-data-based network and loss sensitivity factor approach is used to make best-selected bus to accommodate the BESS such that the associated parameters are maintained optimally. It has been observed that the grid performance improves in terms of power losses because of the local generation of the BESS and the capacitor at the selected buses, thereby eliminating the need for additional power from the source.
Comparative Analysis of Machine Learning Classifiers on Bioinformatics and Clinical Datasets

Looking at the advancements in high-throughput techniques, it is easy to generate huge volume of data and with that comes a problem of processing, analyzing and verifying a data. This paper has analyzed and compared different machine learning based classification techniques for analysis of gene expressions generated through microarrays and clinical decision support using different types of health care datasets. Various classification algorithms, like Decision Tree (DT), Naïve Bayes (NB), and Neural Network (NN) were matched to find the optimum accuracy for diagnosis. The efficiencies of these classification algorithms were examined and equated using glaucoma and cancer datasets. The outcomes were compared based on various parameters, viz. accuracy, sensitivity, dimensionality, and specificity for determining classification algorithm performance. The classifiers indicated enhancements in terms of accuracy after the datasets underwent normalization. This relative analysis depicted that, essentially not a single technique of classification that performs the better than the other. Thus the potential of classification algorithms is highly dependent on several factors of the dataset to be analyzed. In the context of health care datasets, it’s the features of the dataset that are the primary factors affecting the performance.
Interest Flooding Attack Mitigation in Named Data Networking Based VANETs

Nowadays network applications have more focus on content distribution which is hard to tackle in IP based Internet. Information Centric Network (ICN) have the ability to overcome this problem for various scenarios, specifically for Vehicular Ad Hoc Networks (VANETs). Conventional IP based system have issues like mobility management hence ICN solve this issue because data fetching is not dependent on a particular node or physical location. Many initial investigations have performed on an instance of ICN commonly known as Named Data Networking (NDN). However, NDN exposes the new type of security susceptibilities, poisoning cache attack, flooding Interest attack, and violation of privacy because the content in the network is called by the name. This paper focused on mitigation of Interest flooding attack by proposing new scheme, named Interest Flooding Attack Mitigation Scheme (IFAMS) in Vehicular Named Data Network (VNDN). Simulation results depict that proposed IFAMS scheme mitigates the Interest flooding attack in the network.
Buried Power Rails and Back-side Power Grids: Arm® CPU Power Delivery Network Design Beyond 5nm

The technology of buried power rails and back-side power delivery has been proposed for future scaling enablement, beyond the 5nm technology node. This paper studies the CPU design implications of power delivery in the context of these technologies. Employing standard VLSI design flows and sign-off techniques, we benchmark the power delivery designs and technology options using the Arm Cortex-A53 CPU at an imec 3nm technology node. DC and AC analyses of the resulting power delivery networks are presented for the various designs with buried power rails (with front-side and back-side power delivery) and compared to conventional designs without buried power rails. It is shown that buried rails with front-side power delivery can improve the worst-case IR drop from 70mV to 42mV (~1.7X reduction) while buried rails with back-side power delivery substantially reduce IR drop to 10mV (a 7X reduction).
Multilayer Metasurface Simulation in FDTD using Surface Susceptibility Model

Multilayer designs of metasurfaces are required for practical applications because they have more capability to manipulate electromagnetic wave than single-layer designs. This paper thus extend the Surface Susceptibility Model (SSM) integrated in FDTD from single layer to multilayers, consequently accelerating metasurfaces simulations with good accuracy. The SSM is integrated in FDTD by using Generalized Sheet Transition Conditions (GSTCs). To demonstrate the validity of the proposed algorithm, a numerical comparison is presented between simulation of two layer geometrical structures in conventional FDTD and two layer surface susceptibility model in FDTD-SSM.