University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Accelerated HF-rTMS in treatment-resistant unipolar depression: Insights from subgenual anterior cingulate functional connectivity

Abstract: Objectives. Intensified repetitive transcranial magnetic stimulation (rTMS) applied to the left dorsolateral prefrontal cortex (DLPFC) may result in fast clinical responses in treatment resistant depression (TRD). In these kinds of patients, subgenual anterior cingulate cortex (sgACC) functional connectivity (FC) seems to be consistently disturbed. So far, no de novo data on the relationship between sgACC FC changes and clinical efficacy of accelerated rTMS were available. Methods. Twenty unipolar TRD patients, all at least stage III treatment resistant, were recruited in a randomized sham-controlled crossover high-frequency (HF)-rTMS treatment study. Resting-state (rs) functional MRI scans were collected at baseline and at the end of treatment. Results. HF-rTMS responders showed significantly stronger resting-state functional connectivity (rsFC) anti-correlation between the sgACC and parts of the left superior medial prefrontal cortex. After successful treatment an inverted relative strength of the anti-correlations was observed in the perigenual prefrontal cortex (pgPFC). No effects on sgACC rsFC were observed in non-responders. Conclusions. Strong rsFC anti-correlation between the sgACC and parts of the left prefrontal cortex could be indicative of a beneficial outcome. Accelerated HF-rTMS treatment designs have the potential to acutely adjust deregulated sgACC neuronal networks in TRD patients.

TEMDnet: A Novel Deep Denoising Network for Transient Electromagnetic Signal With Signal-to-Image Transformation

Abstract: The considerable prospecting depth and accurate subsurface characteristics can be obtained by the transient electromagnetic method (TEM) in geophysics. Nevertheless, the time-domain TEM signal received by the coil is easily disturbed by environmental background noise, artificial noise, and electronic noise of the equipment. Recently, deep neural networks (DNNs) have been used to solve the TEM denoising problem and have achieved better performance than traditional methods. However, the existing denoising method with DNN adopts fully connected neural networks and is therefore not flexible enough to deal with various signal scales. To address these issues, a novel denoising framework with deep convolutional neural networks (CNNs) of transforming the TEM signal denoising task into an image denoising task (namely, TEMDnet) is proposed in this article. Specifically, a novel signal-to-image transformation method is developed first to preserve the structural features of TEM signals. Then, a novel deep CNN-based denoiser is proposed to further perform feature learning, in which the residual learning mechanism is adopted to model the noise estimation image for different signal features. Extensive experiments demonstrate that the proposed framework can achieve much better performance compared with other state-of-the-art approaches on both simulated signals and real-world signals from a landfill leachate treatment plant in Chengdu, Sichuan, China. Models and code are available at https://github.com/tonyckc/TEMDnet_demo.

Uric Acid Monitoring with a Smartphone as the Electrochemical Analyzer

Abstract: We report the world’s first medical smartphone as an electrochemical analyzer, which is incorporated with an enzymatic test strip for rapid characterization of UA (uric acid) in peripheral whole blood. A disposable electrochemical uric acid test strip was connected to the electrochemical module integrated with the smartphone through a specific interface, a slot around the edge of smartphone. A 3 μL human finger whole blood drop is applied on the strip for UA evaluation and compared to the clinical biochemical analyzer with satisfactory agreement. The measured data was saved and uploaded into a personal health management center through the mobile Internet.

Analysis of ResNet and GoogleNet models for malware detection

Abstract: We have utilized two distinct models to identify the obscure or new sort of malware in this paper GoogleNet and ResNet models are researched and tried which belong to two different platforms ie ResNet belongs to Microsoft and GoogleNet is the intellectual property of Google Two sorts of datasets are utilized for training and validation the models One of the dataset was downloaded from Microsoft which is the combination of 10,868 records and these records are binary records These records are additionally isolated in nine diverse classes Second dataset is considerate dataset and it contains 3000 benign files The said datasets were initially in the form of EXE files and were changed over into opcode, after that changed over into images We got a testing accuracy of 745% on GoogleNet and 8836% precision on ResNet

Adaptive fractional-order switching-type control method design for 3D fractional-order nonlinear systems

Abstract: In this paper, an adaptive sliding mode technique based on a fractional-order (FO) switching-type control law is designed to guarantee robust stability for uncertain 3D FO nonlinear systems. A novel FO switching-type control law is proposed to ensure the existence of the sliding motion in finite time. Appropriate adaptive laws are shown to tackle the uncertainty and external disturbance. The calculation formula of the reaching time is analyzed and computed. The reachability analysis is visualized to show how to obtain a shorter reaching time. A stability criterion of the FO sliding mode dynamics is derived based on indirect approach to Lyapunov stability. Advantages of the proposed control scheme are illustrated through numerical simulations.