Real Time Digital Filter for a Front-End Electronics in Dark Matter and Neutrino Measurements

This paper presents real-time digital filter algorithms to be applied within dark matter and neutrino measurements. The digital signal processing algorithm implements a trapezoidal pulse-shaper programmed on FPGA at 125 MHz. The real-time filter algorithm enhances the SNR of a digitized signal from a photo detection module (SiPM, cryogenic front-end electronics & 14-bits ADC). The trapezoidal filter upgrades the signal to noise ratio (SNR) from 10.4 to 15.4 with a total increment of 50%. The total on-chip power is 0.198 W.

Big Data System for Medical Images Analysis

Big Data in medicine contains conceivably fast processing of large data volumes, alike new and old in perseverance associate the diagnosis and treatment of patients’ diseases. Backing systems for that kind activities may include pre-programmed rules based on data obtained from the medical interview, and automatic analysis of test diagnostic results will lead to classification of observations to a specific disease entity. The current revolution using Big Data significantly expands the role of computer science in achieving these goals, which is why we propose a computer data processing system using artificial intelligence to analyse and process medical images. We conducted research that confirms the need to use GPUs in Big Data systems that process medical images. The use of this type of processor increases system performance.

Detection of Specific Components in a PCA Mixture

Taking a given mixture as an example, 25,000 samples were selected for the detection of 7 indicators. Firstly, the correlation between each indicator and the test result is analyzed, The T test is used to identify the main indicators that can be used to determine the existence of a specific component. Secondly, three comprehensive indexes are obtained by combining PCA. Determine whether there are specific components in the unknown mixture.

Application of Twin Objective Function SVM in Sentiment Analysis

Classification modeling is one of the key issues in sentiment analysis. Support vector machine (SVM) has been widely used in classification as an effective machine learning method. Generally, a common SVM is only for decision-making that sacrifices the distribution of data. In practice, sentiment data are big and mazy, which results in the deficiency of accuracy and stability when common SVM is used. The study investigates sentiment analysis by applying the twin objective function SVM, including nonparallel SVM(NPSVM) and twin SVM (TWSVM). From the experiments, we concluded that twin objective function SVMs are superior to NB and single objective function SVM in accuracy and stability.

A Simple Ensemble Learning Knowledge Distillation

Deep neural network (DNN) has shown significant improvement in learning and generalizing different machine learning tasks over the years. But it comes with an expense of heavy computational power and memory requirements. We can see that machine learning applications are even running in portable devices like mobiles and embedded systems nowadays, which generally have limited resources regarding computational power and memory and thus can only run small machine learning models. However, smaller networks usually do not perform very well. In this paper, we have implemented a simple ensemble learning based knowledge distillation network to improve the accuracy of such small models. Our experimental results prove that the performance enhancement of smaller models can be achieved through distilling knowledge from a combination of small models rather than using a cumbersome model for the knowledge transfer. Besides, the ensemble knowledge distillation network is simpler, time-efficient, and easy to implement.

Two-Dimensional Spatial Resolution in Plane Temperature Monitoring Based on Raman Distributed Temperature Sensor

Spatial resolution is an important parameter that characterizes the detection capability of a system, and there are extremely high requirements for spatial resolution in important fields such as the fossil energy industry and nuclear industry. In order to realize the high-precision distributed monitoring of the optical fiber distributed temperature sensing system (DTS), the factors affecting the spatial resolution of the DTS system were analyzed, and a two-dimensional planar temperature field distribution monitoring scheme based on Raman distributed temperature sensor (RDTS) was proposed. In this scheme, based on the layout of the two-dimensional RDTS heat source positioning system, multimode fiber was adopted. After comparing several sensing fiber routing schemes, the 45∘ skew 2D wiring method of sensing fiber was finally selected. According to the experimental results, the spatial resolution of the temperature field distribution in the monitoring area can break through the limitation of the system resolution. It has more application value than the traditional one-dimensional distributed temperature sensing system.

Secure Server Key Management Designs for the Public Cloud

The Enterprise Level Security (ELS) model focuses on designing secure, distributed web-based systems starting from basic principles. One area of ELS that poses significant design challenges is protection of web server private keys in a public cloud. Web server private keys are of critical importance because they control who can act as the server to represent the enterprise. This includes responding to requests as well as making requests within the enterprise and to its partners. The cloud provider is not part of this trusted network of servers, so the cloud provider should not have access to server private keys. However, current cloud systems are designed to allow cloud providers free access to server private keys. This paper proposes design solutions to securely manage private keys in a public cloud. An examination of commonly used approaches demonstrates the ease with which cloud providers can currently control server private keys. Two designs are proposed to prevent cloud provider access to keys, and their implementation issues are discussed.

Deep Robot-Human Interaction with Facial Emotion Recognition Using Gated Recurrent Units & Robotic Process Automation

This research work proposes a Facial Emotion Recognition (FER) system using deep learning algorithm Gated Recurrent Units (GRUs) and Robotic Process Automation (RPA) for real time robotic applications. GRUs have been used in the proposed architecture to reduce training time and to capture temporal information. Most work reported in literature uses Convolution Neural Networks (CNN), Hybrid architecture of CNN with Long Short Term Memory (LSTM) and GRUs. In this work, GRUs are used for feature extraction from raw images and dense layers are used for classification. The performance of CNN, GRUs and LSTM are compared in the context of facial emotion recognition. The proposed FER system is implemented on Raspberry pi3 B+ and on Robotic Process Automation (RPA) using UiPath RPA tool for robot human interaction achieving 94.66% average accuracy in real time.

An Octave Package to Perform Qualitative Analysis of Nonlinear Systems Immersed in R4

In this work, the AutonomousSystems4D package is presented, which allows the qualitative analysis of non-linear differential equation systems in four dimensions, as well as drawing the phase surfaces by immersing R4 in R3. The package is programmed in the computational tool Octave. As a case study applied to the new Lorenz 4D System, sensitivity was found in the initial conditions, Lyapunov exponents, Kaplan Yorke dimension, a stable and unstable critical point, limit cycle, Hopf bifurcation, and hyperattractors. The package could be adapted to perform qualitative analysis and visualize phase surfaces to autonomous systems, e.g. Sprott 4D, Rossler 4D, etc. The package can be applied to problems such as: design, analysis, implementation of electronic circuits; to message encryption.

Research on Optimization Method of Tool Path in Five-Axis Process Singular Region

For the five-axis machine into the singular region in the process of parts processing, resulting in a discontinuous and rapid rotation of the axis of rotation of large angles. Based on the analysis of the cause of the obvious ripple on the machined surface and the influence on the machining precision, a mathematical model of the singular region is established, and an optimization method of the tool path in the singular region is proposed. The simulation and practical machining results show that the method can effectively overcome the problem of excessive movement of the rotating shaft in the Song singular region of 5-axis machine tool, and solve the surface corrugated defects caused by the problem, while improving the processing efficiency.