FlexRay is a new standard of in-vehicle communication network system,which provides a high speed serial communication, time triggered bus and fault tolerant communication between electronic devices. This paper proposes a network optimization scheme for the composite deadline algorithm for the FlexRay dynamic segment. The priority of the message transmission is allocated by the ratio of the deadline to the message length ratio, which effectively shortens the transmission time and thus improves the network utilization. By analyzing the dynamic segment transmission characteristics and transmission conditions, the most suitable dynamic segment length and minislot number are obtained. By building a network topology architecture model based on CANoe, FlexRay network utilization is up to 31.11% under the condition of many different length messages.
Over 300 million streetlights in cities and campuses throughout the world are powered using electricity generated by burning fossil fuels.Traditional streetlights have mechanical timers to turn the lights ON-OFF for a preset amount of time.Since they are manually operated, they are turned on at full intensity for a fixed period of time regardless of the amount of ambient light, leading to energy wastage.Another disadvantage of these networks is that there is no way of detecting a light when it stops functioning and it takes a lot of time to identify, service and replace it.This paper discusses an off-grid solar powered lighting system for smart cities that is economic, and efficient.The designed system powers DC LED lights.LEDs are used since their power consumption is much less compared to the enormous power consumption of traditional HID lights.The system is integrated with the IoT enabling it to take intelligent decisions without any human involvement.Further, the use of IoT enables remote control and monitoring of the system using Android and iOS applications.The system utilizes sensors to obtain data on the amount of power generated by the solar panels and the amount of power consumed by the LEDs.This data can be monitored using the mobile applications and it is also backed up in a database on the Google Cloud.This data can be analyzed to obtain insights on power generation and power consumption trends.The proposed system is implemented economically, and the performance analysis results are promising compared to traditional lighting systems.
Unbalanced data classification is a research focus for many applications, including financial fraud detection, network intrusion detection and cancer classification. However, unbalanced data classification is rarely investigated in the field of EEG-based sleep staging. Herein, considering the idea that old methods can be exploited in new applications, we propose a practical framework aiming to classify sleep stages with unbalanced data. In this framework, the data are balanced by using a SMOTE algorithm, in which the mean sample number is used for data expansion and the nearest neighbour number is set according to the G-mean values. Subsequently, the features are extracted and selected based on the balanced dataset. The effectiveness of the proposed framework is validated by testing eight sets of Sleep-EDF EEG data in the MIT-BIH physiological information database. From the results, the proposed framework can be used to not only improve the F-score value of the minority class but also to improve the G-mean value and the AUC value of the whole data set, which might benefit sleep studies and disorder diagnoses.
As the world population is increasing day by day and there is an increase in the use of energy in the same order, so it is essential to keep our energy resources very high to meet the demand.It is the fact that due to the rapid depletion of fossil fuel resource worldwide, it became essential to search for alternative sources of energy.For transportation mode, the electric vehicles found the difficulty of charging the batteries after propelling few kilometers.This paper proposes a wind/solar based electric vehicle which can charge the batteries on mobility.It will increase its mileage as well as helping to keep the environment green and clean.The proposed model of the electric vehicle is driven on the same principles as used in electric vehicles.The top (roof/bonnet) of the vehicle is equipped with 420W flexible solar panel, the windscreen and four windows covered by a 410W flexible solar panel which could only be utilized while on parking.The surrounding of the area is equipped with 460W flexible solar panel; a 400W micro wind turbine is located at the front of the vehicle behind the condenser, the combination of small wind turbines adjusted between the two layers of vehicle external body and PV panels.MATLAB/SIMULINK model presented that by utilizing the combination of PV panel and micro wind turbine provide extra power to charge the 30.7 kWh Lithium (Li)-ion battery and is increasing the range up to several kilometers, while the model runs for one sunny day.
This paper presents a thorough review of image processing tasks such as boundary, corner, and edge detection. A clear introduction of these topics is provided for a new researcher, which is followed by a description of the technical details that have been implemented for these topics. In addition, the database list used in the experimentations is also provided. Further, all the techniques are classified and listed in a tabular format. Finally, the open challenges in this field of research are discussed and concluded with the future scope and directions. The authors of this paper expect that this review paper will help new researchers to find an approach to conduct further studies in this topic and also identify some of the unsolved problems. The latter will assist them in solving and reaching close to this research topic.
Cardiovascular disease prediction is very critical area of research. In this work we tried to measure the left ventricular volume which plays an important role in cardiac arrests. In this work we contributed in data pre-processing of the CMR images then applied deep neural network. The data used in this work was Sunnybrook Cardiac Dataset (SCD) and Cardiac Atlas Project (CAP). It is LV CMR images dataset. Convolution neural network and maxpooling with ADAM activation function was applied for the proposed architecture. The results are at very initial stages and further enhancement could be done in the future by applying more efficient pre-processing techniques.
Many algorithms require prior information about test subjects for heart rate (HR) detection.In this paper, we propose a novel algorithm for detecting the HR of previously unknown species based on the Pan-Tompkins (PT) algorithm without a priori knowledge.In this improved PT algorithm, some parameters that need to be predefined can be adaptively adjusted according to the recognition of previous species.In the recognition step, a clustering algorithm is applied to obtain the rough RR intervals, and a decision tree is applied for species recognition by using two features: the rough RR intervals and the proportion of the ECG power of frequency that is less than 5 Hz.The accuracy and the Kappa of species classification in the recognition step can reach 93.15% and 90.23%, respectively.In the HR detection step, the improved PT algorithm is used to precisely detect the HR of rats, mice, humans, frogs and rabbits, and the results show that this method has good performance.In particular, we apply the proposed algorithm to test some ECG signals in humans from the MIT-BIH database.The results show that the accuracy of the proposed algorithm for detection of HR in humans reaches 99.71%.
Taguchi digital dynamic system are problems in which both the input signal and the output responses have values of either 0 or 1 with two misclassification probabilities.Taguchi proposed a two-step procedure for the digital dynamic system under an equalized error rates model and maximized the standardized SN ratio to achieve robust design.This paper proposes a quality loss model to determine the optimal threshold value of a digital dynamic system based on normally distributed data with unequal loss coefficients.We varied the variances of random variable X1 with increment of 0.10 using Excel software.The results are compared with the threshold values obtained by using Taguchi method.When the two loss coefficients and variances of two populations are equal, the optimal threshold value is the same as the threshold value provided by Taguchi method.The maximum error of optimal threshold value is 0.03 compared with actual threshold value.
This paper presents a modeling and design of the wireless controlled self-driving security system using in-wheel drives.The designed system has the wireless main controller, sensor boards, HD-camera, up-down control board and two in-wheel drives to control the In-wheel BLDC(Brushless DC) motor.Each board is connected by the serial communication and the main controller is wireless connected to the remote controller.
The rise in availability of huge amounts of historical data and the need for accurate forecasting techniques of future behavior of electricity consumption emphasize the need for efficient techniques able to reliably estimate the stochastic dependency between the past and future observations in the grid.This study introduces a new time-series forecasting technique for performing short/medium-term electricity consumption forecasting with high accuracy, given a limited period of historical measurements available to extract trends present in time-series.Proposed technique can predict the future energy requirements without the need for additional information such as date or time of the measurements.Described hybrid method can overcome the performance drop issue, where there is redundant or missing data in historical measurements or when the historical measurements are noisy by utilizing three machine learning algorithms; Random Forests, Quinlan's M5 and Linear Regression.The operation of the proposed method is tested on different substations located in central London and the prediction performance established by comparing it to AutoRegressive Integrated Moving Average (ARIMA) and Autoregressive Neural Network (NNAR) time-series forecasting methods.
A railway interlocking system is one example of a critical system, and, therefore, it must have a high level of reliability in order to avoid problems that may result on the loss of people's lives.However, many railway systems are still specified using historical relay-based diagrams, whose analysis are made by human inspection, which is error prone.Relay-based diagrams are specified by nodes and cables in a graphical manner, which resemble undirected graphs.This paper presents a framework for the specification of relay diagrams in a formal language, B-method, based on the specification of a graph and its properties.The use of a formal language allows one to prove the correctness of these railway interlocking systems regarding structural properties.This framework has been evaluated by the specification of a case study.
This paper describes the design of the monopole full-bridge voltage source inverter and demonstrates the working principle with the PSPICE simulation waveform, then proposes a more practical optimization.The simulation result ensures that the inverter has less distortion and smaller phase shift, which is adjustable.And it is useful to the real design and circuit optimization.
Fixed-time convergence control strategies based on adaptive Non-Singular Terminal Sliding Mode are proposed for rigid spacecraft attitude stabilization subject to actuator faults.A novel fixed-time sliding mode surface is used.Then, a fixed-time controller with adaptive law is derived to guarantee that the closed-loop system is stable in the sense of the fixed-time concept.Finally, the Lyapunov stability analysis shows that the controller has a good fault-tolerant performance on actuator faults.Numerical simulation verified the good performance of the controller in the attitude stabilization control.
This paper considers special functioning modes of a buck converter under closed loop feedback, which, depending on the type of a regulator, are characterized by the appearance of bifurcations which are transformed into the chaotic mode, or by undamped oscillations.The paper presents a simple and compact mathematical model.The model is implemented in the Matlab program, which makes it possible to carry out various calculations, including the cyclic ones, followed by graphic and numerical data processing.For normalizing the performance of buck converters, a new method of synchronizing the occurring modes proposed, and its efficiency is tested for various regulators in a feedback system.The accuracy of the proposed mathematical model, and of the adequacy of the obtained results, are tested by the Matlab-Simulink program.
Deep convolutional neural networks (DCNNs) have achieved state-of-the-art results for image recognition.However, these DCNNs with complex structure consist of many layers like convolutional layers, which require high time and computational complexity for training.Therefore, we propose a novel shallow convolutional neural network (SCNND) with dropout to address the problems of the DCNNs for image recognition.The SCNND with 4 layers can fast learning the features of the images, using dropout technology between two convolutional layers to improve recognition performance.Compared to the SCNNs, our SCNND includes 4 layers, with low time complexity and parameters.Experimental results show that our SCNND outperforms shallow CNN methods on Fashion-MNIST dataset.
Video advertising has become important content for web advertising, especially in mobile devices.However, most mobile devices prohibit an auto-play function for video sequences on mobile web browsers.As a result, video coding standards such as MPEG4 and H.265 cannot be used for video advertising on mobile devices.Therefore, CSS image sprites that can show successive images are widely used to play video sequences for web advertising on mobile devices.This paper proposes a novel rate control scheme to improve the coding efficiency, based on CSS image sprites composed of successive still images coded using the JPEG standard.The proposed scheme controls data allocation to blocks according to the importance of the image regions during rate-distortion optimization of the JPEG encoding process.Subjective evaluation based on the Subjective Assessment Methodology for Video Quality (SAMVIQ) method using datasets created using DAVIS2017 and CG showed that the proposed scheme can reduce the total size of coded data while preserving the subjective image quality.
Extraction of feature and classification methods are important phases in recognition system.A good classifier and extraction of features that suits play a very important role in a recognition system to improving recognition rate.In this paper we propose a new system designed to recognize the ten digits of printed Arabic numerals that are the most common symbolic representation of numbers in the world today.This study has been conducted using Hu moment, number of hole and surface which are tolerate to the geometric transformations along with seven different classifiers Naive Bayesian, Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA), Pseudo-Inverse.Support Vector Machine (SVM), Decision Tree and K-Nearest Neighbor (KNN).Classifier combination is considered.Experimental tests demonstrated that our technique achieves good results on multi-font and multi-size printed digit dataset.
Faults lead to de-energising of a section of transmission line thus decreasing the reliability of the system.Power swing affects the distance relays leading to false alarms which were the leading cause of unreliability.Faults cause permanent movement of the operating point in RX plot with a transient oscillation.The locus of power swing forms closed loops.Here, impedance angles were used to discriminate the faults from power swing.The investigation was carried out along with three dimensional plots to visualize the locus of impedance in resistance, reactance and time axis.Results signify that faults were differentiated from power swing using differential impedance angle.The test cases include all the unbalanced faults as well as an unstable swing.Power swing formed bi-conical loops above and below the ideal operating impedance, while differential impedance formed hyperboloid with the mean impedance being zero.Simulations were carried out in a 2 bus system and all the voltages and currents were extracted from the center of transmission line.Visual representation of normal operating impedances and power swing using three dimensional plots (R -X -t space) and (dR/dt -dX/dt -t space) were shown in the Appendix.
