scholarly journals Serial Detection with Neural Network-Based Noise Prediction for Bit-Patterned Media Recording Systems

2021 ◽  
Vol 11 (10) ◽  
pp. 4387
Author(s):  
Thien An Nguyen ◽  
Jaejin Lee
Keyword(s):  
Neural Network ◽  
Data Storage ◽  
Detection Algorithm ◽  
Noise Signal ◽  
High Density ◽  
Noise Prediction ◽  
Magnetic Islands ◽  
Bit Patterned Media ◽  
Proposed Model ◽  
Cross Track

Ultra-high density data storage has gained high significance given the increasing amounts of data; many technologies have been proposed to achieve a high density. Among them, bit-pattern media recording (BPMR) is a promising technology. In BPMR systems, data are stored on magnetic islands. Therefore, high densities can be achieved by reducing the distance between the magnetic islands. Because of the closeness between the magnetic islands, the readback signal is distorted by two-dimensional (2D) interference, which includes the intersymbol interference according to the down-track direction and the intertrack interference according to the cross-track direction. A simple and effective serial detection algorithm was recently proposed to mitigate the 2D interference. However, serial detection utilizes the hard output in inner detection, and this degrades the serial detection performance. To resolve this problem, a subsequent study used feedback to estimate the noise and used this noise signal to create a soft output for inner detection. Following up, in this paper we propose a model that utilizes a neural network for noise prediction. The proposed neural network-based model and the model with the feedback line were compared in terms of bit error rate (BER). The results show that the proposed model achieves a gain of approximately 1 dB at a BER of 10−6.

scholarly journals Effective Generalized Partial Response Target and Serial Detector for Two-Dimensional Bit-Patterned Media Recording Channel Including Track Mis-Registration

2020 ◽  
Vol 10 (17) ◽  
pp. 5738 ◽  
Author(s):  
Thien An Nguyen ◽  
Jaejin Lee
Keyword(s):  
Partial Response ◽  
Data Storage ◽  
High Density ◽  
Cloud Services ◽  
Two Dimensional ◽  
Magnetic Islands ◽  
Bit Patterned Media ◽  
Increasing Demand ◽  
Cross Track ◽  
Response Target

With the development of 5G technology, programs are gradually moving to cloud services. This leads to an increasing demand for storage. In the field of high-density data storage, bit-pattern media recording (BPMR) is considered a promising approach, as it can expand the data density to 4 Tb/in2. However, in high-density BPMR, bits or magnetic islands are very close to each other, leading to significant intertrack interference (ITI) from the cross-track direction and intersymbol interference (ISI) from the down-track direction. To minimize two-dimensional interference, including ITI and ISI, the serial detector method has been highly effective. However, in this method, the signal at the output of the first decoder is still a hard output. Therefore, we suggest methods to convert the output of the first detector into a soft output. Additionally, we have developed a new form of generalized partial response target to overcome the track mis-registration. The results show that our proposed methods apparently improve bit error rate performance.

Research on High-Density Network Intrusion Features Detection Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 1054-1057
Author(s):  
Tai Fu Lv
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Systems ◽  
Feature Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
High Density ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
The Neural Network

Research on high-density network intrusion features problems, which improves the detection accuracy. For high-density network, an intrusion feature detection system based on intelligent expert systems and neural networks in is proposed. First, use expert systems for known high-density network intrusion detection. Use the neural network expert system to detect those which cannot find the unknown high-density network intrusion. Finally test results using neural network expert system rule library to be updated. Experimental results show that this method can effectively detect high-density network intrusion features, which ensures the security of the network and achieves satisfactory results.

scholarly journals Disease Identification in Chilli Leaves using Machine Learning Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 325-329
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Detection System ◽  
Network Models ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Neural Network Models ◽  
The Past ◽  
Learning Techniques ◽  
Proposed Model

Crop diseases reduce the yield of the crop or may even kill it. Over the past two years, as per the I.C.A.R, the production of chilies in the state of Goa has reduced drastically due to the presence of virus. Most of the plants flower very less or stop flowering completely. In rare cases when a plant manages to flower, the yield is substantially low. Proposed model detects the presence of disease in crops by examining the symptoms. The model uses an object detection algorithm and supervised image recognition and feature extraction using convolutional neural network to classify crops as infected or healthy. Google machine learning libraries, TensorFlow and Keras are used to build neural network models. An Android application is developed around the model for the ease of using the disease detection system.

Effect of Attribute Reduction on Rough-Probabilistic Neural Network for Structural Damage Detection

2010 ◽  
Vol 163-167 ◽  
pp. 2482-2487
Author(s):  
Shao Fei Jiang ◽  
Zhao Qi Wu
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Data Storage ◽  
Structural Damage ◽  
Probabilistic Neural Network ◽  
Attribute Reduction ◽  
Measurement Data ◽  
Noise Tolerance ◽  
Structural Damage Detection ◽  
Proposed Model

In this paper, a new rough-probabilistic neural network (RSPNN) model, whereby rough set data and a probabilistic neural network (PNN) are integrated, is proposed. This model is used for structural damage detection, particularly for cases where the measurement data has many uncertainties. To verify the proposed method, an example is presented to identify both single and multi-damage case patterns. The effects of measurement noise and attribute reduction on the damage detection results are also discussed. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also reduces data storage memory requirements.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

scholarly journals Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 920
Author(s):  
Liesle Caballero ◽  
Álvaro Perafan ◽  
Martha Rinaldy ◽  
Winston Percybrooks
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Energy Budget ◽  
Dynamic Models ◽  
Pearson Correlation ◽  
Experimental Conditions ◽  
Grid Map ◽  
Proposed Model ◽  
Exploration Task

This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.

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