scholarly journals HDL Implementation of Scalable Architecture for Packet Classification using Field Split Bit Vector Algorithm

2021 ◽  
Vol 9 (2) ◽  
pp. 125-130
Author(s):  
Sandeep Kakde, Et. al.
Keyword(s):  
Internet Protocol ◽  
Classification Performance ◽  
Packet Classification ◽  
Matching Rule ◽  
Field Programmable ◽  
Rule Sets ◽  
Bit Vector ◽  
Destination Port ◽  
Memory Efficient

The categorization of incoming packets can be considered as a classification based on the fields of the different headers, such as the source-Internet protocol, the target-Internet protocol, the source-port, destination-port and protocol fields. It requires that each packet is compared with rules and each packet is forwarded to the highest priority matching rule. Packet classification performance also depends on the rule sets. The required storage depends generally on the number of rules and the size of the method. In this paper, we described a Modular Field Split Bit-Vector (FSBV) algorithm, with which the Field Programmable Gate Array (FPGA) classification of packets is performed using Xilinx ISE13.1 software, with a few predefined rules. From the results obtained through EDA tools, it can be concluded that the proposed technique is memory-efficient and latency aware.

scholarly journals Enabling Packet Classification with Low Update Latency for SDN Switch on FPGA

2020 ◽  
Vol 12 (8) ◽  
pp. 3068 ◽  
Author(s):  
Chenglong Li ◽  
Tao Li ◽  
Junnan Li ◽  
Zilin Shi ◽  
Baosheng Wang
Keyword(s):  
Real Time ◽  
Field Programmable Gate Array ◽  
High Performance ◽  
Recursive Algorithm ◽  
Packet Classification ◽  
Experimental Results ◽  
The Core ◽  
Core Function ◽  
Field Programmable ◽  
Bit Vector

Field Programmable Gate Array (FPGA) is widely used in real-time network processing such as Software-Defined Networking (SDN) switch due to high performance and programmability. Bit-Vector (BV)-based approaches can implement high-performance multi-field packet classification, on FPGA, which is the core function of the SDN switch. However, the SDN switch requires not only high performance but also low update latency to avoid controller failure. Unfortunately, the update latency of BV-based approaches is inversely proportional to the number of rules, which means can hardly support the SDN switch effectively. It is reasonable to split the ruleset into sub-rulesets that can be performed in parallel, thereby reducing update latency. We thus present SplitBV for the efficient update by using several distinguishable exact-bits to split the ruleset. SplitBV consists of a constrained recursive algorithm for selecting the bit positions that can minimize the latency and a hybrid lookup pipeline. It can achieve a significant reduction in update latency with negligible memory growth and comparable high performance. We implement SplitBV and evaluate its performance by simulation and FPGA prototype. Experimental results show that our approach can reduce 73% and 36% update latency on average for synthetic 5-tuple rules and OpenFlow rules respectively.

scholarly journals Integrated Packet Classification to Support Multiple Security Policies for Robust and Low Delay V2X Services

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jaehyeong Wee ◽  
Wooguil Pak
Keyword(s):  
Unique Feature ◽  
Classification Performance ◽  
Packet Classification ◽  
Performance Evaluations ◽  
Security Policies ◽  
Low Delay ◽  
Network Equipment ◽  
Rule Sets ◽  
Rule Set ◽  
Extensive Performance

One of the key applications in the 5G system is Vehicle-to-Everything (V2X). Ultra-low delay communication is essential for the safety of users and pedestrians in V2X. However, as sophisticated and various cyberattacks are increasing, it becomes hard to satisfy low delay constraints. To protect networks from such attacks, even single network security equipment provides multiple security functions, resulting in the inevitable additive delay in packet processing. In this paper, we suggest a new packet classification paradigm to resolve this issue. The proposed algorithm integrates multiple policy rule-sets into a single rule-set and classifies incoming packets using the integrated rule-set. Thus, it has a unique feature providing high classification performance regardless of the number of security policies. Through extensive performance evaluations, we confirm that the performance improvement is also increased with the total rule-set number increasing without the significant overhead of memory cost. We expect that it will mitigate the delay issue of existing network equipment for upcoming services such as V2X.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Kun Zeng ◽  
Yibin Xu ◽  
Ge Lin ◽  
Likeng Liang ◽  
Tianyong Hao
Keyword(s):  
Clinical Trial ◽  
Ensemble Learning ◽  
Metric Learning ◽  
Classification Performance ◽  
Ensemble Model ◽  
Automated Classification ◽  
Eligibility Criteria ◽  
Data Imbalance ◽  
The Impact

Abstract Background Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. Methods An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. Results Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. Conclusions A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

scholarly journals Space Precession Target Classification Based on Radar High-Resolution Range Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Cunqian Feng ◽  
Yongshun Zhang ◽  
Sisan He
Keyword(s):  
Parameter Extraction ◽  
Classification Performance ◽  
Support Vector ◽  
Electromagnetic Data ◽  
Feature Extractor ◽  
Different Types ◽  
Radar Echo ◽  
High Level ◽  
Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

scholarly journals Real-time classification of hand movements as a basis for intuitive control of grasp neuroprostheses

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Dmitry Amelin ◽  
Ivan Potapov ◽  
Josep Cardona Audí ◽  
Andreas Kogut ◽  
Rüdiger Rupp ◽  
...  
Keyword(s):  
Neural Networks ◽  
Standard Deviation ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Healthy Subjects ◽  
Hand Movements ◽  
Cord Injury ◽  
Field Programmable

AbstractThis paper reports on the evaluation of recurrent and convolutional neural networks as real-time grasp phase classifiers for future control of neuroprostheses for people with high spinal cord injury. A field-programmable gate array has been chosen as an implementation platform due to its form factor and ability to perform parallel computations, which are specific for the selected neural networks. Three different phases of two grasp patterns and the additional open hand pattern were predicted by means of surface Electromyography (EMG) signals (i.e. Seven classes in total). Across seven healthy subjects, CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks) had a mean accuracy of 85.23% with a standard deviation of 4.77% and 112 µs per prediction and 83.30% with a standard deviation of 4.36% and 40 µs per prediction, respectively.

scholarly journals Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

2018 ◽  
Vol 8 (9) ◽  
pp. 1569 ◽  
Author(s):  
Shengbing Wu ◽  
Hongkun Jiang ◽  
Haiwei Shen ◽  
Ziyi Yang
Keyword(s):  
Logistic Regression ◽  
Gene Selection ◽  
Classification Performance ◽  
Cancer Classification ◽  
Sparse Logistic Regression ◽  
The Subject ◽  
Selection For ◽  
Microarray Datasets ◽  
Sparse Methods

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.

scholarly journals Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Shuai Zhang ◽  
Yong Chen ◽  
Xiaoling Huang ◽  
Yishuai Cai
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Differential Evolution Algorithm ◽  
Classification Performance ◽  
Classification Model ◽  
Evolution Algorithm ◽  
Classification Prediction

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.

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