scholarly journals A Robust Document Identification Framework through f-BP Fingerprint

2021 ◽  
Vol 7 (8) ◽  
pp. 126
Author(s):  
Francesco Guarnera ◽  
Oliver Giudice ◽  
Dario Allegra ◽  
Filippo Stanco ◽  
Sebastiano Battiato ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Ad Hoc ◽  
State Of The Art ◽  
Low Cost ◽  
Principal Component ◽  
Industrial Equipment ◽  
Noisy Conditions ◽  
Block Based ◽  
Printed Materials ◽  
Reliable Solution

The identification of printed materials is a critical and challenging issue for security purposes, especially when it comes to documents such as banknotes, tickets, or rare collectable cards: eligible targets for ad hoc forgery. State-of-the-art methods require expensive and specific industrial equipment, while a low-cost, fast, and reliable solution for document identification is increasingly needed in many contexts. This paper presents a method to generate a robust fingerprint, by the extraction of translucent patterns from paper sheets, and exploiting the peculiarities of binary pattern descriptors. A final descriptor is generated by employing a block-based solution followed by principal component analysis (PCA), to reduce the overall data to be processed. To validate the robustness of the proposed method, a novel dataset was created and recognition tests were performed under both ideal and noisy conditions.

scholarly journals Automatic spike sorting for high-density microelectrode arrays

2018 ◽  
Vol 120 (6) ◽  
pp. 3155-3171 ◽  
Author(s):  
Roland Diggelmann ◽  
Michele Fiscella ◽  
Andreas Hierlemann ◽  
Felix Franke
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Microelectrode Arrays ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Sorting Algorithm ◽  
Spike Sorting ◽  
Recording Electrodes

High-density microelectrode arrays can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike sorters must be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multielectrodes, however, suffer from the “curse of dimensionality” and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal component analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently with classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data prewhitening before the principal component analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and that were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation was not required. NEW & NOTEWORTHY We present an automatic spike sorting algorithm that combines three strategies to scale classical spike sorting techniques for high-density microelectrode arrays: 1) splitting the recording electrodes into small groups and sorting them independently; 2) clustering a subset of spikes and classifying the rest to limit computation time; and 3) prewhitening the spike waveforms to enable the use of parameter-free clustering. Finally, we combined these strategies into an automatic spike sorter that is competitive with state-of-the-art spike sorters.

Evaluation of Feature Reduction using Principal Component Analysis and Sequential Pattern Matching for Manet

2017 ◽  
Vol 7 (3) ◽  
pp. 1228
Author(s):  
M. Reji ◽  
P.C. Kishore Raja ◽  
Bhagyalakshmi M
Keyword(s):  
Principal Component Analysis ◽  
Ad Hoc ◽  
Pattern Mining ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction ◽  
Sequential Pattern ◽  
Basic Part ◽  
Information Set ◽  
Hoc Networks

In Mobile Ad hoc Networks (MANETs) there are some security problems because of portability, element topology changes, and absence of any framework. In MANETs, it is of extraordinary significance to identify inconsistency and malignant conduct. With a specific end goal to recognize malignant assaults by means of interruption identification frameworks and dissect the information set, we have to choose some components. Thus, highlight determination assumes basic part in recognizing different assaults. In the writing, there are a few recommendations to choose such elements. For the most part, Principal Component Analysis (PCA) breaks down the information set and the chose highlights. In this paper, we have gathered a list of capabilities from some cutting edge works in the writing. Really, our reproduction demonstrates this list of capabilities identify inconsistency conduct more precise. Likewise, interestingly, we utilize PCA for investigating the information set. In contrast to PCA, our results show Sequential pattern mining (SPM) cannot be affected by outlier data within the network. The  normal and attack states are simulated and the results are analyzed using NS2 simulator.

scholarly journals Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense)

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3225 ◽  
Author(s):  
Grassi ◽  
Benedetti ◽  
Opizzio ◽  
Nardo ◽  
Buratti
Keyword(s):  
Electronic Nose ◽  
Ad Hoc ◽  
Low Cost ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Oxide Semiconductor ◽  
Prediction Ability ◽  
Nearest Neighbours ◽  
Electronic Nose System

The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers’ expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an “ad hoc” designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours’ algorithm and Partial Least Square–Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar’s test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values (p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours’ model is considered the most convenient strategy to assess meat and fish freshness.

scholarly journals Detection of Lethal Bronzing Disease in Cabbage Palms (Sabal palmetto) Using a Low-Cost Electronic Nose

Biosensors ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 188
Author(s):  
Martin J. Oates ◽  
Nawaf Abu-Khalaf ◽  
Carlos Molina-Cabrera ◽  
Antonio Ruiz-Canales ◽  
Jose Ramos ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Discrete Fourier Transform ◽  
Electronic Nose ◽  
Low Cost ◽  
Harmonic Component ◽  
Principal Component ◽  
Component Analysis ◽  
Sabal Palmetto ◽  
Cabbage Palm

Lethal Bronzing Disease (LB) is a disease of palms caused by the 16SrIV-D phytoplasma. A low-cost electronic nose (eNose) prototype was trialed for its detection. It includes an array of eight Taguchi-type (MQ) sensors (MQ135, MQ2, MQ3, MQ4, MQ5, MQ9, MQ7, and MQ8) controlled by an Arduino NANO® microcontroller, using heater voltages that vary sinusoidally over a 2.5 min cycle. Samples of uninfected, early symptomatic, moderate symptomatic, and late symptomatic infected palm leaves of the cabbage palm were processed and analyzed. MQ sensor responses were subjected to a 256 element discrete Fourier transform (DFT), and harmonic component amplitudes were reviewed by principal component analysis (PCA). The experiment was repeated three times, each showing clear evidence of differences in sensor responses between the samples of uninfected leaves and those in the early stages of infection. Within each experiment, four groups of responses were identified, demonstrating the ability of the unit to repeatedly distinguish healthy leaves from diseased ones; however, detection of the severity of infection has not been demonstrated. By selecting appropriate coefficients (here demonstrated with plots of MQ5 Cos1 vs. MQ8 Sin3), it should be possible to build a ruleset classifier to identify healthy and unhealthy samples.

Differentiation between Cerebrospinal Fluid and Serum with Electronic Nose

2005 ◽  
Vol 133 (1) ◽  
pp. 16-19 ◽  
Author(s):  
Anna Aronzon ◽  
C. William Hanson ◽  
Erica R. Thaler
Keyword(s):  
Principal Component Analysis ◽  
Cerebrospinal Fluid ◽  
Electronic Nose ◽  
Clinical Setting ◽  
Sensor Array ◽  
Low Cost ◽  
Principal Component ◽  
Rapid Diagnosis ◽  
Novel Method ◽  
Csf Leaks

OBJECTIVE: The study investigates the ability of the electronic nose to differentiate between cerebrospinal fluid (CSF) and serum and to identify an unknown specimen as CSF or serum. STUDY DESIGN AND SETTING: CSF and serum specimens were heated and tested with an organic semiconductor-based Cyranose 320 electronic nose (Cyrons Sciences, Pasadena, CA). Data from the 32-element sensor array were subjected to principal component analysis to depict differences in odorant patterns. RESULTS: The electronic nose was able to distinguish between CSF and serum isolates with Mahalanobis distance >5. Furthermore, the electronic nose was able to place unknown specimens in the appropriate class of CSF or serum. CONCLUSIONS: The electronic nose is a novel method that may allow rapid, low cost, and reliable distinction between CSF and serum in a clinical setting. SIGNIFICANCE: Because the results are available almost immediately, the electronic nose is a powerful tool that in the future may allow for rapid diagnosis of CSF leaks in the office setting.

scholarly journals Enhancing Detection Performance of Face Recognition Algorithm Using PCA-Faster R-CNN

2021 ◽  
Vol 5 (3) ◽  
pp. 9-13
Author(s):  
Hashiru Isiaka Muhammad ◽  
Kabir Ibrahim Musa ◽  
Mustapha Lawal Abdulrahman ◽  
Abdullahi Abubakar ◽  
Kabiru Umar ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Principal Component ◽  
Component Analysis ◽  
Face Database ◽  
Recognition Time ◽  
Real World Datasets ◽  
Time Image

In this paper, we present a new face detection scheme using deep learning and achieving state-of-the-art recognition performance using real-world datasets.  We designed and implemented a face recognition system using Principal Component Analysis (PCA) and Faster R Convolutional Neural Network (Faster R CNN). In particular, we improve the state-of-the-art Faster RCNN framework by using Principal Component Analysis (PCA) technique and Faster R CNN to detect and recognise faces in a face database.  The Principal Component Analysis (PCA) was used to extract features and dimensionality reduction from the face database, while the Faster R Convolutional Neural Network algorithm was used to identify patterns in the dataset via training the neural network. The three real-world datasets used in our experiment are ORL, Yale, and California face dataset. When implemented on the ORL face dataset, the algorithm achieved average recognition accuracy of 99%, with a recognition time of 147.72 seconds for 10 runs, and the recognition time/image was 0.3 sec/image on 400 images. The Yale face dataset achieved average recognition accuracy of 99.24% with a recognition time of 63.45 seconds for 10 runs, and the recognition time/image was 0.53 sec/image on 120 images. Finally, on California Face Database (CFD), it achieved average recognition accuracy of 99.52% with a recognition time of 226.05 seconds for 10 runs, and the recognition time/image was 0.27 sec/image on 827 images. On the CFD dataset, however, the proposed approach has excellent classification performance when the recall ratio is high. The proposed method achieves a higher recall and accuracy ratio than the Faster RCNN without PCA method. For the F-score, the proposed method achieved 0.98, which is significantly higher than the 0.95 achieved by the Faster-RCNN. This demonstrates the superiority of our model performance-wise as against state-of-the-art, both in terms of accuracy and fast recognition. Therefore our model is more efficient when compared to the latest researches done in the area of facial recognition.

scholarly journals Intrusion Detection System using One Class SVM with and without Feature Selection in Wormhole Attack Detection

2019 ◽  
Vol 9 (2S4) ◽  
pp. 629-638
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Ad Hoc ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis ◽  
Svm Classifier ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Wormhole Attack

An Ad-hoc network is a kind of wireless construction from one to another computer, without having Wi-Fi access point or Router. However, the Ad hoc approach offers marginal security and decreases the data transfer rate. Consequently, it helps the attacker to connect with the ad-hoc network without any trouble. Therefore, a robust and reliable intrusion detection system (IDS) is a necessity of today’s information security domain. These IDS systems play a vital role in monitoring the threats encountered in a network by detecting the change in the normal profile due to attacks. Recently, to detect attacks the IDS are being equipped with machine learning algorithms to attain better accuracy and fast detection speed. Most of the IDS use different network features. However, enormous number of features makes the detection and prevention complicated. The IDS presented in this paper employs random forest and principal component analysis to minimize the number of features for network IDS for wireless ad hoc networks. The one class SVM has been used for detection of worm hole attack with and without feature selection. The performances of these approaches are compared with various existing techniques with false positive rate (FPR), accuracy and detection rate. Here, the accuracy improves and false positive rate reduces when intrusion is detected with feature selection technique. This paper discusses the performance of the one class SVM classifier in the wireless adhoc network IDS with random forest feature selection and principal component analysis feature selection techniques and one class SVM classifier without feature selection technique in the detection of wormhole attack. And the performance of one class SVM IDS is better in the detection of wormhole attack while it is implemented with principal component analysis feature selection technique.

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