Existing Detection Algorithms Research Articles

Simplified Swarm Optimization-Based Function Module Detection in Protein–Protein Interaction Networks

Proteomics research has become one of the most important topics in the field of life science and natural science. At present, research on protein–protein interaction networks (PPIN) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate detection mechanisms of functional modules in PPIN, including open database, existing detection algorithms, and recent solutions. After that, we describe the proposed approach based on the simplified swarm optimization (SSO) algorithm and the knowledge of Gene Ontology (GO). The proposed solution implements the SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species datasets for experiment: fruitfly, mouse, scere, and human. The testing and analysis result show that the proposed solution is feasible, efficient, and could achieve a higher accuracy of prediction than existing approaches.

Semidefinite further relaxation on likelihood ascent search detection algorithm for high‐order modulation in massive MIMO system

Recent studies have shown that existing detection algorithms are not suitable for high-order quadrature amplitude modulation (QAM) in massive MIMO system. In this paper, with an equivalent objective function by QR decomposition and further relaxation on the constrains, the authors develop an improved semidefinite further relaxation detector (SFRD), which is proved to be convex and has solutions within polynomial complexity time. Using the detection result from the proposed SFRD as the initial vector, they propose a novel semidefinite further relaxation on the likelihood ascent search (SFRLAS) detection algorithm. It has been shown through their studies that the proposed SFRLAS scheme can effectively approach the optimum bit error rate from the maximum-likelihood detection algorithm for systems with high-order QAM and large-scale antennas, however, with a lower computational complexity. The spectral efficiency converges to the theoretical value at a much lower required average received signal-to-noise ratio. It is an effective method for high-order QAM signal detection in massive MIMO system.

An expert system for automated identification of obstructive sleep apnea from single-lead ECG using random under sampling boosting

Computerized obstructive sleep apnea detection is necessary to speed-up sleep apnea diagnosis and research and for assisting medical professionals. Moreover, the development of a device to monitor sleep apnea that is low-power and portable, requires a reliable and successful sleep apnea detection scheme. In this article, the problem of automated sleep apnea detection using singe-lead electrocardiogram (ECG) signals has been addressed. At first, segments of ECG signals are decomposed using a data-adaptive signal decomposition scheme, namely- tunable-Q factor wavelet transform (TQWT). Three statistical features are extracted from the TQWT sub-bands and train and test matrices are formed afterwards. These matrices are fed into the classifier to identify non-apneic and apneic ECG signal segments. In this work, a new machine learning algorithm, namely- random under sampling boosting (RUSBoost) is implemented to perform classification. This is for the first time TQWT along with RUSBoost is employed for automatic sleep apnea detection to our knowledge. The overall algorithmic performance of our method is inspected for various values of TQWT parameters. Optimal values of these parameters are investigated and determined. The efficacy and appropriateness of RUSBoost are demonstrated as opposed to the commonly used classification models. The algorithmic performance of our sleep apnea identification scheme is also evaluated against existing detection algorithms in the literature. Experimental outcomes manifest that our sleep apnea identification scheme performs better than the existing works in sensitivity, specificity, and accuracy. It can be anticipated that owing to its use of only one channel of ECG signal, the proposed method will be ideal for device implementation, eliminate the onus of clinicians of analyzing a large bulk of data manually, and expedite sleep apnea diagnosis.

Vehicle Detection Based on Deep Dual-Vehicle Deformable Part Models

Vehicle detection plays an important role in safe driving assistance technology. Due to the high accuracy and good efficiency, the deformable part model is widely used in the field of vehicle detection. At present, the problem related to reduction of false positivity rate of partially obscured vehicles is very challenging in vehicle detection technology based on machine vision. In order to address the abovementioned issues, this paper proposes a deep vehicle detection algorithm based on the dual-vehicle deformable part model. The deep learning framework can be used for vehicle detection to solve the problem related to incomplete design and other issues. In this paper, the deep model is used for vehicle detection that consists of feature extraction, deformation processing, occlusion processing, and classifier training using the back propagation (BP) algorithm to enhance the potential synergistic interaction between various parts and to get more comprehensive vehicle characteristics. The experimental results have shown that proposed algorithm is superior to the existing detection algorithms in detection of partially shielded vehicles, and it ensures high detection efficiency while satisfying the real-time requirements of safe driving assistance technology.

Dual Tree Complex Wavelet Transform Based Compression of Optical Coherence Tomography Images for Glaucoma Detection using Modular Neural Network

Background/Objectives: In worldwide, Glaucoma is basically a second major retinal disease that results in permanent blindness. Loss of Retinal Nerve Fiber Layer (RNFL) is the outcome of glaucoma disease. RNFL thickness is evaluated as a function of spatial information from Optical Coherence Tomography (OCT) images is a significant diagnostics indicator intended for glaucoma disease. However, due to factors such as low image contrast, speckle noise, high spatial resolution, exact compression of OCT is complex. To solve above issues, a Dual Tree Complex Wavelet Transform (DTCWT) based OCT image compression is proposed in this research work. Methods/Statistical analysis: The proposed method consists of five phases such as pre-processing, feature extraction, RNFL segmentation, glaucoma classification and OCT compression. Initially, OCT image is pre-processed for remove the speckle noise using kuan filter. Secondly, the RNFL based texture features are extracted by using Gray Level Covariance Matrix (GLCM) and the scrupulous features are chosen by Principal Component Analysis (PCA). Then, RNFL in OCT is segmented by Improved Artificial Bee Colony (IABC) clustering algorithm. After that the glaucoma is classified as normal, medium and severe by Modular Neural Network (MNN). Finally, DTCWT is used to compress the OCT image. Results: Experimental results show that the proposed MNN is efficient for detecting glaucoma compared with the existing detection algorithms.

A novel method for surface defect inspection of optic cable with short-wave infrared illuminance

Intelligent on-line detection of cable quality is a crucial issue in optic cable factory, and defects on the surface of optic cable can dramatically depress cable grade. Manual inspection in optic cable quality cannot catch up with the development of optic cable industry due to its low detection efficiency and huge human cost. Therefore, real-time is highly demanded by industry in order to replace the subjective and repetitive process of manual inspection. For this reason, automatic cable defect inspection has been a trend. In this paper, a novel method for surface defect inspection of optic cable with short-wave infrared illuminance is presented. The special condition of short-wave infrared cannot only provide illumination compensation for the weak illumination environment, but also can avoid the problem of exposure when using visible light illuminance, which affects the accuracy of inspection algorithm. A series of image processing algorithms are set up to analyze cable image for the verification of real-time and veracity of the detection method. Unlike some existing detection algorithms which concentrate on the characteristics of defects with an active search way, the proposed method removes the non-defective areas of the image passively at the same time of image processing, which reduces a large amount of computation. OTSU algorithm is used to convert the gray image to the binary image. Furthermore, a threshold window is designed to eliminate the fake defects, and the threshold represents the considered minimum size of defects ε. Besides, a new regional suppression method is proposed to deal with the edge burrs of the cable, which shows the superior performance compared with that of Open-Close operation of mathematical morphological in the boundary processing. Experimental results of 10,000 samples show that the rates of miss detection and false detection are 2.35% and 0.78% respectively when ε equals to 0.5mm, and the average processing period of one frame image is 2.39ms. All the improvements have been verified in the paper to show the ability of our inspection method for optic cable.

New framework for unsupervised universal steganalysis via SRISP-aided outlier detection

Formulating steganalysis as a binary classification problem has been highly successful. However, the existing detection algorithms are difficult to obtain high detection accuracy when applied in real-world circumstances. Because so-called model mismatch problem often occurs owing to unknown cover source and embedding parameters. To avoid the mess of model mismatch, we propose a new unsupervised universal steganalysis framework to detect individual stego images. First, cover images with statistical properties similar to those of the given test image are searched from a retrieval cover database to establish an aided cover sample set. Second, unsupervised outlier detection is performed on a test set composed of the given test image and its aided cover sample set to determine the type (cover or stego) of the given test image. Our proposed framework, called Similarity Retrieval of Image Statistical Properties (SRISP)-aided unsupervised outlier detection, requires no training, and thus it does not suffer from model mismatch. The framework employs standard steganalysis features and detects each test image individually. Experimental results illustrate that the framework substantially outperforms one-class support vector machine and the traditional unsupervised outlier detectors without considering SRISP; its detection performance is independent of the proportion of stego images in the test samples.

Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring

Radar is considered an important technology for health monitoring and fall detection in elderly assisted living due to a number of attributes not shared by other sensing modalities. In this article, we describe the signal processing algorithms and techniques involved in elderly fall detection using radar. A human?s radar signal returns differ in their Doppler characteristics, depending on the nature of the human gross motor activities. These signals are nonstationary in nature, inviting time-frequency analysis in both its linear and bilinear aspects, to play a fundamental role in motion identification, including fall features determination and classification. This article employs real fall data to demonstrate the success of existing detection algorithms as well as to report on some of the challenges facing technology developments for fall detection.

Defect Detection in Tire X-Ray Images Using Weighted Texture Dissimilarity

Automatic defect detection is an important and challenging problem in industrial quality inspection. This paper proposes an efficient defect detection method for tire quality assurance, which takes advantage of the feature similarity of tire images to capture the anomalies. The proposed detection algorithm mainly consists of three steps. Firstly, the local kernel regression descriptor is exploited to derive a set of feature vectors of an inspected tire image. These feature vectors are used to evaluate the feature dissimilarity of pixels. Next, the texture distortion degree of each pixel is estimated by weighted averaging of the dissimilarity between one pixel and its neighbors, which results in an anomaly map of the inspected image. Finally, the defects are located by segmenting this anomaly map with a simple thresholding process. Different from some existing detection algorithms that fail to work for tire tread images, the proposed detection algorithm works well not only for sidewall images but also for tread images. Experimental results demonstrate that the proposed algorithm can accurately locate the defects of tire images and outperforms the traditional defect detection algorithms in terms of various quantitative metrics.

Sifting robotic from organic text: A natural language approach for detecting automation on Twitter

Twitter, a popular social media outlet, has evolved into a vast source of linguistic data, rich with opinion, sentiment, and discussion. Due to the increasing popularity of Twitter, its perceived potential for exerting social influence has led to the rise of a diverse community of automatons, commonly referred to as bots. These inorganic and semi-organic Twitter entities can range from the benevolent (e.g., weather-update bots, help-wanted-alert bots) to the malevolent (e.g., spamming messages, advertisements, or radical opinions). Existing detection algorithms typically leverage metadata (time between tweets, number of followers, etc.) to identify robotic accounts. Here, we present a powerful classification scheme that exclusively uses the natural language text from organic users to provide a criterion for identifying accounts posting automated messages. Since the classifier operates on text alone, it is flexible and may be applied to any textual data beyond the Twittersphere.

Detection of K-complexes and sleep spindles (DETOKS) using sparse optimization

BackgroundThis paper addresses the problem of detecting sleep spindles and K-complexes in human sleep EEG. Sleep spindles and K-complexes aid in classifying stage 2 NREM human sleep. New methodWe propose a non-linear model for the EEG, consisting of a transient, low-frequency, and an oscillatory component. The transient component captures the non-oscillatory transients in the EEG. The oscillatory component admits a sparse time–frequency representation. Using a convex objective function, this paper presents a fast non-linear optimization algorithm to estimate the components in the proposed signal model. The low-frequency and oscillatory components are used to detect K-complexes and sleep spindles respectively. Results and comparison with other methodsThe performance of the proposed method is evaluated using an online EEG database. The F1 scores for the spindle detection averaged 0.70 ± 0.03 and the F1 scores for the K-complex detection averaged 0.57 ± 0.02. The Matthews Correlation Coefficient and Cohen's Kappa values were in a range similar to the F1 scores for both the sleep spindle and K-complex detection. The F1 scores for the proposed method are higher than existing detection algorithms. ConclusionsComparable run-times and better detection results than traditional detection algorithms suggests that the proposed method is promising for the practical detection of sleep spindles and K-complexes.

REDHORSE-REcombination and Double crossover detection in Haploid Organisms using next-geneRation SEquencing data.

BackgroundNext-generation sequencing technology provides a means to study genetic exchange at a higher resolution than was possible using earlier technologies. However, this improvement presents challenges as the alignments of next generation sequence data to a reference genome cannot be directly used as input to existing detection algorithms, which instead typically use multiple sequence alignments as input. We therefore designed a software suite called REDHORSE that uses genomic alignments, extracts genetic markers, and generates multiple sequence alignments that can be used as input to existing recombination detection algorithms. In addition, REDHORSE implements a custom recombination detection algorithm that makes use of sequence information and genomic positions to accurately detect crossovers. REDHORSE is a portable and platform independent suite that provides efficient analysis of genetic crosses based on Next-generation sequencing data.ResultsWe demonstrated the utility of REDHORSE using simulated data and real Next-generation sequencing data. The simulated dataset mimicked recombination between two known haploid parental strains and allowed comparison of detected break points against known true break points to assess performance of recombination detection algorithms. A newly generated NGS dataset from a genetic cross of Toxoplasma gondii allowed us to demonstrate our pipeline. REDHORSE successfully extracted the relevant genetic markers and was able to transform the read alignments from NGS to the genome to generate multiple sequence alignments. Recombination detection algorithm in REDHORSE was able to detect conventional crossovers and double crossovers typically associated with gene conversions whilst filtering out artifacts that might have been introduced during sequencing or alignment. REDHORSE outperformed other commonly used recombination detection algorithms in finding conventional crossovers. In addition, REDHORSE was the only algorithm that was able to detect double crossovers.ConclusionREDHORSE is an efficient analytical pipeline that serves as a bridge between genomic alignments and existing recombination detection algorithms. Moreover, REDHORSE is equipped with a recombination detection algorithm specifically designed for Next-generation sequencing data. REDHORSE is portable, platform independent Java based utility that provides efficient analysis of genetic crosses based on Next-generation sequencing data. REDHORSE is available at http://redhorse.sourceforge.net/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1309-7) contains supplementary material, which is available to authorized users.

Neural network pattern recognition of lingual-palatal pressure for automated detection of swallow.

We describe a novel device and method for real-time measurement of lingual-palatal pressure and automatic identification of the oral transfer phase of deglutition. Clinical measurement of the oral transport phase of swallowing is a complicated process requiring either placement of obstructive sensors or sitting within a fluoroscope or articulograph for recording. Existing detection algorithms distinguish oral events with EMG, sound, and pressure signals from the head and neck, but are imprecise and frequently result in false detection. We placed seven pressure sensors on a molded mouthpiece fitting over the upper teeth and hard palate and recorded pressure during a variety of swallow and non-swallow activities. Pressure measures and swallow times from 12 healthy and 7 Parkinson's subjects provided training data for a time-delay artificial neural network to categorize the recordings as swallow or non-swallow events. User-specific neural networks properly categorized 96% of swallow and non-swallow events, while a generalized population-trained network was able to properly categorize 93% of swallow and non-swallow events across all recordings. Lingual-palatal pressure signals are sufficient to selectively and specifically recognize the initiation of swallowing in healthy and dysphagic patients.

Community Detection in Directed Weighted Function-call Networks

Complex network is being considered as a key approach to representing complex systems. We have focused on the static analyses of software systems with the function-call graph and empirically found them to reveal small-world, scale-free features similar to those identified in sociological and technological systems. Another crucial step when studying the structure of networks is to identify communities. Most existing approach to this problem in the previous literature simply ignored the edge direction and applied methods for community detecting in undirected networks. In this paper, we consider the problem of finding communities in directed weighted networks and develop a new community detection algorithm for the networks. Our method regards communities as groups of links rather than nodes in contrast to the existing detection algorithms. This algorithm is tested on function-call graph of artificial system and our experiment is shown to finding communities on the test directed weighted network successfully.

SparseCEM and SparseACE for Hyperspectral Image Target Detection

Due to the limitation of the spatial resolution of hyperspectral sensors, in real hyperspectral remote sensing images, targets of interest usually only occupy a few pixels (or even subpixels). Under such circumstances, we hope that the output of the detection algorithm is sparse. However, the existing detection algorithms seldom restrict this sparsity. Among the developed detection algorithms, the constrained energy minimization (CEM) and the adaptive coherence/cosine estimator (ACE) are two famous and widely used algorithms. In this letter, based on the CEM and the ACE, we propose the novel sparse CEM (SparseCEM) and sparse ACE (SparseACE) using the $\ell_{1}$ -norm regularization term to restrict the output to be sparse. Furthermore, we convert our detection models to second-order cone program problems, which can be efficiently solved by using the interior point method. The experiments on two real hyperspectral images demonstrate the effectiveness of the proposed algorithms.

Multi-source Signal Detection With Arbitrary Noise Covariance

Detecting the presence of signals in noise from multiple sources is a fundamental problem in statistical signal processing. In this paper, we consider multi-antenna signal detection when the noise covariance matrix is assumed to be arbitrary and unknown. We address this problem in the context of cognitive radio, where a multiple-primary-user detector is analyzed. This detector is known as Wilks' detector in statistics literature, which was derived under the generalized likelihood ratio criterion. We calculate the moments of Wilks' detector, which lead to simple and accurate approximate analytical formulae for the false alarm probability, the detection probability and the receiver operating characteristic. From the considered simulation settings, performance gain over existing detection algorithms is observed in scenarios with arbitrary and unknown noise correlation and multiple primary users.

Mchopaodv-Enhanced Cluster-Based Approach Against Wormhole Attacks in Manet

This Mobile Adhoc Network (MANET) is infrastructure less network. Any specialized router is not used in MANET for routing operations because as it is infrastructure less. Instead, all the nodes in MANET act as a router to perform routing operations with the help of routing protocol. The security issue becomes the major problem in MANET because of this feature and it affects the overall performance of the network. Many researchers have proposed various protocols of higher safety to defend against attacks; the increased deployment of ubiquitous MANET has exponentially increased the complexity to detect network attacks and protect against them. In particular Wormhole can affect whole network. The intruder can create wormhole attack by creating tunnel between two nodes with hop or without hops. In this research proposes a detection framework which consists of three phase namely clustering, detection and removal are used to detect both wormhole attacks. The source node will detect the wormhole nodes by tracing the messages received from the nodes. The proposed modified AODV finds the solution using mathematical calculation. it detects the malicious nodes and isolates it from the active data forwarding. The experimental results are compared with the other existing detection algorithm and the results show the performance of the proposed algorithm is better than the other algorithms.

Moving Vehicle Detection and Tracking Algorithm in Traffic Video

Aiming at the defects and shortages of traditional moving vehicles detection algorithms, by the analysis and comparison of the existing detection algorithms, we propose an algorithm that combined with frames with symmetric difference and background difference to detect moving vehicle in this paper. First, two different difference images by using frames with symmetric difference and background difference are gained respectively and two binary images can be gained by the appropriate threshold, then the contour of moving vehicles can be extracted by applying OR operation in the two binary images. Finally, the precise moving vehicles will be gained by mathematic morphological methods. In this paper we use Harris operator, Feature Points such as edges and corners are extracted, followed by block-matching to track the Feature Points in successive viedo frames. Many vehicles can be tracked at the same time automatically since the information is obtained from video sequences. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2613

An Algorithm for Moving Vehicle Detection Based on Traffic Video

Aiming at the defects and shortages of traditional moving vehicles detection algorithms, by the analysis and comparison of the existing detection algorithms, we propose an algorithm that combined with frames with symmetric difference and background difference to detect moving vehicle in this paper. First, two different difference images by using frames with symmetric difference and background difference are gained respectively and two binary images can be gained by the appropriate threshold, then the contour of moving vehicles can be extracted by applying OR operation in the two binary images. Finally, the precise moving vehicles will be gained by mathematic morphological methods. Experimental results show the algorithm used in this paper can detect the moving vehicles better than traditional moving vehicles detection algorithms.

PyCosmic: a robust method to detect cosmics in CALIFA and other fiber-fed integral-field spectroscopy datasets

[Abridged] Detecting cosmic ray hits (cosmics) in fiber-fed IFS data of single exposures is a challenging task, because of the complex signal recorded by IFS instruments. Existing detection algorithms are commonly found to be unreliable in the case of IFS data and the optimal parameter settings are usually unknown a-priori for a given dataset. The CALIFA survey generates hundreds of IFS datasets for which a reliable and robust detection algorithm for cosmics is required as an important part of the fully automatic CALIFA data reduction pipeline. We developed a novel algorithm, PyCosmic, which combines the edge-detection algorithm of L.A.Cosmic with a point-spread function convolution scheme. We generated mock data to compute the efficiency of different algorithms for a wide range of characteristic fibre-fed IFS datasets using the PMAS and VIMOS IFS instruments as representative cases. PyCosmic is the only algorithm that achieves an acceptable detection performance for CALIFA data. We find that PyCosmic is the most robust tool with a detection rate of >~90% and a false detection rate <5% for any of the tested IFS data. It has one less free parameter than the L.A.Cosmic algorithm. Only for strongly undersampled IFS data does L.A.Cosmic exceed the performance of PyCosmic by a few per cent. DCR never reaches the efficiency of the other two algorithms and should only be used if computational speed is a concern. Thus, PyCosmic appears to be the most versatile cosmics detection algorithm for IFS data. It is implemented in the new CALIFA data reduction pipeline as well as in recent versions of the multi-instrument IFS pipeline P3D.