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Efficient utilization of WLAN networks in the next-generation heterogeneous environments

- Haider, Ammar, Gondal, Iqbal, Kamruzzaman, Joarder, Qiu, Bin

Action-02MCF : A robust space-time correlation filter for action recognition in clutter and adverse lighting conditions

- lhaq, Anwaar, Yin, Xiaoxia, Zhang, Yunchan, Gondal, Iqbal

  • Authors: lhaq, Anwaar , Yin, Xiaoxia , Zhang, Yunchan , Gondal, Iqbal
  • Date: 2016
  • Type: Text , Conference paper
  • Relation: 17th International Conference, ACIVS 2016 p. 465-476
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  • Description: Human actions are spatio-temporal visual events and recognizing human actions in different conditions is still a challenging computer vision problem. In this paper, we introduce a robust feature based space-time correlation filter, called Action-02MCF (0’zero-aliasing’ 2M’ Maximum Margin’) for recognizing human actions in video sequences. This filter combines (i) the sparsity of spatio-temporal feature space, (ii) generalization of maximum margin criteria, (iii) enhanced aliasing free localization performance of correlation filtering using (iv) rich context of maximally stable space-time interest points into a single classifier. Its rich multi-objective function provides robustness, generalization and recognition as a single package. Action-02MCF can simultaneously localize and classify actions of interest even in clutter and adverse imaging conditions. We evaluate the performance of our proposed filter for challenging human action datasets. Experimental results verify the performance potential of our action-filter compared to other correlation filtering based action recognition approaches.

Multi-size-window spectral augmentation: Neural network bearing fault classifier

- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 2013 8th IEEE Conference on Industrial Electronics and Applications (ICIEA) p. 261-266
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  • Description: Features extraction has always been crucial in rotary machines for Condition based machine health monitoring. Time-domain-segmentation being among the preliminary steps for further classification process plays a momentous role. Vibration signals from bearing are quasistationary in nature therefore calculation of constituent frequencies amplitudes in the vibration signal is dependent upon time-segmentation-window size. The proposed research confers the effects of time-segmentation window size on spectral features amplitudes calculation and its impacts on classification accuracy of the Artificial Neural Network (ANN). Using multi-size time-segmentation-window, for comprehensive spectral features calculation, ANN pattern classifier has been trained for enhanced classification. ANN learning assigns importance based relative weights to the links using supervised learning. Experimental results have shown that multi-size-window spectral features for ANN fault classifier perform efficiently for quasi-stationary bearing vibrations.

Envelope-Wavelet Packet Transform for Machine Condition Monitoring

- Yaqub, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder

Automated multi-sensor color video fusion for nighttime video surveillance

- Ul-Haq, Anwaar, Gondal, Iqbal, Murshed, Manzur

  • Authors: Ul-Haq, Anwaar , Gondal, Iqbal , Murshed, Manzur
  • Date: 2010
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: In this paper, we present an automated color transfer based video fusion method to attain real-time color night vision capability for night-time video surveillance. We utilize simple RGB Color transfer technique to fused pseudo colored video frames without conversion to any uncorrelated color space. We investigated that final color fusion results greatly depend on the selection of target color Image. Therefore, rather than using any arbitrary target color image based on mere general visual anticipation, we have automated target color image selection using structural similarity and color saturation. We further apply color enhancement to improve final appearance of color fused images. Subjective and objective quality evaluations greatly indicate the effectiveness of our color video fusion method for nighttime video surveillance applications.

Fuzzy logic inspired bearing fault-model membership estimation

- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing p. 420-425
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  • Description: In rotary machines bearings are a primary cause of failure. In order to estimate the time before failure to provide information for timely bearing replacement strategies, condition-based machine health monitoring techniques are employed. This paper discusses a model for estimating the severity of bearing faults that can be used for residual bearing life estimation by processing the vibration signal. The proposed technique used in this model examines the spectral content of vibration signals across frequency bins and then fits Gaussian distributions to each frequency bin. With the use of these Gaussian models and training set examples with different fault severity levels, characteristic membership functions are constructed. This enables estimation of the severity levels of the bearing faults through a fuzzy-logic inspired process, whereby the severity level corresponds to the maximum of the set of corresponding membership functions. Thus based on discrete fault severity levels, trained Gaussian fittings of spectral bins and characteristic fault membership functions are capable to estimate the fault severity on a continuous scale.

Unitary anomaly detection for ubiquitous safety in machine health monitoring

- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 19th International Conference on Neural Information Processing (INCONIP) p. 361-368
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  • Description: Safety has always been of vital concern in both industrial and home applications. Ensuring safety often requires certain quantifications regarding the inclusive behavior of the system under observation in order to determine deviations from normal behavior. In machine health monitoring, the vibration signal is of great importance for such measurements because it includes abundant information from several machine parts and surroundings that can influence machine behavior. This paper proposes a unitary anomaly detection technique (UAD) that, upon observation of abnormal behavior in the vibration signal, can trigger an alarm with an adjustable threshold in order to meet different safety requirements. The normalized amplitude of spectral contents of the quasi stationary time vibration signal are divided into frequency bins, and the summed amplitudes frequencies over bin are used as features. From a training set consisting of normal vibration signals, Gaussian distribution models are obtained for each feature, which are then used for anomaly detection.

Heuristic non parametric collateral missing value imputation : A step towards robust post-genomic knowledge discovery

- Sehgal, Muhammad Shoaib B, Gondal, Iqbal, Dooley, Laurence, Coppel, Ross

How to improve postgenomic knowledge discovery using imputation

- Sehgal, Muhammad Shoaib B, Gondal, Iqbal, Dooley, Laurence, Coppel, Ross

  • Authors: Sehgal, Muhammad Shoaib B , Gondal, Iqbal , Dooley, Laurence , Coppel, Ross
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Eurasip Journal on Bioinformatics and Systems Biology Vol. 2009, no. 1 (2009), p. 1-14
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  • Description: While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputation, which exploit the biological transactional behaviour of functionally correlated genes to afford accurate missing value estimation. This paper examines the influence of missing value imputation techniques upon postgenomic knowledge inference methods with results for various algorithms consistently corroborating that instead of ignoring missing values, recycling microarray data by flexible and robust imputation can provide substantial performance benefits for subsequent downstream procedures

Multiple-points fault signature's dynamics modeling for bearing defect frequencies

- Yaqub, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder

A cross-layer approach for using multiple radio channels with directional beams in a suburban Ad Hoc network

- Rokonuzzaman, S. K., Pose, Ronald, Gondal, Iqbal

  • Authors: Rokonuzzaman, S. K. , Pose, Ronald , Gondal, Iqbal
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: Australasian Telecommunication Networks and Applications Conference
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  • Description: The capacity of wireless ad hoc networks can be increased by using multiple radio channels. But due to interference the capacity is still not fully utilized. This is caused by the limited number of available radio channels. The interference problem can be reduced using directional beams instead of omni-directional beams. This paper presents a novel cross-layer approach to use multiple radio channels with directional antennas. We are using three different radio channels. Each node has three fixed directional beams having fixed beamwidth and with different radio frequency. Two nodes can communicate when both the sending and receiving beams are pointing towards each other using the same frequency channel. In this study the directions of beams cannot be changed dynamically. A modified version of Ad hoc On-demand Distance Vector (AODV) routing protocol has been used. Simulation results show that our approach outperforms other methods using three different radio channels with omni-directional antennas

Smart phone based machine condition monitoring system

- Gondal, Iqbal, Yaqub, Muhammad, Hua, Xueliang

  • Authors: Gondal, Iqbal , Yaqub, Muhammad , Hua, Xueliang
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 19th International Conference on Neural Information Processing p. 488-497
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  • Description: Machine condition monitoring has gained momentum over the years and becoming an essential component in the today’s industrial units. A cost effective machine condition monitoring system is need of the hour for predictive maintenance. In this paper, we have developed a machine condition monitoring system using smart phone, thanks to the rapidly growing smart-phone market both in scalability and computational power. In spite of certain hardware limitations, this paper proposes a machine condition monitoring system which has the tendency to acquire data, build the fault diagnostic model and determine the type of the fault in the case of unknown fault signatures. Results for the fault detection accuracy are presented which validate the prospects of the proposed framework in future condition monitoring services.

Complex anomaly for enhanced machine independent condition monitoring

- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2015
  • Type: Text , Conference proceedings
  • Relation: 9th International Conference on Open Source Systems and Technologies, ICOSST 2015; Lahore, Pakistan; 17th-19th December 2015
  • Full Text: false
  • Description: Safety in machine applications requires tracking machine health during the time of operations. Anomaly detection techniques are used to model normal behavior of the machines and raise an alarm if any anomaly is observed. But traditional anomaly detection techniques do not identify type and severity of aberrance in terms of amplitude, pattern or both. Once the anomalous behavior is observed then fault detection techniques are applied to diagnose faults. For machine independent condition monitoring (MICM) a range of features transforms are needed for autonomous learning of the fault classifiers for different parameters to identify variety of fault types which requires huge amount of time. In this paper a novel complex anomaly plan (CAP) representation has been proposed with amplitude anomalies on real and pattern anomalies on imaginary axis. To plot amplitude and pattern anomalies in the CAP, normal state vibrations frequency features are used to train Gaussian models for each of the frequency. The dynamic location of the anomaly plotted in the CAP gives a measure of the intensity of the anomaly, where real and imaginary axis components help the fault classifier to make an appropriate selection of the transform and thus enhances the efficiency of MICM framework. © 2015 IEEE.
  • Description: ICOSST 2015 - 2015 International Conference on Open Source Systems and Technologies, Proceedings

Dynamic sensor selection for target tracking in wireless sensor networks

- Armaghani, Farzaneh, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Armaghani, Farzaneh , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: IEEE 74th Vehicular Technology Conference, VTC Fall 2011; San Francisco, United States; 5th-8th September 2011 p. 1-6
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  • Description: Optimum selection of sensors in target tracking applications has a great potential to maintain right trade-off between energy consumption and quality of tracking. In this paper, we propose a dynamic sensor selection scheme to achieve energy efficiency while ensuring the required quality of tracking. To this end, relative information utility projection of a target on sensors' observation is used in niche overlap measurements. Niche overlap measures are used to assess the similarity in information utilities where information utility is inversely proportional to error in target's state estimation based on prior distribution. The proposed scheme is a greedy approach in which sensor nodes are selected such that the overall niche overlap of all the selected nodes is maximized until the required level of accuracy is achieved. Our simulation results show significant improvement in tracking accuracy and network's lifetime over the existing methods.

Vibration spectrum imaging : A novel bearing fault classification approach

- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2015
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Electronics Vol. 62, no. 1 (2015), p. 494-502
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  • Description: Incipient fault detection in low signal-to-noise ratio (SNR) conditions requires robust features for accurate condition-based machine health monitoring. Accurate fault classification is positively linked to the quality of features of the faults. Therefore, there is a need to enhance the quality of the features before classification. This paper presents a novel vibration spectrum imaging (VSI) feature enhancement procedure for low SNR conditions. An artificial neural network (ANN) has been used as a fault classifier using these enhanced features of the faults. The normalized amplitudes of spectral contents of the quasi-stationary time vibration signals are transformed into spectral images. A 2-D averaging filter and binary image conversion, with appropriate threshold selection, are used to filter and enhance the images for the training and testing of the ANN classifier. The proposed novel VSI augments and provides the visual representation of the characteristic vibration spectral features in an image form. This provides enhanced spectral images for ANN training and thus leads to a highly robust fault classifier.

Multi-dimensional adaptive SINR based vertical handoff for heterogeneous wireless networks

- Yang, Kemeng, Gondal, Iqbal, Qiu, Bin

  • Authors: Yang, Kemeng , Gondal, Iqbal , Qiu, Bin
  • Date: 2008
  • Type: Text , Journal article
  • Relation: IEEE Communications Letters Vol. 12, no. 6 (2008), p. 438-440
  • Full Text: false
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  • Description: Vertical handoff in next generation heterogeneous wireless networks is a multi-dimensional issue. In this article we propose a multi-dimensional adaptive SINR based vertical handoff algorithm (MASVH) which uses the combined effects of SINR, user required bandwidth, user traffic cost and utilization from participating access networks to make handoff decisions for multi-attribute QoS consideration. Simulation results confirm that the new MASVH algorithm improves the system performance in terms of higher throughput and lower dropping probability, as well as reduces the user traffic cost for accessing the integrated wireless networks.

The role of love stories in Romance Scams : A qualitative analysis of fraudulent profiles

- Kopp, Christian, Layton, Robert, Sillitoe, Jim, Gondal, Iqbal

  • Authors: Kopp, Christian , Layton, Robert , Sillitoe, Jim , Gondal, Iqbal
  • Date: 2016
  • Type: Text , Journal article
  • Relation: International Journal of Cyber Criminology Vol. 9, no. 2 (2016), p. 205-216
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  • Description: The Online Romance Scam is a very successful scam which causes considerable financial and emotional damage to its victims. In this paper, we provide a perspective that might be helpful to explain the success of this scam. In a similar way to the "The Nigerian letter", we propose that the scam techniques appeal to strong emotions, which are clearly involved in Romantic relationships. We also assume that the same success factors found in normal relationships contribute to the success of the romance scam. In an exploratory study using a qualitative analysis of fraudulent profiles from an international dating website, we examined this assumption. The findings show that personal affinities related to personal romantic imaginations, which are described by personal love stories, play an important role in the success of a romance scam. © 2016 International Journal of Cyber Criminology (IJCC).

Machine health monitoring based on stationary wavelet transform and fourth-order cumulants

- Yaqub, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2012
  • Type: Text , Journal article
  • Relation: International Review of Electrical Engineering Vol. 6, no. 1 (2012), p. 238-248
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  • Description: Early stage faults detection for machine health monitoring demands high level of fault classification accuracy under poor signal-to-noise ratio (SNR). Vibration signal which is used for signature matching in case of abnormality detection and diagnosis, requires robust tools such as wavelet transform (WT) for time-frequency analysis. WT is specifically used to deal with nonstationary signals. In order to guarantee improved performance under poor SNR, this paper proposes a scheme for feature extraction based on fourth-order cumulant and stationary wavelet transform (FoCSWT). Higher order cumulants have the tendency to mitigate the impact of Gaussian noise. Fourth-order cumulant corresponds to the "peakedness" of the random distribution and the fault detection capability quantifies it as the most dominant cumulant among higher order statistics. Stationary wavelet transform is used to avoid down-sampling on the vibration data prior to feature extraction which gives better estimation of statistical parameters of the data distribution and gives performance enhancement in terms of fault classification accuracy. Simulation studies show that FoCSWT outperforms the existing techniques in terms of fault detection accuracies under poor SNR.

Dynamic clusters graph for detecting moving targets using WSNs

- Armaghani, Farzaneh, Gondal, Iqbal, Kamruzzaman, Joarder, Green, David

  • Authors: Armaghani, Farzaneh , Gondal, Iqbal , Kamruzzaman, Joarder , Green, David
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 76th IEEE Vehicular Technology Conference, VTC Fall 2012; Quebec City, Canada; 3rd-6th September 2012 p. 1-5
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  • Description: Efficient target tracking applications require active sensor nodes to track a cluster of moving targets. Clustering could lead to significant cost improvement as compared to tracking individual targets. This paper presents accurate clustering of targets for both coherent and incoherent movement patterns. We propose a novel clustering algorithm that utilises an implicit dynamic time frame to assess the relational history of targets in creating a weighted graph of connected components. The proposed algorithm employs key features of localisation algorithms in target tracking, namely, estimated current and predicted locations to determine the relational directions and distances of moving targets. Our simulation results show a significant improvement on the clustering accuracy and computation time by dynamically adjusting the history-window size and predicting the relationships among targets.

Mining associated sensor patterns for data stream of wireless sensor networks

- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2013
  • Type: Text , Conference proceedings
  • Relation: 8th ACM International Workshop on Performance Monitoring, Measurement, and Evaluation of Heterogeneous Wireless and Wired Networks, PM2HW2N 2013, Barcelona; Spain; 3rd-8th November 2013 p. 91-98
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  • Description: WSNs generate a large amount of data in the form of data stream; and mining these streams to extract useful knowledge is a highly challenging task. Existing works proposed in literature use sensor association rules measured in terms of occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. Additionally mining associated sensor patterns from sensor stream data, which is vital for real-time applications, has not been addressed yet in literature. In this paper, we address these problems and propose a new type of sensor behavioral pattern called associated sensor patterns which capture simultaneously association-like co-occurrence as well as substantial temporal correlations implied by such co-occurrences in sensor data. We propose a novel tree structure, called associated sensor pattern stream tree (ASPS-tree) and a new technique, called associated sensor pattern mining of data stream (ASPMS), using sliding window-based associated sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an ASPS-tree, our ASPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering associated sensor patterns over sensor data stream.