Showing items 1 - 20 of 91

Your selections:

A comprehensive spectrum trading scheme based on market competition, reputation and buyer specific requirements

- Hassan, Md Rakib, Karmakar, Gour, Kamruzzaman, Joarder, Srinivasan, Bala

  • Authors: Hassan, Md Rakib , Karmakar, Gour , Kamruzzaman, Joarder , Srinivasan, Bala
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Computer Networks Vol. 84, no. (2015), p. 17-31
  • Full Text:
  • Reviewed:
  • Description: In the exclusive-use model of spectrum trading, cognitive radio devices or secondary users can buy spectrum resources from licensed users or primary users for a short or long period of time. Considering such spectrum access, a trading model is introduced where a buyer can select a set of candidate sellers based on their reputation and their offers in fulfilling its requirements, namely, offered signal quality, contract duration, coverage and bandwidth. Similarly, a seller can assess a buyer as a potential trading partner considering the buyer's reliability, which the seller can derive from the buyer's reputation and financial profile. In our scheme, seller reputation or buyer reliability can be either obtained from a reputation brokerage service, if one exists, or calculated using our model. Since in a competitive market, the price of a seller depends on that of other sellers, game theory is used to model the competition among multiple sellers. An optimization technique is used by a buyer to select the best seller(s) and optimize purchase to maximize its utility. This may result in buying from multiple sellers of certain amount of bandwidth from each, depending on price and meeting requirements and budget constraints. Stability of the model is analyzed and performance evaluation shows that it benefits sellers and buyers in terms of profit and throughput, respectively. © 2015 Elsevier B.V. All rights reserved.

A contender-aware backoff algorithm for CSMA based MAC protocol for wireless sensor network

- Miraz Al-Mamun, Miraz, Karmakar, Gour, Kamruzzaman, Joarder

  • Authors: Miraz Al-Mamun, Miraz , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2010
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Existing contention based nonpersistent medium access control protocols in Wireless Sensor Network (WSN) do not perform well in high contention. Their performances are affected by occurrence of collision due to uniform probability distribution in choosing Time Slot (TS) during backoff period. To address this issue nonuniform probability distribution was proposed. However success rate still drops for higher number of contenders. In this paper we propose CSMA/s (Collision Sense Multiple Access /per Slot based), a new approach in nonuniform contender-aware probability distribution for choosing TS in the backoff period. Rather than taking a premeditated fixed value for contender population size, our proposed scheme embeds neighborhood population size into its bedrock to automatically converge to the actual number of contenders which enables the contender to adaptively choose TS in the backoff period for reducing collision. This method produces better success rate and lower latency for even very high number of contenders.

A dynamic content distribution scheme for decentralized sharing in tourist hotspots

- Kaisar, Shahriar, Kamruzzaman, Joarder, Karmakar, Gour

  • Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Journal of Network and Computer Applications Vol. 129, no. (2019), p. 9-24
  • Full Text:
  • Reviewed:
  • Description: Decentralized content sharing (DCS) is emerging as a suitable platform for smart mobile device users to generate and share contents seamlessly without the requirement of a centralized server. This feature is particularly important for places that lack Internet coverage such as tourist attractions where users can form an ad-hoc network and communicate opportunistically to share contents. Existing DCS approaches when applied for such type of places suffer from low delivery success rate and high latency. Although a handful of recent approaches have specifically targeted improvement of content delivery service in tourist spot like scenario, these and other DCS approaches do not focus on contents’ demand and supply which vary considerably due to visitor in-and-out flow and occurrence of influencing events. This is further compounded by the lack of any content distribution (replication) scheme. The content delivery service will be improved if contents can be proactively distributed in strategic positions based on dynamic demand and supply and medium access contention. In this paper, we propose a dynamic content distribution scheme (DCDS) considering these practical issues for sharing contents in tourist attractions. Simulation results show that the proposed approach significantly improves (7

A robust forgery detection method for copy-move and splicing attacks in images

- Islam, Mohammad, Karmakar, Gour, Kamruzzaman, Joarder, Murshed, Manzur

  • Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Electronics Vol. 9, no. 9 (2020), p. 1-22
  • Full Text:
  • Reviewed:
  • Description: Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy-move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors' physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.
  • Description: This research was funded by Research Priority Area (RPA) scholarship of Federation University Australia.

A rule based inference model to establish strategy-process relationship

- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder, Stranieri, Andrew

  • Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 30th International Business Information Management Association Conference - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth, IBIMA 2017; Madrid, Spain; 8th-9th November 2017 Vol. 2017-January, p. 4544-4556
  • Full Text: false
  • Reviewed:
  • Description: An effective relationship between business processes and their relevant strategies helps enterprises achieve their goals. As a business organisation changes quickly, business processes implement their relevant business operations for efficiency. It is important to know which business process achieves which business strategies dynamically. To the best of our knowledge, there exists a framework which aims to automatically determine the strategy-process relationship (Morrison et al. 2011). However, this framework can only work when the effect of the business process is known, but it is difficult to determine such effect accurately. Moreover, by optimising business processes to satisfy business strategies, higher efficiency may be achieved but there is a high chance of losing discriminative information. It therefore creates certain level of uncertainty in achieving accurate strategy-process relationship. To reduce this uncertainty and determine the relationship accurately between business processes and their relevant strategies as defined by business domain experts, in this paper, we introduce a rule-based inference model. This model not only helps business organisations realize which business processes need to be involved for the organisation to achieve their goals when strategies are made, but also reduces the possibility of losing important details from business process optimisation. We have developed a business case to validate our proposed model and the results show that our model can infer the relation accurately for each rule defined for the related business case.

A smart priority-based traffic control system for emergency vehicles

- Karmakar, Gour, Chowdhury, Abdullahi, Kamruzzaman, Joarder, Gondal, Iqbal

  • Authors: Karmakar, Gour , Chowdhury, Abdullahi , Kamruzzaman, Joarder , Gondal, Iqbal
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Sensors Journal Vol. 21, no. 14 (2021), p. 15849-15858
  • Full Text: false
  • Reviewed:
  • Description: Unwanted events on roads, such as incidents and increased traffic jams, can cause human lives and economic loss. For efficient incident management, it is essential to send Emergency Vehicles (EVs) to the incident place as quickly as possible. To reduce incidence clearance time, several approaches exist to provide a clear pathway to EVs mainly fitted with RFID sensors in the urban areas. However, they neither assign priority to the EVs based on the type and severity of an incident nor consider the effect on other on-road traffic. To address this issue, in this paper, we introduce an Emergency Vehicle Priority System (EVPS) by determining the priority level of an EV based on the type and the severity of an incident, and estimating the number of necessary signal interventions while considering the impact of those interventions on the traffic in the roads surrounding the EV's travel path. We present how EVPS determines the priority code and a new algorithm to estimate the number of green signal interventions to attain the quickest incident response while concomitantly reducing impact on others. A simulation model is developed in Simulation of Urban Mobility (SUMO) using the real traffic data of Melbourne, Australia, captured by various sensors. Results show that our system recommends appropriate number of intervention that can reduce emergency response time significantly. © 2001-2012 IEEE.

A survey on context awareness in big data analytics for business applications

- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder

  • Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
  • Full Text:
  • Reviewed:
  • Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

A trade-off between reliability and energy efficiency for inter-cluster communication in wireless sensor networks

- Sadat, Anwar, Karmakar, Gour

An adaptive approach to opportunistic data forwarding in underwater acoustic sensor networks

- Nowsheen, Nusrat, Karmakar, Gour, Kamruzzaman, Joarder

  • Authors: Nowsheen, Nusrat , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2014
  • Type: Text , Conference proceedings
  • Full Text:
  • Description: Reliable data transfer for underwater acoustic sensor networks (UASNs) is a major research challenge in applications such as pollution monitoring, oceanic data collection, and surveillance due to the long propagation delay and high error rate of the acoustic channel. To address this issue, an opportunistic data forwarding protocol was proposed which achieves high packet delivery success ratio with less routing overhead and energy consumption by selecting the next hop forwarder among a set of candidates based on its link reliability and data transfer reach ability. However, the protocol relies on fixed data hold time approach, i.e., Each node holds data packets for a fixed amount of time before a forwarder discovery process is initiated. Depending on the value of the fixed hold time and deployment contextual scenario, this may incur large end-to-end delay. Moreover, lack of consideration of network condition in hold time limits its performance. In this paper, we propose an adaptive technique to improve its performance. The adaptive approach calculates data hold time at each node dynamically considering a number of 'node and network' metrics including current buffer occupancy, delay experienced by stored data packets, arrival and service rate, neighbors' data transmissions and reach ability. Simulation results show that compared with fixed hold time approach, our adaptive technique reduces end-to-end delay significantly, achieves considerably higher data delivery and less energy consumption per successful packet delivery.

An efficient data delivery mechanism for AUV-based Ad hoc UASNs

- Karmakar, Gour, Kamruzzaman, Joarder, Nowsheen, Nusrat

  • Authors: Karmakar, Gour , Kamruzzaman, Joarder , Nowsheen, Nusrat
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 86, no. (2018), p. 1193-1208
  • Full Text: false
  • Reviewed:
  • Description: Existing 3D Underwater Acoustic Sensor Networks (UASNs) are either fixed having nodes anchored with the seabed or a combination of Autonomous Underwater Vehicles (AUVs) and a fixed UASN where AUVs are controlled to move along paths for data collection. Existing data delivery protocols for such AUV equipped networks are designed in a way where AUVs act as message ferries. These UASNs are deployed for a specific service such as asset (e.g., oil pipes, shipwreck) monitoring and event detection. For a coordinated data collection, to deploy a network for any service like information discovery in an ad hoc manner, it requires a 3D UASN consisting of only AUVs and the movement of an AUV needs to be controlled by another AUV through commands. To our knowledge, no such data delivery protocol required for a 3D UASN comprising only AUVs exists in the current literature that can efficiently handle data collection and delivery. To address this research gap, in this paper, an AUV-based technique for ad hoc underwater network, namely AUV-based Data Delivery Protocol (ADDP), is introduced which ensures data delivery within a given time-constraint by controlling node (i.e., AUV) movement at each hop through commands of a node. The performance of the proposed protocol has also been evaluated and compared with existing relevant protocols in terms of packet delivery ratio, routing overhead and energy consumption considering various network scenarios and sizes. Results exhibit outstanding performance improvement achieved by the proposed protocol for all metrics. © 2017 Elsevier B.V.

An Enhanced Local Texture Descriptor for Image Segmentation

- Tania, Sheikh, Murshed, Manzur, Teng, Shyh, Karmakar, Gour

  • Authors: Tania, Sheikh , Murshed, Manzur , Teng, Shyh , Karmakar, Gour
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE International Conference on Image Processing, ICIP 2020 Vol. 2020-October, p. 1526-1530
  • Full Text: false
  • Reviewed:
  • Description: Texture is an indispensable property to develop many vision based autonomous applications. Compared to colour, feature dimension in a local texture descriptor is quite large as dense texture features need to represent the distribution of pixel intensities in the neighbourhood of each pixel. Large dimensional features require additional time for further processing that often restrict real-time applications. In this paper, a robust local texture descriptor is enhanced by reducing feature dimension by three folds without compromising the accuracy in region-based image segmentation applications. Reduction in feature dimension is achieved by exploiting the mean of neighbourhood pixel intensities radially along lines across a certain radius, which eliminates the need for sampling intensity distribution at three scales. Both the results of benchmark metrics and computational time are promising when the enhanced texture feature is used in a region-based hierarchical segmentation algorithm, a recent state-of-the-art technique. © 2020 IEEE.

An environment-aware mobility model for wireless ad hoc network

- Ahmed, Sabbir, Karmakar, Gour, Kamruzzaman, Joarder

  • Authors: Ahmed, Sabbir , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2010
  • Type: Text , Journal article
  • Relation: Computer Networks Vol. 54, no. 9 (2010), p. 1470-1489
  • Full Text: false
  • Reviewed:
  • Description: Simulation is a cost effective, fast and flexible alternative to test-beds or practical deployment for evaluating the characteristics and potential of mobile ad hoc networks. Since environmental context and mobility have a great impact on the accuracy and efficacy of performance measurement, it is of paramount importance how closely the mobility of a node resembles its movement pattern in a real-world scenario. The existing mobility models mostly assume either free space for deployment and random node movement or the movement pattern does not emulate real-world situation properly in the presence of obstacles because of their generation of restricted paths. This demands for the development of a node movement pattern with accurately representing any obstacle and existing path in a complex and realistic deployment scenario. In this paper, we propose a general mobility model capable of creating a more realistic node movement pattern by exploiting the concept of flexible positioning of anchors. Since the model places anchors depending upon the context of the environment through which nodes are guided to move towards the destination, it is capable of representing any terrain realistically. Furthermore, obstacles of arbitrary shapes with or without doorways and any existing pathways in full or part of the terrain can be incorporated which makes the simulation environment more realistic. A detailed computational complexity has been analyzed and the characteristics of the proposed mobility model in the presence of obstacles in a university campus map with and without signal attenuation are presented which illustrates its significant impact on performance evaluation of wireless ad hoc networks.

An opportunistic message forwarding protocol for underwater acoustic sensor networks

- Nowsheen, Nusrat, Karmakar, Gour, Kamruzzaman, Joarder

  • Authors: Nowsheen, Nusrat , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 2013 19th Asia-Pacific Conference on Communications, APCC 2013; Denpasar; Indonesia; 29th-31st August 2013 p. 1-6
  • Full Text: false
  • Reviewed:
  • Description: Designing message forwarding protocols for underwater acoustic sensor networks (UASNs) is challenging mainly due to high propagation delay, limited bandwidth and high packet loss. Most such protocols operate on the assumption that precise location of sensor nodes is known, which is difficult as GPS waves cannot propagate through water. Moreover, due to the error-prone nature of the acoustic link, message forwarding over multiple hops degrades end-to-end reliability, consumes significant energy and incurs longer delay. In this paper, we propose a location unaware message forwarding technique. It employs opportunistic routing where nodes use accumulate-and-forward paradigm to route data. The technique also exploits nodes' ability to overhear one another's transmission to select reliable route. Our opportunistic model uses independent and local forwarding decisions to select next hop forwarder on-the-fly based on its link transmission reliability and reachability to the gateway. Message ferrying approach is utilized to collect sensor data from gateway nodes of multiple UASNs at high data rate. Our simulation results exhibit its effectiveness and superiority compared with two well established message forwarding algorithms in underwater in terms of packet delivery ratio, routing overhead and energy consumption.

Assessing transformer oil quality using deep convolutional networks

- Alam, Mohammad, Karmakar, Gour, Islam, Syed, Kamruzzaman, Joarder, Chetty, Madhu, Lim, Suryani, Appuhamillage, Gayan, Chattopadhyay, Gopi, Wilcox, Steve, Verheyen, Vincent

  • Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
  • Full Text:
  • Reviewed:
  • Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
  • Description: E1

Assessing trust level of a driverless car using deep learning

- Karmakar, Gour, Chowdhury, Abdullahi, Das, Rajkumar, Kamruzzaman, Joarder, Islam, Syed

  • Authors: Karmakar, Gour , Chowdhury, Abdullahi , Das, Rajkumar , Kamruzzaman, Joarder , Islam, Syed
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 7 (2021), p. 4457-4466
  • Full Text: false
  • Reviewed:
  • Description: The increasing adoption of driverless cars already providing a shift to move away from traditional transportation systems to automated ones in many industrial and commercial applications. Recent research has justified that driverless vehicles will considerably reduce traffic congestions, accidents, carbon emissions, and enhance the accessibility of driving to wider cross-section of people and lifestyle choices. However, at present, people's main concerns are about its privacy and security. Since traditional protocol layers based security mechanisms are not so effective for a distributed system, trust value-based security mechanisms, a type of pervasive security, are appearing as popular and promising techniques. A few statistical non-learning based models for measuring the trust level of a driverless are available in the current literature. These are not so effective because of not being able to capture the extremely distributed, dynamic, and complex nature of the traffic systems. To bridge this research gap, in this paper, for the first time, we propose two deep learning-based models that measure the trustworthiness of a driverless car and its major On-Board Unit (OBU) components. The second model also determines its OBU components that were breached during the driving operation. Results produced using real and simulated traffic data demonstrate that our proposed DNN based deep learning models outperform other machine learning models in assessing the trustworthiness of individual car as well as its OBU components. The average precision of detection accuracies for the car, LiDAR, camera, and radar are 0.99, 0.96, 0.81, and 0.83, respectively, which indicates the potential real-life application of our models in assessing the trust level of a driverless car. © 2000-2011 IEEE.

Attacks on self-driving cars and their countermeasures : a survey

- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Jolfaei, Alireza, Das, Rajkumar

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Jolfaei, Alireza , Das, Rajkumar
  • Date: 2020
  • Type: Text , Journal article , Review
  • Relation: IEEE Access Vol. 8, no. (2020), p. 207308-207342
  • Full Text:
  • Reviewed:
  • Description: Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-To-Vehicle (V2V), Vehicle-To-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-Attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-Attack. We also provide further research directions to improve the security issues associated with self-driving cars. © 2013 IEEE.

Bezier curve-based generic shape encoder

- Sohel, Ferdous, Karmakar, Gour, Dooley, Laurence, Bennamoun, M.

  • Authors: Sohel, Ferdous , Karmakar, Gour , Dooley, Laurence , Bennamoun, M.
  • Date: 2010
  • Type: Text , Journal article
  • Relation: IET Image Processing Vol. 4, no. 2 (2010), p. 92-102
  • Full Text: false
  • Reviewed:
  • Description: Existing Bezier curve-based shape description techniques primarily focus upon determining a set of pertinent control points (CP) to represent a particular shape contour. While many different approaches have been proposed, none adequately consider domain-specific information about the shape contour like its gradualness and sharpness, in the CP generation process which can potentially result in large distortions in the object's shape representation. This study introduces a novel Bezier curve-based generic shape encoder (BCGSE) that partitions an object contour into contiguous segments based upon its cornerity, before generating the CP for each segment using relevant shape curvature information. In addition, although CP encoding has generally been ignored, BCGSE embeds an efficient vertex-based encoding strategy exploiting the latent equidistance between consecutive CP. A non-linear optimisation technique is also presented to enable the encoder is automatically adapt to bit-rate constraints. The performance of the BCGSE framework has been rigorously tested on a variety of diverse arbitrary shapes from both a distortion and requisite bit-rate perspective, with qualitative and quantitative results corroborating its superiority over existing shape descriptors.

Business context in big data analytics

- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder, Stranieri, Andrew

  • Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
  • Date: 2015
  • Type: Text , Conference proceedings
  • Relation: 10th International Conference on Information, Communications and Signal Processing, ICICS 2015; Singapore; 2nd-4th December 2015
  • Full Text: false
  • Reviewed:
  • Description: Big data are generated from a variety of sources having different representation forms and formats, it raises a research question as how important data relevant to a business context can be captured and analyzed more accurately to represent deep and relevant business insight. There is a number of existing big data analytic methods available in the literature that consider contextual information such as the context of a query and its users, the context of a query-driven recommendation system, etc. However, these methods still have many challenges and none of them has considered the context of a business in either data collection or analysis process. To address this research gap, we introduce a big data analytic technique which embeds a business context in terms of the significance level of a query into the bedrock of its data collection and analysis process. We implemented our proposed model under the framework of Hadoop considering the context of a grocery shop. The results exhibit that our method substantially increases the amount of data collection and their deep insight with an increase of the significance level value. © 2015 IEEE.
  • Description: 2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015

Carry me if you can : A utility based forwarding scheme for content sharing in tourist destinations

- Kaisar, Shahriar, Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal

  • Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
  • Date: 2016
  • Type: Text , Conference proceedings
  • Relation: 22nd Asia-Pacific Conference on Communications, APCC 2016; Yogyakarta, Indonesia; 25th-27th August 2016 p. 261-267
  • Full Text:
  • Reviewed:
  • Description: Message forwarding is an integral part of the decentralized content sharing process as the content delivery success highly depends on it. Existing literature employs spatio-temporal regularity of human movement pattern and pre-existing social relationship to take message forwarding decisions. However, such approaches are ineffectual in environments where those information are unavailable such as a tourist spot or camping site. In this study, we explore the message forwarding techniques in such environments considering the information that are readily available and can be gathered on the fly. We propose a utility based forwarding scheme to select the appropriate forwarder node based on co-location stay time, connectivity and available resources. A higher co-location stay time reflects that the forwarder and the destination node is likely to have more opportunistic contacts, while the connectivity and available resource ensure that the selected forwarder has sufficient neighbours and resources to carry the message forward. Simulation results suggest that the proposed approach attains high hit and success rate and low latency for successful content delivery, which is comparable to those proposed for work-place type scenarios with regular movement pattern and pre-existing relationships. © 2016 IEEE.

Consistency driven opinion formation modelling in presence of external sources

- Das, Rajkumar, Kamruzzaman, Joarder, Karmakar, Gour

  • Authors: Das, Rajkumar , Kamruzzaman, Joarder , Karmakar, Gour
  • Date: 2015
  • Type: Text , Conference proceedings
  • Relation: International Joint Conference on Neural Networks, IJCNN 2015; Killarney, Ireland; 12th-17th July 2015
  • Full Text: false
  • Description: Opinion formation in social networks has changed in a more rigorous way due to the inception of Online Social Networks (OSNs) as a platform of generating and sharing huge amount of contents as well as easy and ubiquitous access to varied information sources. Our opinions are not only updated through interactions with our neighbours in OSNs, but also shaped by the opinions received from information sources external to the native OSNs. Current models only consider the neighbours' influence in opinion evolution, thus lack the impact of other information sources, e.g., news media, web search, bulletin board, discussion forum on opinion formation. They consider individual opinion distances to model the influence among interactive neighbours, but fail to capture the influence of majority supported opinions and its possible impact in opinion evolution. Our model explicitly captures the effect of external sources on opinion formation in an OSN. We combine the implication of most perceived opinions in terms of consistency along with opinion distance to emulate the influence of different opinion sources. Consistency is measured by the entropy of opinions derived from a particular source type. Simulation results show that our model properly captures the consensus, polarization and fragmentation properties of opinion evolution. Finally, we investigate the influence of stubborn agents on opinion formation and compare it with a contemporary model. © 2015 IEEE.