| 1. |
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| 2. |
- Ahmad, Naeem, et al.
(författare)
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A taxonomy of visual surveillance systems
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The increased security risk in society and the availability of low cost sensors and processors has expedited the research in surveillance systems. Visual surveillance systems provide real time monitoring of the environment. Designing an optimized surveillance system for a given application is a challenging task. Moreover, the choice of components for a given surveillance application out of a wide spectrum of available products is not an easy job. In this report, we formulate a taxonomy to ease the design and classification of surveillance systems by combining their main features. The taxonomy is based on three main models: behavioral model, implementation model, and actuation model. The behavioral model helps to understand the behavior of a surveillance problem. The model is a set of functions such as detection, positioning, identification, tracking, and content handling. The behavioral model can be used to pinpoint the functions which are necessary for a particular situation. The implementation model structures the decisions which are necessary to implement the surveillance functions, recognized by the behavioral model. It is a set of constructs such as sensor type, node connectivity and node fixture. The actuation model is responsible for taking precautionary measures when a surveillance system detects some abnormal situation. A number of surveillance systems are investigated and analyzed on the basis of developed taxonomy. The taxonomy is general enough to handle a vast range of surveillance systems. It has organized the core features of surveillance systems at one place. It may be considered an important tool when designing surveillance systems. The designers can use this tool to design surveillance systems with reduced effort, cost, and time.
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| 3. |
- Ahmad, Naeem, et al.
(författare)
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Cost Optimization of a Sky Surveillance Visual Sensor Network
- 2012
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Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - Belgium : SPIE - International Society for Optical Engineering. - 9780819491299 ; , s. Art. no. 84370U-
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Konferensbidrag (refereegranskat)abstract
- A Visual Sensor Network (VSN) is a network of spatially distributed cameras. The primary difference between VSN and other type of sensor network is the nature and volume of information. A VSN generally consists of cameras, communication, storage and central computer, where image data from multiple cameras is processed and fused. In this paper, we use optimization techniques to reduce the cost as derived by a model of a VSN to track large birds, such as Golden Eagle, in the sky. The core idea is to divide a given monitoring range of altitudes into a number of sub-ranges of altitudes. The sub-ranges of altitudes are monitored by individual VSNs, VSN1 monitors lower range, VSN2 monitors next higher and so on, such that a minimum cost is used to monitor a given area. The VSNs may use similar or different types of cameras but different optical components, thus, forming a heterogeneous network. We have calculated the cost required to cover a given area by considering an altitudes range as single element and also by dividing it into sub-ranges. To cover a given area with given altitudes range, with a single VSN requires 694 camera nodes in comparison to dividing this range into sub-ranges of altitudes, which requires only 96 nodes, which is 86% reduction in the cost.
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| 4. |
- Ahmad, Naeem, et al.
(författare)
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Model and placement optimization of a sky surveillance visual sensor network
- 2011
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Ingår i: Proceedings - 2011 International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2011. - : IEEE Computer Society. - 9781457714559 ; , s. 357-362
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Konferensbidrag (refereegranskat)abstract
- Visual Sensor Networks (VSNs) are networks which generate two dimensional data. The major difference between VSN and ordinary sensor network is the large amount of data. In VSN, a large number of camera nodes form a distributed system which can be deployed in many potential applications. In this paper we present a model of the physical parameters of a visual sensor network to track large birds, such as Golden Eagle, in the sky. The developed model is used to optimize the placement of the camera nodes in the VSN. A camera node is modeled as a function of its field of view, which is derived by the combination of the lens focal length and camera sensor. From the field of view and resolution of the sensor, a model for full coverage between two altitude limits has been developed. We show that the model can be used to minimize the number of sensor nodes for any given camera sensor, by exploring the focal lengths that both give full coverage and meet the minimum object size requirement. For the case of large bird surveillance we achieve 100% coverage for relevant altitudes using 20 camera nodes per km2 for the investigated camera sensors.
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| 5. |
- Ahmad, Naeem, et al.
(författare)
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Model, placement optimization and verification of a sky surveillance visual sensor network
- 2013
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Ingår i: International Journal of Space-Based and Situated Computing (IJSSC). - 2044-4893 .- 2044-4907. ; 3:3, s. 125-135
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Tidskriftsartikel (refereegranskat)abstract
- A visual sensor network (VSN) is a distributed system of a large number of camera nodes, which generates two dimensional data. This paper presents a model of a VSN to track large birds, such as golden eagle, in the sky. The model optimises the placement of camera nodes in VSN. A camera node is modelled as a function of lens focal length and camera sensor. The VSN provides full coverage between two altitude limits. The model can be used to minimise the number of sensor nodes for any given camera sensor, by exploring the focal lengths that fulfils both the full coverage and minimum object size requirement. For the case of large bird surveillance, 100% coverage is achieved for relevant altitudes using 20 camera nodes per km² for the investigated camera sensors. A real VSN is designed and measurements of VSN parameters are performed. The results obtained verify the VSN model.
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| 6. |
- Ahmad, Naeem, et al.
(författare)
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Modeling and Verification of a Heterogeneous Sky Surveillance Visual Sensor Network
- 2013
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Ingår i: International Journal of Distributed Sensor Networks. - : SAGE Publications. - 1550-1329 .- 1550-1477. ; , s. Art. id. 490489-
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Tidskriftsartikel (refereegranskat)abstract
- A visual sensor network (VSN) is a distributed system of a large number of camera nodes and has useful applications in many areas. The primary difference between a VSN and an ordinary scalar sensor network is the nature and volume of the information. In contrast to scalar sensor networks, a VSN generates two-dimensional data in the form of images. In this paper, we design a heterogeneous VSN to reduce the implementation cost required for the surveillance of a given area between two altitude limits. The VSN is designed by combining three sub-VSNs, which results in a heterogeneous VSN. Measurements are performed to verify full coverage and minimum achieved object image resolution at the lower and higher altitudes, respectively, for each sub-VSN. Verification of the sub-VSNs also verifies the full coverage of the heterogeneous VSN, between the given altitudes limits. Results show that the heterogeneous VSN is very effective to decrease the implementation cost required for the coverage of a given area. More than 70% decrease in cost is achieved by using a heterogeneous VSN to cover a given area, in comparison to homogeneous VSN. © 2013 Naeem Ahmad et al.
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| 7. |
- Ahmad, Naeem
(författare)
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Modelling and optimization of sky surveillance visual sensor network
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A Visual Sensor Network (VSN) is a distributed system of a largenumber of camera sensor nodes. The main components of a camera sensornode are image sensor, embedded processor, wireless transceiver and energysupply. The major difference between a VSN and an ordinary sensor networkis that a VSN generates two dimensional data in the form of an image, whichcan be exploited in many useful applications. Some of the potentialapplication examples of VSNs include environment monitoring, surveillance,structural monitoring, traffic monitoring, and industrial automation.However, the VSNs also raise new challenges. They generate large amount ofdata which require higher processing powers, large bandwidth requirementsand more energy resources but the main constraint is that the VSN nodes arelimited in these resources.This research focuses on the development of a VSN model to track thelarge birds such as Golden Eagle in the sky. The model explores a number ofcamera sensors along with optics such as lens of suitable focal length whichensures a minimum required resolution of a bird, flying at the highestaltitude. The combination of a camera sensor and a lens formulate amonitoring node. The camera node model is used to optimize the placementof the nodes for full coverage of a given area above a required lower altitude.The model also presents the solution to minimize the cost (number of sensornodes) to fully cover a given area between the two required extremes, higherand lower altitudes, in terms of camera sensor, lens focal length, camera nodeplacement and actual number of nodes for sky surveillance.The area covered by a VSN can be increased by increasing the highermonitoring altitude and/or decreasing the lower monitoring altitude.However, it also increases the cost of the VSN. The desirable objective is toincrease the covered area but decrease the cost. This objective is achieved byusing optimization techniques to design a heterogeneous VSN. The core ideais to divide a given monitoring range of altitudes into a number of sub-rangesof altitudes. The sub-ranges of monitoring altitudes are covered by individualsub VSNs, the VSN1 covers the lower sub-range of altitudes, the VSN2 coversthe next higher sub-range of altitudes and so on, such that a minimum cost isused to monitor a given area.To verify the concepts, developed to design the VSN model, and theoptimization techniques to decrease the VSN cost, the measurements areperformed with actual cameras and optics. The laptop machines are used withthe camera nodes as data storage and analysis platforms. The area coverage ismeasured at the desired lower altitude limits of homogeneous as well asheterogeneous VSNs and verified for 100% coverage. Similarly, the minimumresolution is measured at the desired higher altitude limits of homogeneous aswell as heterogeneous VSNs to ensure that the models are able to track thebird at these highest altitudes.
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| 8. |
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| 9. |
- Ahmad, Naeem, et al.
(författare)
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Solution space exploration of volumetric surveillance using a general taxonomy
- 2013
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Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE. - 9780819495044 ; , s. Art. no. 871317-
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Konferensbidrag (refereegranskat)abstract
- Visual surveillance systems provide real time monitoring of the events or the environment. The availability of low cost sensors and processors has increased the number of possible applications of these kinds of systems. However, designing an optimized visual surveillance system for a given application is a challenging task, which often becomes a unique design task for each system. Moreover, the choice of components for a given surveillance application out of a wide spectrum of available alternatives is not an easy job. In this paper, we propose to use a general surveillance taxonomy as a base to structure the analysis and development of surveillance systems. We demonstrate the proposed taxonomy for designing a volumetric surveillance system for monitoring the movement of eagles in wind parks aiming to avoid their collision with wind mills. The analysis of the problem is performed based on taxonomy and behavioral and implementation models are identified to formulate the solution space for the problem. Moreover, we show that there is a need for generalized volumetric optimization methods for camera deployment.
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| 10. |
- Imran, Muhammad, et al.
(författare)
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Analysis and Characterization of Embedded Vision Systems for Taxonomy Formulation
- 2013
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Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - USA : SPIE - International Society for Optical Engineering. - 9780819494290 ; , s. Art. no. 86560J-
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Konferensbidrag (refereegranskat)abstract
- The current trend in embedded vision systems is to propose bespoke solutions for specific problems as each application has different requirement and constraints. There is no widely used model or benchmark which aims to facilitate generic solutions in embedded vision systems. Providing such model is a challenging task due to the wide number of use cases, environmental factors, and available technologies. However, common characteristics can be identified to propose an abstract model. Indeed, the majority of vision applications focus on the detection, analysis and recognition of objects. These tasks can be reduced to vision functions which can be used to characterize the vision systems. In this paper, we present the results of a thorough analysis of a large number of different types of vision systems. This analysis led us to the development of a system’s taxonomy, in which a number of vision functions as well as their combination characterize embedded vision systems. To illustrate the use of this taxonomy, we have tested it against a real vision system that detects magnetic particles in a flowing liquid to predict and avoid critical machinery failure. The proposed taxonomy is evaluated by using a quantitative parameter which shows that it covers 95 percent of the investigated vision systems and its flow is ordered for 60 percent systems. This taxonomy will serve as a tool for classification and comparison of systems and will enable the researchers to propose generic and efficient solutions for same class of systems.
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| 11. |
- Imran, Muhammad, et al.
(författare)
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Architecture Exploration Based on Tasks Partitioning Between Hardware, Software and Locality for a Wireless Vision Sensor Node
- 2012
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Ingår i: International Journal of Distributed Systems and Technologies. - IGI Global, USA. : IGI Global. - 1947-3532 .- 1947-3540. ; 3:2, s. 58-71
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Tidskriftsartikel (refereegranskat)abstract
- Wireless Vision Sensor Networks (WVSNs) is an emerging field which consists of a number of Visual Sensor Nodes (VSNs). Compared to traditional sensor networks, WVSNs operates on two dimensional data, which requires high bandwidth and high energy consumption. In order to minimize the energy consumption, the focus is on finding energy efficient and programmable architectures for the VSN by partitioning the vision tasks among hardware (FPGA), software (Micro-controller) and locality (sensor node or server). The energy consumption, cost and design time of different processing strategies is analyzed for the implementation of VSN. Moreover, the processing energy and communication energy consumption of VSN is investigated in order to maximize the lifetime. Results show that by introducing a reconfigurable platform such as FPGA with small static power consumption and by transmitting the compressed images after pixel based tasks from the VSN results in longer battery lifetime for the VSN.
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| 12. |
- Imran, Muhammad, et al.
(författare)
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Architecture of Wireless Visual Sensor Node with Region of Interest Coding
- 2012
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Ingår i: Proceedings - 2012 IEEE 3rd International Conference on Networked Embedded Systems for Every Application, NESEA 2012. - : IEEE conference proceedings. - 9781467347235 ; , s. Art. no. 6474029-
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Konferensbidrag (refereegranskat)abstract
- The challenges involved in designing a wirelessVision Sensor Node include the reduction in processing andcommunication energy consumption, in order to maximize itslifetime. This work presents an architecture for a wireless VisionSensor Node, which consumes low processing andcommunication energy. The processing energy consumption isreduced by processing lightweight vision tasks on the VSN andby partitioning the vision tasks between the wireless VisionSensor Node and the server. The communication energyconsumption is reduced with Region Of Interest coding togetherwith a suitable bi-level compression scheme. A number ofdifferent processing strategies are investigated to realize awireless Vision Sensor Node with a low energy consumption. Theinvestigation shows that the wireless Vision Sensor Node, usingRegion Of Interest coding and CCITT group4 compressiontechnique, consumes 43 percent lower processing andcommunication energy as compared to the wireless Vision SensorNode implemented without Region Of Interest coding. Theproposed wireless Vision Sensor Node can achieve a lifetime of5.4 years, with a sample period of 5 minutes by using 4 AAbatteries.
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| 13. |
- Imran, Muhammad, et al.
(författare)
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Complexity Analysis of Vision Functions for Comparison of Wireless Smart Cameras
- 2014
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Ingår i: International Journal of Distributed Sensor Networks. - : SAGE Publications. - 1550-1329 .- 1550-1477. ; , s. Art. no. 710685-
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Tidskriftsartikel (refereegranskat)abstract
- There are a number of challenges caused by the large amount of data and limited resources such as memory, processing capability, energy consumption, and bandwidth, when implementing vision systems on wireless smart cameras using embedded platforms. It is usual for research in this field to focus on the development of a specific solution for a particular problem. There is a requirement for a tool which facilitates the complexity estimation and comparison of wireless smart camera systems in order to develop efficient generic solutions. To develop such a tool, we have presented, in this paper, a complexity model by using a system taxonomy. In this model, we have investigated the arithmetic complexity and memory requirements of vision functions with the help of system taxonomy. To demonstrate the use of the proposed model, a number of actual systems are analyzed in a case study. The complexity model, together with system taxonomy, is used for the complexity estimation of vision functions and for a comparison of vision systems. After comparison, the systems are evaluated for implementation on a single generic architecture. The proposed approach will assist researchers in benchmarking and will assist in proposing efficient generic solutions for the same class of problems with reduced design and development costs.
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| 14. |
- Imran, Muhammad, et al.
(författare)
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Complexity Analysis of Vision Functions for implementation of Wireless Smart Cameras using System Taxonomy
- 2012
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Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - Belgium : SPIE - International Society for Optical Engineering. - 9780819491299 ; , s. Art. no. 84370C-
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Konferensbidrag (refereegranskat)abstract
- There are a number of challenges caused by the large amount of data and limited resources such as memory, processing capability, energy consumption and bandwidth when implementing vision systems on wireless smart cameras using embedded platforms. It is usual for research in this field to focus on the development of a specific solution for a particular problem. There is a requirement for a tool which has the ability to predict the resource requirements for the development and comparison of vision solutions in wireless smart cameras. To accelerate the development of such tool, we have used a system taxonomy, which shows that the majority of wireless smart cameras have common functions. In this paper, we have investigated the arithmetic complexity and memory requirements of vision functions by using the system taxonomy and proposed an abstract complexity model. To demonstrate the use of this model, we have analysed a number of implemented systems with this model and showed that complexity model together with system taxonomy can be used for comparison and generalization of vision solutions. Moreover, it will assist researchers/designers to predict the resource requirements for different class of vision systems in a reduced time and which will involve little effort.
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| 15. |
- Imran, Muhammad, et al.
(författare)
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Energy Efficient SRAM FPGA based Wireless Vision Sensor Node: SENTIOF‐CAM
- 2014
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Ingår i: IEEE transactions on circuits and systems for video technology (Print). - 1051-8215 .- 1558-2205. ; 24:12, s. 2132-2143
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Tidskriftsartikel (refereegranskat)abstract
- Many Wireless Vision Sensor Networks (WVSNs) applications are characterized to have a low duty cycling. An individual wireless Vision Senor Node (VSN) in WVSN is required to operate with limited resources i.e., processing, memory and wireless bandwidth on available limited energy. For such resource constrained VSN, this paper presents a low complexity, energy efficient and programmable VSN architecture based on a design matrix which includes partitioning of processing load between the node and a server, a low complexity background subtraction, bi-level video coding and duty cycling. The tasks partitioning and proposed background subtraction reduces the processing energy and design complexity for hardware implemented VSN. The bi-level video coding reduces the communication energy whereas the duty cycling conserves energy for lifetime maximization. The proposed VSN, referred to as SENTIOF-CAM, has been implemented on a customized single board, which includes SRAM FPGA, microcontroller, radio transceiver and a FLASH memory. The energy values are measured for different states and results are compared with existing solutions. The comparison shows that the proposed solution can offer up to 69 times energy reduction. The lifetime based on measured energy values shows that for a sample period of 5 minutes, a 3.2 years lifetime can be achieved with a battery of 37.44 kJ energy. In addition to this, the proposed solution offers generic architecture with smaller design complexity on a hardware reconfigurable platform and offers easy adaptation for a number of applications.
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| 16. |
- Imran, Muhammad, et al.
(författare)
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Implementation of wireless Vision Sensor Node for Characterization of Particles in Fluids
- 2012
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Ingår i: IEEE transactions on circuits and systems for video technology (Print). - 1051-8215 .- 1558-2205. ; 22:11, s. 1634-1643
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Tidskriftsartikel (refereegranskat)abstract
- Wireless Vision Sensor Networks (WVSNs) have a number of wireless Vision Sensor Nodes (VSNs), often spread over a large geographical area. Each node has an image capturing unit, a battery or alternative energy source, a memory unit, a light source, a wireless link and a processing unit. The challenges associated with WVSNs include low energy consumption, low bandwidth, limited memory and processing capabilities. In order to meet these challenges, our research is focused on the exploration of energy efficient reconfigurable architectures for VSN. In this work, the design/research challenges associated with the implementation of VSN on different computational platforms such as micro-controller, FPGA and server, are explored. In relation to this, the effect on the energy consumption and the design complexity at the node, when the functionality is moved from one platform to another are analyzed. Based on the implementation of the VSN on embedded platforms, the lifetime of the VSN is predicted using the measured energy values of the platforms for different implementation strategies. The implementation results show that an architecture, where the compressed images after pixel based operation are transmitted, realize a WVSN system with low energy consumption. Moreover, the complex post processing tasks are moved to a server, with reduced constraints.
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| 17. |
- Imran, Muhammad, et al.
(författare)
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Implementation of Wireless Vision Sensor Node With a Lightweight Bi-Level Video Coding
- 2013
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Ingår i: IEEE Journal on Emerging and Selected Topics in Circuits and Systems. - : IEEE Press. - 2156-3357 .- 2156-3365. ; 3:2, s. 198-209
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Tidskriftsartikel (refereegranskat)abstract
- Wireless vision sensor networks (WVSNs) consist ofa number of wireless vision sensor nodes (VSNs) which have limitedresources i.e., energy, memory, processing, and wireless bandwidth.The processing and communication energy requirements ofindividual VSN have been a challenge because of limited energyavailability. To meet this challenge, we have proposed and implementeda programmable and energy efficient VSN architecturewhich has lower energy requirements and has a reduced designcomplexity. In the proposed system, vision tasks are partitionedbetween the hardware implemented VSN and a server. The initialdata dominated tasks are implemented on the VSN while thecontrol dominated complex tasks are processed on a server. Thisstrategy will reduce both the processing energy consumption andthe design complexity. The communication energy consumption isreduced by implementing a lightweight bi-level video coding on theVSN. The energy consumption is measured on real hardware fordifferent applications and proposed VSN is compared against publishedsystems. The results show that, depending on the application,the energy consumption can be reduced by a factor of approximately1.5 up to 376 as compared to VSN without the bi-level videocoding. The proposed VSN offers energy efficient, generic architecturewith smaller design complexity on hardware reconfigurableplatform and offers easy adaptation for a number of applicationsas compared to published systems.
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| 18. |
- Imran, Muhammad, et al.
(författare)
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Low Complexity Background Subtraction for Wireless Vision Sensor Node
- 2013
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Ingår i: Proceedings - 16th Euromicro Conference on Digital System Design, DSD 2013. - 9780769550749 ; , s. 681-688
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Konferensbidrag (refereegranskat)abstract
- Wireless vision sensor nodes consist of limited resources such as energy, memory, wireless bandwidth and processing. Thus it becomes necessary to investigate lightweight vision tasks. To highlight the foreground objects, many machine vision applications depend on the background subtraction technique. Traditional background subtraction approaches employ recursive and non-recursive techniques and store the whole image in memory. This raises issues like complexity on hardware platform, energy requirements and latency. This work presents a low complexity background subtraction technique for a hardware implemented VSN. The proposed technique utilizes existing image scaling techniques for scaling down the image. The downscaled image is stored in memory of microcontroller which is already there for transmission. For subtraction operation, the background pixels are generated in real time through up scaling. The performance, and memory requirements of the system is compared for four image scaling techniques including nearest neighbor, averaging, bilinear, and bicubic. The results show that a system with lightweight scaling techniques, i.e., nearest neighbor and averaging, up to a scaling factor of 8, missed on average less than one object as compared to a system which uses a full original background image. The proposed approach will reduce the cost, design/implementation complexity and the memory requirement by a factor of up to 64.
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| 19. |
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| 20. |
- Khursheed, Khursheed, et al.
(författare)
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Bi-Level Video Codec for Machine Vision Embedded Applications
- 2013
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Ingår i: Elektronika Ir Elektrotechnika. - : Kaunas University of Technology (KTU). - 1392-1215. ; 19:8, s. 93-96
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Tidskriftsartikel (refereegranskat)abstract
- Wireless Visual Sensor Networks (WVSN) are feasible today due to the advancement in many fields of electronics such as Complementary Metal Oxide Semiconductor (CMOS) cameras, low power electronics, distributed computing and radio transceivers. The energy budget in WVSN is limited due to the small form factor of the Visual Sensor Nodes (VSNs) and the wireless nature of the application. The images captured by VSN contain huge amount of data which leads to high communication energy consumptions. Hence there is a need for designing efficient algorithms which are computationally less complex and provide high compression ratio. The change coding and Region of Interest (ROIs) coding are the options for data reduction of the VSN. But, for higher number of objects in the images, the compression efficiency of both the change coding and ROI coding becomes worse than that of image coding. This paper explores the compression efficiency of the Bi-Level Video Codec (BVC) for several representative machine vision applications. We proposed to implement image coding, change coding and ROI coding at the VSN and to select the smallest bit stream among the three. Results show that the compression performance of the BVC for such applications is always better than that of change coding and ROI coding.
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| 21. |
- Khursheed, Khursheed, et al.
(författare)
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Binary video codec for data reduction in wireless visual sensor networks
- 2013
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Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE - International Society for Optical Engineering. - 9780819494290 ; , s. Art. no. 86560L-
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Konferensbidrag (refereegranskat)abstract
- Wireless Visual Sensor Networks (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. Typical applications of WVSN include environmental monitoring, health care, industrial process monitoring, stadium/airports monitoring for security reasons and many more. The energy budget in the outdoor applications of WVSN is limited to the batteries and the frequent replacement of batteries is usually not desirable. So the processing as well as the communication energy consumption of the VSN needs to be optimized in such a way that the network remains functional for longer duration. The images captured by VSN contain huge amount of data and require efficient computational resources for processing the images and wide communication bandwidth for the transmission of the results. Image processing algorithms must be designed and developed in such a way that they are computationally less complex and must provide high compression rate. For some applications of WVSN, the captured images can be segmented into bi-level images and hence bi-level image coding methods will efficiently reduce the information amount in these segmented images. But the compression rate of the bi-level image coding methods is limited by the underlined compression algorithm. Hence there is a need for designing other intelligent and efficient algorithms which are computationally less complex and provide better compression rate than that of bi-level image coding methods. Change coding is one such algorithm which is computationally less complex (require only exclusive OR operations) and provide better compression efficiency compared to image coding but it is effective for applications having slight changes between adjacent frames of the video. The detection and coding of the Region of Interest (ROIs) in the change frame efficiently reduce the information amount in the change frame. But, if the number of objects in the change frames is higher than a certain level then the compression efficiency of both the change coding and ROI coding becomes worse than that of image coding. This paper explores the compression efficiency of the Binary Video Codec (BVC) for the data reduction in WVSN. We proposed to implement all the three compression techniques i.e. image coding, change coding and ROI coding at the VSN and then select the smallest bit stream among the results of the three compression techniques. In this way the compression performance of the BVC will never become worse than that of image coding. We concluded that the compression efficiency of BVC is always better than that of change coding and is always better than or equal that of ROI coding and image coding. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
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| 22. |
- Khursheed, Khursheed, 1983-, et al.
(författare)
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Detecting and Coding Region of Interests in Bi-Level Images for Data Reduction in Wireless Visual Sensor Network
- 2012
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Ingår i: Wireless and Mobile Computing, Networking and Communications (WiMob), 2012 IEEE 8th International Conference on. - : IEEE conference proceedings. - 9781467314299 ; , s. 705-712
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Konferensbidrag (refereegranskat)abstract
- Wireless Visual Sensor Network (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. The VSNs acquire images of the area of interest in the field, perform some local processing on these images and transmit the results using an embedded wireless transceiver. The energy consumption on transmitting the results wirelessly is correlated with the information amount that is being transmitted. The images acquired by the VSNs contain huge amount of data due to many kinds of redundancies in the images. Suitable bi-level image compression standards can efficiently reduce the information amount in images and will thus be effective in reducing the communication energy consumption in the WVSN. But compression capability of the bi-level image compression standards is limited to the underline compression algorithm. Further data reduction can be achieved by detecting Region of Interest (ROI) in the bi-level images and then coding these ROIs using bi-level image compression method. We explored the compression performance of the lossless ROI detection and coding method for various kinds of changes such as different shapes, locations and number of objects in the continuous set of frames. The CCITT Group 4, JBIG2 and Gzip are used for coding the detected ROIs. We concluded that CCITT Group 4 is a better choice for coding the ROIs in the Bi-level images because of its comparatively good compression performance and less computational complexity. This paper is intended to be a resource for the researchers interested in reducing the amount of data in the bi-level images for energy constrained WVSNs.
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| 23. |
- Khursheed, Khursheed, et al.
(författare)
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Efficient Data Reduction Techniques for Remote Applications of a Wireless Visual Sensor Network
- 2013
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Ingår i: International Journal of Advanced Robotic Systems. - : SAGE Publications. - 1729-8806 .- 1729-8814. ; 10, s. Art. no. 240-
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Tidskriftsartikel (refereegranskat)abstract
- A Wireless Visual Sensor Network (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. After acquiring an image of the area of interest, the VSN performs local processing on it and transmits the result using an embedded wireless transceiver. Wireless data transmission consumes a great deal of energy, where energy consumption is mainly dependent on the amount of information being transmitted. The image captured by the VSN contains a huge amount of data. For certain applications, segmentation can be performed on the captured images. The amount of information in the segmented images can be reduced by applying efficient bi-level image compression methods. In this way, the communication energy consumption of each of the VSNs can be reduced. However, the data reduction capability of bi-level image compression standards is fixed and is limited by the used compression algorithm. For applications attributing few changes in adjacent frames, change coding can be applied for further data reduction. Detecting and compressing only the Regions of Interest (ROIs) in the change frame is another possibility for further data reduction. In a communication system, where both the sender and the receiver know the employed compression standard, there is a possibility for further data reduction by not including the header information in the compressed bit stream of the sender. This paper summarizes different information reduction techniques such as image coding, change coding and ROI coding. The main contribution is the investigation of the combined effect of all these coding methods and their application to a few representative real life applications. This paper is intended to be a resource for researchers interested in techniques for information reduction in energy constrained embedded applications.
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| 24. |
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| 25. |
- Khursheed, Khursheed, 1983-, et al.
(författare)
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Selection of bi-level image compression method for reduction of communication energy in wireless visual sensor networks
- 2012
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Ingår i: SPIE. - : SPIE. - 9780819491299
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Konferensbidrag (refereegranskat)abstract
- Wireless Visual Sensor Network (WVSN) is an emerging field which combines image sensor, on board computation unit, communication component and energy source. Compared to the traditional wireless sensor network, which operates on one dimensional data, such as temperature, pressure values etc., WVSN operates on two dimensional data (images) which requires higher processing power and communication bandwidth. Normally, WVSNs are deployed in areas where installation of wired solutions is not feasible. The energy budget in these networks is limited to the batteries, because of the wireless nature of the application. Due to the limited availability of energy, the processing at Visual Sensor Nodes (VSN) and communication from VSN to server should consume as low energy as possible. Transmission of raw images wirelessly consumes a lot of energy and requires higher communication bandwidth. Data compression methods reduce data efficiently and hence will be effective in reducing communication cost in WVSN. In this paper, we have compared the compression efficiency and complexity of six well known bi-level image compression methods. The focus is to determine the compression algorithms which can efficiently compress bi-level images and their computational complexity is suitable for computational platform used in WVSNs. These results can be used as a road map for selection of compression methods for different sets of constraints in WVSN.
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| 26. |
- Khursheed, Khursheed, et al.
(författare)
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The effect of packets relaying on the implementation issues of the visual sensor node
- 2013
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Ingår i: Electronics and Electrical Engineering. - : Kaunas University of Technology (KTU). - 1392-1215 .- 2029-5731. ; 19:10, s. 155-161
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Tidskriftsartikel (refereegranskat)abstract
- Wireless Visual Sensor Networks (WVSNs) are used for the monitoring of large and inaccessible areas. WVSNs are feasible today due to the advancement in many fields of electronics such as CMOS cameras, low power computing platforms, distributed computing and radio transceivers. The energy budget in a WVSN is limited because of the wireless nature of the applications and the small physical size of the Visual Sensor Node (VSN). The WVSN covers a large area where every node cannot transmit its results directly to the server. Receiving and forwarding other node's packets consumes a large portion of the energy budget of the VSNs. This paper explores the effect of packets relaying in a multihop WVSN on the implementation issues of the VSN. It also explores the effect of node density in the multihop WVSN on the energy consumption, which in turn, has an impact on the lifetime of the VSN. Results show that the network topology does not affect the software implementation of the VSN because of the relatively high execution time of the image processing tasks on the microcontroller. For hardware implementation, network topology and node density does affect the architecture of the VSN due to the fact that communication energy consumption is dominant (because of the low execution time on FPGAs).
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| 27. |
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| 28. |
- Naeem, Samreen, et al.
(författare)
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The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach
- 2021
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Ingår i: Agronomy. - : MDPI AG. - 2073-4395. ; 11:2
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Tidskriftsartikel (refereegranskat)abstract
- This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia.
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| 29. |
- Nawaz, Sadia, et al.
(författare)
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Report of a recurrent mutation in ARS (component B) gene with severe Mal de Meleda in a large consanguineous Pakistani family
- 2011
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Ingår i: Pakistan journal of medical sciences print. - 1682-024X .- 1681-715X. ; 27:3, s. 686-689
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To characterize the disease causing mutation in a large consanguineous Pakistani family with severe Mat de Meleda (MDM) or keratosis palmoplantaris transgrediens, a rare autosomal recessive skin disorder. Methodology: Single nucleotide polymorphism (SNPs) genotyping was performed using the Gene Chip Mapping 250K array (Affymetrix). Homozygosity mapping and sorting of genomic regions were performed with dedicated software called AutoSNPa. Selected regions were further investigated by genotyping with microsatellite markers derived from known and novel pOlymorphic repeats. Two-point LOD score calculation was performed by using the MLINK of Fast link computer package. All three coding exons of ARS (component B) gene were amplified by PCR and sequenced. Conclusion: Sequencing of all the coding exons of ARS (component B) gene in the affected individuals revealed a recurrent missense mutation in exon 3 at base pair 256 from Guanine to Alanine (256G>A) and as a result the amino acid Glycine is replaced by Arginine at position 86 (G86R). This finding will facilitate control of affected MDM births in the Pakistani families.
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