A robust local texture descriptor in the parametric space of the weibull distribution
- Authors: Tania, Sheikh , Karmakar, Gour , Teng, Shyh , Murshed, Manzur
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Multimedia Vol. 25, no. (2023), p. 6053-6066
- Full Text: false
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- Description: Research in texture feature approximation is still in the embryonic stage because of difficulties in developing a sound theoretical model to express the unique pattern in the intensity-variation of pixels in the neighbourhood of the pixel-of-interest so that it can sufficiently discriminate different textures. Local texture descriptors are widely used in image segmentation as they comprise pixel-wise features. The Weber local descriptor (WLD) with differential excitation and gradient orientation components, inspired by Weber's Law, has been leveraged in the state-of-the-art iterative contraction and merging (ICM) image segmentation technique. However, WLD has inherent drawbacks in the formulation of the components that limit its discriminatory capability. This paper introduces a novel texture descriptor by directly modelling the distribution of intensity-variation in the parametric space of the Weibull distribution using its shape and scale parameters. A unified 'joint scale' texture property is introduced, which can discriminate textures better than the individual parameters while keeping the length of the descriptor shorter. Additionally, the accuracy of WLD's gradient orientation component is improved by using an extended Sobel operator and expressing gradients in -
A commonality modeling framework for enhanced video coding leveraging on the cuboidal partitioning based representation of frames
- Authors: Ahmmed, Ashek , Murshed, Manzur , Paul, Manoranjan , Taubman, David
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Multimedia Vol. 24, no. (2022), p. 4446-4457
- Full Text: false
- Reviewed:
- Description: Video coding algorithms attempt to minimize the significant commonality that exists within a video sequence. Each new video coding standard contains tools that can perform this task more efficiently compared to its predecessors. Modern video coding systems are block-based wherein commonality modeling is carried out only from the perspective of the block that need be coded next. In this work, we argue for a commonality modeling approach that can provide a seamless blending between global and local homogeneity information. For this purpose, at first the frame that need be coded, is recursively partitioned into rectangular regions based on the homogeneity information of the entire frame. After that each obtained rectangular region's feature descriptor is taken to be the average value of all the pixels' intensities encompassing the region. In this way, the proposed approach generates a coarse representation of the current frame by minimizing both global and local commonality. This coarse frame is computationally simple and has a compact representation. It attempts to preserve important structural properties of the current frame which can be viewed subjectively as well as from improved rate-distortion performance of a reference scalable HEVC coder that employs the coarse frame as a reference frame for encoding the current frame. © 1999-2012 IEEE.
Discrete cosine basis oriented motion modeling with cuboidal applicability regions for versatile video coding
- Authors: Ahmmed, Ashek , Hamidouche, Wassim , Lambert, Andrew , Pickering, Mark , Murshed, Manzur
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 Picture Coding Symposium, PCS 2022, San Jose, Costa Rica, 7-9 December 2022, 2022 Picture Coding Symposium, PCS 2022 - Proceedings p. 337-341
- Full Text: false
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- Description: The relentless expansion of video based applications is underpinned by video coding technologies. The latest video coding standard i.e. versatile video coding (VVC) can provide superior compression performance than its predecessors. In this regard, motion modeling plays a central role. Experimental results showed that the discrete cosine basis oriented motion model can describe complex motion better than an affine motion model, adopted in the VVC. Hence, in this paper we propose to augment the VVC motion modeling technique with a set of discrete cosine basis oriented motion models and the applicability region of each such motion model is determined by non-overlapping rectangular regions, known as cuboids. Experimental results show a bit rate savings of up to 2.37% is achievable with respect to a VVC reference. © 2022 IEEE.
Dynamic mesh commonality modeling using the cuboidal partitioning
- Authors: Ahmmed, Ashek , Paul, Manoranjan , Murshed, Manzur , Pickering, Mark
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022, Suzhou, China, 13-16 December 2022, 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
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- Description: For 3D object representation, volumetric contents like meshes and point clouds provide suitable formats. However, a dynamic mesh sequence may require significantly large amount of data because it consists of information that varies with time. Hence, for the facilitation of storage and transmission of such content, efficient compression technologies are required. MPEG has started standardization activities aiming to develop a mesh compression standard that would be able to handle dynamic meshes with time varying connectivity information and time varying attribute maps. The attribute maps are features associated with the mesh surface and stored as 2D images/videos. In this paper, we propose to capture the commonality information in the dynamic mesh attribute maps using the cuboidal partitioning algorithm. This algorithm is capable of modeling both the global and local commonality within an image in a compact and computationally efficient way. Experimental results show that the proposed approach can outperform the anchor HEVC codec, suggested by MPEG to encode such sequences, with a bit rate savings of up to 3.66%. © 2022 IEEE.
Efficient scalable 360-degree video compression scheme using 3d cuboid partitioning
- Authors: Afsana, Fariha , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2022
- Type: Text , Conference paper
- Relation: 29th IEEE International Conference on Image Processing, ICIP 2022 p. 996-1000
- Full Text: false
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- Description: Video coding techniques minimize spatial and temporal redundancies inherent in video sequences based on non-overlapping block-based image partitioning. Due to depending on the information from already encoded neighboring blocks, these algorithms lack efficient techniques to exploit the overall global redundancies. Compared to the traditional block-based coding, the cuboid coding (2D) framework has been proven to be a more effective method of image compression that exploits global redundancy by considering homogeneous pixel correlation within a frame. In this paper, we improved the idea of 2D cuboid coding to exploit both local and global redundancy from a video sequence by adopting a three-dimensional (3D) cuboid partitioning scheme for SHVC compression improvement of 360-degree videos. The proposed method considers a group of successive frames as a 3D cuboid and recursively partitions it into sub-3D cuboids where static information over a selected GOP share the same cuboid and moving regions share new cuboids with better-defined objects. All the 3D cuboids are then encoded to create a coarse representation of the video stream. Experiments indicate that the proposed framework significantly outperforms its relevant benchmarks, notably by 17.18% (average) in BD-Rate reduction and 0.82 dB in BD-PSNR gain with respect to the standard SHVC codec. © 2022 IEEE.
Efficient scalable UHD/360-video coding by exploiting common information with cuboid-based partitioning
- Authors: Afsana, Fariha , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Circuits and Systems for Video Technology Vol. 32, no. 6 (2022), p. 3961-3977
- Full Text: false
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- Description: The scalable extension of High Efficiency Video Coding, SHVC can code Ultra High-Definition (UHD) video, including 360-degree video for various devices to serve a single bitstream with different display resolutions and qualities. To improve the SHVC compression efficiency, this paper proposes a novel intra and inter-frame coding scheme by first separating the common/visually important information and then applying cuboid-based variable size block partitioning and coding process for the common/visually important information in the base layer. In cuboid-based partitioning a video frame is partitioned into arbitrary shaped rectangular regions, known as cuboids, based on the distribution of relatively homogeneous pixel values. As the cuboid adopts a variable block partitioning based on the homogeneity of the data value, the partitioned blocks have better alignment with the object boundary. Moreover, in the cuboid coding process, only the partitioning tree information and a single value for each block need to be coded which takes lower number of bits and computational time compared to the traditional SHVC base layer. To verify the performance of the proposed method we embedded the proposed scheme as a base layer into the standard SHVC reference software and used several popular UHD/360-degree videos. The experimental results indicate that the proposed scalable coding strategy achieves an average of 14.04% BD-Rate reduction and 0.61 dB BD-PSNR gain for UHD/360-video compared to the operation points provided by an SHVC conforming encoder. © 1991-2012 IEEE.
Human pose based video compression via forward-referencing using deep learning
- Authors: Rajin, S.M. Ataul Karim , Murshed, Manzur , Paul, Manoranjan , Teng, Shyh , Ma, Jiangang
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022, Suzhou, China,13-16 December 2022, 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
- Full Text: false
- Reviewed:
- Description: To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and object occlusion/deocclusion. Recently, deep learning has been used to model the high-level structure of human pose in specific actions from short videos and then generate virtual frames in future time by predicting the pose using a generative adversarial network (GAN). Therefore, modelling the high-level structure of human pose is able to exploit semantic correlation by predicting human actions and determining its trajectory. Video surveillance applications will benefit as stored 'big' surveillance data can be compressed by estimating human pose trajectories and generating future frames through semantic correlation. This paper explores a new way of video coding by modelling human pose from the already-encoded frames and using the generated frame at the current time as an additional forward-referencing frame. It is expected that the proposed approach can overcome the limitations of the traditional backward-referencing frames by predicting the blocks containing the moving objects with lower residuals. Our experimental results show that the proposed approach can achieve on average up to 2.83 dB PSNR gain and 25.93% bitrate savings for high motion video sequences compared to standard video coding. © 2022 IEEE.
Multi-objective dynamic virtual machine consolidation algorithm for cloud data centers with highly energy proportional servers and heterogeneous workload
- Authors: Khan, Md Anit , Paplinski, Andrew , Khan, Abdul , Murshed, Manzur , Buyya, Rajkumar
- Date: 2022
- Type: Text , Book chapter
- Relation: New Frontiers in Cloud Computing and Internet of Things Chapter 3 p. 69-106
- Full Text: false
- Reviewed:
- Description: Present Dynamic VM Consolidation (DVMC) algorithms assume that optimal energy efficiency can be achieved via maximum load on Physical Machines (PMs). Such assumption has become invalid with the advent of the highly energy proportional PMs. Additionally, these algorithms consider only varying resource demand, ignoring dissimilarity of workload finishing time, aka the VM Release Time (VMRT), whereas both aspects are strongly associated with energy consumption. Consequently, traditional algorithms fail to proffer optimal performance under real Cloud scenarios. Although minimization of VM migration brings massive benefit for Cloud Data Center (CDC), it is complete opposite of what is needed to minimize energy consumption through DVMC. As such, our proposed multi-objective Stochastic Release Time aware DVMC (SRTDVMC) algorithm is unique in addressing concomitant minimization of energy consumption and VM migration in the presence of state-of-the-art PMs and heterogeneous workloads. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Detection of Malleefowl Mounds from Point Cloud Data
- Authors: Parvin, Nahida , Awrangjeb, Mohammad , Irvin, Marc , Florentine, Singarayer , Murshed, Manzur , Lu, Guojun
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021, Gold Coast, 29 November to 1 December 2021
- Full Text: false
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- Description: Airborne light detection and ranging (LiDAR) data have become cost and time-efficient means for estimating the size of timid fauna populations through the identification of artefacts that evidence their occurrence in a large, hostile geographic area. The unobtrusive detection method helps conservation managers to assess the stability of a population and to design appropriate conservation programs. Here we propose a mound (nest) detection method for Australia's native iconic bird, the Malleefowl, from point cloud data, which can be manipulated to act as a surrogate for population data. Existing detection methods are largely through manual observations, and are therefore not efficient for covering large and remote areas. The proposed mound detection method can identify mound feature based on height and intensity values provided by the point cloud data. Each candidate mound point is initially selected by applying a height threshold utilising the classified ground points and their corresponding digital elevation model (DEM). Then, another threshold based on intensity range derived from ground truth mound area analysis is applied on the extracted initial mound points to find the final candidate mound points. These extracted points are then used to generate a binary mask where the potential mound points are found sparse. To connect those points, a morphological filter is applied on the binary image and found the mound separated from other remaining non-mound objects. To obtain the mound from other non-mound objects, a morphological cleaning operation and a connected component analysis are carried out on the mask. The non-mound objects are removed from the mask utilising the area property of mound derived from the empirical analysis of ground-truth observations. Finally, the effectiveness of the proposed technique is calculated based on ground truth. Although the mound shapes and structures are highly variable in nature, our height and intensity-based mound point extraction method detected 55 % of the ground-truthed mounds. © 2021 IEEE.
Dynamic point cloud compression using a cuboid oriented discrete cosine based motion model
- Authors: Ahmmed, Ashek , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 Vol. 2021-June, p. 1935-1939
- Full Text: false
- Reviewed:
- Description: Immersive media representation format based on point clouds has underpinned significant opportunities for extended reality applications. Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression technique projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using modern video coding standard like HEVC. Since the properties of projected texture video frames are different from traditional video frames, HEVC-based commonality modeling can be inefficient. An improved commonality modeling technique is proposed that employs discrete cosine basis oriented motion models and the domains of such models are approximated by homogeneous regions called cuboids. Experimental results show that the proposed commonality modeling technique can yield savings in bit rate of up to 4.17%. ©2021 IEEE
Dynamic point cloud geometry compression using cuboid based commonality modelling framework
- Authors: Ahmmed, Ashek , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE International Conference on Image Processing, ICIP 2021, Anchorage, USA, 19-21 September 2021, Proceedings - International Conference on Image Processing, ICIP Vol. 2021-September, p. 2159-2163
- Full Text: false
- Reviewed:
- Description: Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression (V-PCC) technique projects a dynamic point cloud into geometry and texture video sequences. The projected geometry and texture video frames are then encoded using modern video coding standard like HEVC. However, HEVC encoder is unable to exploit the global commonality that exists within a geometry frame and between successive geometry frames to a greater extent. This is because in HEVC, the current frame partitioning starts from a rigid 64 × 64 pixels level without considering the structure of the scene need be coded. In this paper, an improved commonality modeling framework is proposed, by leveraging on cuboid-based frame partitioning, to encode point cloud geometry frames. The associated frame-partitioning scheme is based on statistical properties of the current geometry frame and therefore yields a flexible block partitioning structure composed of cuboids. Additionally, the proposed commonality modeling approach is computationally efficient and has a compact representation. Experimental results show that if the V-PCC reference encoder is augmented by the proposed commonality modeling technique, a bit rate savings of 2.71% and 4.25% are achieved for full body and upper body of human point clouds’ geometry sequences respectively. © 2021 IEEE.
An Enhanced Local Texture Descriptor for Image Segmentation
- 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.
Efficient low bit-rate intra-frame coding using common information for 360-degree video
- Authors: Afsana, Fariha , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2020
- Type: Text , Conference paper
- Relation: 22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020
- Full Text: false
- Reviewed:
- Description: With the growth of video technologies, super-resolution videos, including 360-degree immersive video has become a reality due to exciting applications such as augmented/virtual/mixed reality for better interaction and a wide-angle user-view experience of a scene compared to traditional video with narrow-focused viewing angle. The new generation video contents are bandwidth-intensive in nature due to high resolution and demand high bit rate as well as low latency delivery requirements that pose challenges in solving the bottleneck of transmission and storage burdens. There is limited optimisation space in traditional video coding schemes for improving video coding efficiency in intra-frame due to the fixed size of processing block. This paper presents a new approach for improving intra-frame coding especially at low bit rate video transmission for 360-degree video for lossy mode of HEVC. Prior to using traditional HEVC intra-prediction, this approach exploits the global redundancy of entire frame by extracting common important information using multi-level discrete wavelet transformation. This paper demonstrates that the proposed method considering only low frequency information of a frame and encoding this can outperform the HEVC standard at low bit rates. The experimental results indicate that the proposed intra-frame coding strategy achieves an average of 54.07% BD-rate reduction and 2.84 dB BD-PSNR gain for low bit rate scenario compared to the HEVC. It also achieves a significant improvement in encoding time reduction of about 66.84% on an average. Moreover, this finding also demonstrates that the existing HEVC block partitioning can be applied in the transform domain for better exploitation of information concentration as we applied HEVC on wavelet frequency domain. © 2020 IEEE.
Enhanced transfer learning with ImageNet trained classification layer
- Authors: Shermin, Tasfia , Teng, Shyh Wei , Murshed, Manzur , Lu, Guojun , Sohel, Ferdous , Paul, Manoranjan
- Date: 2019
- Type: Text , Book chapter
- Relation: Image and Video Technology Chapter 12 p. 142-1455
- Full Text: false
- Reviewed:
- Description: Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained source network are transferred to the target network followed by fine-tuning. Prior research has shown that this approach is capable of improving task performance. However, the impact of the ImageNet pre-trained classification layer in parameter fine-tuning is mostly unexplored in the literature. In this paper, we propose a fine-tuning approach with the pre-trained classification layer. We employ layer-wise fine-tuning to determine which layers should be frozen for optimal performance. Our empirical analysis demonstrates that the proposed fine-tuning performs better than traditional fine-tuning. This finding indicates that the pre-trained classification layer holds less category-specific or more global information than believed earlier. Thus, we hypothesize that the presence of this layer is crucial for growing network depth to adapt better to a new task. Our study manifests that careful normalization and scaling are essential for creating harmony between the pre-trained and new layers for target domain adaptation. We evaluate the proposed depth augmented networks for fine-tuning on several challenging benchmark datasets and show that they can achieve higher classification accuracy than contemporary transfer learning approaches.
Measuring trustworthiness of IoT image sensor data using other sensors' complementary multimodal data
- Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019 p. 775-780
- Full Text: false
- Reviewed:
- Description: Trust of image sensor data is becoming increasingly important as the Internet of Things (IoT) applications grow from home appliances to surveillance. Up to our knowledge, there exists only one work in literature that estimates trustworthiness of digital images applied to forensic applications, based on a machine learning technique. The efficacy of this technique is heavily dependent on availability of an appropriate training set and adequate variation of IoT sensor data with noise, interference and environmental condition, but availability of such data cannot be assured always. Therefore, to overcome this limitation, a robust method capable of estimating trustworthy measure with high accuracy is needed. Lowering cost of sensors allow many IoT applications to use multiple types of sensors to observe the same event. In such cases, complementary multimodal data of one sensor can be exploited to measure trust level of another sensor data. In this paper, for the first time, we introduce a completely new approach to estimate the trustworthiness of an image sensor data using another sensor's numerical data. We develop a theoretical model using the Dempster-Shafer theory (DST) framework. The efficacy of the proposed model in estimating trust level of an image sensor data is analyzed by observing a fire event using IoT image and temperature sensor data in a residential setup under different scenarios. The proposed model produces highly accurate trust level in all scenarios with authentic and forged image data. © 2019 IEEE.
- Description: E1
An algorithm for network and data-aware placement of multi-tier applications in cloud data centers
- Authors: Ferdaus, Md Hasanul , Murshed, Manzur , Calheiros, Rodrigo , Buyya, Rajkumar
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 98, no. (2017), p. 65-83
- Full Text: false
- Reviewed:
- Description: Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%. © 2017 Elsevier Ltd
Cuboid segmentation for effective image retrieval
- Authors: Murshed, Manzur , Teng, Shyh , Lu, Guojun
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 International Conference on Digital Image Computing : Techniques and Applications (DICTA); Sydney, Australia; 29th November-1st December 2017 p. 884-891
- Full Text: false
- Reviewed:
- Description: Region-based image retrieval has been proven to be effective in finding relevant images. In this paper, we propose a cuboid im-age segmentation method which results in rectangle image partitions. Rectangle partitions are more suitable for image compression, retrieval and other image operations. We apply partitions in image retrieval in this paper. Our experimental results have shown that (1) the proposed partitioning method is effective in segmenting images into meaningful rectangles; (2) using colour partitions for image retrieval is more effective than using whole images; and (3) the partitioned approach has additional advantage of letting users to select certain objects/colours as queries to find more relevant images/objects. These three advantages could be important in crime scene investigation image indexing and retrieval. Moreover, the proposed technique is amenable to compressed-domain applications.
Development of a cylindrical polar coordinates shallow water storm surge model for the coast of Bangladesh
- Authors: Paul, Gour , Murshed, Manzur , Haque, Rabiul , Rahman, Mizanur , Hoque, Ashabul
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of coastal conservation Vol. 21, no. 6 (2017), p. 951-966
- Full Text: false
- Reviewed:
- Description: The coast of Bangladesh is funnel shaped. The narrowing of the Meghna estuary along with its peculiar topography creates a funneling effect that has a large impact on surge response. In order to have an accurate estimation of surge levels, the impacts of the estuary should be treated with due importance. To represent in detail the real complexities of the estuary, a very high resolution is required, which in turn necessitates more computational cost. Considering the facts into account, a location specific vertically integrated shallow water model in cylindrical polar coordinates is developed in this study to foresee water levels associated with a storm. A one-way nested grid technique is used to incorporate coastal complicities with minimum cost. In specific, a fine mesh scheme (FMS) capable of incorporating coastal complexities with acceptable accuracy is nested into a coarse mesh scheme (CMS) covering up to 15°N latitude in the Bay of Bengal. The coastal and island boundaries are approximated through appropriate stair step representation and the model equations are solved by a conditionally stable semi-implicit finite difference technique using a structured C-grid. Numerical experiments are performed using the model to estimate water levels due to surge associated with the April 1991 and AILA, 2009 cyclones, which struck the coast of Bangladesh. Time series of tidal level is generated from an available tide table through a cubic spline interpolation method. The computed surge response is superimposed linearly with the generated time series of tidal oscillation to obtain the time series of total water levels. The model results exhibit a good agreement with observation and reported data.
Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management : A review
- Authors: Khan, Md Anit , Paplinski, Andrew , Khan, Abdul , Murshed, Manzur , Buyya, Rajkumar
- Date: 2017
- Type: Text , Book chapter
- Relation: Sustainable Cloud and Energy Services : Principles and Practice Chapter 6 p. 135-165
- Full Text: false
- Reviewed:
- Description: As envisioned by Leonard Kleinrock [1], Cloud computing has transformed the dream of “computing as a utility” into reality, so much so it has turned out as the latest computing paradigm [2]. Cloud computing is called as Service-on-demand, as Cloud Service Providers (CSPs) assure users about potentially unlimited amount of resources that can be chartered on demand. It is also known as elastic computing, since Cloud Service Users (CSUs) can dynamically scale, expand, or shrink their rented resources anytime and expect to pay for the exact tenure of resource usage under Service Level Agreements (SLA). Through such flexibilities and financial benefits, CSPs have been attracting millions of clients who are simultaneously sharing the underlying computing and storage resources that are collectively known as Cloud data centers.
A Centroid Algorithm for Stabilization of Turbulence-Degraded Underwater Videos
- Authors: Halder, Kalyan Kumar , Paul, Manoranjan , Tahtali, Murat , Anavatti, Sreenatha G. , Murshed, Manzur
- Date: 2016
- Type: Text , Conference paper
- Relation: 2016 International Conference on Digital Image Computing: Techniques and Applications DICTA 2016 p. 1-6
- Full Text: false
- Reviewed:
- Description: This paper addresses the problem of stabilizing underwater videos with non-uniform geometric deformations or warping due to a wavy water surface. It presents an improved method to correct these geometric deformations of the frames, providing a high-quality stabilized video output. For this purpose, a non-rigid image registration technique is employed to accurately align the warped frames with respect to a prototype frame and to estimate the deformation parameters, which in turn, are applied in an image dewarping technique. The prototype frame is chosen from the video sequence based on a sharpness assessment. The effectiveness of the proposed method is validated by applying it on both synthetic and real- world sequences using various quality metrics. A performance comparison with an existing method confirms the higher efficacy of the proposed method.