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An effective feature representation can boost recognition tasks in the sketch domain. Due to an abstract and diverse structure of the sketch relatively with a natural image, it is complex to generate a discriminative features representation for sketch recognition. Accordingly, this article presents a novel scheme for sketch recognition. It generates a discriminative features representation as a result of integrating asymmetry essential information from deep features. This information is kept as an original feature‐vector space for making a final decision. Specifically, five different well‐known pre‐trained deep convolutional neural networks (DCNNs), namely, AlexNet, VGGNet‐19, Inception V3, Xception, and InceptionResNetV2 are fine‐tuned and utilised for feature extraction. First, the high‐level deep layers of the networks were used to get multi‐features hierarchy from sketch images. Second, an entropy‐based neighbourhood component analysis was employed to optimise the fusion of features in order of rank from multiple different layers of various deep networks. Finally, the ranked features vector space was fed into the support vector machine (SVM) classifier for sketch classification outcomes. The performance of the proposed scheme is evaluated on two different sketch datasets such as TU‐Berlin and Sketchy for classification and retrieval tasks. Experimental outcomes demonstrate that the proposed scheme brings substantial improvement over human recognition accuracy and other state‐of‐the‐art algorithms.

The scattering of atmospheric particles significantly alters images captured under hazy weather condition. Images appear distorted, blurry and low in contrast attenuation, which extensively affects computer vision systems. There has been development of several prior based methods to address this problem. However, these methods come at a high computational cost. We present a fast, single image dehazing method based on dark channel prior and Rayleigh scattering. Firstly, we present a simple but effective methodology for estimating the atmospheric light through the computation of average, minimum and maximum of the pixels in each of the three RGB colour channels. Then, using the theory of Rayleigh scattering, we model a scattering coefficient to estimate the initial transmission map. Also, a fast-guided filter is adopted to refine the initial transmission map due to inaccurate halo edges. Finally, we restore the haze-free image through the atmospheric scattering model. Extensive qualitative and computational experiments on hazy outdoor images demonstrate that the proposed method produces excellent results whiles achieving a faster processing time.

The scattering of climatic particles significantly alters images captured under unfavorable weather situations. Although many traditional methods have efficiently committed to annihilating haze, they pose some limitation due to their hand-crafted features, e.g., dark channel, maximum contrast, and colour disparity. This paper presents an end-to-end algorithm to restore a hazy image using a residual-based deep CNN straightforwardly. The proposed algorithm is non-subject to the climatic dispersing model, yet it learns the mapping relationship within the hazy input image and their corresponding transmission map. The network architecture constitutes a convolution kernel and multi-scale fusion layers in extracting relevant features in predicting a holistic propagation map. Ultimately, we obtain the residual image and circumvent the loss of information using a residual network. Comprehensive empirical results demonstrate that the proposed technique outperforms multiple conventional algorithms.

Consumer-Grade global positioning system (GPS) is widely used in many domains. The obvious issue of this consumer-grade device is low accuracy and reading fluctuation results. In terms of using an application that requires a more precise location, the output could be difficult. In this study, the authors deploy various methods to reduce the global positioning system data fluctuation and present field test results. Two main types of the device worked together to collect data from global positioning systems, such as Microcontroller for algorithm processing and presenting data and global positioning system receivers for receiving data from a satellite. We combine three global positioning system modules to received signals in a single device and test calculated data compared with the Kalman filtering methods in many cases, including moving and static devices. Implementing the Standard Kalman Filter to multiple global positioning system Modules has improved the constancy of cheap global positioning system equipment. The experiment algorithm is presented significant improvement to overcome the retrieved data fluctuation problem. This study's contribution will enable creating a cheap global positioning system locator device for various applications that require more accuracy than the standard consumer-grade receiver.

Due to the advancement of technology, devices generate huge working files at a rapid rate. These data, which are of considerable scale and are created very fast, can be called big data. Keeping such files in one storage device is impossible. Therefore, a large file size is recommended for storage in a cloud storage service. Although this concept is a solution to solve the storage problem, it still faces challenges in terms of reliability and security. The main issues are the unreliability of single cloud storage when its service is down, and the risk of insider attack from the storage service. Therefore, this paper proposes a file sharing scheme that increases both the reliability of the file fragments using a multi-cloud storage system and decreases the risk from insider attack. The dew computing concept is used as a contributor to the file-sharing scheme. The working file is split into fragments. Each fragment is deployed to cloud storage services, one fragment per one cloud provider manner. The dew server controls users&rsquo, access and monitors the availability of fragments. Finally, we verify the proposed scheme in aspects of the downloading performance, and security.

Big data is the name that defines data which has enormous size and unstructured. Due to the file size is pretty huge. It is impracticable to store a large file in one storage volume. However, cloud computing is a solution to this impossible. Data owner can store the file in a cloud storage provider (CSP). Nevertheless, the new dilemma has arisen. Relying on single cloud storage may generate trouble for the customer. A CSP may stop its service anytime. Moreover, the CSP is the third party that user have to trust without verification. In that case, the privacy or unauthorized accessing of data may be violated without notice. To overcome this risk, we propose secure data storage scheme for big data storing on multiple CSPs. The one big data file is split into chunks and distributed to multiple cloud storage provider. After splitting the file, metadata is generated. Metadata is a place to keep chunks information, includes; chunk locations, access paths, username and password of the data owner, methods to connect each CSP. The metadata is encrypted and transferred to the user who requests to access the file. The user utilizes the metadata and chunks of the file to compose the original file. This method will minimize the risk of privacy. The goal of this paper is to provide the method to protect the privacy of data stored on multiple cloud storage providers. Furthermore, we discuss and analyze how this data storage scheme promote the protection of big data privacy.

Recently, cloud computing is an emerging technology along with big data. Both technologies come together. Due to the enormous size of data in big data, it is impossible to store them in local storage. Alternatively, even we want to store them locally, we have to spend much money to create bit data center. One way to save money is store big data in cloud storage service. Cloud storage service provides users space and security to store the file. However, relying on single cloud storage may cause trouble for the customer. CSP may stop its service anytime. It is too risky if data owner hosts his file only single CSP. Also, the CSP is the third party that user have to trust without verification. After deploying his file to CSP, the user does not know who access his file. Even CSP provides a security mechanism to prevent outsider attack. However, how user ensure that there is no insider attack to steal or corrupt the file. This research proposes the way to minimize the risk, ensure data privacy, also accessing control. The big data file is split into chunks and distributed to multiple cloud storage provider. Even there is insider attack; the attacker gets only part of the file. He cannot reconstruct the whole file. After splitting the file, metadata is generated. Metadata is a place to keep chunk information, includes, chunk locations, access path, username and password of data owner to connect each CSP. Asymmetric security concept is applied to this research. The metadata will be encrypted and transfer to the user who requests to access the file. The file accessing, monitoring, metadata transferring is functions of dew computing which is an intermediate server between the users and cloud service.

When dealing with the quality of software system there are many Complex issues and challenges established, Such complex issue appears due to uncleared, ambiguous, and non-compete of functional as well as non-functional requirements during the requirement gathering process. This MDA is well known and used by Object Management Group(OMG) which raises abstraction level. The work has been done on model-based development which is very helpful to reduce the efforts and time and also achieve pure and error-free code. This paper describes the use of Model Driven architecture in web application area, to reduce the web application system problem. The model-driven web engineering facilitates to develop the web application using models to explain web site in different detail level. The existing web developments strategy can be polished by using MDA process. This paper discusses the basic concepts and presents the software development strategy based on MDA. Our focus is on code generation by using models. We take the scenario of Multi-Level marketing in order to design the UML diagram of Multi-level marketing for code generation. We have successfully received our desired code by Using ANDROMDA source code generator supported by MDA.

Drone is not the only high-tech toy anymore. It has been using an onboard range of applications from civilian to military, including indoor and outdoor operations. Outdoor drone relies on GPS signal to locate its position. It is different from the indoor drone. In this paper, the indoor drone is a review in many aspects also it is current one of must interested topic for researchers who have the main area in autonomous robotic and aerial technology.