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Music plays an important role in our society and has applications broader than just entertainment and pleasure due to its social and physiological effects. There has been recent interest in music, and two active research topics are music information retrieval and music emotion recognition, where data mining and machine learning techniques are integrated with music features and annotations to extract...
Recently, capabilities of many computer vision tasks have significantly improved due to advances in Convolutional Neural Networks. In our research, we demonstrate that it can be also used for face detection from low resolution thermal images, acquired with a portable camera. The physical size of the camera used in our research allows for embedding it in a wearable device or indoor remote monitoring...
Hypervisor-based virtualization rapidly becomes a commodity, and it turns valuable in many scenarios such as resource optimization, uptime maximization, and consolidation. Container-based application virtualization is an appropriate solution to develop a light weighted partitioning by providing application isolation with less overhead. Undoubtedly, container based virtualization delivers a lightweight...
Deep feature learning methods have been aggressively applied in the field of music tagging retrieval Genre categorization, mood classification, and chord detection are the most common tags from local spectral to temporal structure. Convolutional Neural networks (CNNs) using kernels extract the local features that are in different levels of hierarchy while Recurrent Neural Networks (RNNs) discover...
Speaker identification systems are becoming more important in today's world. This is especially true as devices rely on the user to speak commands. In this article, an analysis of how a text-independent voice identification system can be built is presented. Extracting the Mel-Frequency Cepstral Coefficients is evaluated and a support vector machine is trained and tested on two different data sets,...
Pedestrian recognition is a key problem for a number of application domains namely autonomous driving, search and rescue, surveillance and robotics. Real-time pedestrian recognition entails determining if a pedestrian is in an image frame. State-of-art pedestrian detection convolution neural networks(CNN) such as Fast R-CNN depend on computationally expensive region detection algorithms to hypothesize...
Autonomous driving has been a hot topic with companies like Google, Uber, and Tesla because of the complexity of the problem, seemingly endless applications, and capital gain. The technology's brain child is DARPA's autonomous urban challenge from over a decade ago. Few companies have had some success in applying algorithms to commercial cars. These algorithms range from classical control approaches...
Robot navigation requires specific techniques for guiding a mobile robot to a desired destination. In general, a desired path is required in an environment described by different terrain and a set of distinct objects, such as obstacles and particular landmarks. In this paper, a new approach for autonomous navigation is presented using machine learning techniques such as Convolutional Neural Network...
Thermal imaging has shown potential in assisting many aspects of smart irrigation management. This article examines key technical and legal issues and requirements supporting the use of Cloud of Things for managing water source-related data prior to discussing potential solutions.
License Plate Recognition System (LPRS) plays a vital role in smart city initiatives such as traffic control, smart parking, toll management and security. In this article, a cloud-based LPRS is addressed in the context of efficiency where accuracy and speed of processing plays a critical role towards its success. Signature-based features technique as a deep convolutional neural network in a cloud...
Image analysis plays a crucial role in the field of medicine, as the analysis guides the radiologist towards perfect diagnosis and treatment planning. This paper presents a novel method to enhance computerized tomography (CT) and magnetic resonance (MR) images. The proposed algorithm uses three techniques, namely, Domain Transform, Shape-adaptive edge enhancement, and Adaptive histogram equalization...
Histogram-Based image enhancement techniques attempts to divide the histogram vertically and horizontally to overcome the saturation effect of intensities and over-enhancement. The proposed cloud-based method makes an effort to modify a classic histogram-based image enhancement by concept of cascade control. Two control loops check the quality of the output image by an inner and outer feedback signals...
While all cloud based platforms possess security vulnerabilities, the additional security challenges with container systems stem from the sharing of Host OS among independent containers. If a malicious application was to break into the root of container Daemon, it could gain root access into the host kernel thereby compromising the entire system. It could create Denial-Of-Service attack for other...
The paper proposes a software architecture for cloud robotics which intends three subsystems in the cloud environment: Middleware Subsystem, Background Tasks Subsystem, and Control Subsystem. The architecture invokes cloud technologies such as cloud computing, cloud storage, and other networking platforms arranged on the assistances of congregated infrastructure and shared services for robotics, for...
Dynamic distributed algorithm for provisioning of resources has been proposed to support heterogeneous multi-cloud environment. Multi-cloud infrastructure heterogeneity implies the presence of more diverse sets of resources and constraints that aggravate competition among providers. Sigmoidal and logarithmic functions have been used as the utility functions to meet the indicated constraints in the...
Automated, efficient software deployment is essential for today's modern cloud hosting providers. With advances in cloud technology, on demand cloud services offered by public providers are becoming increasingly powerful, anchoring the ecosystem of cloud services. Cloud infrastructure services are appealing in part because they enable customers to acquire and release infrastructure resources on demand...
In this paper, we describe three new soft computing methods for segmentation of both gray level and color images by using a fuzzy entropy based cost function for the genetic algorithm. The presented methods allow us to find optimized set of parameters for a predefined cost function. Particularly, we found the optimum set of membership functions by maximizing the fuzzy entropy and based on the membership...
In this paper, we describe a scalable and economical architecture for performing container based parallelization to obtain the best possible quantized image using different quantization techniques on the cloud. This approach using containers can be scaled to be used with huge datasets. The quantization techniques used in this paper are fuzzy entropy and genetic algorithm based techniques. Different...
The increase of the cloud file sharing storage as infrastructure for serving large amounts of images over the internet inspires new data analytics paradigms. In this paper, we sketch the idea of expanding the cloud file sharing capabilities from only storing images to also performing encryption and analytics by moving and executing user defined programs near the data inside of an object storage cloud...
In this paper, we sketch the idea of double image encryption service to provide the privacy and authentication on big-data image libraries on cloud computing environment. The encoding of the image is done using the P-Fibonacci transform of Discrete Cosine Coefficients "PFCC" algorithm. First, using Discrete Cosine Transfer (DCT), we transfer an image from the spatial domain to the frequency...
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