varunr14/newsummary huggingface dataset:
Cloud computing provides an effective approach to deliver multimedia services to end users with the desired user quality of experience . However cloud based multimedia applications require many of servers and consume huge energy . To reduce energy consumption a multimedia service provider should balance the energy and QoE . In this paper a theoretic model is developed to explore the trade off between energy consumption and QoE for multimedia cloud . Based on objective factor a QoE quantifying model is proposed . Employing Lyapunov Optimization techniques an optimal control framework is designed and analyzed to make energy and QoE decisions in MSPs . An approximate online algorithm is proposed with the explicitly provable performance upper bound . Extensive experiments have been conducted to verify the effectiveness of EUE RP algorithm in the practical settings . The algorithm can guarantee desired QoE and reduce energy consumption even without any information about the future fluctuation of user demands . 


Summary: We investigate the trade off of QoE and energy in multimedia cloud. A QoE quantifying model is proposed based on objective factor. We develop a theoretic model by applying Lyapunov optimization method. An approximate online algorithm EUE RP is proposed with the explicitly provable performance upper bound. The proposed method flexibly manages the trade off between QoE and energy for cloud based multimedia services.
Mobile Cloud Computing augments capabilities of mobile devices by offloading applications to cloud . Resource allocation is one of the most challenging issues in MCC which is investigated in this paper considering neighboring mobile devices as service providers . The objective of the resource allocation is to select service providers minimizing the completion time of the offloading along maximizing lifetime of mobile devices satisfying deadline constraint . The paper proposes a two stage approach to solve the problem first Non dominated Sorting Genetic Algorithm II is applied to obtain the Pareto solution set second entropy weight and Technique for Order Preference by Similarity to Ideal Solution method are employed to specify the best compromise solution . Furthermore a context aware offloading middleware is developed to collect contextual information and handle offloading process . Moreover to stimulate selfish users a virtual credit based incentive mechanism is exploited in offloading decision . The experimental results demonstrate the ability of the proposed resource allocation approach to manage the trade off between time and energy comparing to traditional algorithms . 


Summary: A resource allocation method to minimize time and energy of tasks in mobile cloud is proposed. A two stage approach using NSGA II entropy and TOPSIS methods is employed. A context aware Offloading Middleware for Mobile Cloud is developed to manage offloading. A virtual credit based incentive mechanism is used to motivate users to cooperate in offloading. The proposed method appropriately manages the trade off between time and energy.
Provisioning of appropriate resources to cloud workloads depends on the Quality of Service requirements of cloud workloads . Based on application requirements of cloud users discovery and allocation of best workload resource pair is an optimization problem . Acceptable QoS can not be provided to the cloud users until provisioning of resources is offered as a crucial ability . QoS parameters based resource provisioning technique is therefore required for efficient provisioning of resources . In this paper QoS metric based resource provisioning technique has been proposed . The proposed technique caters to provisioned resource distribution and scheduling of resources . The main aim of this research work is to analyze the workloads categorize them on the basis of common patterns and then provision the cloud workloads before actual scheduling . The experimental results demonstrate that QoS metric based resource provisioning technique is efficient in reducing execution time and execution cost of cloud workloads along with other QoS parameters . 


Summary: Cloud workloads have been analyzed and clustered through workload patterns. QoS metrics of each workload have been identified. We have analyzed the effect of number of workloads and resources on execution time and cost. Proposed technique demonstrates the minimization of cost and time simultaneously while adhering to workload deadline.
Independent fine grain web services can be integrated to a value added coarse grain service through service composition technologies in Service Oriented Architecture . With the advent of cloud computing more and more web services in cloud may provide the same function but differ in performance . In addition the development of cloud computing presents a geographically distributed manner which elevates the impact of the network on the QoS of composited web services . Therefore a significant research problem in service composition is how to select the best candidate service from a set of functionally equivalent services in terms of a service level agreement . In this paper we propose a composition model that takes both QoS of services and cloud network environment into consideration . We also propose a web service composition approach based on genetic algorithm for geo distributed cloud and service providers who want to minimize the SLA violations . 


Summary: A service composition model in geo distributed cloud environment is proposed. The composition approach considers both QoS of Web services and QoS of network. We propose a genetic algorithm to solve the composition problem. The notion of skyline is used to generate the initial population.
Wireless sensor networks are formed by a large number of sensor nodes which are commonly known as motes . In the past few years several reliable congestion controlled and energy efficient transport layer protocols in wireless sensor networks have been developed and proposed in the literature . In this paper we have presented a hybrid and dynamic reliable transport protocol which provides the mechanism to dynamically assign the timing parameters to the nodes as well as enhance the protocol performance by using a hybrid Acknowledgement Negative Acknowledgement scheme . The performance of proposed protocol is tested under TinyOS Simulator varying different parameters and protocol settings and found that proposed protocol is able to program all the nodes when given proper pump fetch ratios is able to solve the booting sensor nodes problem by being able to wait till all the nodes finished booting and solves the all packets lost problem by acknowledging the receipt of its first packet delivered that is the inform message . 


Summary: A hybrid and dynamic reliable transport protocol for wireless sensor networks is proposed. It provides hybrid Acknowledgement Negative Acknowledgement scheme. It controls the booting sensor nodes problem and the all packets lost problem. The performance of proposed protocol is tested under TinyOS Simulator.
This paper presents an important step towards the standardization of research works on Optical Character Recognition in Persian language. It describes the formations of a standard handwritten database including isolated digits isolated signs multi digit numbers numerical strings courtesy amounts and postal codes. In this regard binary images of 72 180 samples were extracted from the designed forms. These forms were filled by 180 writers selected from different ages genders and jobs. Then these forms were scanned at 300 dpi with a high speed scanner. Finally forms are segmented into samples and are stored in bitmap format. This database is named PHOND Persian Handwritten Optical Numbers Digits and it is available to the research community. Comparisons with the previous related databases illustrate the advantages of PHOND against other databases. Different experiments are done using PHOND database and the results are compared with other research works in handwritten recognition. 


Summary: Introduction of a new database PHOND for handwriting Recognition of Digits Signs etc. in Persian. Presentation of a modified framing feature for handwriting Recognition. Recognition of digits signs and numerical strings in Persian using PHOND and other databases.
Extreme Learning Machine proposes a non iterative training method for Single Layer Feedforward Neural Networks that provides an effective solution for classification and prediction problems . Its hardware implementation is an important step towards fast accurate and reconfigurable embedded systems based on neural networks allowing to extend the range of applications where neural networks can be used especially where frequent and fast training or even real time training is required . This work proposes three hardware architectures for on chip ELM training computation and implementation a sequential and two parallel . All three are implemented parameterizably on FPGA as an IP core . Results describe performance accuracy resources and power consumption . The analysis is conducted parametrically varying the number of hidden neurons number of training patterns and internal bit length providing a guideline on required resources and level of performance that an FPGA based ELM training can provide . 


Summary: Extreme Learning Machine ELM on chip learning is implemented on FPGA. Three hardware architectures are evaluated. Parametrical analysis of accuracy resource occupation and performance is carried out.
The present work is dedicated to the detection of Lagrangian Coherent Structures in viscous flows through the Finite Time Lyapunov Exponents which have been addressed by several works in the recent literature . Here a novel numerical technique is presented in the context of the Smoothed Particle Hydrodynamics models . Thanks to the Lagrangian character of SPH the trajectory of each fluid particle is explicitly tracked over the whole simulation . This allows for a direct evaluation of the FTLE field supplying a new way for the data analysis of complex flows . The evaluation of FTLE can be either implemented as a post processing or nested into the SPH scheme conveniently . In the numerical results three test cases are presented giving a proof of concept for different conditions . The last test case regards a naval engineering problem for which the present algorithm is successfully used to capture the submerged vortical tunnels caused by the splashing bow wave . 


Summary: Algorithms for the detection of Lagrangian Coherent Structures LCSs are presented. Formulae for the Finite Time Lyapunov Exponent FTLE are derived for SPH models. The FTLE dependence on the use of enhanced SPH models is discussed. Post processing and nested in time implementations are discussed. Some test cases are considered giving a proof of concept for different conditions.
The druse an abnormal yellow white deposit on retina is a dominant characteristic of age related macular degeneration AMD which is a retinal disorder associated with age. The early detection of drusen is useful for ophthalmologists to diagnose the patients that suffer from AMD. An automated method has been proposed in this work to detect and segment drusen using retinal fundus images by gradient based segmentation to find true edges of drusen connected component labeling to remove suspicious pixels from drusen region and edge linking to connect all labeled pixels into a meaningful boundary. The proposed method outperforms other existing methods in detection of drusen with an accuracy sensitivity specificity of 96.17 89.81 99.00 on two publicly available retinal image databases. In order to grade the severity of AMD the detected drusen by the proposed method are further quantified into small intermediate and large with an accuracy of 88.46 98.55 and 88.37 respectively. 


Summary: Designed a new drusen detection and segmentation method finding meaningful drusen boundaries . To find true edges of drusen a gradient based segmentation procedure is described . Connected component labeling is applied to remove suspicious pixels from drusen region . Edge linking is used to connect all labeled pixels into a meaningful boundary to detect drusen . The performance of proposed method is evaluated by statistical measures and quantification of drusen to grade severity of age related macular degradation . The proposed work characterizes the detected drusen in small intermediate and large soft to show its ability to grade age related macular degradation severity level helpful in early age related macular degradation diagnosis .
In this paper we use and extend a parallel optoelectronic processor for image preprocessing and implement software tools for testing and evaluating the presented algorithms . After briefly introducing the processor and showing how images can be stored in it we adapt a number of local image preprocessing algorithms for smoothing edge detection and corner detection such that they can be executed on the processor in parallel . These algorithms are performed on all pixels of the input image in parallel and as a result in steps independent of its dimensions . We also develop a compiler and a simulator for evaluating and verifying the correctness of our implementations . 


Summary: We use a parallel optoelectronic processor for image preprocessing. We extend the processor to improve its parallelism and to make it more compact. We adapt several preprocessing operators for the processor. We write a compiler and simulator for evaluating the presented algorithms.
Spectrum sensing is an important aspect of cognitive radios . This paper describes a method for spectrum sensing based on the autocorrelation of the received samples . The proposed method was evaluated by means of experiments wherein the probabilities of detection and false alarm at different signal to noise ratios were observed . The platform used for the experiments was a set of Universal Software Radio Peripheral devices acting as radio frequency front ends in combination with GNU Radio software . Since the signal processing was performed in the software domain Gaussian noise of different levels was emulated by changing the standard deviation of a Python random number generator . In addition the output power of a signal generator was varied to obtain different levels of SNR . A metric called the Euclidean distance was derived to analyze the autocorrelation of the samples received by the USRP device in order to decide between two possible situations only noise present or signal plus noise present . The proposed method was compared with two methods one based on the value of the autocorrelation at the first lag and another one based on the power of the signal known as energy detection spectrum sensing technique . 


Summary: Gaussian noise samples are delta correlated ACF t t . We can do spectrum sensing with the Euclidean distance between ACF t and a reference line. The Euclidean distance method performs better than the ED and ACF 1 methods.
In ubiquitous high performance computing performing concurrent services is an important task of middleware . Because a major development effort is not easily achieved isolating a virtual machine may be a helpful solution but will likely suffer from additional overhead costs . This challenge can also be resolved by reusing the device driver . However these solutions are difficult to implement without the VM technique . In this study we present an alternative approach to minimizing overhead and develop a middleware called userspace virtualized middleware . Instead of bypassing the VM the proposed approach depends on userspace transparency and contention management to shift the VM concept into middleware . We introduce two strategies to enhance the system adaptively comprehensive restructuring by simplifying the VM memory mechanism and implementing zero copy buffers to reuse devices . The result demonstrates that Uware is feasible and could be applied in a broad variety of UHPC . 


Summary: Uware is a virtualization based approach to effectively improve the UHPC application design. Uware is an alternative approach to minimize the overhead and to develop a middleware. The approach relies on contention management and zero copy buffer mechanism to from isolation model.
Cognitive radio technology can solve the problems of spectrum scarcity and low spectrum utilization . However random behavior of the primary user appears to be an enormous challenge . In this paper we propose a PU behavior aware joint channel selection and allocation scheme . In the first step the channels are ranked based on statistics of the PU usage whereas in the second phase a proportional fair oriented channel allocation scheme is employed to allocate channels among CRs . We also introduce the concept of a time varying framing process that minimizes the overall data transmission time . Simulation results show that the proposed scheme outperforms existing schemes in terms of the transmission time and the number of collisions with the PUs . In addition it helps to save a significant amount of transmission power . Moreover it provides a significantly higher system throughput as compared to the existing schemes . 


Summary: A proportional fair oriented sharing scheme is proposed to allocate channels. A PU activity model is developed to provide most stable channels to users. A time varying framing process is used to make variable sized frames at MAC layer. The proposed scheme reduces the delay collisions and increases the throughput.
In this paper a locality aware NoC communication architecture is proposed . The architecture may reduce the energy consumption and latency in MultiProcessor Systems on Chips . It consists of two network layers which one layer is dedicated to the packets transmitted to near destinations and the other layer is used for the packets transmitted to far destinations . The actual physical channel width connecting the cores is divided between the two layers . The locality is defined based on the number of hops between the nodes . The relative significances of the two types of communications determine the optimum ratio for the channel width division . To assess the efficiency of the proposed method we compare its communication latency with that of conventional one for different channel widths communication traffic profiles and mesh sizes . 


Summary: We propose two layer network on chip to separate local and non local traffics. For each locality rate we obtain the best division ratio for the bitwidth of channels. We define locality based on the number of hops between source and destination nodes. For each traffic locality is defined to include 50 percent of all communication.
The increasing demand of cloud computing motivates the researchers to make cloud environment more efficient for its users and more profitable for the providers . Though virtualization technology helps to increase the resource utilization still the operational cost of cloud gradually increases mainly due to the consumption of large amount of electrical energy . So to reduce the energy consumption virtual machines are dynamically consolidated to lesser number of physical machines by live VM migration technique . But this may cause SLA violation and the provider is penalized . So to maintain an energy performance trade off the number of VM migration should be minimized . VM migration primarily takes place in two cases for hotspot mitigation and to switch off the underutilized nodes by migrating all its VMs . If a host is found to be overloaded then instead of immediately migrating some of its VMs we can check whether the migration is really required or not . For this we have proposed a load prediction algorithm to decide whether the migration will be performed or not . After the decision has been taken the algorithm finds a suitable destination host where the VM will be shifted . For this we have proposed a novel approach to decide whether a particular host is suitable as destination depending on its probable future load . We have simulated our algorithms in CloudSim using real world workload traces and compared them with the existing benchmark algorithms . Results show that the proposed methods significantly reduce the number of VM migration and subsequent energy consumption while maintaining the SLA . 


Summary: Time series based forecasting methods are used to predict future load of a system. If the current and the next predicted load of a server exceed the dynamic upper threshold then migration will take place. The forecasting methods are also used to predict multiple n future load of the system. Our algorithms are able to find more suitable destination host for VM placement. They are capable of saving energy by reducing number of over utilized hosts and virtual machine migration. Maximum QoS requirements are fulfilled due to less violation of SLA.
Industrial image processing tasks especially in the domain of optical metrology are becoming more and more complex . While in recent years standard PC components were sufficient to fulfill the requirements special architectures have to be used to build high speed image processing systems today . For example for adaptive optical systems in large scale telescopes the latency between capturing an image and steering the mirrors is critical for the quality of the resulting images . Commonly the applied image processing algorithms consist of several tasks with different granularities and complexities . Therefore we combined the advantages of multicore CPUs GPUs and FPGAs to build a heterogeneous image processing pipeline for adaptive optical systems by presenting new architectures and algorithms . Each architecture is well suited to solve a particular task efficiently which is proven by a detailed evaluation . With the developed pipeline it is possible to achieve a high throughput and to reduce the latency of the whole steering system significantly . 


Summary: Image processing applications can benefit from heterogeneous computing architectures. Using FPGAs GPUs and CPUs together enables a fast image processing pipeline. FPGA architectures within smart cameras can increase throughput and decrease latency. The image processing pipeline was demonstrated at the example of optical metrology.