Image super resolution model enabled by wavelet lifting with optimized deep convolutional neural network

This paper plans to develop an intelligent super resolution model with the linkage of Wavelet lifting scheme and Deep learning algorithm. Before initiating the resolution procedure, the entire HR images are converted into Low Resolution (LR) images using bicubic interpolation-based downsampling and upsampling. Further, the Wavelet lifting scheme helps to generate the four subbands of each image like LR wavelet Sub-Bands for LR images, and High Resolution (HR) wavelet Sub-Bands for HR images. The residual image is generated by taking the difference between the LR wavelet Sub-Bands and HR wavelet Sub-Bands images. The proposed model involves two main phases: Training phase and Testing. The training phase trains the residual image of all images by Deep Convolutional Neural Network with LR wavelet Sub-Bands as input and residual image as target. On the other hand, in testing phase, the LR wavelet Sub-Bands query image is subjected to Deep Convolutional Neural Network, which outputs the concerned residual image. This generated residual image is summed with LR wavelet Sub-Bands image, followed by inverse wavelet lifting scheme to obtain the final super resolution image. The main contribution of this paper is to improve the conventional Deep Convolutional Neural Network by optimizing the number of hidden layer, and hidden neurons using modified Whale Optimization Algorithm called Average Fitness Enabled Whale Optimization Algorithm by considering the objective of maximizing the Peak Signal-to-Noise Ratio. Finally, the proposed method achieves an improved quality of the results which is comparable the existing models.     

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.     

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material, and multi-product purchasing and production planning process. The objective function is the expected total cost after two stages, and the results include detailed plans for purchasing and production in each demand scenario. Small-scale problems are solved through a deterministic equivalent transformation technique. To solve the problems in the large scale, an algorithm combining metaheuristic and sample average approximation is suggested. This algorithm can be implemented in parallel to utilize the power of the solver. The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given, then the problems of the first and second stages can be decomposed.     

基于HAGA的D2D-NOMA资源分配优化算法

针对无线系统带宽资源有限、基站负载压力大、传输时延长等问题,提出一种基于非正交多址接入技术的D2D系统吞吐量最大化资源分配算法。在不同用户的服务质量约束条件下,建立D2D系统吞吐量最大化资源分配模型。该模型的优化目标是一个混合整数非线性规划问题,将其解耦为信道匹配与功率分配2个子问题并分别进行处理,利用自适应惩罚函数法处理约束条件并提出一种基于爬山策略的自适应遗传算法以对问题进行求解。仿真结果表明,与GA、AGA算法相比,该算法能够有效提高D2D系统的吞吐量,且收敛性能更好。     

Design and Configuration Rationales for Digital Video Storage and Delivery Systems

Recent advances in computing technology have brought multimedia information processing to prominence. The ability to digitize, store, retrieve, process, and transport analog information in digital form has changed the dimensions of information handling. Several architectural and network configurations have been proposed for efficient and reliable digital video delivery systems. However, these proposals succeed only in addressing subsets of the whole problem. In this paper, we discuss the characteristics of video services. These include Cable Television, Pay-Per-View, and Video Repository Centers. We also discuss requirements for Video On Demand services. With respect to these video services, we analyze two important video properties: image quality and response time. We discuss and present configurations of a Digital Video Delivery System (DVDS) from three general system components - servers, clients, and connectivities. Pertinent issues in developing each component are also analyzed. We also present an architecture of a DVDS that can support the various functionalities that exist in the various video services. Lastly, we discuss data allocation strategies which impact performance of interactive video on demand (IVOD). We present preliminary results from a study using a limited form of mirroring to support high performance IVOD.     

DES加密算法在秘密共享中的应用

秘密共享思想提出后,经过几十年的发展已经出现了很多不同方案、模型等。本文根据秘密共享的特性提出了一种基于对称密钥系统的秘密共享方案。该方案解决了一些秘密共享现有的问题的,尤其是可验证性。存储在密钥分发者的信息都是加密后的密文,因此,其抗攻击的强度就是使用的加密算法的强度。通过简单C＋＋程序实现这一方案。对方案的可行性进行分析。     

地方高校计算机应用人才培养的困难与对策研究

介绍在计算机科学与技术专业规范指导下,我校以理论素质培养与技术能力掌握为导向,对计算机科学与技术两个方向的教学培养计划、课程改革、教学组织设计和课程评价等方面进行的一些探索,希望能为其他地方院校提供参考。     

A spatial orthogonal allocation and heterogeneous cultural hybrid algorithm for multirobot exploration mission planning

A spatial orthogonal allocation method is devised for multirobot tasks allocation.A 3D space model is adopted to describe exploration mission;meanwhile spatial orthogonal tentative technology is utilized to update the attractor position for load balance.Heterogeneous interactive cultural hybrid architecture is proposed to solve a robot route planning problem;it utilizes good-point-set to initialize population spaces,redefine novel evolution model and particle evolution ability,and introduce near-neighbor lo...     

A spatial orthogonal allocation and heterogeneous cultural hybrid algorithm for multirobot

A spatial orthogonal allocation method is devised for multirobot tasks allocation.A 3D space model is adopted to describe exploration mission;meanwhile spatial orthogonal tentative technology is utilized to update the attractor position for load balance.Heterogeneous interactive cultural hybrid architecture is proposed to solve a robot route planning problem;it utilizes good-point-set to initialize population spaces,redefine novel evolution model and particle evolution ability,and introduce near-neighbor lo...     

Control allocation for a V/STOL aircraft based on robust fuzzy control

This study addresses the design of an output-feedback H-infinity and fuzzy mixed controller for an advanced vertical and/or short take-off and landing aircraft that exhibits satisfactory global stability criteria. A fuzzy logic based optimization approach is employed to solve autonomously the constrained control allocation problem by suitably adjusting the components of the output vector and finding a proper vector in the attainable moment set. Simulations show that the designed fuzzy controller is better s...