Large-Scale Convex Optimization for Ultra-Dense Cloud-RAN

The heterogeneous cloud radio access network (Cloud-RAN) provides a revolutionary way to densify radio access networks. It enables centralized coordination and signal processing for efficient interference management and flexible network adaptation. Thus, it can resolve the main challenges for next-generation wireless networks, including higher energy efficiency and spectral efficiency, higher cost efficiency, scalable connectivity, and low latency. In this article, we shall provide an algorithmic thinking on the new design challenges for the dense heterogeneous Cloud-RAN based on convex optimization. As problem sizes scale up with the network size, we will demonstrate that it is critical to take unique structures of design problems and inherent characteristics of wireless channels into consideration, while convex optimization will serve as a powerful tool for such purposes. Network power minimization and channel state information acquisition will be used as two typical examples to demonstrate the effectiveness of convex optimization methods. We will then present a two-stage framework to solve general large-scale convex optimization problems, which is amenable to parallel implementation in the cloud data center.Comment: to appear in IEEE Wireless Commun. Mag., June 201

Fog computing and convolutional neural network enabled prognosis for machining process optimization

Cloud enabled prognosis systems have been increasingly adopted by manufacturing industries. The effectiveness of the cloud systems is, however, crippled by the high latency of data transfer between shop floors and the cloud.To overcome the limitation, this paper presents an innovative fog enabled prognosis system for machining process optimization. The system functions include: (1) dynamic prognosis - Convolutional Neural Network (CNN) based prognosis is implemented to detect potential faults from customized machining processes. Preprocessing mechanisms of the CNN are designed for partitioning and de-noising monitored signals to strengthen the performance of the system in practical manufacturing situations; (2) an innovative fog enabled prognosisarchitecture for machining process optimization – it consists of a terminal layer, a fog layer and a cloud layer to minimize data traffic and improve system efficiency. Under the architecture, monitored signals during machining collected on the terminal layer are processed using the trained CNN deployed on the fog layer to efficiently detect abnormal situations. Intensive computing activities like training of the CNN and system re-optimization responding to detected faults are carried out dynamically on the cloud layer to leverage its computation powers. The system was validated in a UK machining company. With the system deployment, the efficiency of energy and production was improved for 29.25% and 16.50% on average. In comparison with a cloud system, this fog system achieved 70.26% reduction in the bandwidth requirement between shop floors and cloud, and 47.02% reduction in data transfer time. This research, sponsored by EU projects, demonstrates that industrialartificial intelligence can facilitate smart manufacturing practices effectively

Optimization Model for Planning Precision Grasps with Multi-Fingered Hands

Precision grasps with multi-fingered hands are important for precise placement and in-hand manipulation tasks. Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, high-dimensionality, collision and undesired properties of the sensing and positioning. This paper proposes an optimization model to search for precision grasps with multi-fingered hands. The model takes noisy point cloud of the object as input and optimizes the grasp quality by iteratively searching for the palm pose and finger joints positions. The collision between the hand and the object is approximated and penalized by a series of least-squares. The collision approximation is able to handle the point cloud representation of the objects with complex shapes. The proposed optimization model is able to locate collision-free optimal precision grasps efficiently. The average computation time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise of the point cloud. The effectiveness of the algorithm is demonstrated by experiments.Comment: Submitted to IROS2019, experiment on BarrettHand, 8 page

Optimal Control of Applications for Hybrid Cloud Services

Development of cloud computing enables to move Big Data in the hybrid cloud services. This requires research of all processing systems and data structures for provide QoS. Due to the fact that there are many bottlenecks requires monitoring and control system when performing a query. The models and optimization criteria for the design of systems in a hybrid cloud infrastructures are created. In this article suggested approaches and the results of this build.Comment: 4 pages, Proc. conf. (not published). arXiv admin note: text overlap with arXiv:1402.146

Theoretical study of a cold atom beam splitter

A theoretical model is presented for the study of the dynamics of a cold atomic cloud falling in the gravity field in the presence of two crossing dipole guides. The cloud is split between the two branches of this laser guide, and we compare experimental measurements of the splitting efficiency with semiclassical simulations. We then explore the possibilities of optimization of this beam splitter. Our numerical study also gives access to detailed information, such as the atom temperature after the splitting