In this paper, we propose a novel approach of optimal power allocation and signal phase selection for NOMA wireless relay networks. Specifically, to optimize the network throughput, the base station and the stronger relay optimally allocate their transmission power, while the weaker relay selects an optimal signal phase. To maximize the end-to-end sum rate, we formulate a non-convex optimization problem and derive novel analytical results. In addition, we propose the four-candidate greedy algorithm for optimal power allocation and signal phase selection in NOMA wireless relay networks. Simulation results show that the proposed greedy algorithm could significantly improve the system performance.

Industrial arm-type robots have multiple degrees-of-freedom (DoFs) and high dexterity but the use of the roll-pitch-roll wrist configuration yields singularities inside the reachable workspace. Excessive joint velocities will occur when encountering these singularities. Arm-type robots currently don't have enough dexterity to move the end-effector path away from the wrist singularities. Robots with redundant DoFs can be used to provide additional dexterity to avoid the singularities and reduce the excessive joint velocity. An end-effector wrist module is proposed to provide two redundant DoFs when interfaced with an existing 6-DoF robot. The new 8-DoF robot has a compact roll-pitch-yaw wrist that has no singularities inside the reachable workspace. The highly redundant robot can also be used to avoid collisions in various directions. Path tracking simulation examples are provided to show the advantages of the proposed design when compared with existing redundant or nonredundant robots. We expect that this module can serve as a cost-effective solution in applications where singularity-free motion or collision-free motion is required.

In this paper, we present a new hybrid representation of slicing trees and multi-packing trees, called multi-domain-packing trees (MDP-trees), for macro placement to handle ultra large-scale multi-domain mixed-size designs. A multi-domain design typically consists of a set of mixed-size domains, each with hundreds/thousands of large macros and (tens of) millions of standard cells, which is often seen in modern high-end applications (e.g., 4G LTE products and upcoming 5G ones). To the best of our knowledge, there is still no published work specifically tackling the domain planning and macro placement simultaneously. Based on binary trees, the MDP-tree is very efficient and effective for handling macro placement with multiple domains. Previous works on macro placement can handle only single-domain designs, which do not consider the global interactions among domains. In contrast, our MDP-trees plan domain regions globally, and optimize the interconnections among domains and macro/cell positions simultaneously. The placement area of each domain is well reserved, and the macro displacement is minimized from initial macro positions of the design prototype. Experimental results show that our approach can significantly reduce both the average half-perimeter wirelength and the average global routing wirelength.

As interconnects dominate circuit performance in modern chip designs, placement becomes an essential stage in optimizing timing. Recent timing-driven placement (TDP) techniques focus mainly on optimizing late slack rather than early slack. This paper presents a TDP algorithm to improve the early slack while preserving an optimized late slack. The preservation is achieved by accurately predicting optimal Steiner tree topologies after each move in our TDP algorithm. An optimality-preserving pruning scheme for each move is proposed to speed up the optimization process, without sacrificing the solution quality. Experimental results show that our algorithm can substantially improve the early slacks and the overall quality scores of the top-2 winning placers of the 2015 ICCAD Incremental Timing-Driven Placement Contest, while preserving their late slacks.

In this paper, a fast CU depth decision algorithm based on support vector machine (SVM) is proposed to reduce the computational complexity of HEVC intra coding. It is systematic to develop the criterion of early CU splitting and termination by applying SVM. Appropriate features for training SVM models are extracted from spatial domain and pixel domain. Artificial neural network is used to analyze the impact of each extracted feature on CU size decision, and different weights are assigned to the output of SVMs. The experimental results show that the proposed fast algorithm provides 58.9% encoding time saving at most, and 46.5% time saving on average compared with HM 12.1.

In this paper, we develop an economic model to study the impact of commercial software and open source codes on the solutions offered by IT consulting firms. Robust support and trust are the most important assets of the IT solution built on commercial software, whereas cost savings is the major reason for adopting the IT solution developed by open source codes. The study aims to characterize the difference between the IT solutions by the demand for the number of licenses and the distribution of IT knowledge level. We find that the distribution of IT knowledge level on the pricing strategy of the IT consulting firm offering the solution built on open source codes is not monotonic. Moreover, from the viewpoint of efficiency and profit maximization, we find that IT consulting firm should help enterprise customers enhance their IT knowledge as it can offer the solutions built on different software packages.