Improving cache locality for GPU-based volume rendering

We present a cache-aware method for accelerating texture-based volume rendering on a graphics processing unit (GPU). Because a GPU has hierarchical architecture in terms of processing and memory units, cache optimization is important to maximize performance for memory-intensive applications. Our method localizes texture memory reference according to the location of the viewpoint and dynamically selects the width and height of thread blocks (TBs) so that each warp, which is a series of 32 threads processed simultaneously, can minimize memory access strides. We also incorporate transposed indexing of threads to perform TB-level cache optimization for specific viewpoints. Furthermore, we maximize TB size to exploit spatial locality with fewer resident TBs. For viewpoints with relatively large strides, we synchronize threads of the same TB at regular intervals to realize synchronous ray propagation. Experimental results indicate that our cache-aware method doubles the worst rendering performance compared to those provided by the CUDA and OpenCL software development kits.     

Schemes for remotely preparing an arbitrary four-qubit \chi -state

Two schemes via different entangled resources as the quantum channel are proposed to realize remote preparation of an arbitrary four-particle \(\chi \) -state with high success probabilities. To design these protocols, some useful and general measurement bases are constructed, which have no restrictions on the coefficients of the prepared states. It is shown that through a four-particle projective measurement and two-step three-particle projective measurement under the novel sets of mutually orthogonal basis vectors, the original state can be prepared with the probability 50 and 100 %, respectively. And for the first scheme, the special cases of the prepared state that the success probability reaches up to 100 % are discussed by the permutation group. Furthermore, the present schemes are extended to the non-maximally entangled quantum channel, and the classical communication costs are calculated.     

Dynamic surface control of a piezoelectric fuel injector during rate shaping

Fuel injection rate shaping is a strategy to improve fuel efficiency and reduce harmful emissions in diesel engines. Due to their fast response, piezoelectric fuel injectors are capable of rate shaping operation. This paper describes a model-based closed-loop controller of injection flow rate for a piezoelectric fuel injector. This within-an-engine-cycle control strategy utilizes a dynamic surface control scheme and shows an injection flow rate tracking capability. Practical issues with LabVIEW FPGA control implementation are also addressed. The performance of the controller is verified with simulation and experimental results at different rail pressures and desired injection rates. The experiments show a maximum error of total fuel per one injection event of 2.5%. A stability analysis is also included.     

Asymptotical mean square stability of cellular neural networks with random delay

In this paper,the asymptotical mean-square stability analysis problem is considered for a class of cellular neural networks (CNNs) with random delay. Compared with the previous work,the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed cellular neural network is asymptotic mean-square stability. By using some stochastic analysis techniques and Lyapunov-Krasovskii functional,some conditions are derived to ensure that the cellular neural networks with random delay is asymptotical mean-square stability. A numerical example is exploited to show the vadlidness of the established results.