Incorporating insights from human visual perception into 3D object processing has become an important research field in computer graphics during the past decades. Many computational models for different applications have been proposed, such as mesh saliency, mesh roughness and mesh skeleton. In this letter, we present a novel Skeleton Modulated Topological Visual Perception Map (SMTPM) integrated with visual attention and visual masking mechanism. A new skeletonisation map is presented and used to modulate the weight of saliency and roughness. Inspired by salient viewpoint selection, a new Loop subdivision stencil decision based rapid viewpoint selection algorithm using our new visual perception is also proposed. Experimental results show that the SMTPM scheme can capture more richer visual perception information and our rapid viewpoint selection achieves high efficiency.

3D Mesh segmentation has become an important research field in computer graphics during the past few decades. Many geometry based and semantic oriented approaches for 3D mesh segmentation has been presented. However, only a few algorithms based on Markov Random Field (MRF) has been presented for 3D object segmentation. In this letter, we present a definition of mesh segmentation according to the labeling problem. Inspired by the capability of MRF combining the geometric information and the topology information of a 3D mesh, we propose a novel 3D mesh segmentation model based on MRF and Graph Cuts. Experimental results show that our MRF-based schema achieves an effective segmentation.

Multi-cell cooperation is a promising technique to mitigate inter-cell interference arising from universal frequency reuse in cellular networks. Sharing channel state information (CSI) in neighboring cells can help enhance the overall system capacity at the cost of high feedback burden. In this paper, an asymmetric CSI feedback strategy is proposed for multi-cell cooperation beamforming. In order to improve the overall system performance, we optimize the limited feedback bandwidth based on the average received power from both serving and neighboring cells. Simulation results show that the proposed strategy utilizes the limited feedback bandwidth more efficiently, thereby achieving a higher sum rate.

To learn the working situation of rod-pumped wells under ground, we always need to analyze dynamometer diagrams, which are generated by the load sensor and displacement sensor. Rod-pumped wells are usually located in the places with extreme weather, and these sensors are installed on some special oil equipments in the open air. As time goes by, sensors are prone to generating unstable and incorrect data. Unfortunately, load sensors are too expensive to frequently reinstall. Therefore, the resulting dynamometer diagrams sometimes cannot make an accurate diagnosis. Instead, as an absolutely necessary equipment of the rod-pumped well, the electric motor has much longer life and cannot be easily impacted by the weather. The electric power curve during a swabbing period can also reflect the working situation under ground, but is much harder to explain than the dynamometer diagram. This letter presented a novel deep learning architecture, which can transform the electric power curve into the dimensionless dynamometer diagram image. We conduct our experiments on a real-world dataset, and the results show that our method can get an impressive transformation accuracy.

Many state-of-the-art embedded systems adopt scratch-pad memory (SPM) as the main on-chip memory due to its advantages in terms of energy consumption and on-chip area. The cache is automatically managed by the hardware, while SPM is generally manipulated by the software. Traditional compiler-based SPM allocation methods commonly use static analysis and profiling knowledge to identify the frequently used data during runtime. The data transfer is determined at the compiling stage. However, these methods are fragile when the access pattern is unpredictable at compile time. Also, as embedded devices diversify, we expect a novel SPM management that can support embedded application portability over platforms. This paper proposes a novel runtime SPM management method based on the core working set (CWS) theory. A counting-based CWS identification algorithm is adopted to heuristically determine those data blocks in the program's working set with high reference frequency, and then these promising blocks are allocated to SPM. The novelty of this SPM management method lies in its dependence on the program's dynamic access pattern as the main cue to conduct SPM allocation at runtime, thus offloading SPM management from the compiler. Furthermore, the proposed method needs the assistance of MMU to complete address redirection after data transfers. We evaluate the new approach by comparing it with the cache system and a classical profiling-driven method, and the results indicate that the CWS-based SPM management method can achieve a considerable energy reduction compared with the two reference systems without notable degradation on performance.

Visual degradation is usually introduced during 3D mesh simplification. The main issue in mesh simplification is to maximize the simplification ratio while minimizing the visual degradation. Therefore, effective and objective evaluation of the visual degradation is essential in order to select the simplification ratio. Some objective geometric and subjective perceptual metrics have been proposed. However, few objective metrics have taken human visual characteristics into consideration. To evaluate the visual degradation introduced by mesh simplification for a 3D triangular object, we integrate the structural degradation with mesh saliency and propose a new objective and multi-scale evaluation metric named Global Perceptual Structural Degradation (GPSD). The proper selection of the simplification ratio under a given distance-to-viewpoint is also discussed in this paper. The accuracy and validity of the proposed metric have been demonstrated through subjective experiments. The experimental results confirm that the GPSD metric shows better 3D model-based multi-scale perceptual evaluation capability.

A stabilized local oscillator is one of the key components for any radar system, especially for a Doppler radar in detecting slowly moving targets. Based on hybrid semiconductor/superconductor circuitry, the HTS local oscillator produces stable, low noise performance superior to that achieved with conventional technology. The device combines a high Q HTS sapphire cavity resonator (f=5.6 GHz) with a C-band low noise GsAs HEMT amplifier. The phase noise of the oscillator, measured by a HP 3048A noise measurement system, is -134 dBc/Hz at 10 kHz offset at 77 K.