Real-time robotic control relies on high-speed tactile arrays, but increasing the number of sensing pixels to cover large areas often leads to greater scanning delays, with readout speeds for large arrays rarely exceeding 100 Hz. To overcome this restriction, we developed compressive tactile subsampling methods that take advantage of spatial patterns in tactile data. By sampling fewer pixels in each frame and reconstructing the tactile signal using a learned tactile dictionary, these methods enable quicker readout. Using a$32\times 32$tactile sensor array, we evaluated classification accuracy and reconstruction error for tactile interactions with 30 daily and 3D printed objects using a robotic arm. Compared to traditional raster scanning, our method produced 18 times faster frame rates while maintaining minimal reconstruction and classification error. By implementing this scalable technique into software, low-cost tactile arrays may be transformed and robots can attain high-resolution, high-speed touch sensing across their bodies. More details in our preprint [1].

In this paper, we develop Targeted Dot Product Representation (TarDPR), a DPR-based feature selection and combination framework for friend recommendation in online social networks (OSNs). Our approach modifies conventional DPR techniques and makes itself applicable to OSNs by focusing on computing a consistent representation while minimizing unnecessary suggestions made outside these interested regions. A notable property of TarDPR is its ability to effectively incorporate different types of social features and produce new meaningful features that help competitive approaches to significantly improve their recommendation quality. We derive an iterative algorithm for TarDPR that is supported by mathematical analysis, and is efficient on large social traces. To certify the usability of our approach, we conduct empirical experiments on real social traces including Facebook and Foursquare social networks. The competitive experimental results show that TarDPR achieves up to 15% improvement in comparison with other competitive methods. These results consequently confirm the efficacy of our suggested framework.

This paper presents an adaptive block-based image coding method, which combines the advantages of variable block size transform and adaptive pre-/post-filtering scheme. Our approach partitions an image into blocks with different sizes, which are best suitable for the characteristics of the underlying data in the rate-distortion (RD) sense. The adaptive block decomposition mitigates the ringing artifacts by adopting a small block size transform in nonstationary regions, and improves the coding efficiency by using a large block size transform in homogenous regions. Moreover, pre-/post-filtering is adaptively applied along the block boundaries to improve coding efficiency and minimize blocking artifacts. Simulation results show that the proposed coder can achieve competitive objective performance as well as yield superior reconstruction visual quality, compared with the RD-optimized JPEG2000 and H.264/AVC I-frame coder.

Summary form only given. Seismic data compression is desirable in geophysics for both storage and transmission stages. Wavelet coding methods have generated interesting developments, including a real-time field test trial in the North Sea in 1995. Previous work showed that GenLOT with basic optimization also outperforms state-of-the-art biorthogonal wavelet coders for seismic data. In this paper, we focus on the problem of filter bank optimization using various properties of seismic data. It is often desirable to evaluate the compression performance of a transform on a set of data using a priori objective measures, to reduce extensive testings by selecting only good a priori transforms, and to tailor transforms to the statistical properties of the data set. In the scope of this work, we use symmetric AR models up to order 4 to obtain an average model of the horizontal and vertical signals of a seismic stack section. Rosten et al. (1999), have already shown that order 1 or 2 models give good results in filter bank optimization for non-unitary filter banks, using coding gain optimization. Several other criteria may be used for transform optimization. Following the theory in Tran and Nguyen (1999), we use a weighted combination of C/sub o/=k/sub C/C/sub C/+k/sub S/C/sub S/+k/sub d/C/sub D/ of coding gain, stopband attenuation and DC leakage minimization functions.