11911656
802.11 Link Interference: A Simple Model and A Performance Enhancement
Recent findings have revealed that the carrier-sensing range set in current major implementations does not detect all interference signals in 802.11 networks. In this paper, we investigate the effect of interference and develop a mathematical model for it. The accuracy of our model is verified via simulations. Based on an insight from our model, we present a simple protocol that operates on the top of 802.11 MAC layer and achieves more throughput than rate-adjustment schemes.
16045709
A General Model and Analysis of Physical Layer Capture in 802.11 Networks
While packet capture has been observed in real implementations of 802.11 devices, there is a lack of accurate models that describe the phenomenon. We present a general analytical model and an iterative method that predicts error probabilities and throughputs of packet transmissions with multiple senderreceiver pairs. Our model offers a more accurate prediction than previous work by taking into account the cumulative strength of interference signals and using the BER model to convert a signal to interference and noise ratio value to a bit error probability. This permits the analysis of packet reception at any transmission rate with interference from neighbors at any set of locations. We also prove that our iterative method converges, and we verify the accuracy of our model through simulations in Qualnet. Last, we present a rate assignment algorithm to reduce the average delay as an application of our analysis.
25776373
Iteration-free fractal image coding based on efficient domain pool design
The domain pool design is one of the dominant issues which affect the coding performance of fractal image compression. In this paper, we employ the LBG algorithm and propose a block averaging method to design the efficient domain pools based on a proposed iteration-free fractal image codec. The redundancies between the generated domain blocks are reduced by the proposed methods. Therefore, we can obtain the domain pools that are more efficient than those in the conventional fractal coding schemes and thus the coding performance is improved. On the other hand, the iteration process in the conventional fractal coding scheme not only requires a large size of memory and a high computation complexity but also prolongs the decoding process. The proposed iteration-free fractal codec can overcome the problems above. In computer simulation, both the LBG-based and block-averaging methods for the domain pool design in the proposed iteration free scheme achieve excellent performances. For example, based on the proposed block-averaging method, the decoded Lena image has at least a 0.5 dB higher PSNR (under the same bit rate) and an eight-time faster decoding speed than the conventional fractal coding schemes that require iterations.
15864534
Real-Time Disk Scheduling with On-Disk Cache Conscious
Previous real-time disk scheduling algorithms assume that each disk request incurs a disk mechanical operation and only consider how to move the disk head under real-time constraints. However, with the increased capacity of on-disk cache, modern disk drives read-ahead data aggressively. Thus, the on-disk cache may service lots of requests without incurring physical disk access. By exploring the design methodology of on-disk cache, in this paper, we propose CARDS: a cache-aware real-time disk scheduling algorithm that takes the on-disk cache into consideration during scheduling. Therefore, the scheduling algorithm can help to minimize the cache miss ratio. Besides, the service timing estimation is more accurate in schedulability analysis since the cache effect is considered. A simulation-based evaluation shows CARDS to be highly successful as compared to the classical real-time disk scheduling algorithms. For example, under sequential workload with 10 sequential streams, the data throughput of CARDS is 1.1 times of DM-SCAN.
4841642
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the conventional clustering problem called semi-supervised clustering, which performs clustering in the presence of some background knowledge or supervisory information expressed as pairwise similarity or dissimilarity constraints. However, existing metric learning methods for semi-supervised clustering mostly perform global metric learning through a linear transformation. In this paper, we propose a new metric learning method that performs nonlinear transformation globally but linear transformation locally. In particular, we formulate the learning problem as an optimization problem and present three methods for solving it. Through some toy data sets, we show empirically that our locally linear metric adaptation (LLMA) method can handle some difficult cases that cannot be handled satisfactorily by previous methods. We also demonstrate the effectiveness of our method on some UCI data sets. Besides applying LLMA to semi-supervised clustering, we have also used it to improve the performance of content-based image retrieval systems through metric learning. Experimental results based on two real-world image databases show that LLMA significantly outperforms other methods in boosting the image retrieval performance.
1730596
A Scalable Kernel-Based Algorithm for Semi-Supervised Metric Learning
In recent years, metric learning in the semisupervised setting has aroused a lot of research interests. One type of semi-supervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale up well with the data set size. In this paper, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.
2551829
Adaptive Schemes for Improving Fractal Block Coding of Images
In this paper, we propose several adaptive methods to improve the fractal block coding (FBC) of still images. First of all, the range of the contrast scaling is adaptively selected according to its statistical property: then, we search for the best quantized value in the selected range. Then, the domain pool of the current range block is predicted using a finite-state method, which uses the information of previous best-match domain blocks and domain pools. The size of the domain pool is automatically adjusted; however, conventional studies can only determine a fixed-size domain pool. Therefore, the search process for finding the best-match domain block in our approach is more flexible. In addition, we propose a finite-state method to estimate the mean (average pixel value) of the current range block by using the means of the previous blocks. From the simulation, we achieve 0.35 dB improvement in peak signal-to-noise ratio and 10.5% reduction in bit rate on average compared with the basic FBC scheme. Finally, the encoding and decoding time of different FBC schemes is also evaluated for completeness.