11395837
Information-Bearing Noncoherently Modulated Pilots for MIMO Training
In this correspondence, we deal with noncoherent communications over multiple-input-multiple-output (MIMO) wireless links. For a Rayleigh flat block-fading channel with M transmit- and N receive-antennas and a channel coherence interval of length T, it is well known that for TGtM, or, at high signal-to-noise-ratio (SNR) rhoGt1 and Mlesmin{N,lfloorT/2rfloor}, unitary space-time modulation (USTM) is capacity-achieving, but incurs exponential demodulation complexity in T. On the other hand, conventional training-based schemes that rely on known pilot symbols for channel estimation simplify the receiver design, but they induce certain SNR loss. To achieve desirable tradeoffs between performance and complexity, we propose a novel training approach where USTM symbols over a short length Ttau(<T) are used as pilots which carry information to the receiver, and coherent communication is used for the remainder of the block. This new approach considerably reduces the receiver complexity when Ttau is a small fraction of T, and recovers part of the SNR loss experienced by the conventional training-based schemes. When rhorarrinfin and TgesTtau ges2M=2Nrarrinfin, but the ratios alpha=M/T, alpha1 =Ttau/T are fixed, we obtain analytical expressions of the asymptotic SNR loss for both the conventional and new training-based approaches, serving as a guideline for practical designs
495865
Achieving Wireline Random Access Throughput in Wireless Networking Via User Cooperation
Well appreciated at the physical layer, user cooperation is introduced here as a diversity enabler for wireless random access (RA) at the medium access control sublayer. This is accomplished through a two-phase protocol in which active users start with a low power transmission attempting to reach nearby users and follow up with a high power transmission in cooperation with the users recruited in the first phase. We show that such a cooperative protocol yields a significant increase in throughput. Specifically, we prove that for networks with a large number of users, the throughput of a cooperative wireless RA network operating over Rayleigh-fading links approaches the throughput of an RA network operating over additive white Gaussian noise links-thus justifying the title of the paper. The message borne out of this result is that user cooperation offers a viable choice for migrating diversity benefits to the wireless RA regime, thus bridging the gap to wireline RA networks, without incurring a bandwidth or energy penalty
12790826
SICTA: a 0.693 contention tree algorithm using successive interference cancellation
Contention tree algorithms have provable stability properties, and are known to achieve stable throughput as high as 0.487 for the infinite population Poisson model. A common feature in all these random access protocols is that collided packets at the receive-node are always discarded. In this paper, we derive a novel tree algorithm (TA) that we naturally term SICTA because it relies on successive interference cancellation to resolve collided packets. Performance metrics including throughput and delay are analyzed to establish that SICTA outperforms existing contention tree algorithms reaching 0.693 in stable throughput.
8460642
Recovering photometric properties of architectural scenes from photographs
In this paper, we present a new approach to producing photorealistic computer renderings of real architectural scenes under novel lighting conditions, such as at different times of day, starting from a small set of photographs of the real scene. Traditional texture mapping approaches to image-based modeling and rendering are unable to do this because texture maps are the product of the interaction between lighting and surface reflectance and one cannot deal with novel lighting without dissecting their respective contributions. To obtain this decomposition into lighting and reflectance, our basic approach is to solve a series of optimization problems to find the parameters of appropriate lighting and reflectance models that best explain the measured values in the various photographs of the scene. The lighting models include the radiance distributions from the sun and the sky, as well as the landscape to consider the effect of secondary illumination from the environment. The reflectance models are for the surfaces of the architecture. Photographs are taken for the sun, the sky, the landscape, as well as the architecture at a few different times of day to collect enough data for recovering the various lighting and reflectance models. We can predict novel illumination conditions with the recovered lighting models and use these together with the recovered reflectance values to produce renderings of the scene. Our results show that our goal of generating photorealistic renderings of real architectural scenes under novel lighting conditions has been achieved. CR Categories: I.2.10 [Artificial Intelligence]: Vision and Scene Understanding—modeling and recovery of physical attributes I.3.7 [Computer Graphics]: Three-dimensional Graphics and Realism—color, shading, shadowing, and texture , visible line/surface algorithms I.4.8 [Image Processing]: Scene Analysis—color, photometry, shading
8259100
Adapt Bagging to Nearest Neighbor Classifiers
It is well-known that in order to build a strong ensemble, the component learners should be with high diversity as well as high accuracy. If perturbing the training set can cause significant changes in the component learners constructed, then Bagging can effectively improve accuracy. However, for stable learners such as nearest neighbor classifiers, perturbing the training set can hardly produce diverse component learners, therefore Bagging does not work well. This paper adapts Bagging to nearest neighbor classifiers through injecting randomness to distance metrics. In constructing the component learners, both the training set and the distance metric employed for identifying the neighbors are perturbed. A large scale empirical study reported in this paper shows that the proposed BagInRand algorithm can effectively improve the accuracy of nearest neighbor classifiers.
4001661
Configuring common personal software: a requirements-driven approach
We investigate the personalization capabilities of common personal software systems. We use a typical e-mail client as an example of such a system, and examine the configuration screens it offers to its users. We discover that each configuration value reflects each of the ways with which the user goals can be satisfied. Thus, we construct a goal model in which alternative ways for satisfying high level goals are matched with alternative system configurations. This way, automatic configuration of the system by reasoning about the overlaying goal model can be achieved. We find that the vast majority of the configuration options that refer to system functionality can be configured using this method, facilitating thereby the personalization tasks for users with no technical background, and ensuring, at the same time, consistency and meaningfulness in the configuration result.
10354510
Using familiar single-user editors for collaborative editing
A number of real-time group editors have been developed as vehicles for investigating various technical issues in computer-supported cooperative work (CSCW). Excellent as they are in serving academic purposes, those research prototypes are not (and will probably not be) widely used by ordinary users for "serious" group editing activities. The reason is primarily that they are generally not as powerful or usable as single-user editors that people are already familiar with. In this paper, we propose an approach which converts existing single-user editors to group editors without modifying their source code. As a result, different editors such as Word and GVim can be shared by a group of distributed users to edit the same document simultaneously. Users can use familiar single-user editors for collaborative editing which are allowed to be heterogeneous. Towards this end we have been working on a research project called intelligent collaboration transparency or ICT to address related technical issues. Our work compared to existing application-sharing systems especially in that it is able to interoperate heterogeneous single-user applications. We report our latest progress on this project.
1372639
Motion Field Estimation for Temporal Textures
This paper presents a novel approach for estimating the flow fields of dynamic temporal textures whose motion differs radically from that of rigid bod- ies. Our approach is based on a local flow probability distribution function at each pixel using the STAR model and the data from a spatio-temporal neighborhood. The peak of this density function can be regarded as the estimated local flow vec- tor. Our complete algorithm exploits a two-stage process. The first stage of the algorithm applies a simple tensor method to estimate the direction of the optical flow at each pixel in the texture. In the second stage, the flow probability function is used to perform a one-dimensional search along the flow direction to obtain the magnitude. Performance analysis and experiments with real video sequences show that our methods can successfully estimate flow fields.