Image enhancement has played a significant role in analyzing and synthesizing images in the modern era. This study proposed a method to improve the visual information, discrete entropy, peak signal-to-noise ratio, and the visual quality of low-light and hazy image data. In this paper a technique for improving low-light and hazy images using illumination estimates, improved haze removal filter coefficients, and discrete wavelet transform fusion has been presented. The presented technique eliminates the low-light effect from the image to help improve the observation quality of low-light and hazy images. In order to eliminate the haze from the image, the optimal de-haze to the inverted image is applied. A better-quality image has been obtained by fusing both of the obtained images through discrete wavelet transform fusion. The simulation study showed a significant improvement in visible and quantitative aspects over previously proposed methods.

It is well known that a packet loss in 802.11 can happen either due to collision or an insufficiently strong signal. However, discerning the exact cause of a packet loss, once it occurs, is known to be quite difficult. In this paper we take a fresh look at this problem of wireless packet loss diagnosis for 802.11-based communication and propose a promising technique called COLLIE. COLLIE performs loss diagnosis by using newly designed metrics that examine error patterns within a physical-layersymbolin order to expose statistical differences between collision and weak signal based losses. We implement COLLIE through custom driver-level modifications in Linux and evaluate its performance experimentally. Our results demonstrate that it has an accuracy ranging between 60-95% while allowing a false positive rate of up to 2%. We also demonstrate the use of COLLIE in subsequent link adaptations in both static and mobile wireless usage scenarios through measurements on regular laptops and the Netgear SPH101 Voice-over-WiFi phone. In these experiments, COLLIE led to throughput improvements of 20- 60% and reduced retransmission related costs by 40% depending upon the channel conditions.

A wide range of transmit power control (TPC) algorithms have been proposed in recent literature to reduce interference and increase capacity in 802.11 wireless networks. However, few of them have made it to practice. In many cases this gap is attributed to lack of suitable hardware support in wireless cards to implement these algorithms. In particular, many research efforts have indicated that wireless card vendors need to support power control mechanisms in a fine-grained manner - both in the number of possible power levels and the time granularity at which the controls can be applied. In this paper we claim that even if fine-grained power control mechanisms were to be made available by wireless card vendors, algorithms would not be able to properly leverage such degrees of control in typical indoor environments. We prove this claim through rigorous empirical analysis and then build a tunable empirical model (Model-TPC) that can determine the granularity of power control that is actually useful. To illustrate the importance of our solution, we conclude by demonstrating the impact of choice of power control granularity on Internet applications where wireless clients interact with servers on the Internet. We observe that the number of feasible power was found to be between 2-4 for most indoor environments. We believe that the results from this study can serve as the right set of assumptions to build practically realizable TPC algorithms in the future.

A radio frequency identifier (RFID) system consists of inexpensive, uniquely-identifiable tags that are mounted on physical objects, and readers that track these tags (and hence these physical objects) through RF communication. In this paper we, therefore, address this load balancing problem for readers - given a set of tags that are within range of each reader, which of these tags should each reader be responsible for such that the cost for monitoring tags across the different readers is balanced, while guaranteeing that each tag is monitored by at least one reader. We show that a generalized variant of the load balancing problem is NP-hard and hence present a 2-approximation centralized algorithm. We next present an optimal centralized solution for a specialized variant. Subsequently, we present a localized distributed algorithm that is probabilistic in nature and closely matches the performance of the centralized algorithms. Our results demonstrate that our schemes achieve very good performance even in highly dynamic large-scale RFID systems.

Packet classification is the foundation of many Internet functions such as QoS and security. A long thread of research has proposed efficient software-based solutions to this problem. Such software solutions are attractive because they require cheap memory systems for implementation, thus bringing down the overall cost of the system. In contrast, hardware-based solutions use more expensive memory systems, e.g., TCAMs, but are often preferred by router vendors for their faster classification speeds. The goal of this paper is to find a "best-of-both-worlds" solution -- a solution that incurs the cost of a software-based system and has the speed of a hardware-based one. Our proposed solution, called smart rule cache achieves this goal by using minimal hardware -- a few additional registers -- to cache evolving rules which preserve classification semantics, and additional logic to match incoming packets to these rules. Using real traffic traces and real rule sets from a tier-1 ISP, we show such a setup is sufficient to achieve very high hit ratios for fast classification in hardware. Cache miss ratios are 2 ∼ 4 orders of magnitude lower than flow cache schemes. Given its low cost and good performance, we believe our solution may create significant impact on current industry practice.

Wireless 802.11 hotspots have grown in an uncoordinated fashion with highly variable deployment densities. Such uncoordinated deployments, coupled with the difficulty of implementing coordination protocols, has often led to conflicting configurations (e.g., in choice of transmission power and channel of operation) among the corresponding Access Points (APs). Overall, such conflicts cause both unpredictable network performance and unfairness among clients of neighboring hotspots. In this paper, we focus on the fairness problem for uncoordinated deployments. We study this problem from the channel assignment perspective. Our solution is based on the notion of channel-hopping, and meets all the important design considerations for control methods in uncoordinated deployments - distributed in nature, minimal to zero coordination among APs belonging to different hotspots, simple to implement, and interoperable with existing standards. In particular, we propose a specific algorithm called MAXchop, which works efficiently when using only non-overlapping wireless channels, but is particularly effective in exploiting partially-overlapped channels that have been proposed in recent literature. We also evaluate how our channel assignment approach complements previously proposed carrier sensing techniques in providing further performance improvements. Through extensive simulations on real hotspot topologies and evaluation of a full implementation of this technique, we demonstrate the efficacy of these techniques for not only fairness, but also the aggregate throughput, metrics.We believe that this is the first work that brings into focus the fairness properties of channel hopping techniques and we hope that the insights from this research will be applied to other domains where a fair division of a system's resources is an important consideration.