Improving Efficiency of Optical Camera Communication using Dynamic Rate Adaptation

A novel dynamic joint rate and power adaptation framework is proposed for downlink data transmission in a multicell variable spreading factor (VSF) WCDMA system where the different classes of users have different signal-to-interference ratio (SIR) requirements. Based on a general downlink SIR model, the problem of optimal dynamic rate and power adaptation under multiple SIR constraints is also formulated, for which the rate and power allocation can be found by an exhaustive search. Performance of the dynamic joint rate and power adaptation under the proposed framework is evaluated under random micro-mobility model with uncorrelated long-term fading and a directional micro-mobility model with correlated long-term fading in a cellular WCDMA environment for both homogeneous (or uniform) and the non-homogeneous (or non-uniform) traffic load scenarios.

Recent studies showed that, in practical situations, the primary user (PU) access to the channel is better modeled as a time dependent random process. Taking this into account, we address the optimal rate adaptation problem of a cognitive radio (CR) link. A secondary user (SU) link detects an idle channel and starts the transmission with the goal of transmitting a given amount of data packets within a given time. During the transmission, and taking into account frame retransmission, the transmitter dynamically adapts the frames rate, from a finite number of available rates. If the PU accesses the channel, the SU immediately stops the transmission. The problem is formulated as an episodic Markov decision process (MDP). We show that selecting the best stationary policy (using the same rate for the whole transmission) can perform close to optimal.

Based on state projection method with two-step operations, this paper deals with the model reduction problem by analyzing the information descriptions of system states. Our basic idea in obtaining the reduced-order models is to minimize the information loss or the conditional information loss caused by truncation by eliminating the state variables with the least contribution to system information. Before truncation, an entropy preserving transformation of the original state is required. The derived minimum information loss (MIL) and minimum conditional information loss (MCIL) methods are proved to be efficient for approximating stable and unstable systems, respectively, and connected with the balanced truncation methods firmly. Illustrative examples are given.

To meet the dramatically increasing traffic demand, licensed-assisted access (LAA) has been proposed for long-term evolution (LTE) systems to operate on the unlicensed spectrum. However, it is a huge challenge to achieve fair and efficient coexistence between LAA and Wi-Fi on the same band. In this paper, we devise a novel channel access approach for LAA eNBs to improve system throughput and achieve fair coexistence with Wi-Fi nodes. Specifically, Carrier Sense Multiple Access with Enhanced Collision Avoidance (CSMA/ECA), which is able to reach and maintain collision-free operation by deterministic backoff (DB) after successful transmissions, is introduced to the listen-before-talk (LBT) procedure of LAA eNBs to improve the coexistence system throughput. However, note that the fixed DB value in traditional CSMA/ECA cannot ensure fair coexistence all the time corresponding to different network sizes. Therefore, an adjustable DB value scheme is devised to replace the fixed one, in order to achieve fair coexistence whatever the number of Wi-Fi nodes and LAA eNBs is. Aiming to obtain the maximum system throughput improvement, the behavior of a LAA eNB is modeled as a Markov chain, and the throughput of the coexistence network is derived. Then, we formulate a throughput optimization problem and develop an algorithm of DB value adjustment, which can not only achieve the maximum throughput improvement but also maintain the fair coexistence whatever the number of nodes in the coexistence network is. At last, numerical results are presented which validate the accuracy of our analysis model and demonstrate the effectiveness of the proposed algorithm.

With the growing technology and amount of data, the use of cloud and centralized servers is increasing. When the data of the individuals is stored by the third party it leads to the insecurity of misuse of their information in the individuals. Data Mining is carried on this data stored over cloud to get useful information, analysis and decision making purposes. But along with the process of mining, privacy preservation is the key concern. While performing data mining over large set of data there are chances of privacy violation, loss of information, loss of privacy of individuals. While concentrating only on preserving privacy can increase inefficiency and complexity of data mining techniques. Techniques used for achieving privacy lead to a significant information loss. The methods concerned about information loss generally lack behind in achieving a good level of privacy. Thus it is important to find the efficient data mining technique which can preserve privacy without causing high loss of information. This paper analyses various privacy preserving data mining techniques for their pros and cons and then gives a proposed concept to achieve privacy with minimum information loss.

Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details.

Throughput and lifetime are usually conflicting objectives in designing wireless sensor networks; hence, the right balance needs to be found. With this aim in view, we address in this paper the problem of minimizing the frame length defined within a time division multiple access scheme and the problem of maximizing network lifetime subject to a maximum frame length. The pursued solution in either case leverages a wide range of parameters related to coverage, routing, transmission power, and data rate. Furthermore, it is consistent with the physical interference model. To this end, we rely on column generation technique to derive near‐optimal solutions even when the integrality constraints on coverage and flow variables are enforced. Moreover, we propose a polynomial‐time heuristic algorithm to solve efficiently the underlying NP‐hard problem of concurrent link selection with discrete power control and rate adaptation. Simulation results show that our heuristic algorithm leads to solutions within 3% of optimality while saving around 99% of computation time. Besides, the results illustrate the significant impact of power control and rate adaptation on throughput and lifetime improvement. Interestingly, we found that network lifetime can be significantly prolonged when traffic demands are sufficiently low at the affordable cost of small decrease in throughput. Copyright © 2015 John Wiley & Sons, Ltd.

Optical camera communication (OCC) shows promise for optical wireless communication (OWC) in vehicular networks. However, vehicle mobility-induced angular distortions hinder system throughput by degrading non-isotropic vehicular OCC channel gain. Few of the prior works have ever made a comprehensive analysis of their impact, especially based on the pixel value which reflects the camera imaging features. To address this knowledge gap, a pixel value-described vehicular OCC system model accounting for transmitter imaging location and intensity from the geometry and radiometry aspects is presented in this paper with common types of the offset and rotation angles included. We integrate a MATLAB-based simulated vehicular OCC system with an experimentally designed testbed for validation and performance analysis. For a single-time snapshot, we investigate the impacts of common angular distortion types in vehicular OCC systems on maximum pixel value, imaging location, and communication-related metrics. Furthermore, we statistically analyze their influences by considering two driving scenarios with respective angular distributions. The angular distortion characterization from this work is expected to lay a stepping stone to addressing mobility in vehicular OCC systems.

A criterion is presented to indicate a strategy for additively combining corresponding discrete rows and columns of a square array to form an array of reduced size such that the loss of information thereby incurred is minimized. The objective function is expressed in terms of an information loss criterion between the original array and an array of the same size acting as a surrogate for the reduced array. In contrast to the usual application of information theory in planning, where arrays are filled out based on incomplete information, the converse case is implied here, in which a particular partial representation of the array is sought (that is, the aggregated array) which most closely approaches the information content of the original full array. The method is expected to be useful for situations where available large arrays of data need to be appropriately reduced in size so that they can be efficiently used in further computations. Alternatively, the original array may contain current observed data, and the primary aim may be to determine from this array the aggregation scheme to use in future forecasting calculations. Typical applications may include the aggregation of spatial zones, the aggregation of economic sectors in input-output analysis, as well as various clustering problems.

Even in a scenario where Big Data has increasingly become a part of company structure, it is not uncommon to come across statistical or mathematical analysis processes lacking sufficient available observations to consider all the business variables of interest. As a consequence, it affects, in different levels, all analytic methods whether statistical or mathematical. Specifically in the case of the benchmarking model known as DEA (Data Envelopment Analysis), the convergence of involved algorithms is directly dependent on the relation between the quantity of observations and the quantity of variable considered. Any attempt to circumvent the conditions imposed by these methodologies will result in loss of information. The scope of this study is to propose an alternative method to deal with this problem when the DEA model is applied and minimize information loss.

The prevalence of missing values in the data streams collected in real environments makes them impossible to ignore in the privacy preservation of data streams. However, the development of most privacy preservation methods does not consider missing values. A few researches allow them to participate in data anonymization but introduce extra considerable information loss. To balance the utility and privacy preservation of incomplete data streams, we present a utility-enhanced approach for Incomplete Data strEam Anonymization (IDEA). In this approach, a slide-window-based processing framework is introduced to anonymize data streams continuously, in which each tuple can be output with clustering or anonymized clusters. We consider the dimensions of attribute and tuple as the similarity measurement, which enables the clustering between incomplete records and complete records and generates the cluster with minimal information loss. To avoid the missing value pollution, we propose a generalization method that is based on maybe match for generalizing incomplete data. The experiments conducted on real datasets show that the proposed approach can efficiently anonymize incomplete data streams while effectively preserving utility.

Modern navigation systems and logistics services rely heavily on digital road networks, which can be accurately constructed from crowd-sourced GPS trajectories. Yet, most existing trajectory-based road network generators project GPS coordinates to 2D grid, followed by semantic segmentation to obtain road heatmap, and further processed to road network graph via heuristic processing. The data through this pipeline involve multiple cross-modal heuristic conversions, which cause serious information loss and harm overall robustness. In this paper, we propose a novel end-to-end learnable deep learning approach, named GraphWalker, to generate accurate road network graphs directly from raw trajectories with minimal information loss. In particular, GraphWalker is a latent diffusion model (LDM) consisting of two key components: the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trajectory-to-Walks Diffusion Transformer</b> (T2W-DiT) generates <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">walks</i> with trajectories as conditions, and the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Walks-to-Graph Variational Auto-Encoder</b> (W2G-VAE) reconstructs road network graphs from walks. GraphWalker keeps the data in geographical space throughout the pipeline to retain all information within raw trajectories. Additionally, the end-to-end learnable design removes the heuristic processing and enhances the robustness and generalizability. Extensive experiments on multiple datasets demonstrate that the proposed GraphWalker can effectively generate high-quality road networks from noisy and sparse trajectories, showcasing significant improvements of up to 55.43% over state-of-the-art.

SummaryAnonymization is a practical solution for preserving user's identity privacy before data publishing. There are various anonymity techniques can be applied to maintain data utility of micro‐data and social networks, however these methods lead to a high runtime or low anonymous graph utility. In this article, an efficient, utility‐preserving approach has been proposed to reduce anonymization runtime as well as the amount of information loss incurred by graph anonymization. We craft our anonymization algorithm by combining greedy partition‐based aggregating with multi‐dimensional sorting as main heuristic tools. The proposed algorithm generates a partial order of the vertices so that the vertex at top rank and another vertex at bottom rank can never be aggregated in the same group, the runtime is reduced. Greedy partition‐based aggregating is employed to create ‐anonymous clusters which minimizing information loss. Experimental results on real‐world datasets show the proposed method has good performance and is superior to the existing methods.

A reconstruction decoder for computing with words

The new members of mathematical physics of heat conduction in stationary state with their subharmonic solution originate from a real variational procedure named as “recency principle”. As this variational principle is not a so‐called “quasivariational” one therefore it is suitable to show irreversibility in stationary state process. The physical meaning is here the minimum production of entropy (the minimum information loss) as the internal irreversible process source. We show for the dissipation of information explanations from the set theory. Neighborhood, closure, boundary, cluster point, and accumulation point etc. are necessary to form well‐posed boundary conditions. Irreversible non‐equilibrium process causes energy degradation which can be interpreted both geometrically and mathematically as compared with the Laplace potential equation for conservative field. The energy degradation comes from information loss equivalence. For subharmonic solution we use Perron theorem.