Mobility profiler: A framework for discovering mobility profiles of cell phone users

Mobility path information of cell phone users play a crucial role in a wide range of cell phone applications, including context based search and advertising, early warning systems, city-wide sensing applications such as air pollution exposure estimation and traffic planning. However, there is a disconnect between the low level location data logs available from the cell phones and the high level mobility path information required to support these cell phone applications. In this paper, we present formal definitions to capture the cell phone users’ mobility patterns and profiles, and provide a complete framework, Mobility Profiler, for discovering mobile cell phone user profiles starting from cell based location data. We use real-world cell phone log data (of over 350 K h of coverage) to demonstrate our framework and perform experiments for discovering frequent mobility patterns and profiles. Our analysis of mobility profiles of cell phone users expose a significant long tail in a user’s location-time distribution: A total of 15% of a cell phone user’s time is spent on average in locations that each appears with less than 1% of total time.     

Improved assignment with ant colony optimization for multi-target tracking

Detecting and tracking ground targets is crucial in military intelligence in battlefield surveillance. Once targets have been detected, the system used can proceed to track them where tracking can be done using Ground Moving Target Indicator (GMTI) type indicators that can observe objects moving in the area of interest. However, when targets move close to each other in formation as a convoy, then the problem of assigning measurements to targets has to be addressed first, as it is an important step in target tracking. With the increasing computational power, it became possible to use more complex association logic in tracking algorithms. Although its optimal solution can be proved to be an NP hard problem, the multidimensional assignment enjoyed a renewed interest mostly due to Lagrangian relaxation approaches to its solution. Recently, it has been reported that randomized heuristic approaches surpassed the performance of Lagrangian relaxation algorithm especially in dense problems. In this paper, impelled from the success of randomized heuristic methods, we investigate a different stochastic approach, namely, the biologically inspired ant colony optimization to solve the NP hard multidimensional assignment problem for tracking multiple ground targets.     

Routing table updating by using intelligent agents

In a network, one of the important problems is making an efficient routing decision. Many studies have been carried out on making a decision and several routing algorithms have been developed. In a network environment, every node has a routing table and these routing tables are used for making routing decisions. Nowadays, intelligent agents are used to make routing decisions. Intelligent agents have been inspired by social insects such as ants. One of the intelligent agent types is self a cloning ant. In this study, a self cloning ant colony approach is used. Self cloning ants are a new synthetic ant type. This ant assesses the situation and multiplies through cloning or destroying itself. It is done by making a routing decision and finding the optimal path. This study explains routing table updating by using the self cloning ant colony approach. In a real net, this approach has been used and routing tables have been created and updated for every node.     

How to trust a few among many

The presence of numerous and disparate information sources available to support decision-making calls for efficient methods of harnessing their potential. Information sources may be unreliable, and misleading reports can affect decisions. Existing trust and reputation mechanisms typically rely on reports from as many sources as possible to mitigate the influence of misleading reports on decisions. In the real world, however, it is often the case that querying information sources can be costly in terms of energy, bandwidth, delay overheads, and other constraints. We present a model of source selection and fusion in resource-constrained environments, where there is uncertainty regarding the trustworthiness of sources. We exploit diversity among sources to stratify them into homogeneous subgroups to both minimise redundant sampling and mitigate the effect of certain biases. Through controlled experiments, we demonstrate that a diversity-based approach is robust to biases introduced due to dependencies among source reports, performs significantly better than existing approaches when sampling budget is limited and equally as good with an unlimited budget.     

Certifying spatially uniform behavior in reaction–diffusion PDE and compartmental ODE systems

We present a condition that guarantees spatial uniformity for the asymptotic behavior of the solutions of a reaction–diffusion PDE with Neumann boundary conditions. This condition makes use of the Jacobian matrix of the reaction terms and the second Neumann eigenvalue of the Laplacian operator on the given spatial domain, and eliminates the global Lipschitz assumptions commonly used in mathematical biology literature. We then derive numerical procedures that employ linear matrix inequalities to certify this condition, and illustrate these procedures on models of several biochemical reaction networks. Finally, we present an analog of this PDE result for the synchronization of a network of identical ODE models coupled by diffusion terms. From a systems biology perspective, the main contribution of the paper is to blend analytical and numerical tools from nonlinear systems and control theory to derive a relaxed and verifiable condition for spatial uniformity of biological processes.     

Equilibrium-independent passivity: A new definition and numerical certification

We extend the traditional notion of passivity to a forced system whose equilibrium is dependent on the control input by defining equilibrium-independent passivity, a system property characterized by a dissipation inequality centered at an arbitrary equilibrium point. We provide a necessary input/output condition which can be tested for systems of arbitrary dimension and sufficient conditions to certify this property for scalar systems. An example from network stability analysis is presented which demonstrates the utility of this new definition. We then proceed to numerical certification of equilibrium-independent passivity using sum-of-squares programming. Finally, through numerical examples we show that equilibrium-independent passivity is less restrictive than incremental passivity.     

Use of sesame stalk biomass for the removal of Ni(II) and Zn(II) from aqueous solutions

In this study an agricultural residue, sesame stalk, was evaluated for the removal of Ni(II) and Zn(II) metal ions from aqueous solutions. Biosorption studies were carried out at different pH, biosorbent dosage, initial metal ion concentrations, contact time, and solution temperature to determine the optimum conditions. The experimental data were modeled by Langmuir, Freundlich, Dubinin-Radushkevich (D-R) and Temkin isotherm models. Langmuir model resulted in the best fit of the biosorption data. The pseudo-first-order and pseudo-second-order kinetic models were used to describe the kinetic data and to evaluate rate constants. The best correlation was provided by the second-order kinetic model. The thermodynamic parameters such as ΔG°, ΔH° and ΔS° were calculated for predicting the nature of adsorption. The experimental results showed that sesame stalk can be used as an effective and low-cost biosorbent precursor for the removal of heavy metal ions from aqueous solutions.     

A stochastic neighborhood search approach for airport gate assignment problem

An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for ?stanbul Atatürk Airport.     

Model Inadequacy and Residuals Control Charts for Autocorrelated Processes

As a result of time series parameter estimation based on previous data, the probability content of residuals control charts may vary when standard control limits are used. In this paper, we consider the AR(1) process with the autoregressive parameter being estimated from a sample of observations. The performance of the exponentially weighted moving average (EWMA) control chart for residuals is investigated. Modified control limits that account for the uncertainty in the parameter estimate are provided. Comparisons through simulation signify the importance of the modified control limits. Copyright © 2004 John Wiley & Sons, Ltd.