Pricing strategy and technology choices: an empirical investigation of ‘Everyday Low Price’ in the domestic US Airline sector

The Conference program's website is located at http://www.krannert.purdue.edu/faculty/kkarthik/wise12/program.aspINTRODUCTION: There is a rich literature in economics on factors that govern airline prices. With approximately 50% of airline tickets sold online, there is now a renewed interest in investigating airline pricing particularly amongst Information Systems (IS) researchers. While market transparency created by online travel agents (OTAs) is a motivation enough to reexamine airline pricing, one missing piece calls for a thorough empirical investigation: In all extant studies (economics, marketing and IS), pricing by two major airli…postprin

Devendrakula Vellalar Journalism Records and Ethnographic Theory

A man is strong in society because of his protective, mentality. Throughout human history, defensive behavious and instinct have kept humanity constantand grouring. In the battle for life that human face, revolutions are inevitable class conflicts. protests and revolutionsin human society bring about a dynamic transformation. The foundation of racial representation, tenacity and flight. Which in fact make thehuman race vibrant and multiplay, is formed by revolution, conflict, creative and critical literature. The literature has the ability to bring revolution more than any other weapon, according to the current educated culture

Lease based addressing for event-driven wireless sensor networks

Sensor Networks have applications in diverse fields. They can be deployed for habitat modeling, temperature monitoring and industrial sensing. They also find applications in battlefield awareness and emergency (first) response situations. While unique addressing is not a requirement of many data collecting applications of wireless sensor networks it is vital for the success of applications such as emergency response. Data that cannot be associated with a specific node becomes useless in such situations. In this work we propose an addressing mechanism for event-driven wireless sensor networks. The proposed scheme eliminates the need for network wide Duplicate Address Detection (DAD) and enables reuse of addresses. <br /

Texture analysis via unsupervised and supervised learning

A framework for texture analysis based on combined unsupervised and supervised learning is proposed. The textured input is represented in the frequency-orientation space via a Gabor-wavelet pyramidal decomposition. In the unsupervised learning phase a neural network vector quantization scheme is used for the quantization of the feature-vector attributes and a projection onto a reduced dimension clustered map for initial segmentation. A supervised stage follows, in which labeling of the textured map is achieved using a rule-based system. A set of informative features are extracted in the supervised stage as congruency rules between attributes using an information-theoretic measure. This learned set can now act as a classification set for test images. This approach is suggested as a general framework for pattern classification. Simulation results for the texture classification are given

Multi-resolution texture classification based on local image orientation

The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, in this study the main emphasis is placed on the implementation of multi-resolution texture analysis schemes. The experimental results were obtained when the analysed texture descriptors were applied to standard texture databases

Factorized variational approximations for acoustic multi source localization

Estimation based on received signal strength (RSS) is crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present a Gaussian approximation of the Chi distribution that is applicable to general RSS source localization problems in sensor networks. Using our Gaussian approximation, we provide a factorized variational Bayes (VB) approximation to the location and power posterior of multiple sources using a sensor network. When the source signal and the sensor noise have uncorrelated Gaussian distributions, we demonstrate that the envelope of the sensor output can be accurately modeled with a multiplicative Gaussian noise model. In turn, our factorized VB approximations decrease the computational complexity and provide computational robustness as the number of targets increases. Simulations are provided to demonstrate the effectiveness of the proposed approximations

Joint acoustic-video fingerprinting of vehicles, part I

We address vehicle classification and mensuration problems using acoustic and video sensors. In this paper, we show how to estimate a vehicle's speed, width, and length by jointly estimating its acoustic wave-pattern using a single passive acoustic sensor that records the vehicle's drive-by noise. The acoustic wave-pattern is approximated using three envelope shape (ES) components, which approximate the shape of the received signal's power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM and number of cylinders to create a vehicle profile vector that forms an intuitive discriminatory feature space. In the companion paper, we discuss vehicle classification and mensuration based on silhouette extraction and wheel detection, using a video sensor. Vehicle speed estimation and classification results are provided using field data

Learning texture discrimination rules in a multiresolution system

We describe a texture analysis system in which informative discrimination rules are learned from a multiresolution representation of time textured input. The system incorporates unsupervised and supervised learning via statistical machine learning and rule-based neural networks, respectively. The textured input is represented in the frequency-orientation space via a log-Gabor pyramidal decomposition. In the unsupervised learning stage a statistical clustering scheme is used for the quantization of the feature-vector attributes. A supervised stage follows in which labeling of the textured map is achieved using a rule-based network. Simulation results for the texture classification task are given. An application of the system to real-world problems is demonstrated

Automated Teller Machine (ATM)- a Pathogen City a Surveillance Report From Locations in and Around Madurai City, Tamil Nadu, India

ATM is used by millions of people in a day. It is meant to be a public utility device. Hence the microorganisms plays a major role in accommodating the safer place, ATM. Hence to this account an elaborate survey was taken for complete assessment of microbiology in and around Madurai city. Swabs were collected from each ATM screen, buttons, floor, users hand, exposure of plates and also extended the work in relation with microorganisms prevalent in ladies toilet the samples collected from ATM were plated in nutrient agar plates. The results showed the presence of increased bacterial count subsequently, most pathogens on characterization extended revealed the genus of the particular organism E-coli, Pseudomonas, Staphylococcus aures, Klebsiella, Micrococcus, Salmonella, Serratia and fungal species included Aspergillus sp, Mucor sp and Fusarium. Antibiogram study of bacteria also provides us information about the antibiotic resistance pattern of the bacterial isolates