Showing papers in "Human-centric Computing and Information Sciences in 2015"

The BAU GIS system using open source mapwindow

Abstract: “BAU GIS” is an efficient and flexible Geographic Information System (GIS) that supports manipulation, analysis, and viewing of geospatial data and associated attribute data in various GIS data formats. “BAU GIS” system is a stand-alone application, developed using (Map Window Open Source GIS) and (visual basic 10.0). It has been designed and developed to address the need for a GIS programming tool that could be used in engineering research and project software, without requiring end users to purchase a complete GIS system, or become a GIS experts. It is both a GIS modeling system, and a GIS application programming interface (API); all in a convenient redistributable package. The BAU GIS application is free and extensible GIS that can be used as an open-source alternative to desktop GIS, in order to distribute data to others and to develop and distribute custom spatial data analysis tools.

Augmented reality applications for cultural heritage using Kinect

Abstract: This paper explores the use of data from the Kinect sensor for performing augmented reality, with emphasis on cultural heritage applications. It is shown that the combination of depth and image correspondences from the Kinect can yield a reliable estimate of the location and pose of the camera, though noise from the depth sensor introduces an unpleasant jittering of the rendered view. Kalman filtering of the camera position was found to yield a much more stable view. Results show that the system is accurate enough for in situ augmented reality applications. Skeleton tracking using Kinect data allows the appearance of participants to be augmented, and together these facilitate the development of cultural heritage applications.

Assessment of combined textural and morphological features for diagnosis of breast masses in ultrasound

Abstract: The objective of this study is to assess the combined performance of textural and morphological features for the detection and diagnosis of breast masses in ultrasound images We have extracted a total of forty four features using textural and morphological techniques Support vector machine (SVM) classifier is used to discriminate the tumors into benign or malignant The performance of individual as well as combined features are assessed using accuracy(Ac), sensitivity(Se), specificity(Sp), Matthews correlation coefficient(MCC) and area AZ under receiver operating characteristics curve The individual features produced classification accuracy in the range of 6166% and 9083% and when features from each category are combined, the accuracy is improved in the range of 7916% and 9583% Moreover, the combination of gray level co-occurrence matrix (GLCM) and ratio of perimeters (P ratio ) presented highest performance among all feature combinations (Ac 9585%, Se 96%, Sp 9146%, MCC 09146 and AZ 09444)The results indicated that the discrimination performance of a computer aided breast cancer diagnosis system increases when textural and morphological features are combined

Implementation of hybrid image fusion technique for feature enhancement in medical diagnosis

Abstract: Image fusion is used to enhance the quality of images by combining two images of same scene obtained from different techniques. In medical diagnosis by combining the images obtained by Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI) we get more information and additional data from fused image. This paper presents a hybrid technique using curvelet and wavelet transform used in medical diagnosis. In this technique the image is segmented into bands using wavelet transform, the segmented image is then fused into sub bands using curvelet transform which breaks the bands into overlapping tiles and efficiently converting the curves in images using straight lines. These tiles are integrated together using inverse wavelet transform to produce a highly informative fused image. Wavelet based fusion extracts spatial details from high resolution bands but its limitation lies in the fusion of curved shapes. Therefore for better information and higher resolution on curved shapes we are blending wavelet transform with curvelet transform as we know that curvelet transform deals effectively with curves areas, corners and profiles. These two fusion techniques are extracted and then fused implementing hybrid image fusion algorithm, findings shows that fused image has minimum errors and present better quality results. The peak signal to noise ratio value for the hybrid method was higher in comparison to that of wavelet and curvelet transform fused images. Also we get improved statistics results in terms of Entropy, Peak signal to noise ratio, correlation coefficient, mutual information and edge association. This shows that the quality of fused image was better in case of hybrid method.

A novel method for expert finding in online communities based on concept map and PageRank

Abstract: An online community is a virtual community where people can express their opinions and their knowledge freely. There are a great deal of information in online communities, however there is no way to determine its authenticity. Thus the knowledge which has been shared in online communities is not reliable. By determining expertise level of users and finding experts in online communities the accuracy of posted comments can be evaluated. In this study, a hybrid method for expert finding in online communities is presented which is based on content analysis and social network analysis. The content analysis is based on concept map and the social network analysis is based on PageRank algorithm. To evaluate the proposed method java online community was selected and then correlation between our results and scores prepared by java online community was calculated. Based on obtained results Spearman correlation for 11 subcategories of java online community using this method is 0.904, which is highly an acceptable value.

A review on stochastic approach for dynamic power management in wireless sensor networks

Abstract: Wireless sensor networks (WSNs) demand low power and energy efficient hardware and software. Dynamic Power Management (DPM) technique reduces the maximum possible active states of a wireless sensor node by controlling the switching of the low power manageable components in power down or off states. During DPM, it is also required that the deadline of task execution and performance are not compromised. It is seen that operational level change can improve the energy efficiency of a system drastically (up to 90%). Hence, DPM policies have drawn considerable attention. This review paper classifies different dynamic power management techniques and focuses on stochastic modeling scheme which dynamically manage wireless sensor node operations in order to minimize its power consumption. This survey paper is expected to trigger ideas for future research projects in power aware wireless sensor network arenas.

Modeling and discovering human behavior from smartphone sensing life-log data for identification purpose

Abstract: Today, personal data is becoming a new economic asset. Personal data which generated from our smartphone can be used for many purposes such as identification, recommendation system, and etc. The purposes of our research are to discover human behavior based on their smartphone life log data and to build behavior model which can be used for human identification. In this research, we have collected user personal data from 37 students for 2 months which consist of 19 kinds of data sensors. There is still no ideal platform that can collects user personal data continuously and without data loss. The data which collected from user’s smartphone have various situations such as the data came from multiple sensors and multiple source information which sometimes one or more data does not available. We have developed a new approach to building human behavior model which can deal with those situations. Furthermore, we evaluate our approach and present the details in this paper.

Effects of blended e-Learning: a case study in higher education tax learning setting

Abstract: This article has two main objectives. First, we describe the design of an e-learning system for a University Income Tax Law course. Second, we analyze and explore learning results in terms of students’ learning satisfaction and learning achievement. Learning achievement was examined by questions derived from the course content while learning satisfaction was analyzed based on an adaptation of the Technology Acceptance Model (TAM). Results indicate that neither gender nor the school system affect students’ e-learning system satisfaction. Since students’ knowledge and exposure to computers are equal regardless of gender or educational background this reduces the significance of both these variables. Participating samples are divided into three groups: traditional, fully on-line and blended learning. We find, however, a statistically significant difference existed in learning achievement among groups. The blended learning group, combining on- line learning with paper-and-pencil testing, has the best learning achievement among the three groups.

Implementation of searchable symmetric encryption for privacy-preserving keyword search on cloud storage

Abstract: Ensuring the cloud data security is a major concern for corporate cloud subscribers and in some cases for the private cloud users. Confidentiality of the stored data can be managed by encrypting the data at the client side before outsourcing it to the remote cloud storage server. However, once the data is encrypted, it will limit server’s capability for keyword search since the data is encrypted and server simply cannot make a plaintext keyword search on encrypted data. But again we need the keyword search functionality for efficient retrieval of data. To maintain user’s data confidentiality, the keyword search functionality should be able to perform over encrypted cloud data and additionally it should not leak any information about the searched keyword or the retrieved document. This is known as privacy preserving keyword search. This paper aims to study privacy preserving keyword search over encrypted cloud data. Also, we present our implementation of a privacy preserving data storage and retrieval system in cloud computing. For our implementation, we have chosen one of the symmetric key primitives due to its efficiency in mobile environments. The implemented scheme enables a user to store data securely in the cloud by encrypting it before outsourcing and also provides user capability to search over the encrypted data without revealing any information about the data or the query.

Local quantized extrema patterns for content-based natural and texture image retrieval

Abstract: This paper proposes a novel feature descriptor, named local quantized extrema patterns (LQEP) for content based image retrieval. The standard local quantized patterns (LQP) collect the directional relationship between the center pixel and its surrounding neighbors and the directional local extrema patterns (DLEP) collect the directional information based on local extrema in 0°, 45°, 90°, and 135° directions for a given center pixel in an image. In this paper, the concepts of LQP and DLEP are integrated to propose the LQEP for image retrieval application. First, the directional quantized information is collected from the given image. Then, the directional extrema is collected from the quantized information. Finally, the RGB color histogram is integrated with the LQEP for a feature vector generation. The performance of the proposed method is tested by conducting three experiments on Coel-1K, Corel-5K and MIT VisTex databases for natural and texture image retrieval. The performance of the proposed method is evaluated in terms of precision, recall, average retrieval precision and average retrieval rate on benchmark databases. The results after investigation show a considerable improvements in terms of their evaluation measures as compared to the existing methods on respective databases.

An optimizing pipeline stall reduction algorithm for power and performance on multi-core CPUs

Abstract: The power-performance trade-off is one of the major considerations in micro-architecture design. Pipelined architecture has brought a radical change in the design to capitalize on the parallel operation of various functional blocks involved in the instruction execution process, which is widely used in all modern processors. Pipeline introduces the instruction level parallelism (ILP) because of the potential overlap of instructions, and it does have drawbacks in the form of hazards, which is a result of data dependencies and resource conflicts. To overcome these hazards, stalls were introduced, which are basically delayed execution of instructions to diffuse the problematic situation. Out-of-order (OOO) execution is a ramification of the stall approach since it executes the instruction in an order governed by the availability of the input data rather than by their original order in the program. This paper presents a new algorithm called Left-Right (LR) for reducing stalls in pipelined processors. This algorithm is built by combining the traditional in-order and the out-of-order (OOO) instruction execution, resulting in the best of both approaches. As instruction input, we take the Tomasulo's algorithm for scheduling out-of-order and the in-order instruction execution and we compare the proposed algorithm's efficiency against both in terms of power-performance gain. Experimental simulations are conducted using Sim-Panalyzer, an instruction level simulator, showing that our proposed algorithm optimizes the power-performance with an effective increase of 30% in terms of energy consumption benefits compared to the Tomasulo's algorithm and 3% compared to the in-order algorithm.

Knowledge-based adaptable scheduler for SaaS providers in cloud computing

Abstract: Software as a Service (SaaS) in Cloud Computing offers reliable access to software applications for end users over the Internet without direct investment in infrastructure and software. SaaS providers utilize resources of internal datacenters or rent resources from a public Infrastructure as a Service (IaaS) provider in order to serve their customers. Internal hosting can increase cost of administration and maintenance, whereas hiring from an IaaS provider can impact quality of service due to its variable performance. To surmount these challenges, we propose a knowledge-based admission control along with scheduling algorithms for SaaS providers to effectively utilize public Cloud resources in order to maximize profit by minimizing cost and improving customers’ satisfaction level. In the proposed model, the admission control is based on Service Level Agreement (SLA) and uses different strategies to decide upon accepting user requests for that minimal performance impact, avoiding SLA penalties that are giving higher profit. However, because the admission control can make decisions optimally, there is a need of machine learning methods to predict the strategies. In order to model prediction of sequence of strategies, a customized decision tree algorithm has been used. In addition, we conducted several experiments to analyze which solution in which scenario fit better to maximize SaaS provider’s profit. Results obtained through our simulation shows that our proposed algorithm provides significant improvement (up to 38.4 % cost saving) compared to the previous research works.

Trusted and secure clustering in mobile pervasive environment

Abstract: Pervasive computing has the potential to offer low-cost, high performance, and user centric solutions to exchange the information and communicate seamlessly in highly dynamic, heterogeneous environment. Here small and influential dissimilar devices or nodes have to set up independent network unknown by the user. Communicating devices are resource-restricted and equipped with micro or bio-sensors to acknowledge the signals where traditional security systems based on cryptography and encryption are not enough for promising level of security assurance. In this paper we explore trust and security challenges and appraise the opportunities in autonomous mobile pervasive ad-hoc networks to improve security. Trust management models in human-centric applications can enhance the security assurance. Many researchers proposed various trust models for different scenarios. Inspiring from such models we propose a trust computation metric based on node’s impulsive behavior to become malicious node in dynamic scenario and breach the security. In winding up, we put our efforts to present energy efficient, secure and trusted clustering to enhance the security assurance and significant adaptation of trustworthy communication in user-centric m-healthcare applications where information is ubiquitous.

Testing of the voice communication in smart home care

Abstract: This article is aimed to describe the method of testing the implementation of voice control over operating and technical functions of Smart Home Come. Custom control over operating and technical functions was implemented into a model of Smart Home that was equipped with KNX technology. A sociological survey focused on the needs of seniors has been carried out to justify the implementation of voice control into Smart Home Care. In the real environment of Smart Home Care, there are usually unwanted signals and additive noise that negatively affect the voice communication with the control system. This article describes the addition of a sophisticated system for filtering the additive background noise out of the voice communication with the control system. The additive noise significantly lowers the success of recognizing voice commands to control operating and technical functions of an intelligent building. Within the scope of the proposed application, a complex system based on fuzzy-neuron networks, specifically the ANFIS (Adaptive Neuro-Fuzzy Interference System) for adaptive suppression of unwanted background noises was created. The functionality of the designed system was evaluated both by subjective and by objective criteria (SSNR, DTW). Experimental results suggest that the studied system has the potential to refine the voice control of technical and operating functions of Smart Home Care even in a very noisy environment.

Provably secure attribute-based encryption with attribute revocation and grant function using proxy re-encryption and attribute key for updating

Abstract: Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is suitable for data access control on a cloud storage system. In CP-ABE, the data owner encrypts data under the access structure over attributes and a set of attributes assigned to users is embedded in user’s secret key. A user is able to decrypt if his attributes satisfy the ciphertext’s access structure. In CP-ABE, processes of user’s attribute revocation and grant are concentrated on the authority and the data owner. In this paper, we propose a ciphertext-policy attribute-based encryption scheme delegating attribute revocation processes to Cloud Server by proxy re-encryption. The proposed scheme does not require generations of new secret key when granting attributes to a user and supports any Linear Secret Sharing Schemes (LSSS) access structure. We prove that the proposed scheme is secure against attack by unauthorized users and Cloud Server.

Re-purchase intentions and virtual customer relationships on social media brand community

Abstract: The purpose of this study was to demonstrate how to manage digital customer relationships (i.e. relationships with the brand, the product, the company, and other fans) on social media based community (i.e. Facebook brand fan-pages) and to influence post-purchase intentions (i.e. word-of-mouth and re-purchase intentions). This study used partial least squares to test the hypotheses and analyze the data. The results of this study indicated that all these four customer-community relationships can enhance post-purchase behaviors by improving individual community participation or identification. The findings are of benefit to both academics and practitioners and this research is one of the first to demonstrate how to manage digital customer relationships on social media brand community.

Heart rate monitoring using human speech spectral features

Abstract: This paper attempts to establish a correlation between the human speech, emotions and human heart rate. The study highlights a possible contactless human heart rate measurement technique useful for monitoring of patient condition from real-time speech recordings. The distance between the average peak-to-peak distances in speech Mel-frequency cepstral coefficients are used as the speech features. The features when tested on 20 classifiers from the data collected from 30 subjects indicate a non-separable classification problem, however, the classification accuracies indicate the existence of strong correlation between the human speech, emotion and heart-rates.

s-ITSF: a service based intelligent transportation system framework for smart accident management

Abstract: Recently, traffic jams and accidents increase due to increase of traffic volume. Thus, intelligent transport system (ITS) is developed to provide efficient road situation and information is actively studying. However, it is mainly focused on traffic jam and management of traffic situation that a systematic management is not provided when an accident occurs. An action to prevent an accident providing the reliable information and a systematic management after accident is needed. In this paper we propose a service based intelligent transportation system framework (s-ITSF) to provide efficient and systematic accident management. The s-ITSF provides an efficient ITS service such as before/after accident management using the accident prevention and management and traffic information data through provision of highly reliable information through encryption and authentication based on vehicular cloud computing (VCC) environment. In addition, it provides systematic accident management which may reduce loss of lives and properties from the traffic accident by quick settlement.

Personalized fitting recommendation based on support vector regression

Abstract: Collaborative filtering (CF) is a popular method for the personalized recommendation. Almost all of the existing CF methods rely only on the rating data while ignoring some important implicit information in non-rating properties for users and items, which has a significant impact on the preference. In this study, considering that the average rating of users and items has a certain stability, we firstly propose a personalized fitting pattern to predict missing ratings based on the similarity score set, which combines both the user-based and item-based CF. In order to further reduce the prediction error, we use the non-rating attributes, such as a user’s age, gender and occupation, and an item’s release date and price. Moreover, we present the deviation adjustment method based on the support vector regression. Experimental results on MovieLens dataset show that our proposed algorithms can increase the accuracy of recommendation versus the traditional CF.

INSPECT: extending plane-casting for 6-DOF control

Abstract: INSPECT is a novel interaction technique for 3D object manipulation using a rotation-only tracked touch panel. Motivated by the applicability of the technique on smartphones, we explore this design space by introducing a way to map the available degrees of freedom and discuss the design decisions that were made. We subjected INSPECT to a formal user study against a baseline wand interaction technique using a Polhemus tracker. Results show that INSPECT is 12% faster in a 3D translation task while at the same time being 40% more accurate. INSPECT also performed similar to the wand at a 3D rotation task and was preferred by the users overall.

Identifying the value co-creation behavior of virtual customer environments using a hybrid expert-based DANP model in the bicycle industry

Abstract: Since 2008, soaring international oil prices and environmental awareness have pushed bicycle to be a green transport vehicle to reduce greenhouse gas emissions as a significant global trend. Consequently, Taiwan’s bicycle industry earned the “bicycle kingdom” has entered a new peak period of demand under popular social trends of bicycling for health conscious and a healthy exercise tool; thus, to co-create value with customers to retain the reputation is important for Taiwan’s bicycle industry. In Internet age, plus the prevailing of service-dominant logic, virtual customer environments (VCEs) can be greatly leveraged to promote customers’ active engagement in the value co-creation activities. After an extensive literature review, this study organizes a hybrid expert-based DANP model based on the applications of multi-criteria decision making (MCDM) tools, such as decision-making trial and evaluation laboratory (DEMATEL)-based analytical network process (ANP), for investigating the iterative and dynamic nature of customer’s engagement and value co-creation behavior in the key bicycle industry in Taiwan. In the empirical study of analysis, the use and gratification framework of prior studies is validated on concerning the dynamic value co-creation behavior in bicycling VCEs and yields the following empirical results: (1) Tribal behavior drives the pursuit of realized benefits through VCE engagement and affects the related participation and citizenship behaviors in turn; (2) recognize the importance of social influences toward personal commitment and engagement of bicycling activities and related VCEs; and (3) four broad types of interaction-based benefits derived from engagement in VCEs include cognitive, social integrative, personal integrative, and hedonic benefits. The major research findings on theoretical implications and managerial implications provide helpful insights on marketing of Taiwan’s bicycle industry.

Time evolution of face recognition in accessible scenarios

Abstract: Up to now, biometric recognition has shown significant advantages as to be considered a reliable solution for security systems in mobile environments. Nevertheless, due to the short lifetime of biometrics in mobile devices, a handful of concerns regarding usability and accessibility need to be covered in order to meet users’ requirements. This work is focused on analysing the usability and accessibility of a face recognition system used by visually impaired people, focusing on the time spent in the process, which is a critical aspect. Specifically, we cover different key questions including which kind of feedback is more useful for visually impaired users and beneficial for performance and how is the performance evolution in contrast with the time spent in the recognition. Our findings suggest that several parameters improve along with the time spent in the process, including performance. The audio feedback provided in real time involves also better performance and user experience than instructions given previously.

Design of fusion technique-based mining engine for smart business

Abstract: Keys to successful implementation of smart business require a wide spectrum of domain knowledge, experts, and their correlated experiences. Excluding those external factors—which can be collected by well-deployed sensors—being aware of user (or consumer) has the highest priority on the to-do-list. The more user is understood, the more user can be satisfied from an intuitive point of view, and thus, data plays a rather essential role in the scenario. However, it is never easy to achieve comprehensive understanding as the data requires further processing before its values can be extracted and used. So how the data can be properly transformed into something useful for smart business development is exactly what we pursue in this study. As a pioneer, three major tasks are focused. First, a data mining engine based on the concept of the KID model is designed and developed to be responsible for the universal collection of data and mining valuable information which is primarily from real world, cyber world, and social world. Second, we go further into the fusion process of the collected data and meaningful information extracted and interpreted by algorithms or fused algorithms in the data mining engine (e.g., the consumer purchase data shared by real-world company) and turn them into valuable knowledge about the situation of customers and business situations based on the concept of knowledge, information, and data. A three-layer analysis and mining procedure is designed to enhance the mining engine through conventional RFM (Recency, Frequency, and Monetary Value) model and a set of fusion techniques. And in the end, we make planning-based predictions for a real-world company for expansion of the business interests.

Designing a collaborative visual analytics system to support users’ continuous analytical processes

Abstract: In recent, numerous useful visual analytics tools have been designed to help domain experts solve analytical problems. However, most of the tools do not reflect the nature of solving real-world analytical tasks collaboratively because they have been designed for single users in desktop environments. In this paper, a complete visual analytics system is designed for solving real-world tasks having two integrated components: a single-user desktop system and an extended system suitable for a collaborative environment. Specifically, we designed a collaborative touch-table application (iPCA-CE) by adopting an existing single-user desktop analytical tool (iPCA). With the system, users can actively transit from individual desktop to shared collaborative environments without losing track of their analysis. They can also switch their analytical processes from collaborative to single-user workflows. To understand the usefulness of the system for solving analytical problems, we conducted a user study in both desktop and collaborative environments. From this study, we found that both applications are useful for solving analytical problems individually and collaboratively in different environments.

How does this message make you feel? A study of user perspectives on software update/warning message design

Abstract: Software update messages are commonly used to inform users about software updates, recent bug fixes, and various system vulnerabilities, and to suggest recommended actions (e.g., updating software). While various design features (e.g., update options, message layout, update message presentation) of these messages can influence the actions taken by users, no prior study can be found that investigated users opinions regarding various design alternatives. To address this void, this paper focuses on identifying software update message design features (e.g., layout, color, content) that may affect users positively or negatively. Toward that, we conducted a user study where users are shown 13 software update messages along with 1 virus warning message. We collect responses from 155 users through an online survey. Participants gave a total of 809 positive comments and 866 negative comments along with ranking of each image in terms of perceived importance, noticeability, annoyance and confusion. As many of the comments are repetitive and often contain multiple themes, we manually analyzed and performed a bottom-up, inductive coding to identify and refine the underlying themes. Over multiple iterations, positive comments were grouped into 52 categories which were subsequently grouped under four themes. Similarly, negative comments were first grouped into 38 categories which were subsequently grouped under four themes. Based on our analysis, we present the list of design features that are found to be highly correlated to confusion, annoyance, noticeability, and importance, either positively or negatively.

Designing challenge questions for location‐based authentication systems: a real‐life study

Abstract: Online service providers often use challenge questions (a.k.a. knowledge‐based authentication) to facilitate resetting of passwords or to provide an extra layer of security for authentication. While prior schemes explored both static and dynamic challenge questions to improve security, they do not systematically investigate the problem of designing challenge questions and its effect on user recall performance. Interestingly, as answering different styles of questions may require different amount of cognitive effort and evoke different reactions among users, we argue that the style of challenge questions itself can have a significant effect on user recall performance and usability of such systems. To address this void and investigate the effect of question types on user performance, this paper explores location‐based challenge question generation schemes where different types of questions are generated based on users’ locations tracked by smartphones and presented to users. For evaluation, we deployed our location tracking application on users’ smartphones and conducted two real‐life studies using four different kinds of challenge questions. Each study was approximately 30 days long and had 14 and 15 users respectively. Our findings suggest that the question type can have a significant effect on user performance. Finally, as individual users may vary in terms of performance and recall rate, we investigate and present a Bayesian classifier based authentication algorithm that can authenticate legitimate users with high accuracy by leveraging individual response patterns while reducing the success rate of adversaries.

Graph-based motor primitive generation framework

Abstract: Unmanned aerial vehicles (UAVs) have many potential applications, such as delivery, leisure, and surveillance. To enable these applications, making the UAVs fly autonomously is the key issue, and requires defining UAV motor primitives. Diverse attempts have been made to automatically generate motor primitives for UAVs and robots. However, given that UAVs usually do not fly as expected because of external environmental factors, a novel approach for UAVs needs to be designed. This paper proposes a demonstration-based method that generates a graph-based motor primitive. In the experiment, an AR.Drone 2.0 was utilized. By controlling the AR.Drone 2.0, four motor primitives are generated and combined as a graph-based motor primitive. The generated motor primitives can be performed by a planner or a learner, such as a hierarchical task network or Q-learning. By defining the executable conditions of the motor primitives based on measured properties, the movements of the graph-based motor primitive can be chosen depending on changes in the indoor environment.

Image contour based on context aware in complex wavelet domain

Abstract: Active contours are used in the image processing application including edge detection, shape modeling, medical image-analysis, detectable object boundaries, etc. Shape is one of the important features for describing an object of interest. Even though it is easy to understand the concept of 2D shape, it is very difficult to represent, define and describe it. In this paper, we propose a new method to implement an active contour model using Daubechies complex wavelet transform combined with B-Spline based on context aware. To show the superiority of the proposed method, we have compared the results with other recent methods such as the method based on simple discrete wavelet transform, Daubechies complex wavelet transform and Daubechies complex wavelet transform combined with B-Spline.

Fairness scheme for energy efficient H.264/AVC-based video sensor network

Abstract: The availability of advanced wireless sensor nodes enable us to use video processing techniques in a wireless sensor network (WSN) platform. Such paradigm can be used to implement video sensor networks (VSNs) that can serve as an alternative to existing video surveillance applications. However, video processing requires tremendous resources in terms of computation and transmission of the encoded video. As the most widely used video codec, H.264/AVC comes with a number of advanced encoding tools that can be tailored to suit a wide range of applications. Therefore, in order to get an optimal encoding performance for the VSN, it is essential to find the right encoding configuration and setting parameters for each VSN node based on the content being captured. In fact, the environment at which the VSN is deployed affects not only the content captured by the VSN node but also the node’s performance in terms of power consumption and its life-time. The objective of this study is to maximize the lifetime of the VSN by exploiting the trade-off between encoding and communication on sensor nodes. In order to reduce VSNs’ power consumption and obtain a more balanced energy consumption among VSN nodes, we use a branch and bound optimization techniques on a finite set of encoder configuration settings called configuration IDs (CIDs) and a fairness-based scheme. In our approach, the bitrate allocation in terms of fairness ratio per each node is obtained from the training sequences and is used to select appropriate encoder configuration settings for the test sequences. We use real life content of three different possible scenes of VSNs’ implementation with different levels of complexity in our study. Performance evaluations show that the proposed optimization technique manages to balance VSN’s power consumption per each node while the nodes’ maximum power consumption is minimized. We show that by using that approach, the VSN’s power consumption is reduced by around 7.58% in average.

A spatial data warehouse recommendation approach: conceptual framework and experimental evaluation

Abstract: Spatial data warehouses store a large amount of historized and aggregated data. They are usually exploited by Spatial OLAP (SOLAP) systems to extract relevant information. Extracting such information may be complex and difficult. The user might ignore what part of the warehouse contains the relevant information and what the next query should be. On the other hand, recommendation systems aim to help users to retrieve relevant information according to their preferences and analytical objectives. Hence, developing a SOLAP recommendation system would enhance spatial data warehouses exploitation. This paper proposes a SOLAP recommendation approach that aims to help users better exploit spatial data warehouses and retrieve relevant information by recommending personalized spatial MDX (Multidimensional Expressions) queries. The approach detects implicitly the preferences and needs of SOLAP users using a spatio-semantic similarity measure. The approach is described theoretically and validated by experiments.