Virtual image sensors to track human activity in a smart house

With the advancement of computer technology, demand for more accurate and intelligent monitoring systems has also risen. The use of computer vision and video analysis range from industrial inspection to surveillance. Object detection and segmentation are the first and fundamental task in the analysis of dynamic scenes. Traditionally, this detection and segmentation are typically done through temporal differencing or statistical modelling methods. One of the most widely used background modeling and segmentation algorithms is the Mixture of Gaussians method developed by Stauffer and Grimson (1999). During the past decade many such algorithms have been developed ranging from parametric to non-parametric algorithms. Many of them utilise pixel intensities to model the background, but some use texture properties such as Local Binary Patterns. These algorithms function quite well under normal environmental conditions and each has its own set of advantages and short comings. However, there are two drawbacks in common. The first is that of the stationary object problem; when moving objects become stationary, they get merged into the background. The second problem is that of light changes; when rapid illumination changes occur in the environment, these background modelling algorithms produce large areas of false positives.These algorithms are capable of adapting to the change, however, the quality of the segmentation is very poor during the adaptation phase. In this thesis, a framework to suppress these false positives is introduced. Image properties such as edges and textures are utilised to reduce the amount of false positives during adaptation phase. The framework is built on the idea of sequential pattern recognition. In any background modelling algorithm, the importance of multiple image features as well as different spatial scales cannot be overlooked. Failure to focus attention on these two factors will result in difficulty to detect and reduce false alarms caused by rapid light change and other conditions. The use of edge features in false alarm suppression is also explored. Edges are somewhat more resistant to environmental changes in video scenes. The assumption here is that regardless of environmental changes, such as that of illumination change, the edges of the objects should remain the same. The edge based approach is tested on several videos containing rapid light changes and shows promising results. Texture is then used to analyse video images and remove false alarm regions. Texture gradient approach and Laws Texture Energy Measures are used to find and remove false positives. It is found that Laws Texture Energy Measure performs better than the gradient approach. The results of using edges, texture and different combination of the two in false positive suppression are also presented in this work. This false positive suppression framework is applied to a smart house senario that uses cameras to model ”virtual sensors” to detect interactions of occupants with devices. Results show the accuracy of virtual sensors compared with the ground truth is improved

Impact of Social Networking Websites on Business Today

A social networking website is a social structure build-up of individuals or organizations called “nodes”, which are connected by one or more specific types of mutuality, such as friendship, common interest, financial exchange, dislike relationships of beliefs, knowledge, prestige and information. This article focuses on social networking websites and their impact on business. It aims to identify opportunities and ways to be effective as promoters of businesses to internet users all over the world, 24 hours and 7 days, creating unlimited possibilities for advertising potential. This article reviews the best social networking websites currently used as well as examples of ways in which business can use these types of websites to expand their target markets. This article also gives insight about the threats and challenges associated with social networking websites, as well as things for businesses to watch out for if they determine to use these types of websites. Social networking websites will continue to shape the ways in which businesses collaborate and communicate, both inside and outside of enterprise

Interference reduced routing for sensor networks

Construction of interference reduced routes is an all-important problem in sensor network. We propose a model for extracting a small size backbone network from a given background network. The extracted network possesses the property of reduced static interference. A backbone structure, constructed on the top of a planar sensor network can be used to route message with lower interference. We propose two centralized algorithms for constructing the backbone network. The first algorithm is based on the spanning tree construction of inner holes of sensor network. The second algorithm builds the backbone network by using the Delaunay triangulation of the center of gravity of holes in the network, which runs in O(n2) time. We also present a distributed localized implementation of the proposed algorithm by using the quasi Voronoi diagram and medial axis formed by the distribution of network holes. We describe an experimental investigation of the proposed algorithm. The results of the simulation show that the routing guided by the proposed backbone network is effective in reducing interference

Bring the Real-World Digital Marketing Experience to Classroom: Google Online Marketing Challenge

Experiential learning is a process of learning through experience and reflection. It is different from the traditional didactic learning, in which students read or are told about others\u27 experiences of the subject. In many university classrooms, faculty members have incorporated hands-on projects to stimulate learning experience. However, experience doesn\u27t always lead to experiential learning. A well-designed experiential learning can produce benefits such as 1) enhancing deep learning, 2) ensuring engagement, and 3) developing employable skills. The students who enrolled in an advanced e-Commerce course participated in the Google Online Marketing Challenge (GOMC), in which each team used $250 provided from Google on helping small business clients to promote their businesses. Incorporating the GOMC project in class produced the aforementioned benefits of experiential learning. The experiential learning activity seems to be a promising pedagogy to engage students and prepare them to be the future workforce of fashion marketing and branding in this Internet era

A large-scale sentiment analysis using political tweets

Twitter has become a key element of political discourse in candidates’ campaigns. The political polarization on Twitter is vital to politicians as it is a popular public medium to analyze and predict public opinion concerning political events. The analysis of the sentiment of political tweet contents mainly depends on the quality of sentiment lexicons. Therefore, it is crucial to create sentiment lexicons of the highest quality. In the proposed system, the domain-specific of the political lexicon is constructed by using the supervised approach to extract extreme political opinions words, and features in tweets. Political multi-class sentiment analysis (PMSA) system on the big data platform is developed to predict the inclination of tweets to infer the results of the elections by conducting the analysis on different political datasets: including the Trump election dataset and the BBC News politics. The comparative analysis is the experimental results which are better political text classification by using the three different models (multinomial naïve Bayes (MNB), decision tree (DT), linear support vector classification (SVC)). In the comparison of three different models, linear SVC has the better performance than the other two techniques. The analytical evaluation results show that the proposed system can be performed with 98% accuracy in linear SVC

Religiosity and Store Choice Criteria: Exploring Christian Consumers’ Apparel Shopping Behavior in the United States

Religiosity is “the degree to which a person adheres to his or her religious values, beliefs, and practices, and uses them in daily live” (Worthington et al., 2003, p.85). As a key element of culture, religion not only affects a society’s value system and provides conduct code to its believers, but also affects consumers’ consumption and shopping behavior (e.g. Bailey and Sood, 1993). It is a relatively new subject in marketing and consumer behavior research. Limited studies have investigated the effect of religiosity on retail patronage behavior; even fewer have focused on the US markets. However, the United States is a highly religious country with 76% of US adults being Christians (US Census, 2012). Therefore, religiosity might have been an important affecting factor in US markets

Interference Analysis of Medium Voltage Air Line 20 KV Feeder Using Failure Mode and Effects Analysis Method

This article discusses the interference analysis of medium voltage air line 20 kv feeder using failure mode and effects analysis method. The distribution network consists of two parts, the first the distribution network consists of two parts, the first is the medium / primary voltage (JTM) network, which supplies electrical power from the sub-transmission substation to the distribution substation, the primary distribution network uses three wires or four wires for three phases. the impact of the reliability index from the calculation of the impact of the reliability index based on the number of disturbances (SAIFI), it shows that in January 2019 it has the highest index value, namely SAIFI, 1,695 disturbances/ subscribers. From the results of the calculation of the impact of the reliability index based on the number of blackouts (SAIDI), it shows that in January 2019 the SAIDI index value was 3,883 hours/customer

Religiosity, Faith Driven Consumption, and Apparel Shopping Orientation

Faith driven consumers refer to devout Christian consumers whose consumption and shopping behavior is strongly influenced by Christian values and worldviews (Faithnomics.com, 2012). This segment of consumers accounts for 17% of the US population, or 41 million people. By size, it is similar to the Hispanic segment of the market; however, with $1.75 trillion, the annual spending power of this segment is 75% more than that of the Hispanic segment (Faithnomics.com, 2012). Strong moral and Christian values rather than simple consumer needs drive their purchase decisions in the marketplace. In this study, the authors define the consumption behavior based on the biblical views and teachings as faith driven consumption (FDC)

Effects of Religiosity on Apparel Shopping Orientation: An Exploratory Study

Although religion is an important cultural element that affects a society’s value system and its people’s behavior, limited research has studied effects of religion on consumers’ patronage behavior, especially when it comes to apparel. This study, therefore, explored religiosity and consumers’ apparel shopping orientation. The findings reveal that religiosity significantly affect Christian consumers’ apparel shopping orientation. Specifically, religiosity has a significant positive direct effect on quality consciousness, fashion consciousness, and price consciousness. The study indicates fashion retailers should understand the role of religiosity on consumers’ patronage behavior, thus delivering better value to their customers