Sequential slotted amplify-decode-and-forward

In this paper, we propose a novel slotted hybrid cooperative protocol named the sequential slotted amplify-decodeand-forward (SADF) protocol and evaluate its performance in terms of diversity-multiplexing trade-off (DMT). The relays between the source and destination are divided into two different groups and each relay either amplifies or decodes the received signal. We first compute the optimal DMT of the proposed protocol with the assumption of perfect decoding at the DF relays. We then derive the DMT closed-form expression of the proposed sequential-SADF and obtain the proximity gain bound for achieving the optimal DMT. With the proximity gain bound, we then found the distance ratio to achieve the optimal DMT performance. Simulation result shows that the proposed protocol with high proximity gain outperforms other cooperative communication protocols in high SNR regime.

Anomalous event detection using a semi-two dimensional hidden Markov model

The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Spatio temporal feature evaluation for action recognition

Spatio-Temporal interest points are the most popular feature representation in the field of action recognition. A variety of methods have been proposed to detect and describe local patches in video with several techniques reporting state of the art performance for action recognition. However, the reported results are obtained under different experimental settings with different datasets, making it difficult to compare the various approaches. As a result of this, we seek to comprehensively evaluate state of the art spatio- temporal features under a common evaluation framework with popular benchmark datasets (KTH, Weizmann) and more challenging datasets such as Hollywood2. The purpose of this work is to provide guidance for researchers, when selecting features for different applications with different environmental conditions. In this work we evaluate four popular descriptors (HOG, HOF, HOG/HOF, HOG3D) using a popular bag of visual features representation, and Support Vector Machines (SVM)for classification. Moreover, we provide an in-depth analysis of local feature descriptors and optimize the codebook sizes for different datasets with different descriptors. In this paper, we demonstrate that motion based features offer better performance than those that rely solely on spatial information, while features that combine both types of data are more consistent across a variety of conditions, but typically require a larger codebook for optimal performance.

The complementary potential of multimedia and science demonstrations

Science is often considered as one of the cornerstones of human advancement. Despite its importance in our society, science as a subject in schools appears to be losing ground. Lack of relevance, the nature of the curriculum and the pedagogical approach to teaching are some of the reasons which researchers believe are causing a “swing” away from science. This paper will argue for the effectiveness of simple science demonstrations as a feasible pedagogical option with a high task value and which has the potential to reengage and reinvigorate student interest in the subject. This paper describes a case study (N = 25) in which the Integrative problem based learning model for science was implemented in a year nine science class. The study was conducted at a secondary school in Australia. Teacher demonstrations were situated in classroom activities in a “Why is it so?” problem/question format. Qualitative data gathered from students demonstrated a number of benefits of this approach. This paper then explores ways in which Web 2.0 technologies could be incorporated to enhance the value of science demonstrations

Practical insights into curricula integration for primary science

As indicated in a previous Teaching Science article, effective planning for curricula integration requires using standards from two (or more) subject areas (e.g., science and English, science and art or science and mathematics), which also becomes the assessment foci for teaching and learning. Curricula integration of standards into an activity necessitates pedagogical knowledge for developing students’ learning in both subject areas. For science education, the skills and tools for curricula integration include the use of other key learning areas (KLAs). A balance between teacher and student-centred science education programs that draw on democratic processes (e.g., Beane, 1997) can be used to make real-world links to target students’ individual needs. This article presents practical ways to commence thinking about curricula integration towards using Australian curriculum standards.

Reputation modelling in Citizen Science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Data reliability issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. Participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data reliability has become an urgent demand. This study aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we propose to design a reputation framework to enhance data reliability and also investigate some critical elements that should be aware of during developing and designing new reputation systems.

Understanding the participatory library through a grounded theory study

Lankes and Silverstein (2006) introduced the “participatory library” and suggested that the nature and form of the library should be explored. In the last several years, some attempts have been made in order to develop contemporary library models that are often known as Library 2.0. However, little research has been based on empirical data and such models have had a strong focus on technical aspects but less focus on participation. The research presented in this paper fills this gap. A grounded theory approach was adopted for this study. Six librarians were involved in in-depth individual interviews. As a preliminary result, five main factors of the participatory library emerged including technological, human, educational, social-economic, and environmental. Five factors influencing the participation in libraries were also identified: finance, technology, education, awareness, and policy. The study’s findings provide a fresh perspective on contemporary library and create a basis for further studies on this area.

Phenomenography : opening a new territory for library and information science research

Phenomenography is a research approach devised to allow the investigation of varying ways in which people experience aspects of their world. Whilst growing attention is being paid to interpretative research in LIS, it is not always clear how the outcomes of such research can be used in practice. This article explores the potential contribution of phenomenography in advancing the application of phenomenological and hermeneutic frameworks to LIS theory, research and practice. In phenomenography we find a research toll which in revealing variation, uncovers everyday understandings of phenomena and provides outcomes which are readily applicable to professional practice. THe outcomes may be used in human computer interface design, enhancement, implementation and training, in the design and evaluation of services, and in education and training for both end users and information professionals. A proposed research territory for phenomenography in LIS includes investigating qualitative variation in the experienced meaning of: 1) information and its role in society 2) LIS concepts and principles 3) LIS processes and; 4) LIS elements.

The science curriculum : the decline of expertise and the rise of bureaucratise

The content for the school science curriculum has always been an interplay or contest between the interests of a number of stakeholders, who have an interest in establishing it at a new level of schooling or in changing its current form. For most of its history, the interplay was dominated by the interests of academic scientists, but in the 1980s the needs of both future scientists and future citizens began to be more evenly balanced as science educators promoted a wider sense of science. The contest changed again in the 1990s with a super-ordinate control being exerted by government bureaucrats at the expense of the subject experts. This change coincides with the rise in a number of countries of a market view of education, and of science education in particular, accompanied by demands for public accountability via simplistic auditing measures. This shift from expertise to bureaucratise and its consequences for the quality of science education is illustrated with five case studies of science curriculum reform in Australia.

Preparing citizens for a complex world : the grand challenge of teaching socio-scientific issues in science education

The dawn of the twenty-first century encouraged a number of scientific and technological organisations to identify what they saw as ‘Grand Challenges and Opportunities’. Issues of environment and health featured very prominently in these quite short lists, as can be seen from a sample of these challenges in Table 1. Indeed, the first two lists of challenges in Table 1 were identified as for the environment and for health, respectively.

The challenge of generic competences to science education

Since 2000 there has been pressure on education systems for develop in students a number of competences that are described as generic. This pressure stems from studies of the changing nature of work in the Knowledge Society that is now so dominant. The DeSeCo project identified a number of these competences, and listed them under the headings of communicative, analytical and personal. They include thinking, creativity, communication skills, knowing how to learn, working in teams, adapting to change, and problem solving. These competences pose a substantial challenge to the manner in which education as a whole, and science education in particular, has hitherto been generally conceived. It is now common to find their importance acknowledged in new formulation of the curriculum. The paper reviews a number of these curriculum documents and how they have tried to relate these competences to the teaching and learning of Science, a subject with its own very specific content for learning. It will be suggested that the challenge provides an opportunity for a reconstruction of the teaching and learning of science in schools that will increase its effectiveness for more students.

Power electronic converters for high power ultrasound transducers

Piezoelectric transducers convert electrical energy to mechanical energy and play a great role in ultrasound systems. Ultrasound power transducer performance is strongly related to the applied electrical excitation. To have a suitable excitation for maximum energy conversion, it is required to analyze the effects of input signal waveform, medium and input signal distortion on the characteristic of a high power ultrasound system (including ultrasound transducer). In this research, different input voltage signals are generated using a single-phase power inverter and a linear power amplifier to excite a high power ultrasound transducer in different medium (water and oil) in order to study the characteristic of the system. We have also considered and analyzed the effect of power converter output voltage distortions on the performance of the high power ultrasound transducer using a passive filter.

Integration of opinion into customer analysis model

As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. In order to enhance customer satisfaction and their shopping experiences, it has become important to analysis customers reviews to extract opinions on the products that they buy. Thus, Opinion Mining is getting more important than before especially in doing analysis and forecasting about customers’ behavior for businesses purpose. The right decision in producing new products or services based on data about customers’ characteristics means profit for organization/company. This paper proposes a new architecture for Opinion Mining, which uses a multidimensional model to integrate customers’ characteristics and their comments about products (or services). The key step to achieve this objective is to transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations. This research presents a comprehensive way to calculate customers’ orientation for all possible products’ attributes.