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Previous studies in computer science education show that game playing is negatively correlated with success in introductory programming classes in (Wilson, Shrock ACM SIGCSE Bulletin, vol 33, pp 184–188, 2001). However, informally, we observed that students who have previous gaming experience take to programming tasks easier than those without gaming experience. There have also been recent studies that show that playing strategic video games can improve problem-solving skills which is an essential skill in program design. This chapter presents the findings of our study to identify if a correlation between previous gaming experience (game playing) and individual computational thinking (CT) skills exists. To achieve this, a survey was administered on undergraduate students undertaking an introductory computing course to collect data on their gaming history and an individual assignment on Scratch. Each project was subsequently analysed to determine the level of mastery of core CT skills. Cochran–Armitage test of trend was then executed on each CT skill category with respect to the coded gaming experience. The results obtained during our analysis shows a correlation between gaming experience and specific categories of the CT skills domain particularly in the area of abstraction and problem-solving and user interactivity. The outcome of our study should be beneficial as ways to leverage on students’ gaming experience in the classroom will also be discussed.

The objective of this study is to investigate the feasibility of a single-electrode electroencephalogram (EEG)-based brain-computer interface (BCI) in differentiating two conditions. This approach has the potential to be implemented as a computer input device for users to express choices (e.g., left and right, yes and no). The attentional allocation of participants among boxes that each flicker at a different frequency (e.g., 8.6 Hz and 12 Hz) can be distinguished based on EEG alone. Traditionally, steady-state visual evoked potentials (SSVEPs) are studied using multi-channel EEG systems, which greatly hinders the user's mobility. Although SSVEPs are mostly examined in the frequency domain and from the occipital region of the brain, we tested five classifiers with 44 features extracted from the EEG, recorded using an electrode at the frontopolar area (FP1). Apart from using frequency-domain features, such as fast Fourier transform (FFT) coefficients and power spectral density (PSD) features, we also included time-domain features from the pre-frontal region and achieved an average classification accuracy of 74.58% using a random forest (RF) classifier.

Extracting information from a large number of scientific documents prepared in portable document format (PDF) is a time-consuming process, if all this is to be done without the help of an automated system. However, the missing of structural information in PDF can create a lot of issues during the extraction process. Watermark is one of the objects that can have a negative effect on this. When PDF extraction tool is applied to PDF with watermark, the watermark can affect the order of the text and is often extracted as part of the text. If the text is to be used for analysis in the future, the watermark might affect the accuracy in the results, since they should not be taken into consideration. In this paper, an approach that can be used to overcome the issue above is proposed. The proposed approach makes use of direct text recognition from PDF and optical character recognition (OCR) to produce two version of digital text that can be combined for better accuracy. The results shown that the proposed approach is capable of extracting text from PDF with different watermark patterns.

In this article, a multiview image compression framework, which involves the use of Block-based Compressive Sensing (BCS) and Joint Multiphase Decoding (JMD), is proposed for a Visual Sensor Network (VSN). In the proposed framework, one of the sensor nodes is configured to serve as the reference node, the others as nonreference nodes. The images are encoded independently using the BCS to produce two observed measurements that are transmitted to the host workstation. In this case, the nonreference nodes always encoded the images (I NR ) at a lower subrate when compared with the images from the reference nodes (I R ). The idea is to improve the reconstruction of I NR using I R . After the two observed measurements are received by the host workstation, they are first decoded independently, then image registration is applied to align I R onto the same plane of I NR . The aligned I R is then fused with I NR , using wavelets to produce the projected image I P . Subsequently, the difference between the measurements of the I P and I NR is calculated. The difference is then decoded and added to I P to produce the final reconstructed I NR . The simulation results show that the proposed framework is able to improve the quality of I NR on average by 2dB to 3dB at lower subrates when compared with other Compressive Sensing (CS)--based multiview image compression frameworks.

This paper demonstrates electroencephalogram (EEG) analysis in MATLAB environment with the objective to investigate effectiveness of cognitive stress recognition algorithm using EEG from single-electrode BCI. 25 subjects' EEG were recorded in MATLAB with the use of Stroop color-word test as stress inducer. Questionnaire on subjects' self-perceived stress scale during Stroop test were gathered as classification's target output. The main analysis tool used were MATLAB, coupled with the use of Discrete Cosine Transform (DCT) as dimension reduction technique to reduce data size down to 2% of the origin. Three pattern classification algorithms' - Artificial Neural Network (ANN), k-Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) were trained using the resulted 2% DCT coefficients. Our study discovered the use of DCT along with KNN offers highest average classification rate of 72% compared to ANN and LDA.

Devices in a visual sensor network (VSN) are mostly powered by batteries, and in such a network, energy consumption and bandwidth utilization are the most critical issues that need to be taken into consideration. The most suitable solution to such issues is to compress the captured visual data before transmission takes place. Compressive sensing (CS) has emerged as an efficient sampling mechanism for VSN. CS reduces the total amount of data to be processed such that it recreates the signal by using only fewer sampling values than that of the Nyquist rate. However, there are few open issues related to the reconstruction quality and practical implementation of CS. The current studies of CS are more concentrated on hypothetical characteristics with simulated results, rather than on the understanding the potential issues in the practical implementation of CS and its computational validation. In this paper, a low power, low cost, visual sensor platform is developed using an Arduino Due microcontroller board, XBee transmitter, and uCAM-II camera. Block compressive sensing (BCS) is implemented on the developed platform to validate the characteristics of compressive sensing in a real-world scenario. The reconstruction is performed by using the joint multi-phase decoding (JMD) framework. To the best of our knowledge, no such practical implementation using off the shelf components has yet been conducted for CS.

The main problem for event gathering in wireless sensor networks (WSNs) is the restricted communication range for each node. Due to the restricted communication range and high network density, event forwarding in WSNs is very challenging and requires multihop data forwarding. Currently, the energy-efficient ant based routing (EEABR) algorithm, based on the ant colony optimization (ACO) metaheuristic, is one of the state-of-the-art energy-aware routing protocols. In this paper, we propose three improvements to the EEABR algorithm to further improve its energy efficiency. The improvements to the original EEABR are based on the following: (1) a new scheme to intelligently initialize the routing tables giving priority to neighboring nodes that simultaneously could be the destination, (2) intelligent update of routing tables in case of a node or link failure, and (3) reducing the flooding ability of ants for congestion control. The energy efficiency improvements are significant particularly for dynamic routing environments. Experimental results using the RMASE simulation environment show that the proposed method increases the energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR without incurring a significant increase in complexity. The method is also compared and found to also outperform other swarm-based routing protocols such as sensor-driven and cost-aware ant routing (SC) and Beesensor.

Asian students with conservative behaviors tend to feel embarrassed and reluctant to ask questions in large classrooms. This paper proposed a mobile-compatible web-based question and answer application to address the aforementioned problem. This proposed web application is known as Quick Question (QQ). QQ is designed to allow students to post questions anonymously and rapidly during class hours. QQ can be accessed online from a web hosting server, or offline from a Raspberry Pi without Internet access. QQ can run on multiple platforms with different screen sizes and capable of serving 250 concurrent users while using the Raspberry Pi. QQ also includes a profanity filter that censor bad words with an accuracy of 86.11%. Furthermore, a term extraction and learning mechanism is implemented to help in grouping and sorting questions posted by students in a class.

When the embedded zerotree wavelet (EZW) algorithm was first introduced by Shapiro, four types of symbols (zerotree (ZTR), isolated zero (IZ), positive (POS), and negative (NEG)) were used to represent the tree structure. An improved version of EZW, the set partitioning in hierarchical trees (SPIHT) algorithm was later proposed by Said and Pearlman. SPIHT removed the ZTR symbol, while keeping the other three symbols in a slightly different form. In the SPIHT algorithm, the coding of the parent node is isolated from the coding of its descendants in the tree structure. Therefore, it is no longer possible to encode the parent and its descendants with a single symbol. When both the parent and its descendants are insignificant (forming a degree-0 zerotree (ZTR)), it cannot be represented using a ZTR symbol. From our observation, the number of degree-0 ZTRs can occur very frequently not only in natural and synthesis images, but also in video sequences. Hence, the ZTR symbol is reintroduced into SPIHT in our proposed SPIHT-ZTR algorithm. In order to achieve this, the order of sending the output bits was modified to accommodate the use of ZTR symbol. Moreover, the significant offspring were also encoded using a slightly different method to further enhance the performance. The SPIHT-ZTR algorithm was evaluated on images and video sequences. From the simulation results, the performance of binary-uncoded SPIHT-ZTR is higher than binary-uncoded SPIHT and close to SPIHT with adaptive arithmetic coding.

This paper introduces a smoothing and preprocessing (S+P) technique for a line-based one-bit-transform (1BT) motion estimation scheme. In the proposed algorithm, a smoothing threshold ( Threshold S) is incorporated into the 1BT convolutional kernel. By using the smoothing threshold, scattering noise which is a common problem in most 1BT images can be greatly reduced. After the transformation, the 1BT images for the current and reference frames are divided into a number of macroblocks. The macroblock in the current frame is first compared with the macroblock at the same position in the reference frame. If the Sum of Absolute Difference (SAD) is below a certain preprocessing threshold ( Threshold P), the macroblock in the current frame is considered to have negligible movement and motion search is not performed. Simulation results show that this technique achieves high performance and greatly reduces the number of search operations. By incorporating the S+P technique, the PSNR achieved by the 1BT is approaches the performance of the 8-bit Full Search Block Matching Algorithm (FSBMA), and the difference is as low as 0.08 dB. In addition, this technique outperforms current state-of-the-art 1BT motion estimation techniques. An improvement in PSNR performance by up to 0.6 dB and a reduction in the number of search operations by 60% to 93% is achieved using video conferencing sequences.

In this paper, a Multi-phase Reconstruction (MPR) framework that uses certain key frames to produce some Side Information (SI) to improve the reconstruction quality of the non-key frames is proposed. After a sequence of frames has been encoded using Block Compressive Sensing (BCS) and transmitted to the host workstation, some SI is produced by first aligning the key frames to the non-key frames. The aligned frames are then fused together using Wavelet to exploit the spatial and temporal correlations between them, and to generate a set of predicted non-key frames. Next, the difference between the initially reconstructed and the predicted non-key frames at the measurement level is calculated. The difference is then decoded to recover a set of residual frames. The reconstruction of the final non-key frames is completed by adding the residual frames to the predicted non-key frames. The experimental results show that the proposed framework is able to outperform other frameworks by 1.5–3.0 dB at lower sub-rates.

This paper describes the pedagogy of how the 7-week course on computer fundamentals is introduced and taught to a class of 700 first year undergraduate students from the schools of business and computing. The computer fundamentals course itself covers theoretical topics such as the hardware and software of computers, information systems and programming basics as well as practical topics such as using office productivity software and basic programming. This course was conducted using a hybrid learning approach in which video lectures were used in conjunction with face-to-face practical work. The design, method, experience and recommendations from conducting this course will be presented. Feedback from 261 students regarding how the teaching had benefited their learning on average is 61% positive, 11% negative and the remaining were neutral.

Recent studies reveal that driving without sufficient sleep would increase the risk of road traffic accident. With the aim to facilitate a safer driving experience, eye activity detection algorithm were studied actively. Though the use of wired multi-channel brain computer interface (BCI) to monitor driver's mental state has shown promising results, but the actual practicality were limited by its inconvenience. Consequently, we examined the effectiveness of a wireless and wearable single-channel BCI in detecting driver's eye-states. Using the NeuroSky MindWave headset that entailed a single-electrode for prefrontal cortex, we observed an increment of low alpha activity during the transition from eyes-open to eyes-closed state. A monitoring system to keep drivers awake by means of alarm notifications is then implemented using adaptive percentage threshold algorithm for alarm-triggering purpose. Through simulation, our algorithm has demonstrated an EEG eye-states recognition system with: adequate detection rate of 31% per second, negligible false alarm rate of 0.5%, and minimum latency of 2 seconds.

The aim of this paper is to investigate the effects of rainfall on image stitching by measuring the performance of the Harris corner detector, Scale-Invariant Feature Transform (SIFT) detector, and the Speeded Up Robust Feature (SURF) detector. A set of images were captured by using two cameras during light rain and during heavy rain. The number of detected features points was used as the performance measure. The results indicate that all the detectors are capable to extract a substantial amount of feature points. However, the rainfall does reduce the number of detected feature points by 48 and 67% when the Harris corner detector and the SURF detector were adopted respectively. At the end, the images were stitched together based on the feature points extracted by using the Harris corner detector. The results indicate that the effect of rainfall is negligible as long as there is a substantial amount of distinctive feature points.

This paper proposed an energy efficient multi-view visual processing scheme for the visual sensor networks (VSNs). The aim of this scheme is to reduce the energy consumption by reducing the amount of data need to be transmitted over the network. This is achieved by using two approaches. First, an image compression based on the Set Partitioning in Hierarchical Trees (SPIHT) [1] algorithm is implemented to compress the images before transmitting them over the network. Second, in cases where the Field-of-View (FoV) of different visual nodes is overlapped, the overlapping regions (redundant information) between the images will be identified at the host workstation to prevent them from being transmitted multiple times. This is to further reduce the data transmission. In this case, only one visual node is required to transmit the overlapping regions. The information obtained from the aforementioned visual node will be used to reconstruct the overlapping regions of other visual nodes at the host workstation. In other words, other visual nodes are only required to transmit the non-overlapping regions. Instead of transmitting the entire image over the network, only part of it will be transmitted. The simulations results show that our proposed scheme can reduce the data transmission for 50%. Furthermore, the quality of reconstructed images is acceptable and can be improved in further works.

The wireless multimedia sensor network (WMSN) is an example of a platform for a multiple camera application. Since the devices in the WMSN are powered by batteries, energy consumption remains a critical issue. The previous chapters have discussed methods for single-view information reduction by removing the redundancy present in an image using event compression or by removing unimportant image frames using event detection. For multiple camera applications, additional redundancy can be found in cases where there is an overlap in the field of view (FOV) of different nodes. This is considered as the inter-camera redundancy that can be exploited to further reduce the data requirements for transmission. In this chapter, several approaches that have been proposed for multiple-view information reduction will be reviewed. The chapter begins by discussing approaches to remove or reduce the inter-camera redundancy. This is followed by a discussion on feature extraction which is a core technique used in searching for the similarities between two images. Then, an overview of the image stitching process will be provided. The performance of using different feature extraction methods to locate the same features in small size images and perform the stitching will be presented as well. The chapter concludes with an implementation of the proposed framework on the WMSN hardware platform.

This chapter presents the designs and implementations for the FPGA wireless multimedia sensor node (WMSN) hardware platforms. Two platforms will be described: a low-cost platform using the Celoxica RC10 FPGA board and a medium-cost platform using the Celoxica RC203E FPGA board. A strip-based low-memory processor based on a modified MIPS architecture will be implemented on the FPGA. For efficient processing, the strip-based MIPS processor contains customised instructions to perform the discrete wavelet transform (DWT). The chapter begins with a discussion of FPGA-based soft-core processors in wireless sensor systems and then moves on to describe the WMSN hardware platforms using the Celoxica FPGA boards. Next, the datapath and control architectures for the strip-based MIPS are discussed. The chapter concludes with an illustrative implementation of the DWT on the hardware platform using the Handel-C hardware description language. The DWT implementation will also be used in the later chapters on event detection and event compression.

This chapter discusses the hardware technology and programming languages for reconfigurable devices. The first part of the chapter discusses the technology available and the advantages of field programmable gate arrays (FPGAs) for implementation in hardware constrained environments such as wireless multimedia sensor networks and describes the range of FPGA technology and internal architectures using examples from Xilinx and Altera FPGA families. The second part of the chapter presents an overview of hardware description languages for reconfigurable devices. Here, ranges of available languages from lower-level languages like VHDL and Verilog to higher-level C-based languages are discussed. The chapter also contains an introduction to the Handel-C programming language which will be used in the next chapter for the processor specification. The DK Design Suite methodology used by Celoxica is also discussed.

A typical configuration of Visual Sensor Network (VSN) usually consists of a set of vision nodes, network motes, and a base station. The vision node is used to capture image data and transmit them to the nearest network mote. Then, the network motes will relay the data within the network until it reaches the base station. Since the vision node is usually small in size and battery-powered, it restricts the resources that can be incorporated onto it. In this chapter, a Field Programmable Gate Array (FPGA) implementation of a low-complexity and strip-based Microprocessor without Interlocked Pipeline Stage (MIPS) architecture is presented. In this case, the image data captured by the vision node is processed in a strip-by-strip manner to reduce the local memory requirement. This allows an image with higher resolution to be captured and processed with the limited resources. In addition, parallel access to the neighbourhood image data is incorporated to improve the accessing speed. Finally, the performance of visual saliency in using the proposed architecture is evaluated.

A degree-k zerotree coder codes the significance of a spatial orientation tree (SOT) up to k levels. A SOT is referred to as a degree-k zerotree if all the nodes are insignificant with respect to a threshold except for the nodes in the top k levels. Coding with a higher degree zerotree requires fewer encoding bits especially at lower bit-rates where most of the coefficients are insignificant. However, it may be less efficient at higher bit-rates since the wavelet coefficients are more likely to be significant as the number of planes encoded is increased. In this paper, a tuned degree-k zerotree wavelet (TDKZW) coding scheme where the degree of zerotree tested is tuned at each coding pass is presented. The concept of virtual coding is also applied in conjunction with the proposed algorithm. Simulation results show that our proposed coding scheme consistently outperforms the traditional SPIHT binary encoded coding.

A multiview image compression framework for the Wireless Multimedia Sensor Network (WMSN) based on image stitching and SPIHT coding is proposed. This framework is designed to work with the mesh network topology that is very robust. In this framework, the images taken by neighboring sensors are stitched together with an image stitching technique to remove the overlap redundancy. The image stitching process will be carried out in certain intermediate nodes along the way towards a centralized decoder. This will help to conserve more power since the amount of data to be transmitted is reduced. In addition, the tree structure adapted by SPIHT is modified to improve the flexibility in coding the stitched image. Simulation results show that the use of image stitching with SPIHT coding can greatly remove the overlap and spatial redundancy.

Face recognition using 3D images is an important area of research due to its ability to solve problems faced by 2D images like pose changes. In this chapter, a 3D face range recognition and compression system is proposed and the effect of using compressed 3D range images on the recognition rate is investigated. Compression is used to reduce the file size for faster transmission and is performed using the Set Partitioning in Hierarchical Trees (SPIHT) coding method, which is an improvement of the Embedded Zerotree Wavelet (EZW) coding method. Arithmetic Coding (AC) is also performed after SPIHT to further reduce the amount of bits transmitted. Comparing the uncompressed probe images and probe images compressed using SPIHT coding, simulation results show that the compressed image recognition rate ranges from being lower to being slightly higher than uncompressed probe image recognition rate, depending on bit rate. This proves that a 3D face range recognition system using compressed images is a feasible alternative to a system without using compressed images and should be investigated since the benefits like smaller file storage size, faster image transmission time and better recognition rates are important.

A high performance 2D one-bit-transform (1BT) motion estimation algorithm with smoothing and preprocessing (S + P) is introduced in this paper. The 1BT technique is used to transform an 8-bit image into a 1-bit representation image (1BT image). In the 1BT motion estimation algorithm, the 8-bit current frame (c frame) and reference frame (p frame) are first transformed into their 1BT image respectively, before calculating the Sum of Absolute Difference (SAD) and performing the search operations using the Full Search Block Matching Algorithm (FSBMA). In our proposed algorithm, a smoothing threshold (ThresholdS) is incorporated into the filtering kernel, which is used to perform the transformation from 8-bit image into the 1BT image. The smoothing technique can greatly reduce the scattering noise created in the 1BT image. This will help to improve the accuracy when performing the search operations. After the transformation, the 1BT image for the c frame and p frame is divided into number of macroblocks. The macroblock in the c frame will be first compared to the macroblock at the same position in the p frame. If the SAD is below the preprocessing threshold (ThresholdP), the macroblock is considered to have negligible movement and search operation is not required. This preprocessing technique can greatly reduce the total number of search operations. Simulation results show that an improvement up to 0.65 dB, with reduction in search operation up to 95.07% is achieved. Overall, the proposed S + P technique is very suitable to be used in applications such as video conferencing and monitoring.

In conventional video coding, motion compensation is commonly being used to remove the temporal redundancy. But the computational complexity introduced by the motion compensation can be very high, especially when the full search is adopted. The 3DSPIHT resolves this problem by replacing the motion compensation with the temporal wavelet transform, which require lower computational complexity. However, the performance of the 3DSPIHT is optimal only when Arithmetic Coding (AC) is incorporated. This contradicts the initial idea of reducing the computational complexity with the wavelet transform, because the computational complexity introduced by AC is sufficiently high. In this paper, an efficient binary-uncoded algorithm for video coding called the 3DSPIHT-ZTR is proposed. The idea is to re-introduce the use of ZTR symbol, which is initially used in the Embedded Zerotree Wavelet (EZW) algorithm, into 3DSPIHT. In addition, the significant offspring are also encoded in a slightly different way to further enhance the performance. The performance of 3DSPIHT-ZTR is approximately 1.5 dB better than the conventional 3DSPIHT in terms of PSNR. Its performance is also comparable to 3DSPIHT with AC under certain circumstances.

The proposed scheme is an extension from Set Partitioning In Hierarchical Trees (SPIHT) algorithm. It combines the SPIHT algorithm with Slepian-Wolf (SW) theorem. In most of the video coding scheme that adapted the Slepian-Wolf or Wyner-Ziv (WZ) theorem, a feedback channel is usually incorporated to correct errors that occurred at the decoder side. The used of feedback is a big drawback in system where latency or memory constraints are a critical issue. In order to investigate the possibility in tolerating the errors, and achieve a reasonable result, the feedback channel is not adapted in the proposed scheme. In the proposed scheme, the video sequence will be divided into two types, which defined as Key frame and SW frame. The Key frame is intracoded with SPIHT algorithm alone, while the SW frame is coded using the proposed algorithm. First of all, the SW frame is coded using the SPIHT algorithm to extract the Significant bits, Sign bits, and Refinement bits. Then, the Refinement bits will be further coded by a Coset encoder. The results show that the proposed scheme is perform slightly better than using SPIHT algorithm alone.