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 singlechannel 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.
This paper presents a very low-memory video compression architecture for implementation in a wireless multimedia sensor network. The approach employs a strip-based processing technique where a group of image sequences is partitioned into strips, and each strip is encoded separately. A new one-dimensional, memory-addressing method is proposed to store the wavelet coefficients at predetermined locations in the strip buffer for ease of coding. To further reduce the memory requirements, the video-coding scheme uses a modified set-partitioning in hierarchical trees algorithm to give a high compression performance. The proposed work is implemented using a soft-core microprocessor-based approach. Simulation tests conducted have verified that even though the proposed video compression architecture using strip-based processing requires a much less complex hardware implementation and its efficient memory organisation uses a lesser amount of embedded memory for processing and buffering, it can still achieve a very good compression performance
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.
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.
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.
Abstract-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.
Due to the limited Field-Of-View (FOV) of a single camera, it is sometimes desired to extend the FOV using multiple cameras. Image stitching is one of the methods that can be used to exploit and remove the redundancy created by the overlapping FOV. However, the memory requirement and the amount of computation for conventional implementation of image stitching are very high. In this paper, this problem is resolved by performing the image stitching and compression in a strip-by-strip manner. First, the stitching parameters are determined by transmitting two reference images to an intermediate node to perform the processing. Then, these parameters are transmitted back to the visual node and stored in there. These parameters will be used to determine the way of stitching the incoming images in a strip-by-strip manner. After the stitching of a strip is done, it can be further compressed using a strip-based compression technique
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