81 research outputs found
Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues
No full textA three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human vision system (HVS). The reason for the usage of these high-cost and time-consuming subjective tests is due to the lack of an objective video Quality of Experience (QoE) evaluation method that models the HVS. In this paper, we propose a hybrid 3D-video QoE evaluation method based on spatial resolution associated with depth cues (i.e., motion information, blurriness, retinal-image size, and convergence). The proposed method successfully models the HVS by considering the 3D video parameters that directly affect depth perception, which is the most important element of stereoscopic vision. Experimental results show that the measurement of the 3D-video QoE by the proposed hybrid method outperforms the widely used existing methods. It is also found that the proposed method has a high correlation with the HVS. Consequently, the results suggest that the proposed hybrid method can be conveniently utilized for the 3D-video QoE evaluation, especially in real-time applications
Watermarking of Parkinson Disease Speech in Cloud-Based Healthcare Framework
No full textMobile healthcare in a cloud-based system increases the easiness and the ubiquitous nature of patient-doctor relationship. One of the major issues of this healthcare is secure transmission and data authenticity. If the data is not transmitted securely or not authenticated, the clients may face embarrassment. In this paper, we propose a cloud-based healthcare framework that will authenticate speech data from a patient suspected to have Parkinson's disease. The patient sends his or her speech signal recorded via a smart phone through Internet to the cloud. A discrete wavelet transform- (DWT-) singular value decomposition (SVD) based speech watermarking module is run in the cloud to embed watermark to the signal. In case of authentication, watermark is extracted from the questioned signal and matched with the stored watermark. Experimental results indicate that the proposed DWT-SVD based watermarking system achieves imperceptibility and is robust against attacks such as additive white Gaussian noise and filtering
Image Tampering Detection Based on Local Texture Descriptor and Extreme Learning Machine
No full textDeep learning framework for short term power load forecasting, a case study of individual household energy customer
No full textData Reduction Using Change Coding for Remote Applications of wireless Visual Sensor Networks
No full textThe data reduction capability of image compression schemes is limited by the underlying compression technique. For applications with minor changes between consecutive frames, change coding can be used to further reduce the data. We explored the efficiency of change coding for data reduction in a wireless visual sensor network (WVSN). This paper presents an analysis of the compression efficiency of change coding for a variety of changes, such as different shapes, sizes, and locations of white objects in adjacent sets of frames. Compressing change frame provides a better performance compared with compressing the original frames for up to 95% changes in the number of objects in adjacent frames. Due to illumination noise, the size of the objects increases at its boundaries, which negatively affects the performance of change coding. We experimentally proved that the negative impact of illumination noise could be reduced by applying morphology on the change frame. Communication energy consumption of the VSN is dependent on the data that are transmitted to the server. Our results show that the communication energy consumption of the VSN can be reduced by 27%, 29%, and 46% by applying change coding in combination with JBIG2, Group4, and Gzip_pack, respectively. The findings presented in this paper will aid researchers in enhancing the compression potential of image coding schemes in the energy-constrained applications of WVSNs.SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle
Analysis of Binary Image Coding Methods for Outdoor Applications of Wireless Vision sensor Networks
No full textThe processing of images at the vision sensor nodes (VSN) requires a high computation power and their transmission requires a large communication bandwidth. The energy budget is limited in outdoor applications of wireless vision sensor networks (WVSN). This means that both the processing of images at the VSN and the communication to server must be energy efficient. The wireless communication of uncompressed data consumes huge amounts of energy. Data compression methods are efficient in reducing data in images and can be used for the reduction in transmission energy. We have evaluated seven binary image coding techniques. Our evaluation is based on the processing complexity and energy consumption of the compression methods on the embedded platforms. The focus is to come up with a binary image coding method, which has good compression efficiency and short processing time. An image coding method with such attributes will result in reduced total energy requirement of the node. We have used both statistically generated images and real captured images, in our experiments. Based on our results, we conclude that International Telegraph and Telephone Consultative Committee Group 4, gzip_pack and JPEG-LS are suitable coding methods for the outdoor applications of WVSNs.SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle
Improved Particle Swarm Optimization Based on Velocity Clamping and Particle Penalization
No full textRetinal Vessel Segmentation Based on the Anam-Net Model
No full textAccurate segmentation of retinal blood vessels can help ophthalmologists diagnose eye-related diseases such as diabetes and hypertension. The task of segmentation of the vessels comes with a number of challenges. Some of the challenges are due to haemorrhages and microaneurysms in fundus imaging, while others are due to the central vessel reflex and low contrast. Encoder-decoder networks have recently achieved excellent performance in retinal vascular segmentation at the trade-off of increased computational complexity. In this work, we use the Anam-Net model to accurately segment retinal vessels at a low computational cost. The Anam-Net model consists of a lightweight convolutional neural network (CNN) along with bottleneck layers in the encoder and decoder stages. Compared to the standard U-Net model and the R2U-Net model, the Anam-Net model has 6.9 times and 10.9 times fewer parameters. We evaluated the Anam-Net model on three open-access datasets: DRIVE, STARE, and CHASE_DB. The results show that the Anam-Net model achieves better segmentation accuracy compared to several state-of-the-art methods. For the DRIVE, STARE, and CHASE DB datasets, the model achieved {sensitivity and accuracy} of {0.8601, 0.9660}, {0.8697, 0.9728}, and {0.8553, 0.9746}, respectively. On the DRIVE, STARE, and CHASE_DB datasets, we also conduct cross-training experiments. The outcome of this experiment demonstrates the generalizability and robustness of the Anam-Net model
Reconfigurable intelligent surfaces (RIS) using NOMA with thermal energy harvesting
No full textInternational audienceThe integration of Reconfigurable Intelligent Surfaces (RIS) with Non-Orthogonal Multiple Access (NOMA) and thermal energy harvesting presents a novel approach to enhancing wireless communication networks. RIS technology optimizes signal propagation and improves network efficiency through programmable surface elements, while NOMA increases spectral efficiency by allowing multiple users to share the same frequency resource. When combined with thermal energy harvesting, which captures ambient heat and converts it into electrical power, this integration offers a sustainable solution to power the RIS infrastructure. This paper explores the synergistic benefits of RIS using NOMA with thermal energy harvesting, examining its impact on network performance, energy efficiency, and sustainability. Through a review of recent advancements and research, we discuss how this combined approach can address key challenges in modern wireless communications and contribute to the development of greener, more efficient networks
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