The impact of state-of-the-art techniques for lossless still image compression
A great deal of information is produced daily, due to advances in telecommunication, and
the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data …
the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data …
Computational 2D and 3D medical image data compression models
In this world of big data, the development and exploitation of medical technology is vastly
increasing and especially in big biomedical imaging modalities available across medicine …
increasing and especially in big biomedical imaging modalities available across medicine …
An optimal adaptive reweighted sampling-based adaptive block compressed sensing for underwater image compression
Abstract The use of Block Compressed Sensing (BCS) as an alternative to conventional
Compressed Sensing (CS) in image sampling and acquisition has gained attention due to …
Compressed Sensing (CS) in image sampling and acquisition has gained attention due to …
Automatic recognition algorithm of traffic signs based on convolution neural network
H Xu, G Srivastava - Multimedia Tools and Applications, 2020 - Springer
Because of the hierarchical significance of traffic sign images, the traditional methods do not
effectively control and extract the brightness and features of layered images. Therefore, an …
effectively control and extract the brightness and features of layered images. Therefore, an …
RETRACTED ARTICLE: a discrete wavelet transform and recurrent neural network based medical image compression for MRI and CT images
SR Sabbavarapu, SR Gottapu, PR Bhima - Journal of Ambient Intelligence …, 2021 - Springer
Medical imaging is an active and develo** field that has an impact on recognition,
diagnosis and surgical planning of the disease. The image compression is introduced in the …
diagnosis and surgical planning of the disease. The image compression is introduced in the …
Optimized active contor segmentation model for medical image compression
Nowadays, medical imaging systems tend to have greatest impact on disease identification,
diagnosis, and surgical preparation. To save hardware space and transmission bandwidth, it …
diagnosis, and surgical preparation. To save hardware space and transmission bandwidth, it …
An efficient priority‐based convolutional auto‐encoder approach for electrocardiogram signal compression in Internet of Things based healthcare system
RK Mahendran, P Velusamy… - Transactions on …, 2021 - Wiley Online Library
Due to advancements in healthcare monitoring systems, the Internet of Things concepts are
proficiently utilized in the medical field to detect and diagnose the physical health problems …
proficiently utilized in the medical field to detect and diagnose the physical health problems …
Gps interference signal recognition based on machine learning
J Xu, S Ying, H Li - Mobile Networks and Applications, 2020 - Springer
Abstract The Global Positioning System (GPS) is not only widely used in navigation,
measurement and other services, but also an indispensable key equipment for the military …
measurement and other services, but also an indispensable key equipment for the military …
Micro-distortion detection of lidar scanning signals based on geometric analysis
When detecting micro-distortion of lidar scanning signals, current hardwires and algorithms
have low compatibility, resulting in slow detection speed, high energy consumption, and …
have low compatibility, resulting in slow detection speed, high energy consumption, and …
Segmentation based medical image compression of brain magnetic resonance images using optimized convolutional neural network
Image compression plays a crucial role in the field of medical imaging, including Magnetic
Resonance Imaging (MRI). The MRI images are typically large and high-resolution, which …
Resonance Imaging (MRI). The MRI images are typically large and high-resolution, which …