Approaches for M-Health Environment

It is a well-known fact that when a camera or other imaging system captures an image, often, the vision system for which it is captured cannot implement it directly. There may be several reasons behind this fact such as there can exist random intensity variation in the image. There can also be illumination variation in the image or poor contrast. These drawbacks must be tackled at the primitive stages for optimum vision processing. This chapter will discuss different filtering approaches for this purpose. The chapter begins with the Gaussian filter, followed by a brief review of different often used approaches. Moreover, this chapter will also render different filtering approaches including their hardware architectures.

The Padding of Vein Image Features and Hardware Designs in M-Health Environments

This chapter describes the timing diagrams of padding features and hardware designs of segmentation, controllers, and filters. Further, the authors have described that the hardware design concept of segmentation task can be performed online in a distributed cloud computing m-health environment. The segmentation phase uses two Gaussian filter functions with different sizes of filter masks and standard deviation with a threshold value to make a distinction between veins image patterns and the corresponding backgrounds in the cloud IoT-based m-health environment. In order to design the hardware architecture of the median filter, the superior moving window architecture is used by researchers to accommodate a larger size median filter in the cloud IoT-based m-health environment.

Algorithms for Vein Image Enhancement and Matching in the Cloud IoT-Based M-Health Environment

In this chapter, the authors have described the methodologies to achieve the objectives of veins image enhancement, feature extractions, and matching with other veins images in the cloud IoT-based m-health environment. The initial steps to propose the algorithms for veins image enhance and feature extractions will have five parts. Once the proposed algorithm is written, the hardware architecture designs of the proposed veins image enhancements and feature extraction algorithm will be described by the authors. The hardware designs are presented in subsequent sections of this chapter. Further, the hardware designs are elaborated in detail for each of the techniques. The presented algorithms are implemented in MATLAB 11.0 software, and these algorithms are simulated and integrated with different veins sample images. The hardware designs of veins image enhancements and feature extractions are implemented using Verilog Hardware Language Description (VHLD), and these implemented results are simulated using MSA (Model-Sim-Altera) for sample images of different types of veins.

Future Generation Computing in M-Health

The implementation of healthcare-related big data in m-health has constantly been considered as the most prevalent technological breakthrough of the modern era. Indeed, the use of healthcare-related big data in m-health is a pivotal and substantially challenging task and is still not chiefly considered by the researchers. This is predominantly indispensable owing to the perpetual cascading of structured and unstructured datasets being elicited abundantly from multifold m-health applications within the purview of diverse healthcare systems. Perhaps, there are many innovative paradigms, which, if synergistically used in the domain of m-health, can generate the next level of computing in this purview. This chapter will render the relevance of big data from the point of view of m-health as well as the existing and future attributions of different machine and deep learning techniques in the pursuit of m-health.

A Glimpse of Hardware Design Approaches

In this chapter, the authors have described that in order to design the vein enhancement and feature extraction algorithm, different modules such as DSP, embedded processor, hardware accelerator, and FPGA are implemented. Further, it has also been revealed in this chapter that the performance of the vein algorithm implemented on the Nios-II and DSP processor is not considered fast though the DSP processor is designed for signal processing applications. The FPGA is an acceptable choice for researchers due to low-cost factors. The FPGA is implemented for the hardware design of the vein algorithm. However, the performance result was not fast. Furthermore, to cater to the need for better performance, innovative hardware design architecture is the need of the time. It is observed that if there are considerable calculations in the algorithm, the optimization of the algorithm with the parallel processing capabilities of hardware will be a good choice as it can mitigate the error of the calculations.

Results and Discussions of Palm-Dorsa-Veins-Based Systems in the Cloud IoT-Based M-Health Environment

The results of palm-dorsa-veins-based m-health systems in a cloud-computing environment are discussed and analyzed in a detailed way in this chapter of the book. The sample images S1, S2, S3, and S4 are being used for hardware designs and performance evaluation in the cases of re-sampling, segmentation, median filters, thinning and Top veins, which will be used for critically ill and general patients' identity verification in the cloud IoT-based m-health environments. The ModelSim-Altera hardware design language is used as a simulator tool to simulate the hardware design with sample veins images. Further, the ModelSim-Altera simulation outcomes are compared with MATLAB implementations for evaluating the performances of hardware designs of the described algorithms in the cloud IoT-based m-health environment. The outcomes are analyzed, and the details of these outcomes are discussed in this chapter.

The Panoramic Views of Cloud IoT-Based M-Health Biometrics

The veins-based biometric systems use the molds and patterns of the veins' images of the human body for identification in standalone systems or a cloud internet of things (IoT)-based networking environment. The beauty of using veins-based systems for identification is that the vein pattern cannot be stolen or duplicated or washed out because of its availability in the human body. Currently, vein patterns of fingers, hand, palm, heart, head, palm-dorsa, and wrist of humans are used for biometric identification purposed in cloud and IoT-based network environments. In this chapter, the authors have described different types of algorithms including parallel algorithms for identifying persons in clouds and IoT-based environments. The authors observed that many researchers have designed and developed several algorithms to improve and extract the veins patterns from different parts of the human body for identification in different types of environments including clouds and the internet of things.

Performance Evaluation of Hardware Designs, Thinning, and Segmentation Algorithms in M-Health Environments

In this chapter, the authors have described the experimental analysis steps required for converting original veins images into thinned veins images by applying resample, segmentation, filtering, and thinning algorithms in the cloud IoT-based m-health environments. It is a little bit difficult to make a distinction between the vein pattern and the surroundings particularly in the cases of unclear and thin veins images. However, after applying the resample, segmentation, median filters, and thinning algorithms in the cloud IoT-based m-health environment, the superior quality veins image patterns of a single line are obtained.