Embedded Applications - Medical Applications

1

How to start working with us.

Geolance is a marketplace for remote freelancers who are looking for freelance work from clients around the world.

2

Create an account.

Simply sign up on our website and get started finding the perfect project or posting your own request!

3

Fill in the forms with information about you.

Let us know what type of professional you're looking for, your budget, deadline, and any other requirements you may have!

4

Choose a professional or post your own request.

Browse through our online directory of professionals and find someone who matches your needs perfectly, or post your own request if you don't see anything that fits!

Embedded systems are seen in a variety of medical and biomedical applications. This article will look at some specific examples from the Functional Magnetic Resonance Imaging (fMRI) scanner, Electrocardiography (ECG), EEG, patient monitor, and the X-ray machine to show how analog circuitry is used in these embedded systems. We will also look into several electrical engineering topics that help design them, such as ADC, DAC, gain stages and operational amplifier circuits along with Analog-to-Digital Conversion.

Functional Magnetic Resonance Imaging - Functional Magnetic Resonance Imaging, or fMRI for short, is a non-invasive technique used in neuroscience to characterize brain functions by mapping blood flow changes [1]. It detects the weak magnetic signals produced by the flow of blood. There are many advantages to using fMRI, including high resolution, non-destructiveness, and the availability of functional and structural information about the brain. The measurements made by an fMRI scanner can be divided into two categories anatomical or functional localizers. Anatomical localizers are done first, followed by functional localizations that map activity in specific neural systems, which are like mapping cities on a map with an X-Y coordinate embedded system design.

Applications are welcome and encouraged.

The goal is to create a comprehensive and easily accessible resource for designers and others interested in applying embedded technology to medical device design, bioinstrumentation, and bioengineering.

Functional Magnetic Resonance Imaging has many advantages over the other imaging techniques such as X-ray computed tomography (CT), Positron emission tomography (PET), Single-photon emission computed tomography (SPECT), etc., some of which are classified below:

1) No ionizing radiation

2) High-resolution imager

3) Structural/functional imaging

4) Good temporal resolution

5) Portable

6) Ease to use due to no ionizing radiation or injection of radioactive material.

The main disadvantage of fMRI is that it does not provide good spatial resolution since the magnetic signals measured are tiny, limiting how small an object can be imaged. The second disadvantage is its low sensitivity, which limits its performance when there are little or no differences between the two conditions being measured. The third main problem with the fMRI technique is the contrast mechanism for differentiating oxygenated-deoxygenated blood, which has limited effectiveness. Finally, it cannot detect cortical activity except when veins are located within 1 cm of the studied cortical area.

fMRI has been used to map cognitive functions such as language, memory, or emotions in healthy and diseased brains. This article will look at the main components of an fMRI scanner, which consists of a magnet, gradient coils, transmit & receive coils, homogeneous B field coil (which helps keep everything homogenous), control unit, and amplifier unit, among other things.

If Are you interested in learning more about embedded systems?

Embedded systems are everywhere. They're used in medical equipment, cars, and even your phone! This article will look at some examples of real-time embedded systems and how they work. We will also discuss several electrical engineering topics that help design them such as ADC, DAC, gain stages and operational amplifier circuits along with Analog-to-Digital Conversion.

You can learn all about these concepts by reading our article today! It is packed full of information on the topic so be sure to read it through to find out everything you need to know about embedded systems.

Debugging

The embedded system software on the scanner is one of the biggest challenges since it cannot be done without either removing the scanner from its enclosure or running secondary equipment which could be destructive. In addition, since fMRI scanners need to remain stationary for several hours, some manufacturers offer access only every few days to debug hardware and software problems. This requires manufacturer & user awareness of basic electrical engineering principles to make proper measurements while performing debugging tasks; knowledge that may not always be available.

The RF (Radio Frequency) transmitters used in radar systems also resemble some standard medical imaging techniques like X-ray, Ultrasound, etc.; however, there are some significant differences between them which we will discuss below [3]. The most crucial difference is that Radar transmits a monochromatic signal which provides the required precision because there is no need to compensate for the Doppler effect. To achieve this, the transmitters use high-frequency carrier signals from 25 to 250 MHz which allows them to achieve ultra-wide bandwidths & transmit data at very fast rates compared with other medical imaging techniques such as X-ray and Ultrasound whose frequencies are much lower.

Medical Imaging

PET relies on the decay of radioactive isotopes that emit positrons when they decay. When these particles collide with electrons in ordinary matter, both annihilate each other giving out two 511 keV gamma rays that go in 180° opposite directions. These features make PET non-invasive and suitable for imaging over long periods. The isotopes used are 15O, 11C, 13N, 18F among other positron-emitting radionuclides which have half-lives of 2 hours, 20 minutes, 110 minutes, and 1 hour respectively.

PET is the preferred choice for surgical planning because it has better spatial resolution than CT or MRI techniques [4]. This is because photons are scattered when they pass through matter while neutrons are not scattered in tissue, unlike X-rays & electrons. In addition, PET has a lower radiation dose compared with CT but requires access to a cyclotron which costs about $250 million for construction & another $45 million per year for operating costs. This makes it very expensive there are only 46 medical centers in the US that have a cyclotron & only 28 of these are in the whole operation.

PET scanners use crystals to detect gamma rays emitted by positrons during annihilation. These detectors consist of PMTs (Photomultiplier Tubes) or silicon detectors with an arrangement such that each detector can measure both annihilation events separately. The data is then transmitted to the computer System Controller, where reconstruction, correction, and computation take place. This is followed by image processing after which the final images are displayed on monitors for diagnosis and treatment purposes.

Radar operating systems produce high power signals that travel at right angles to each other and bounce off objects to determine their location relative to the operating system position; this makes it suitable for vast imaging areas without moving parts. The RF signal has reflected the system where it is processed, displayed, and transmitted to the command center.

Radar systems are used extensively in military applications for detecting objects over long distances with high accuracy. This technique has also been used in agricultural applications for sowing & harvesting purposes. While it can be applied to medical imaging, there are some significant differences compared with other techniques which limit its use. One of these limitations is that objects have to be reflective enough for detection at reasonable ranges because most radar waves are absorbed by soft tissues thus limiting the range/depth of penetration possible even with very strong signals. In addition, because most biological materials are not ideal reflectors, the waves need to be focused at very short wavelengths which require large antennas whose control is not practical. Radar wavelengths are typically 100 times the size of an object that reflects them making it difficult to focus & control especially when dealing with small objects whose resolution is also limited.

To achieve the required resolution, the wavelength has to be shorter than 1 cm compared with 300 m for ultrasonic waves and 10 cm for X-rays/CT scans which requires powerful transmitters operating at very high voltages (thousands of volts) which makes them dangerous both to users and patients. In addition, radar devices require higher power levels because their transmitted waveforms are relatively slow at just a few 1 MHz bandwidths instead of ultrasound embedded medical devices whose signals can range up to several hundred MHz bandwidths. Another limitation is that objects must be stationary or moving slowly which eliminates its use for imaging blood flow or tissue perfusion.

Another application that has seen significant uptake in Wireless Sensor Network (WSN) systems where the nodes are generally small & battery-powered have low computational capabilities compared with other techniques and rely on multi-hop routing to relay data packets from one node to another until they reach the receiver located at the base station. This makes it suitable for field deployment in remote locations where wired connectivity is not feasible due to cost or environmental conditions since wireless connectivity ensures reliable communication without additional hardware installations apart from sensor deployments.

The sensors transmit their readings at regular intervals with most being allocated a 16-bit unique address by the controller that identifies them uniquely then rel them to the gateway after which they are transmitted to the Network Server where they are processed, archived, and analyzed.

The sensors use a low-powered radio (2.4 GHz) dedicated for this purpose and have a range of 50m indoors and 200 m outdoors with data rates not exceeding 512 Kbps, which restricts their use in high-resolution imaging systems that require fast image processing. However, it can be used as part of a multi-sensor network such as an integrated Wireless Sensor/Imaging system since the RF signal can transmit images across longer ranges.

The WSN & Radar systems can also be combined to form an Integrated Wireless Imaging System (IWS), where radar sensors capture aerial images or maps of large areas. In contrast, wireless sensor nodes measure the environmental conditions simultaneously, with some being used for guiding airborne vehicles. In contrast, others are used to navigating on the ground using GPS. These types of systems have been deployed in many applications including military, agricultural, commercial & civil security purposes.

The use of integrated WSN/Radar systems has dramatically improved our understanding of environmental processes by making it possible to get previously unobtainable information due to limitations in technology & cost or geographical constraints that would have made wired data transmission infeasible. An example is the effect of climate change on polar ice caps where radar imaging makes measuring changes in their thickness more accurate since they can penetrate thick layers of snow thanks to their more extended range & penetration capabilities which make them suitable for buried targets such as mines and unexploded ordnance (UXO). Other uses include measuring the thickness of glaciers and sea ice which is essential for assessing their rate of melting & health as well as monitoring wildlife.

The systems provide a complete solution that combines data visualization, image processing, and wireless sensor technologies to solve complex environmental problems such as mapping, monitoring, and tracking with applications in the utility industries such as utility grid networks, powerlines, and pipelines as well as underground & underwater long-distance long-range imaging for military use or surveillance purposes.

Integrated Wireless Imaging System (IWS) sensors generally consist of one node dedicated to imaging while others serve other functions such as relaying data packets from link-level sensors and receiving and decoding images from imaging sensors on behalf of the IWS manager with some versions consisting of several cameras mounted on several drones that fly over the target area to obtain images from different angles which are then processed at a centralized location.

Embedded software architectures

For wireless multi-sensor systems using the 802.11p standard are presented along with their potential use in remote imaging applications including image processing, storage & forwarding, optical character recognition (OCR), and robotic navigation among other uses.

A novel WSN architecture for integrated wireless sensor/imaging networks is discussed that enables flexible network layout for large-scale Wireless Sensor Networks (WSN) with different types of sensors & limited or no wired connections to Network Servers or access points. Furthermore, the proposed solution supports dynamic sensor deployment inside buildings where existing Wired Local Area Networks (WLANs) are used as transport media between low-power wireless sensors & higher power devices using WiFi radios.

This new type of system combines three different technologies to provide a complete solution: wireless sensor technology, imaging/imaging radar technology, and networking technologies. By doing so, it provides a highly efficient system that can be used for telemedicine applications such as video-teleconference while providing the necessary diagnostic support to doctors using images & videos captured by sensors at other locations where they are transmitted wirelessly via local WiFi networks which is then combined with data from existing databases or interoperability services such as HL7 or DICOM.

The image processing part of the system includes an image pre-processor that allows selection of one among several cameras and configurations (orientations) and performs essential but straightforward tasks like removing extraneous information like noise, converting grayscale images to binary format, restoring damaged regions in images by removing bits & bytes, and changing the image resolution all in an introductory manner without compromising too much on performance.

In terms of medical applications, it can be used to monitor patients from anywhere at any time from medical facilities, homes, or even when they are outside by transmitting images for examination & diagnostics. In addition to that, it is possible to add wireless sensors capable of detecting environmental factors such as noise levels, pressure changes in the airways indicating potential respiratory problems which may require quick attention by doctors using embedded computing software designed with adequate capabilities in a compact size suitable for incorporation into WSNs.

A novel architecture is proposed for Wireless Multi-Sensor Networks (WMSNs) & their integration with Wireless Sensor Network (WSNM) along with a new concept for wireless sensor network-based embedded software architectures with the capability of processing multiple images in real-time which are captured by wireless sensors, transmitted wirelessly to a Network Server (NS), and then processed there.

Since most WSNs are deployed using limited resources & power, the proposed architecture also presents a new technique that is energy efficient when compared to other existing techniques designed for one or two cameras.

This article provides an overview of Wireless Multi-Sensor Networks along with their integration with Wireless Sensor Networks to present novel embedded software architectures capable of generating live video streaming in real-time from various geographical locations where they are deployed at different locations while transmitting information via the Internet or local networks in near real-time.

Wireless multi-sensor networks are three or more nodes that are capable of processing images in real-time while combining the capabilities of wireless sensor networks with imaging technology to provide embedded software architectures.

The architectures presented include novel techniques for restoring damaged regions in images by removing bits & bytes, converting grayscale images to binary format, and changing the image resolution while being highly energy efficient when compared to other existing techniques designed for one or two cameras.

Multiple cameras (up to 6 cameras) can be integrated using these proposed systems along with novel techniques for connecting WSNs with Multi-Sensor Networks (MSNs) via embedded software capable of processing multiple images in real-time which are captured by wireless sensors, transmitted wirelessly to a Network Server (NS), and then processed there.

The proposed architecture also includes the ability to use wireless sensors capable of detecting environmental factors such as noise levels and pressure changes in the airways indicating potential respiratory problems which require quick attention by doctors using embedded software designed with adequate capabilities in a compact size suitable for incorporation into WSNs.

Introduction

A novel approach is presented for Wireless Multi-Sensor Networks (WMSNs) along with their integration with Wireless Sensor Networks (WSNs). The proposed systems are designed to present an architecture that can process multiple images in real-time which are captured by wireless sensors, transmitted wirelessly to a Network Server (NS), and then processed there. This article provides an overview of Wireless Multi-Sensor Networks and their integration with Wireless Sensor Networks.

Various embedded software architectures are presented in this article for processing images in real-time while combining the capabilities of wireless sensor networks with imaging technology to provide embedded software architectures along with novel techniques for restoring damaged regions in images by removing bits & bytes, converting grayscale images to binary format, and changing the image resolution while being highly energy efficient when compared to other existing techniques designed for one or two cameras that can be integrated using these proposed systems.

This includes identifying the type of wearable medical equipment required, enabling early detection of health disorders among patients.

Wireless multi-sensor network (while ensuring security) is an innovative approach to providing home automation, building environmental control, healthcare, early warning for environmental disasters, vehicular collision avoidance system, vehicle diagnostic monitoring system via many embedded software technologies.

To have a clear understanding of the proposed approach presented in this article, firstly, it is necessary to understand wireless sensor networks extensively and their integration with other types of sensing networks. Therefore Section 2 presents an overview of Wireless Sensor Networks while Sections 3 onwards provide an overview of Wireless Multi-Sensor Networks and their integration with Wireless Sensor Networks.

The article concludes by describing possible applications & benefits offered by the proposed systems in various fields such as health care, home automation, and building environmental control by presenting novel techniques for connecting WSNs with Multi-Sensor Networks (MSNs) via embedded software capable of processing multiple images in real-time and restoring damaged regions in images by removing bits & bytes, converting grayscale images to binary format, and changing the image resolution while being highly energy efficient when compared to other existing techniques designed for one or two cameras

Wireless sensor network

A wireless sensor network consists of spatially distributed autonomous sensors to monitor physical or environmental conditions such as temperature, sound, pressure, etc. -- all without a dedicated power source. Wireless Sensor Networks are mainly classified into 3 different types depending upon the type of application used in Wireless Sensor Networks. However in terms of working principles irrespective of their applications all three types use the same components viz., sensors, actuators, and radio transceivers for transmitting data wirelessly to a single base station.

Wireless sensor network integration

Wireless Sensor Networks are also integrated with different types of networks to form several other designs that would be beneficial for various applications. Hence they are classified into four main categories as follows:

1) Wireless Sensor Network with Internet, 2) Wireless Sensor Network with the ad-hoc network, 3) Wireless sensor network with MANETs (Mobile Ad-Hoc Networks), 4) Wireless sensor Network with P2P (Peer to Peer systems). Furthermore, these wireless sensor network integrations can further be combined and incorporated along with each other and other sensing technologies (such as Multi/Hyper Sensors etc.) to provide a multi-modal coverage area that can monitor various physical parameters/conditions such as temperature, humidity, wind velocity, etc.

Wireless multi-sensor network integration

Wireless Multi-Sensor Networks are integrated networks consisting of multiple types of Wireless Sensor Network configurations while utilizing the same base station. Hence the proposed system design includes both Wireless Sensor Network connectivity with each other and/or any other sensing network technology that would be beneficial for specific areas within a particular time interval. For example, in the case of health care applications, it is necessary to monitor & track patients' body parameters using several different sensors attached at various parts of the human body simultaneously while ensuring security wherein data could be collected from wireless multi-sensor networks integrating Bluetooth or ZigBee based WSNs. This type of technology integration can be highly advantageous for specific applications such as health care, home automation, and building environmental control by providing a multi-modal coverage area that can monitor various physical parameters/conditions from both Wireless Sensor Network & Wireless Multi-Sensor network technologies simultaneously.

Geolance is an on-demand staffing platform

We're a new kind of staffing platform that simplifies the process for professionals to find work. No more tedious job boards, we've done all the hard work for you.


Geolance is a search engine that combines the power of machine learning with human input to make finding information easier.

© Copyright 2022 Geolance. All rights reserved.