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Neural Networks and the Future of Electrical and Computer Engineering

Neural Networks in Electrical and Computer EngineeringEngineers can harness machine learning and artificial intelligence for effective, data-driven approaches to complex problems. For example, how can we monitor and protect endangered animals without resorting to highly disruptive techniques like capturing and tagging them? Researchers at Michigan State University’s Department of Computer Science and Engineering found an answer by building facial recognition software that identifies individual primates in the wild.

A convolutional neural network—the same technology commonly used for other image processing applications like the computer vision that guides autonomous vehicles—made it possible to track specific lemurs, chimpanzees and golden monkeys with a high level of accuracy. And by implementing the capabilities of the PrimNet architecture through a mobile app, the developers made it simple to keep an eye on the animals over time.

Picking a primate out of the crowd is just one of the countless ways that recent advances in artificial intelligence have unlocked remarkable potential for scientific insights and business innovation. And artificial neural networks, which mimic the structures of organic brains, offer some of the most exciting possibilities for expanding what we can accomplish with machine learning.

Electrical and computer engineers can build their careers in contributing to the next generation of machine learning solutions. In turn, the growing engineering applications of neural networks are transforming how technical professionals address a variety of challenges in fields like signal processing and energy production.

What is a Neural Network?

Neural networks are different from other forms of artificial intelligence because they’re designed to resemble human and animal brains. Layers of connected nodes, referred to as artificial neurons, receive, process and transmit data. Each node performs a mathematical function to weigh the data it receives, which determines whether the input exceeds a certain threshold. When the output exceeds that threshold, the neuron fires and the data passes through to the next layer. Using a variety of learning models, these nodes can be trained to automatically adjust coefficients in a way that achieves the desired results.

In a deep learning architecture, multiple layers of machine learning algorithms extract gradually higher-level features from data input. These systems progress from identifying broad features in data, images or speech to noting the fine details. Automating these tasks enables revolutionary leaps in a range of fields, powering technology such as a portable device that automatically translates full sentences of American Sign Language into spoken English.

Many deep learning architectures rely on a method called stochastic gradient descent to optimize functions. In a supervised learning process, a neural network can be taught to determine the right classifications for the information it receives by running a backpropagation algorithm to compute gradient of loss functions. That means the system evaluates how accurately an algorithm models the relevant data and adjusts accordingly. Through the optimization process, a system may learn to accomplish tasks like finding a specific type of object in a series of images or recognizing the words in a recording of human speech.

While deep learning has received a great deal of attention, neuroevolution is another promising way of generating neural networks. This technique employs evolutionary algorithms instead of gradient-based optimization, imitating the biological development that resulted in the brains of living organisms. The process starts with randomly created neural networks, all of which likely struggle to perform basic functions. However, those that are most successful are selected to propagate the next generation. Over numerous iterations, the neural networks breed increasingly effective offspring that can handle complicated jobs like controlling robots or playing a video game.

While neural networks may resemble the human brain, there is still a tremendous amount of work ahead for scientists and engineers before computers can imitate human-level intelligence. MSU faculty in the biology and computer science and engineering departments collaborated to help us understand the challenges involved by modeling how organisms evolved the ability to learn. As the authors pointed out, an artificial neural network has one major advantage over the systems created through biological mutation because the artificial neurons are purposely designed to form associations from the start.

Exploring the Engineering Applications of Neural Networks

System architectures that learn to perform essential tasks or watch out for anomalies can be a major asset for engineers. Professionals have demonstrated the versatility of this form of artificial intelligence in a wide range of projects, some of which could hold enormous implications for the future of transportation and healthcare:

  • Self-driving cars emerged as one of the most significant fields for putting deep learning to work. Neural networks allow autonomous vehicles to gather sensory information about the road ahead, react to obstacles and make decisions such as planning a route.
  • Researchers in MSU’s Connected and Autonomous Networked Vehicles for Active Safety (CANVAS) group are developing a deep learning framework known as the CANVAS Brain that perceives the world through sensor fusion. This approach merges data from radar, LiDAR, and multispectral cameras to enable situational awareness that can inform real-time decisions in autonomous vehicles or issue warnings to human drivers about hazardous road conditions.
  • Automotive engineers have proposed other applications for neural networks in cars like identifying the physical and chemical properties of various types of fuel to help engines operate more efficiently.
  • The many possible implementations for neural networks on aircraft include collision avoidance systems that could lead to safer and more efficient performance. A study suggested that a convolutional recurrent neural network would provide accurate 4D trajectory predictions that account for factors such as wind, temperature and storms.
  • Machine learning experts are investigating ways to improve healthcare delivery and outcomes. Some of these proposals include predicting how often aging populations need to visit a hospital, detecting early signs of Alzheimer’s disease in MRI scans and speeding up the automated processing of clinical notes with systems that can make sense of abbreviations.

Electrical Engineering and Machine Learning Applications for Neural Networks

Deep learning and other forms of artificial intelligence have produced powerful tools and strategies for electrical engineering. For example, fuzzy logic control systems, which are able to account for a continuum of conditions when establishing rules for how machines respond to inputs, have become a common application for AI. Researchers are investigating how neural networks could offer further advantages for controlling grids and improve performance in dynamic systems.

AI and machine learning play a growing role in signal detection and processing, improving quality and accuracy. For example, algorithms have been deployed to model signals, draw inferences about data input and adjust signal output. Studies have suggested the potential for training neural networks that improve multiple-signal detection and modulation. Deep-learning systems could perform statistical signal processing to reduce the noise that would otherwise interfere with image recognition or speech enhancement.

Neural networks may also help in reliably producing renewable energy. One of the greatest disadvantages of relying on sources like solar and wind power is that the sun doesn’t always shine and the wind doesn’t always blow. That makes power system scheduling vital to maintain a consistent supply. A neural network can inform planning by processing large amounts of data rapidly to develop more accurate wind speed predictions. Implementing a multivariable model, a data monitoring system can measure time intervals down to milliseconds as it notes temperature, wind speed, wind direction and weather pressure.

For engineers, advances in neural networks deliver endless opportunities for experimentation and creative problem-solving. Building expertise in electrical and computer engineering can help professionals discover new ways to achieve results with artificial intelligence and deep learning. The curriculum in Michigan State University’s online Master of Science in Electrical and Computer Engineering prepares students to implement deep neural networks and execute deep learning projects. This program helps electrical and computer engineers develop the advanced technical skills to drive the next generation of solutions in fields such as autonomous vehicles, renewable energy and healthcare.

About Michigan State University’s Online Master of Science in Electrical & Computer Engineering

Michigan State University’s online Master of Science in Electrical & Computer Engineering program readies students to excel in the field by expanding their technical knowledge and focusing on the real problems spurring technical innovation. Courses in the online program are taught by MSU’s faculty of pioneering researchers and experienced educators. Online students can choose from two plans of study, selecting a thesis or non-thesis option as they complete a graduate education from an R1 research institution ranked among the Top 100 Global Universities by U.S. News & World Report.

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