Did you know that mobile phones, computers, radios, and TVs work because of signal processing? Signal processing technology power so much of our daily lives. This technology is the science behind our connected digital lives. Read on to find out more…
What Is Signal Processing?
Signal processing simply means processing signals--analog or digital--in such a way that any kind of computer can interpret it. The signals can be any form of data that can be digitized, such as video, images, audio, and sensor data.
For instance, when a computer receives a sound wave, it only perceives some analog or discrete value of certain parameters like frequency and pitch. The role of signal processing is to process these signals in a way that a computer can interpret the sound just the way human hears it, i.e. In terms of words and sound. Today, signal processing has made waves in deep learning.
Deep Learning for Signal Processing
Deep learning achieves recognition with the highest accuracy. That has offered new opportunities to industries such as aerospace, defense & communications, automotive, and digital health to develop efficient systems such as driverless cars.
What’s more, recent developments in deep learning have improved substantially to a point where it has outdone human capabilities in certain tasks such as classifying objects in images.
Although deep learning was first introduced in the 1980s, it gained popularity lately because of the following reasons:
- Deep learning requires large quantities of labeled data. For instance, driverless car development needs millions of images and many hours of video.
- It also requires a lot of computing power. The High-performance GPUs have a parallel architecture, which, when combined with cloud computing or clusters, it enables engineers to reduce training time from weeks to hours or even less.
Deep Learning Neural Networks
For starters, a thorough understanding of signal data and signal processing is required to use machine-learning techniques. Here are the essential tools you need to make your work easier.
Since labeling large sets of data is hectic; you need a Labeler app to help you go through your samples without taking too much time.
These enable you to analyze data by minimizing the risk of losing memory. You will use the ‘transform’ option to process and transmit essential features. The other important feature with a data store is that it separates data files according to their functions and specification.
Synthetic data generation:
This one will help you when you get unreasonable results with your data samples. Also, it’s applicable when you need more data to simulate accurate results.
Now that you’re well aware of the various variables you need in the deep learning process, here are useful steps that you can take:
The first step is to collect data from various sources, which involves reading and storing preprocessed data. You then separate your data into different sets of samples. It is here that you are required to label your samples for algorithm purposes.
The next step is to manage the data to identify the preprocessing and extraction methods needed for optimal results. You then identify the critical features for your signal data set. You are going to use a signal analyzer App to browse through the data signals on time.
Lastly, you are required to change and separate other different features from your data, such as signal models, transformation points, and data peaks.
Signal Processing Versus Image Processing
The signal processing involves data preprocessing and feature extraction stages that other image processing apps do not possess. Even though image data is created under low costs, signal sets are tough to develop because of other variable features like noise and voice distortion.
Data versus Knowledge
It’s not easy to quantify signal data sets. For instance, you cannot predict the remaining number of years a particular machine will serve you. The probability of a successful deep learning process depends on your knowledge, the dataset size, and your computation skills.
You can therefore say that the higher the quality of a particular set of data, the more comfortable and closer you are to being able to achieve positive results with deep learning. Besides, if your computation skills are superior, you will perform better.
If you have a clear understanding of data, you will be an advantageous position with a conventional machine even without the need of a well-established dataset.
Long Short- Term Memory Models for Human Activity recognition
Human activity recognition (HAR) has been successful for years with rising technological advancements. This technology is useful in industries such as healthcare, military, gaming, fitness, and navigation. There are two main types of HAR, namely:
These are wearable devices with inbuilt sensors that send signals and can be segmented and identified at the same time. Nowadays, sensor technology is growing fast because it’s affordable and more efficient.
External device HAR:
Deep learning methods have been used to fend off challenges with machine learning methods that follow commands set by the user. Examples of HAR self-driven devices include temperature-tracking devices, heart rate tracking device, among others.
Signal Processing on GPU
If you are still using a CPU computer and not the GPU based workstation, you’re missing out a lot. Signal processing on a GPU’s is 30 times faster than the standard PC. Furthermore, using a GPU based system means you can open several source libraries at the same time for optimal results.
Signal Processing on FPGA’s
Also referred to as a Field-programmable gate array, FPGA is an efficient hardware solution to processing data-intensive workloads. The advantage of the FPGA is they can offload repetitive processing procedures, therefore, boosting the overall performance of apps.
The decision on whether to go for the FPGA or GPU depends on what you plan to achieve. It is, therefore, crucial to have in-depth discussions with the vendor to establish your goals and what machine to buy.
The world we live in today is filled with signals. Taking time to understand them helps overcome threats and provide solutions. This technology is useful in the analysis of brain signals, audio signals, and financial signals. It has also helped scientists come up with new medicines, innovations, or even new technologies.