In an era dominated by IoT applications, sensors are everywhere—embedded in our homes, vehicles, industries, and even our bodies. They generate an immense amount of data that holds valuable insights waiting to be uncovered. Traditional DSP algorithms like the Fast Fourier Transform (FFT) and Kalman filters have been fundamental in analysing this data, effectively extracting features of interest and filtering out noise. These algorithms excel in tasks such as frequency analysis, state estimation, and noise reduction, providing precise and reliable results.

However, as the complexity and volume of sensor data grow, relying solely on DSP algorithms is no longer sufficient. The patterns and anomalies within large-scale, multidimensional data streams often exceed the capabilities of traditional methods. This is where AI and ML models become indispensable. AI/ML models are adept at handling complex, nonlinear patterns and can make predictions based on learned data. Yet, they lack common sense of the process that they are modelling and are also highly dependent on the quality of the input data.

Combining the strengths of both DSP algorithms and AI/ML models leads to more robust and efficient sensor data processing systems. DSP techniques can preprocess and enhance the data, making it cleaner and more relevant for AI models to analyse. Arm Cortex processors play a pivotal role in this augmentation. Renowned for their efficiency and performance, they are widely used in AIoT (Artificial Intelligence of Things) solutions, enabling the simultaneous execution of DSP algorithms and AI/ML models directly on edge devices. This combination allows for intelligent data processing that is both rapid and power-efficient, meeting the demands of modern technology applications.

The Necessity of DSP Algorithms

DSP algorithms are essential for transforming raw sensor data into meaningful information. Sensors often collect data that is noisy or distorted, making direct interpretation challenging. DSP algorithms tackle these issues by performing noise reduction, signal enhancement, and feature extraction.

For example, the FFT converts time-domain signals into frequency-domain representations, revealing patterns crucial for applications like vibration analysis and audio. Digital filters such as lowpass, bandpass and high-pass eliminate unwanted frequency regions, isolating signals of interest and improving data quality.

Without DSP techniques, valuable insights within sensor data might remain hidden. DSP algorithms lay the groundwork by refining the data, ensuring that both traditional analysis methods and AI/ML models receive high-quality inputs. They provide reliable results based on established mathematical principles and human reasoning, which is essential in critical applications like medical devices, aerospace, and industrial automation where precision and repeatability are paramount.

As such, it’s important to realise that preprocessing of sensor data with DSP algorithms is an essential step, since AI/ML models rely heavily on the quality of input data for accurate predictions.

Moreover, DSP algorithms are efficient and can operate in real-time on devices with limited resources, such as Arm Cortex processors, making them ideal for edge computing where real-time processing is needed.

The Necessity of AI/ML Models

While DSP algorithms are powerful, they are generally designed to address specific problems and may not scale well with the increasing complexity and volume of sensor data. AI/ML models come into play by offering the ability to learn from data, identify complex patterns, and make predictions without explicit programming for each task. They are particularly useful when:

• Patterns are too complex for manual feature extraction: In cases like image and speech recognition, where the features of interest are not easily extracted using traditional DSP methods.
• Data is high-dimensional or unstructured: AI/ML models can handle large datasets with numerous variables, finding relationships that may not be apparent using scientific reasoning.
• Adaptive learning is required: ML models can be improved over time with more training data as it becomes available.

However, it is important to realise that AI/ML models lack common sense and are heavily reliant on the data they are trained on. As such, they may misinterpret or overlook important features if the input data is noisy or lacks proper pre-processing.

Augmenting DSP and AI/ML: a complementary approach

To maximize the benefits of sensor data processing, a hybrid approach that combines DSP algorithms with AI/ML models is often the most effective. Here’s how they complement each other:

1. Pre-processing with DSP:
   • Noise Reduction: Digital filters (e.g. lowpass) can be used to clean up the signal before it reaches the ML model.
   • Feature Extraction: Algorithms like FFT or DWT can extract meaningful features that reduce the dimensionality of the data and highlight important patterns.
2. AI/ML for Pattern Recognition:
   • Classification and Regression: ML models can take the features extracted by DSP algorithms and perform tasks like anomaly detection, predictive maintenance, and classification.
   • Adaptive Learning: ML models can adapt to new data trends over time, improving their accuracy and usefulness.
3. Feedback Mechanisms:
   • Model Refinement: The outputs from AI/ML models can inform adjustments in DSP algorithms, creating a feedback loop that enhances overall system performance.

Example Application: Vibration analysis in Industrial equipment

• DSP Stage:
  • FFT Analysis: Converts vibration signals (usually captured from an accelerometer) from the time to frequency domain to identify characteristic frequencies associated with specific mechanical faults.
  • Feature Extraction: Extracts features like peak frequencies, amplitudes, and harmonics. These amplitude features can be further scaled (using properties of the FFT) to extract velocity or displacement estimates from the original acceleration data.
• AI/ML Stage:
  • Fault Classification: An ML model trained on labelled data predicts the type of fault (e.g., imbalance, misalignment, bearing wear) based on the extracted features.
  • Predictive Maintenance Scheduling: Regression models estimate the remaining useful life of equipment, allowing for proactive maintenance.

Benefits of augmentation:

• Improved Accuracy: Pre-processing with DSP algorithms enhances the quality of data fed into AI/ML models.
• Efficiency: Reduces computational load by focusing on relevant features, which is especially important for edge devices with limited resources.
• Reliability: Combining deterministic DSP outputs with probabilistic AI/ML predictions leads to more robust systems.

Key takeaways

The fusion of DSP algorithms and AI/ML models represents a powerful paradigm for sensor data processing in modern technology. DSP algorithms provide the necessary tools for signal enhancement and feature extraction, ensuring that the data is in the best possible form for analysis. Despite lacking any common sense (see here for a previous article), AI/ML models certainly excel at finding complex patterns and making predictions based on the processed data, making them attractive for many modern AIoT applications.

Arm Cortex processors play a pivotal role in this integration, offering the computational capabilities required to run both DSP algorithms and AI/ML models efficiently on the same platform. This synergy enables the development of advanced AIoT solutions that are capable of processing sensor data intelligently at the edge, leading to faster decisions and reduced latency. This is further strengthend with Arm’s TrustZone extension, that provides developers with a hardware data security model, offering a high level of security against hacking, stealing of encryption keys and counterfeiting.

As the volume and complexity of sensor data continue to grow, leveraging the strengths of both DSP and AI/ML will be essential for advancing technology across industries. By adopting a complementary approach and utilising decent computational platforms such as Arm’s Cortex family of processors, we can build more effective, efficient, and intelligent systems that meet the demands of the future.