187 Proofread all the .qmd files

contents/conventions.qmd

@@ -2,66 +2,66 @@
 
 Please follow these conventions as you contribute to this online book:
 
-1. **Clear Structure and Organization**:
+1. **Clear Structure and Organization:**
 
-    - **Chapter Outlines**: Begin each chapter with an outline that provides an
+    - **Chapter Outlines:** Begin each chapter with an outline that provides an
       overview of the topics covered.
-    - **Sequential Numbering**: Utilize sequential numbering for chapters,
+    - **Sequential Numbering:** Utilize sequential numbering for chapters,
       sections, and subsections to facilitate easy reference.
 
-2. **Accessible Language**:
+2. **Accessible Language:**
 
-    - **Glossary**: Include a glossary that defines technical terms and jargon.
-    - **Consistent Terminology**: Maintain consistent use of terminology
+    - **Glossary:** Include a glossary that defines technical terms and jargon.
+    - **Consistent Terminology:** Maintain consistent use of terminology
       throughout the book to avoid confusion.
 
-3. **Learning Aids**:
+3. **Learning Aids:**
 
-    - **Diagrams and Figures**: Employ diagrams, figures, and tables to visually
+    - **Diagrams and Figures:** Employ diagrams, figures, and tables to visually
       convey complex concepts.
-    - **Sidebars**: Use sidebars for additional information, anecdotes, or to
+    - **Sidebars:** Use sidebars for additional information, anecdotes, or to
       provide real-world context to the theoretical content.
 
-4. **Interactive Elements**:
+4. **Interactive Elements:**
 
-    - **Exercises and Projects**: Integrate exercises and projects at the end of
+    - **Exercises and Projects:** Integrate exercises and projects at the end of
       each chapter to encourage active learning and practical application of
       concepts.
-    - **Case Studies**: Incorporate case studies to provide a deeper
+    - **Case Studies:** Incorporate case studies to provide a deeper
       understanding of how principles are applied in real-world situations.
 
-5. **References and Further Reading**:
+5. **References and Further Reading:**
 
-    - **Bibliography**: Include a bibliography at the end of each chapter for
+    - **Bibliography:** Include a bibliography at the end of each chapter for
       readers who wish to delve deeper into specific topics.
-    - **Citations**: Maintain a consistent style for citations, adhering to
+    - **Citations:** Maintain a consistent style for citations, adhering to
       recognized academic standards like APA, MLA, or Chicago.
 
-6. **Supporting Materials**:
+6. **Supporting Materials:**
 
-    - **Supplementary Online Resources**: Provide links to supplementary online
+    - **Supplementary Online Resources:** Provide links to supplementary online
       resources, such as video lectures, webinars, or interactive modules.
-    - **Datasets and Code Repositories**: Share datasets and code repositories
+    - **Datasets and Code Repositories:** Share datasets and code repositories
       for hands-on practice, particularly for sections dealing with algorithms
       and applications.
 
-7. **Feedback and Community Engagement**:
+7. **Feedback and Community Engagement:**
 
-    - **Forums and Discussion Groups**: Establish forums or discussion groups
+    - **Forums and Discussion Groups:** Establish forums or discussion groups
       where readers can interact, ask questions, and share knowledge.
-    - **Open Review Process**: Implement an open review process, inviting
+    - **Open Review Process:** Implement an open review process, inviting
       feedback from the community to continuously improve the content.
 
-8. **Inclusivity and Accessibility**:
+8. **Inclusivity and Accessibility:**
 
-    - **Inclusive Language**: Utilize inclusive language that respects diversity
+    - **Inclusive Language:** Utilize inclusive language that respects diversity
       and promotes equality.
-    - **Accessible Formats**: Ensure the textbook is available in accessible
+    - **Accessible Formats:** Ensure the textbook is available in accessible
       formats, including audio and Braille, to cater to readers with
       disabilities.
 
-9. **Index**:
-    - **Comprehensive Index**: Include a comprehensive index at the end of the
+9. **Index:**
+    - **Comprehensive Index:** Include a comprehensive index at the end of the
       book to help readers quickly locate specific information.
 
 Implementing these conventions can contribute to creating a textbook that is

contents/dsp_spectral_features_block/dsp_spectral_features_block.qmd

@@ -16,15 +16,15 @@ But how does it work under the hood? Let's dig into it.
 
 Extracting features from a dataset captured with inertial sensors, such as accelerometers, involves processing and analyzing the raw data. Accelerometers measure the acceleration of an object along one or more axes (typically three, denoted as X, Y, and Z). These measurements can be used to understand various aspects of the object's motion, such as movement patterns and vibrations. Here's a high-level overview of the process:
 
-**Data collection**: First, we need to gather data from the accelerometers. Depending on the application, data may be collected at different sampling rates. It's essential to ensure that the sampling rate is high enough to capture the relevant dynamics of the studied motion (the sampling rate should be at least double the maximum relevant frequency present in the signal).
+**Data collection:** First, we need to gather data from the accelerometers. Depending on the application, data may be collected at different sampling rates. It's essential to ensure that the sampling rate is high enough to capture the relevant dynamics of the studied motion (the sampling rate should be at least double the maximum relevant frequency present in the signal).
 
-**Data preprocessing**: Raw accelerometer data can be noisy and contain errors or irrelevant information. Preprocessing steps, such as filtering and normalization, can help clean and standardize the data, making it more suitable for feature extraction.
+**Data preprocessing:** Raw accelerometer data can be noisy and contain errors or irrelevant information. Preprocessing steps, such as filtering and normalization, can help clean and standardize the data, making it more suitable for feature extraction.
 
 > The Studio does not perform normalization or standardization, so sometimes, when working with Sensor Fusion, it could be necessary to perform this step before uploading data to the Studio. This is particularly crucial in sensor fusion projects, as seen in this tutorial, [Sensor Data Fusion with Spresense and CommonSense](https://docs.edgeimpulse.com/experts/air-quality-and-environmental-projects/environmental-sensor-fusion-commonsense).
 
-**Segmentation**: Depending on the nature of the data and the application, dividing the data into smaller segments or **windows** may be necessary. This can help focus on specific events or activities within the dataset, making feature extraction more manageable and meaningful. The **window size** and overlap (**window span**) choice depend on the application and the frequency of the events of interest. As a rule of thumb, we should try to capture a couple of "data cycles."
+**Segmentation:** Depending on the nature of the data and the application, dividing the data into smaller segments or **windows** may be necessary. This can help focus on specific events or activities within the dataset, making feature extraction more manageable and meaningful. The **window size** and overlap (**window span**) choice depend on the application and the frequency of the events of interest. As a rule of thumb, we should try to capture a couple of "data cycles."
 
-**Feature extraction**: Once the data is preprocessed and segmented, you can extract features that describe the motion's characteristics. Some typical features extracted from accelerometer data include:
+**Feature extraction:** Once the data is preprocessed and segmented, you can extract features that describe the motion's characteristics. Some typical features extracted from accelerometer data include:
 
 -   **Time-domain** features describe the data's [statistical properties](https://www.mdpi.com/1424-8220/22/5/2012) within each segment, such as mean, median, standard deviation, skewness, kurtosis, and zero-crossing rate.
 -   **Frequency-domain** features are obtained by transforming the data into the frequency domain using techniques like the [Fast Fourier Transform (FFT)](https://en.wikipedia.org/wiki/Fast_Fourier_transform). Some typical frequency-domain features include the power spectrum, spectral energy, dominant frequencies (amplitude and frequency), and spectral entropy.
@@ -361,7 +361,7 @@ plt.legend(loc='upper right')
 plt.xlabel('Frequency (Hz)')
 #plt.ylabel('PSD [V**2/Hz]')
 plt.ylabel('Power')
-plt.title('Power spectrum P(f) using Welch\'s method')
+plt.title('Power spectrum P(f) using Welch's method')
 plt.grid()
 plt.box(False)
 plt.show()