This tool implements three different noise reduction algorithms for smoothing data: Rectangular Averaging, Binomial Median Filtering, and Binomial Averaging. It processes data from a list and displays the results in another list.
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Minor bugs fixed.
Explained NoiseReductionKernelWidth and updated algorithm details in README.md.
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Rectangular Method
- How it works: This method calculates the average of a window of values centered around each data point. The width of the window (kernel width) determines the number of neighboring data points included in the averaging process.
- Principle: By averaging the values within the window, the noise (random fluctuations) is smoothed out, resulting in a clearer, more stable signal.
- Code Implementation:
// Rectangular if (rbtnRect.Checked == true) { for (int i = 0; i < listBox1.Items.Count; i++) { double sum = 0; int count = 0; for (int kernel_index = -NoiseReductionKernelWidth; kernel_index <= NoiseReductionKernelWidth; kernel_index++) { int dataIndex = i + kernel_index; if (dataIndex >= 0 && dataIndex < listBox1.Items.Count) { count++; sum += Convert.ToDouble(listBox1.Items[dataIndex]); } } listBox2.Items.Add(sum / count); lblCnt2.Text = "Count : " + listBox2.Items.Count; } }
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Binomial Coefficients Method (Median)
- How it works: This method uses the binomial coefficients to weight the data points within the window. The median of the weighted values is then calculated.
- Principle: The median is less sensitive to extreme values (outliers) than the average, making it effective for noise reduction while preserving the overall trend of the data.
- Code Implementation:
// Binomial coefficient (median) else if (rbtnMed.Checked == true) { for (int i = 0; i < listBox1.Items.Count; i++) { List<Tuple<double, int>> weightedValues = new List<Tuple<double, int>>(); for (int kernel_index = -NoiseReductionKernelWidth; kernel_index <= NoiseReductionKernelWidth; kernel_index++) { int dataIndex = i + kernel_index; if (dataIndex >= 0 && dataIndex < listBox1.Items.Count && (NoiseReductionKernelWidth + kernel_index) >= 0 && (NoiseReductionKernelWidth + kernel_index) < binomialCoefficients.Length) { double value = Convert.ToDouble(listBox1.Items[dataIndex]); int weight = binomialCoefficients[Math.Abs(kernel_index)]; for (int w = 0; w < weight; w++) { weightedValues.Add(new Tuple<double, int>(value, weight)); } } } if (weightedValues.Count > 0) { weightedValues.Sort((x, y) => x.Item1.CompareTo(y.Item1)); double weightedMedian; int midIndex = weightedValues.Count / 2; if (weightedValues.Count % 2 == 0) { weightedMedian = (weightedValues[midIndex - 1].Item1 + weightedValues[midIndex].Item1) / 2.0; } else { weightedMedian = weightedValues[midIndex].Item1; } listBox2.Items.Add(weightedMedian); lblCnt2.Text = "Count : " + listBox2.Items.Count; } } }
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Binomial Coefficients Method (Average)
- How it works: This method uses binomial coefficients to weight the values within the kernel. The weighted average of these values is then calculated.
- Principle: Binomial coefficients give more weight to the central values in the window, which helps to preserve the central trend while smoothing out noise.
- Code Implementation:
// Binomial coefficient (average) else if (rbtnAvg.Checked == true) { binomialCoefficients = AvgCalcBinomialCoefficients(NoiseReductionKernelWidth * 2 + 1); for (int i = 0; i < listBox1.Items.Count; i++) { List<double> values = new List<double>(); int coefficientSum = 0; for (int kernel_index = -NoiseReductionKernelWidth; kernel_index <= NoiseReductionKernelWidth; kernel_index++) { int dataIndex = i + kernel_index; if (dataIndex >= 0 && dataIndex < listBox1.Items.Count) { values.Add(Convert.ToDouble(listBox1.Items[dataIndex]) * binomialCoefficients[NoiseReductionKernelWidth + kernel_index]); coefficientSum += binomialCoefficients[NoiseReductionKernelWidth + kernel_index]; } } if (values.Count > 0) { double sum = values.Sum(); double average = sum / coefficientSum; listBox2.Items.Add(average); lblCnt2.Text = "Count : " + listBox2.Items.Count; } } }
- Calculating Binomial Coefficients
- Principle: Binomial coefficients are derived from Pascal's triangle and represent the coefficients in the expansion of a binomial expression.
- Code Implementation:
private int[] AvgCalcBinomialCoefficients(int windowSize) { int[] coefficients = new int[windowSize]; coefficients[0] = 1; for (int i = 1; i < windowSize; i++) { coefficients[i] = coefficients[i - 1] * (windowSize - i) / i; } return coefficients; }
These algorithms help smooth out noise in data by averaging or filtering based on the chosen method.
- Implemented drag-and-drop functionality to allow users to easily add data to the application.
- Used regular expressions to extract and parse numerical data from various formats.
- Designed and developed a user-friendly interface with interactive elements like buttons and list boxes.
- Provided real-time feedback to the user by updating counts and results dynamically.
- Allowed users to select the noise reduction method and kernel width through the interface.
- Enabled users to calibrate and fine-tune the noise reduction process based on their specific needs.
By implementing these techniques, this project effectively reduces noise from the given data, providing clearer and more reliable results.
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