ഈറ്റൺ തരംഗരൂപങ്ങൾ അവസാനിച്ചുview വിപുലമായ വിശകലനം

 

Waveforms overview

• Waveforms are foundational to the representation and analysis of electrical signals in systems like power electronics, control systems, and embedded hardware. In electrical engineering, understanding the characteristics and behavior of various waveform types is essential for designing, operating, and troubleshooting circuits and systems that generate, transmit, or manipulate electrical energy
  വിവരങ്ങളും.
• In electronics, the term is usually applied to time-varying voltages, currents, or electromagnetic fields. In acoustics, it is usually applied to steady periodic sounds — variations of pressure in air or other media. In these cases, the waveform is an attribute that is independent of the frequency, amplitude, or phase shift of the signal.
• The waveform of an electrical signal can be visualized with an oscilloscope or any other device that can capture and plot its value at various times, with suitable scales in the time and value axes. The electrocardiograph is a medical device to record the waveform of the electric signals that are associated with the beating of the heart and, this waveform has important diagnostic value. Waveform  generators, which can output a periodic voltage or current with one of several waveforms, are a common tool in electronics laboratories and workshops.
• Electrical waveforms are visual representations of the variation of voltage and current over time. In simple language if we plot on a graph, this voltage or current variations on x- axis against time (t) on y- axis, the resulting plot or drawing would represent the shape of a waveform.
• This paper focusses specifically on the waveforms generated by electrical signals in terms of voltage, current, and frequency analysis.

There are many different types of electrical waveforms available but generally they can all be broken down into two distinctive groups:

• Uni-directional waveforms: These electrical waveforms are always positive or negative in nature flowing in one forward direction only and they never cross the zero-axis point. Common uni-directional waveforms are square-wave timing signals, clock pulses, and trigger pulses.
• Bi-directional waveforms: These electrical waveforms are also called alternating waveforms as they alternate from a positive direction to a negative direction constantly crossing the zero-axis point. This bidirectional nature allows them to represent alternating current (AC) signals, which are fundamental in power distribution and many electronic systems. Common examples include sine waves, triangle waves, and sawtooth waves. These waveforms are essential in applications where signal polarity changes over time, like in audio signals, radio transmissions, and alternating current (AC) power systems.

Table 1. Common types of waveforms

 Fundamentals of waveform analysis

Waveform analysis is a fundamental concept in signal processing, electrical engineering, and various scientific fields. It involves examining the shape and characteristics of a signal over time.

Key parameters in waveform analysis are:

പാരാമീറ്റർ വിവരണം

• Amplitude Maximum value of the waveform
• Frequency (f) Number of cycles per second (Hz)
• Period (T) Time taken for one complete cycle (T = 1/f)
• Phase Shift of the waveform in time
• Wavelength Distance between repeating units of the waveform
• Duty Cycle Ratio of the high time to the total period (for square waves)

Table 2. Key parameters in waveform analysis

A waveform can tell you many things about a signal, like:

• The time and voltage values of a signal
• The frequency of an oscillating signal
• The “moving parts” of a circuit represented by the signal
• The frequency with which a particular portion of the signal occurs relative to other portions
• Whether or not a malfunctioning component is distorting the signal
• How much of a signal is direct current (DC) or alternating current (AC)

Metric Description

• RMS (Root Mean Square) Measures signal power
• Peak-to-Peak Difference between max and min ampഅക്ഷാംശം
• Crest Factor Ratio of peak to RMS
• THD (ആകെ ഹാർമോണിക് ഡിസ്റ്റോർഷൻ)
• Measures harmonic distortion
• Wavelength Measures signal clarity
• SNR (സിഗ്നൽ-ടു-നോയിസ് അനുപാതം)
• Measures signal clarity

Table 3. Key signal metrics and their descriptions, commonly used in signal analysis and diagnostics

അപേക്ഷകൾ
Waveform analysis is a powerful technique used across many fields to interpret and extract meaningful information from signals. Here are some key applications of waveform analysis:

1.  Electrical and power systems
   In the realm of electrical and power systems, waveform analysis plays a pivotal role in ensuring the reliability, efficiency, and safety of the grid. Engineers rely on waveform data to monitor power quality, which involves detecting deviations from ideal voltage and current waveforms. These deviations may manifest as voltage sags, swells, or harmonic distortions; all of these issues can compromise equipment performance and system stability. Beyond quality monitoring, waveform analysis is instrumental in detecting faults. By examining the shape and behavior of current and voltage waveforms, it becomes possible to identify short circuits, insulation failures, or equipment malfunctions in real time. Additionally, waveform data supports load analysis, helping utilities and facility managers understand consumption patterns, optimize energy usage, and plan for future demand.
2.  Audio and speech processing
   In audio and speech processing, waveform analysis enables machines to interpret human sounds, forming the basis for technologies like speech recognition, noise reduction, and music analysis. It helps systems identify spoken words, filter out background noise, and analyze musical elements such as rhythm and pitch.
3. മെഡിക്കൽ ഡയഗ്നോസ്റ്റിക്സ്
   In medical diagnostics, waveform analysis is essential for monitoring physiological signals. Tools like electrocardiograms (ECGs) and electroencephalograms (EEGs) use waveform patterns to detect heart and brain conditions, allowing for non-invasive diagnosis and real-time health monitoring.

Oscillography in power systems

• Oscilloscopes are one of the main tools for analyzing electrical signals. The primary information obtained from the waveform of the signal is visualization of its amplitude variation over time. This capability makes them indispensable for tasks such as testing, debugging, and troubleshooting electronic systems.
• Oscillography in power systems refers to the use of oscillographs or digital fault recorders (DFRs) to capture and analyze electrical waveforms—typically voltage and current—during normal operation and especially during disturbances like faults, switching events, or equipment failures. Oscilloscopes come in various types, each suited for specific applications in electronics, power systems, and signal analysis.

Here’s a breakdown of the main types of oscilloscopes:

1. Analog oscilloscope
   The analog oscilloscope is the earliest form of this instrument, developed in the early 20th century and widely adopted after World War II. It operates using a cathode-ray tube (CRT), where an electron beam is deflected horizontally and vertically to trace the waveform of an electrical signal on a phosphorescent screen. The vertical deflection corresponds to the voltage of the signal, while the horizontal deflection is controlled by a time base, allowing the waveform to be displayed over time. One of the key strengths of analog oscilloscopes is their real-time response. They can display fast transients and signal changes as they happen, which is particularly useful in educational settings and for quick diagnostics. However, they lack the ability to store waveforms or perform digital analysis. Their bulky design, limited measurement precision, and absence of memory make them less suitable for modern applications, but they remain valuable for teaching fundamental concepts of signal behavior.
2. Digital Storage Oscilloscope (DSO)
   The DSO represents a significant advancement over analog models. It digitizes incoming analog signals using analog-to-digital converters (ADCs), which sample the signal at high speed and convert it into a digital format. This digital data is then stored in memory, allowing for post-capture analysis, zooming, triggering, and waveform comparison.
   DSOs are equipped with microprocessors and software that enable a wide range of features, including automatic measurements, FFT analysis, and protocol decoding. They are widely used in power systems, embedded systems, and research and development laboratories due to their versatility and precision.
3.  Digital Phosphor Oscilloscope (DPO)
   The DPO builds on the DSO architecture but introduces a higher waveform capture rate and intensity grading. This means that the oscilloscope can capture and display thousands or even millions of waveforms per second, layering them with varying brightness to indicate frequency of occurrence. This feature mimics the visual persistence of analog CRTs while offering the benefits of digital processing.
   DPOs are particularly effective for jitter analysis, glitch detection, and signal integrity testing in high-speed digital systems. They allow engineers to visualize subtle anomalies that might be missed by conventional oscilloscopes.
4.  COMTRADE standards
   • The COMTRADE standard, short for Common Format for Transient Data Exchange for Power Systems, is a globally adopted file format designed to store and exchange waveform data recorded during power system disturbances. Defined by the IEEE Standard C37.111, COMTRADE plays a critical role in the post-event analysis and simulation of electrical faults and protection system behavior.
   • This standard is essential because it ensures interoperability across a wide range of tools and devices used by utilities, relay manufacturers, and system analysts. When a disturbance such as a fault or switching event occurs, digital fault recorders (DFRs), protective relays, and other monitoring equipment capture high-resolution waveform data. COMTRADE provides a standardized way to store this data, making it possible to share and analyze it across different platforms and software environments.
   • One of the most valuable applications of COMTRADE is post-event analysis. Engineers use recorded data to reconstruct the sequence of events, verify the correct operation of protection relays, and identify any anomalies in system behavior. This analysis is crucial for improving the reliability and responsiveness of protection schemes. Additionally, COMTRADE files are widely used in training and simulation environments, where real-world disturbance data can be replayed to test relay logic, validate system models, or train operators and engineers under realistic conditions.
   • COMTRADE standards have evolved to accommodate the growing complexity and data requirements of modern power systems. The original version, introduced in 1991, used a simple ASCII format to store waveform data and configuration information. While effective for its time, this format had limitations in terms of data size and flexibility. In 1999, the standard was updated to support binary data formats, which allowed for more efficient storage and faster processing, as well as the ability to handle multiple sampling rates within a single dataset—an important feature for capturing both high-speed transients and slower system dynamics.
   • In 2013 COMTRADE introduced XML-based configuration files and the ability to package all related files into a single ZIP archive. This enhancement improved data organization, readability, and integration with modern software tools. The XML format also made it easier to parse and interpret configuration data programmatically, further enhancing automation and interoperability.
   • In summary, COMTRADE is a foundational standard in the power systems domain and enables consistent, accurate, and efficient handling of transient waveform data. Its continued evolution reflects the growing demands of digital substations, smart grids, and advanced protection systems.

Key components of COMTRADE

File extension Description

• CFG Configuration file: defines channel names, scaling factors, sampലിംഗ് നിരക്ക് മുതലായവ.
• DAT Data file: contains the actual sampled waveform data (ASCII or binary)
• HDR (optional) Header file: includes metadata about the recording device or event
• INF (optional) Information file: provides additional context or comments

Figure 1. COMTRADE in Power System Event Analysis Workflow

Advanced waveform analysis techniques

• Modern power systems are becoming increasingly complex due to the integration of renewable energy sources, power electronics, and smart grid technologies. These advancements introduce new challenges in monitoring, protection, and control, particularly in the accurate analysis of electrical waveforms. Traditional signal processing methods often fall short in detecting subtle anomalies, transient disturbances, or nonlinear behaviors in voltagഇ, നിലവിലെ തരംഗരൂപങ്ങൾ.
• Advanced waveform analysis techniques offer powerful tools to address these challenges. By enabling high-resolution, adaptive, and intelligent analysis of power system signals, these methods enhance fault detection, power quality assessment, and system stability monitoring. This white paper highlights the most impactful techniques that transform waveform analysis in modern power systems.

Key techniques for power system waveform analysis

1. Fast Fourier Transform (FFT)
   FFT is a cornerstone technique in signal processing, widely used for analyzing the frequency content of electrical waveforms. In power systems, FFT is primarily applied to identify and quantify harmonic components in voltage and current signals. By transforming time-domain signals into the frequency domain, FFT reveals the presence of harmonic distortion, which is critical for power quality assessment and compliance with standards like IEEE 519.
   Although FFT is highly efficient and accurate for stationary signals, it assumes that the signal characteristics do not change over time. This makes it less suitable for analyzing transient or time-varying phenomena, where other techniques may be more appropriate.
2.  Wavelet Transform (WT)
   WT addresses the limitations of FFT by offering a time-frequency representation of signals. Unlike FFT, which uses fixed sinusoidal basis functions, WT uses scalable and translatable wavelets that adapt to the signal characteristics.
   This allows for multi-resolution analysis, making it ideal for detecting transient events such as faults, switching operations, and lightning strikes. In power systems, WT is particularly valuable for fault detection and localization, as it can pinpoint the exact time and frequency of disturbances. Its ability to analyze both high-frequency transients and low-frequency trends makes it a versatile tool for modern grid monitoring.
3. Independent Component Analysis (ICA)
   ICA is a statistical technique used to separate a multivariate signal into additive, independent components. In power systems, ICA is employed to isolate sources of interference or noise from measurement data, especially in multi-phase systems or sensor arrays. By identifying statistically independent sources, ICA enhances the clarity of waveform data and improves the accuracy of subsequent analysis. It is commonly used in power quality monitoring, fault detection, and signal denoising.
4.  Machine learning-based techniques
   With the rise of big data and intelligent systems, machine learning has become an integral part of waveform analysis. Techniques such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks are capable of learning complex patterns directly from raw waveform data. These models are used for a wide range of applications, including automated
   fault classification, anomaly detection, load forecasting, and predictive maintenance. Machine learning offers the advantage of adaptability and scalability, making it a powerful tool for modern power system analytics.

Figure 2. Waveforms Analysis in Power Systems

ഹാർമോണിക് വിശകലനം

• In power systems, harmonics refer to voltage or current waveforms that occur at frequencies which are integer multiples of the system’s fundamental frequency—typically 50 Hz in countries like India or 60 Hz in the United States. These harmonic components are superimposed on the fundamental waveform, distorting its ideal sinusoidal shape. For instance, a second harmonic in a 50 Hz system would appear at 100 Hz, a third at 150 Hz, and so on. As these higher-frequency components accumulate, they alter the waveform symmetry and smoothness, leading to a range of power quality issues.
• The primary sources of harmonics in modern electrical networks are nonlinear loads. These are devices that draw current in abrupt pulses rather than in a smooth, sinusoidal manner. Common examples include variable frequency drives (VFDs), which are used to control motor speeds; uninterruptible power supplies (UPS), which ensure continuous power during outages; and a wide array of electronic equipment like computers, printers, and LED lighting. Inverters used in solar and wind energy systems are also significant contributors, especially as renewable energy penetration increases in the grid.
• The presence of harmonics in a power system is not merely a theoretical concern—it has tangible and often costly consequences. Harmonic distortion can lead to overheating in transformers, motors, and cables, reducing their lifespan and efficiency. Protective relays, which are designed to respond to specific waveform characteristics, may malfunction or fail to operate correctly in the presence of distorted signals. Additionally, harmonics increase system losses and can create resonance conditions, where certain frequencies are amplified, potentially causing severe equipment damage.
• Given these risks, harmonic analysis becomes a critical aspect of power system monitoring and design. It enables engineers
  to assess the extent of distortion, identify its sources, and implement appropriate mitigation strategies. One of the most widely used tools for this purpose is the FFT. This mathematical technique decomposes a complex waveform into its constituent frequency components, revealing the magnitude and phase of each harmonic. FFT is particularly effective for analyzing stationary signals, where the waveform characteristics remain relatively constant over time.

 Process of harmonic analysis on web അപേക്ഷ

Step-by-step flow

• Capture or receive signal (e.g., from COMTRADE or sensor)
• Segment the signal into frames (e.g., 1024 sampലെസ്)
• Apply a window function to each frame
• Perform FFT on the window frame
• Visualize or analyze the frequency spectrum

Memory efficiency tips

• Use Typed Arrays (Float32Array, Float64Array) for performance
• Reuse buffers instead of reallocating
• Avoid unnecessary data copies or JSON serialization

Library Features Size Notes

• fft.js Pure JS FFT Small Fast, Typed Arrays
• DSP.js FFT + filters Medium Good for audio
• Meyda Audio features Large Built on Web Audio API

Table 5. Libraries for FFT in JS 

Figure 3. Process of Harmonic Analysis on Web അപേക്ഷ

1.  Signal acquisition
   • COMTRADE DATA fetching parsing
     The signal acquisition process begins with a backend service that receives or accesses COMTRADE files, which typically include a configuration file (.CFG) and a data file (.DAT). The .CFG file is parsed to extract metadata such as:
   • ചാനലുകളുടെ എണ്ണം
   • Sampലിംഗ് നിരക്ക്
   • ചാനലിന്റെ പേരുകൾ
   • Scaling factors
     The .DAT file contains the actual recorded signal values. These values are scaled using the extracted factors to convert raw digital readings into meaningful physical units like volts or ampഈറസ്.
     Once the time-series data is extracted and structured, it is serialized into a JSON format. Each entry includes a timestamp and the corresponding values for each channel. This structured data is then made available to the frontend via a RESTful API endpoint.
   • Plotting sinewave based on channels
     On the front end, users can select specific parameters or channels—such as VGA, VBA, and others—to visualize their waveforms. These parameters typically represent voltage or current measurements from different phases or locations in the power system. For exampLe:
   • VGA: Voltage on phase A at a generator terminal
   • VBA: Voltage on phase A at a bus
     A dropdown or list interface allows users to choose from the available channels. Once selected, the corresponding time-series data is plotted, typically showing sinusoidal waveforms under normal operating conditions.
2. വിൻഡോ
   After the sinewave is plotted, users are provided with a graphical interface to select a small window—usually one complete cycle of the waveform. This is done using a draggable or resizable selection box over the graph.
   Once a window is selected:
   • The corresponding subset of the time-series data is extracted
   • A windowing function (such as Hann or Hamming) is applied to taper the edges of the signal
   • The window signal is then prepared for FFT computation
   • This step is crucial in power systems where signals are periodic. Selecting exactly one cycle ensures that the FFT captures the fundamental frequency and harmonics with minimal distortion, while the windowing function reduces spectral leakage.
3. Fast Fourier Transform computation
   • The Fast Fourier Transform (FFT) is applied to convert the time-domain signal into the frequency domain. This reveals the harmonic content of the signal. The FFT is computed using the following formula:
     X[k] = Σ (x[n] * e^(-j2πkn/N)) for n = 0 to N-1
     എവിടെ:
   • X[k] is the k-th frequency component
   • x[n] is the n-th time-domain sample
   • N is the total number of sampലെസ്
   • j is the imaginary unit
     In fft.js, the FFT is computed using the Cooley-Tukey algorithm, which recursively breaks down a DFT of size NN into smaller DFTs.
     The following is a simplified overview പ്രക്രിയയുടെ:
   • Input preparation
   • The input signal is usually an array of real numbers
     • If the signal is real-only, it’s converted into a complex array; each value becomes a complex number with an imaginary part of 0
   • Bit-reversal permutation
     The input array is reordered in a specific way (bit-reversal order) to prepare the recursive FFT steps
   • Butterfly computation
     The core of the FFT algorithm involves combining pairs of values using the butterfly operation:
   • u=x[n]t=x[n+m]•e−j2πNkx[n]=u+tx[n+m]=u−tutx[n]x[n+m]
   • =x[n]=x[n+m]•e−jN2πk=u+t=u−t
     This is repeated in stages, doubling the size of the sub-DFTs each time
   • ഔട്ട്പുട്ട്
     The result is an array of complex numbers representing the amplitude and phase of each frequency component
4. ദൃശ്യവൽക്കരണങ്ങൾ
   The final step is visualization, where the frequency-domain data is rendered into an interactive and interpretable format using ECharts, a powerful JavaScript-based charting library.
   Once the FFT is computed, the resulting frequency spectrum—typically consisting of magnitude values across frequency bins—is passed to charts. Users can view this data as:
   • Line charts
   • Bar graphs
   • ഹീറ്റ്മാപ്പുകൾ

ECharts supports interactive features such as zooming, tooltips, legends, and responsive layouts, making it ideal for exploring signal characteristics in detail. This visualization is essential for identifying patterns, detecting anomalies, and making informed decisions based on the frequency content of the signal—such as spotting harmonics, noise, or unexpected frequency spikes.

 Harmonic mitigation strategies
As power systems evolve with the integration of nonlinear loads and renewable energy sources, harmonic distortion has become a critical concern. Harmonics can lead to equipment overheating, disoperation of protective devices, and increased system losses. To address these challenges, several mitigation strategies have been developed, each suited to different system configurations and harmonic profiles.

1. Passive filters
   Passive filters are among the most traditional and widely used solutions for harmonic mitigation. These filters consist of inductors and capacitors configured to form resonant circuits tuned to specific harmonic frequencies. When connected to the power system, they provide a low-impedance path for targeted harmonics, effectively diverting them away from sensitive equipment. While passive filters are cost-effective and simple to implement, they are fixed in nature and may not adapt well to varying harmonic conditions. Additionally, improper tuning can lead to resonance issues, potentially amplifying rather than mitigating distortion.
2.  Active Power Filters (APFs)
   APF represent a more dynamic approach to harmonic mitigation. These devices use power electronics to continuously monitor the system and inject compensating currents that cancel out unwanted harmonics. Unlike passive filters, APFs can adapt to real-time changes in load and harmonic content. They are particularly effective in environments with fluctuating nonlinear loads, such as industrial plants or data centers. However, their complexity and higher cost can be a barrier to widespread adoption, especially in smaller installations.
3.  Hybrid filters
   Hybrid filters combine the strengths of both passive and active filtering techniques. Typically, a passive filter is used to handle lower-order harmonics, while an active filter addresses higher-order or dynamically changing harmonics. This combination provides a balanced solution that offers both cost efficiency and adaptability. Hybrid filters are especially useful in systems where harmonic profiles are partially predictable but still subject to occasional variation.

ഉപസംഹാരം

• Advanced waveform analysis is a cornerstone of modern engineering, particularly in power systems, where it enables precise monitoring, fault detection, and optimization of complex electrical networks. Techniques such as FFT, WT, EMD, HHT, ICA, and machine learning models empower engineers to uncover hidden patterns and ensure system reliability
• Harmonic analysis is critical for maintaining power quality, as harmonics from nonlinear loads can distort waveforms and damage equipment. Tools like FFT and wavelet-based analysis are essential for identifying and mitigating these distortions
• A deep understanding of electrical parameters—voltage, current, frequency, harmonics, and transients—is fundamental for assessing system health. These insights extend beyond power systems into fields like audio processing and medical diagnostics, where waveform analysis supports applications such as speech recognition and ECG interpretation
• The COMTRADE standard ensures consistent storage and analysis of transient data, supporting post-event diagnostics and smart grid integration. Harmonic mitigation strategies, including passive, active, and hybrid filters, further enhance system stability and compliance
• At the core of this analytical process lies signal acquisition, followed by FFT-based decomposition and visualization through charts and time-frequency plots. These tools transform raw data into actionable insights and enable engineers to detect anomalies, optimize performance, and make informed decisions
• Waveform analysis continues to evolve as a transformative force, driving smarter, safer, and more resilient systems across industries

ഈറ്റനെ കുറിച്ച്
Eaton is an intelligent power management company dedicated to protecting the environment and improving the quality of life for people everywhere. We make products for the data center, utility, industrial, commercial, machine building, residential, aerospace and mobility markets. We are guided by our commitment to do business right, to operate sustainably and to help our customers manage power—today and well into the future.

By capitalizing on the global growth trends of electrification and digitalization, we’re accelerating the planet’s transition to renewable energy sources, helping to solve the world’s most urgent power management challenges, and building a more sustainable society for people today and generations to come. Eaton was founded in 1911 and has been listed on the New York Stock Exchange for more than a century. We reported revenues of $24.9 billion in 2024 and serve customers in more than 160 countries.
For more information, visit Eaton.com

റഫറൻസുകൾ

1. Harmonic Analysis and Fourier Transform – University of Maryland
2. Signal Analysis Resources – University of North Carolina Wilmington
3. Harmonic Analysis Lecture Notes – University of Illinois at Urbana–Champഎയിൻ
4. Harmonic Analysis with Symmetrical Space Phasor Components – IEEE Xplore
5. Applications and Practicalities of Synchronized Waveform Monitoring – PAC World
6. Electrical Waveforms and Electrical Signal Types – Electronics Tutorials

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• Eaton.com/BrightlayerDataCenters
• Eaton.com/foreseer

© 2025 ഈറ്റൺ
എല്ലാ അവകാശങ്ങളും നിക്ഷിപ്തം
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Publication No. WP027030EN / SMC September 2025

ഈറ്റൺ ഒരു രജിസ്റ്റർ ചെയ്ത വ്യാപാരമുദ്രയാണ്.
മറ്റെല്ലാ വ്യാപാരമുദ്രകളും അവയുടെ ഉടമസ്ഥരുടെ സ്വത്താണ്.

പതിവായി ചോദിക്കുന്ന ചോദ്യങ്ങൾ

What are some common types of waveforms?

Common types of waveforms include sine wave, square wave, triangle wave, and sawtooth wave.

What information can be derived from waveform analysis?

Waveform analysis provides insights into signal characteristics such as ampലിറ്റിയൂഡ്, ഫ്രീക്വൻസി, പിരീഡ്, ഫേസ്, ആർഎംഎസ്, പീക്ക്-ടു-പീക്ക് മൂല്യങ്ങൾ, ക്രെസ്റ്റ് ഫാക്ടർ, ടിഎച്ച്ഡി, എസ്എൻആർ.

പ്രമാണങ്ങൾ / വിഭവങ്ങൾ

ഈറ്റൺ തരംഗരൂപങ്ങൾ അവസാനിച്ചുview വിപുലമായ വിശകലനം [pdf] ഉടമയുടെ മാനുവൽ തരംഗരൂപങ്ങൾ കഴിഞ്ഞുview and Advanced Analysis, Waveforms, Overview and Advanced Analysis, Advanced Analysis, Analysis

റഫറൻസുകൾ

• ഉപയോക്തൃ മാനുവൽ