scispace - formally typeset
Search or ask a question

What is Frequency Counting? 


Best insight from top research papers

Frequency counting is a method used in various applications such as radio frequency devices, sensor platforms, and recommendation engines. It involves capturing and analyzing the frequency of signals or events. For instance, a frequency counting module can monitor carrier frequency variations in signals . In the context of sensor platforms, a technique for determining resonance frequency involves counting oscillations per time during sensor ring-down, enhancing measurement resolution . Additionally, electronic devices utilize memory-efficient methods for storing frequency data at different levels of detail, facilitating event frequency analysis and sorting for applications like Internet bookmark management . By combining techniques like FFT and frequency counting, systems can achieve more accurate data for applications like automobile Doppler radar systems, enhancing safety and efficiency .

Answers from top 4 papers

More filters
Papers (4)Insight
Patent
18 Aug 2010
10 Citations
Frequency counting is efficiently tracking event occurrences. This method uses primary and secondary storages with detailed and summarized data, beneficial for tasks like grading bookmarks in browsers.
Frequency counting is the process of capturing and counting the carrier frequency variations of signals, particularly AM and FM modulation signals, to optimize communication effectiveness through adjustment based on test results.
Proceedings ArticleDOI
Tao Tang, Jin Pan, Deqiang Yang 
01 Dec 2018
Frequency Counting is a method used in frequency measurement alongside FFT in FPGA for automobile Doppler radar systems. It enhances accuracy in detecting vehicles' frequencies, aiding in accident prevention.
Frequency counting is a process of measuring the number of occurrences of a particular radio frequency signal. The radio frequency counting device in the paper automates scanning and precise measurements.

Related Questions

What is a frequency?5 answersFrequency is a central concept in Mathematics, Physics, and Signal Processing. It is the main tool for describing the oscillatory behavior of signals, which is usually argued to be the manifestation of some of their key features, depending on their nature. In applied behavior analysis and behavior measurement, frequency refers to cycles per unit time or a count divided by the time during which it occurred. However, in statistics, frequency refers to a count of items in a data set. It is important to note that within applied behavior analysis and behavior measurement, the term "frequency" should not refer to "count" but instead to "rate." Counts of behavior should always be accompanied by information about the observation time to provide rate data.
What is frequency?5 answersFrequency is a central concept in Mathematics, Physics, and Signal Processing. It is the main tool for describing the oscillatory behavior of signals, which is usually argued to be the manifestation of some of their key features, depending on their nature. In applied behavior analysis and behavior measurement, frequency refers to cycles per unit time or a count divided by the time during which it occurred. It is important to note that the term "frequency" should not refer to "count" but instead to "rate" within applied behavior analysis and behavior measurement. Frequency measurements provide precise descriptions of the dimensional quantity of a repeating event and can be used to compare various phenomena, such as sound waves or behavioral responses.
What is frequency in research?4 answersFrequency in research refers to the number of occurrences of a specific linguistic structure or signal within a given system or dataset. It is an important factor in usage-based models of language change, acquisition, and processing. In the context of high-precision data acquisition technology, frequency measurement involves processing a large amount of data while maintaining high precision. Frequency estimation methods are used in various fields such as wireless communication, speech processing, and radar measurements. Word frequencies are commonly used in disciplines like linguistics, psychology, and pedagogy to study behavior and structure. In the field of power systems, frequency is crucial for improving the security and reliability of isolated power grids.
What is frequency?5 answersFrequency refers to the number of occurrences of a particular event or item within a given time frame. It can be defined as the count or rate of events or behaviors per unit of time. In applied behavior analysis and behavior measurement, frequency is often used to refer to the count of behaviors divided by the time during which they occur. However, in linguistics, frequency is defined as the number of occurrences of a linguistic structure in a particular linguistic system or sub-system. Frequency can be represented both propositionally and in analog form, depending on the context and the type of information being measured. It is an important factor in usage-based models of language change, language acquisition, and language processing. The term "frequency" can be ambiguous and its meaning may vary depending on the field of study and the specific context in which it is used. In the field of applied behavior analysis, it is recommended to use frequency as a synonym for count rather than rate.
What is frequency analysis?4 answersFrequency analysis is the process of studying the frequency content of a signal. It involves techniques such as Fourier transform theory, statistics, and digital signal processing to analyze the oscillatory behavior of signals and estimate their spectra. Frequency analysis is widely used in various fields, including noise and vibration analysis, fluid power systems, and neural networks. In noise and vibration analysis, frequency analysis is based on the discrete Fourier transform and involves estimating the spectrum of a signal from measured samples. In fluid power systems, frequency analysis is used to study the frequency response of motor-valve and cylinder-valve drives for parameter studies. In the context of neural networks, frequency analysis helps understand the mechanisms of representation learning and the presence of frequency shortcuts in classification tasks. Overall, frequency analysis is a fundamental tool for understanding and analyzing signals in various domains.
What is frequency in regards to data collection?4 answersFrequency in data collection refers to the rate at which data is collected or sampled. It determines how often data is gathered and recorded for a specific attribute or point type. The collection frequency is determined based on the value of the attribute and the type of point being monitored. For example, in a system for collecting metric data, a sample table is created to store the collected data, and the collection frequency is used to determine the structure of the table. In the context of RF environment data collection, a device initiates an RF scan in response to a request from a network controller, and the collected data is transmitted back to the controller. In the case of security-related information transfer, DNS requests are used as a reporting vehicle, and the frequency of these requests indicates that a data block has been accessed. The goal of data collection systems is to reduce communication amounts while minimizing processing loads on devices.

See what other people are reading

How do ambient light levels affect photoplethysmography?
5 answers
Ambient light levels significantly impact photoplethysmography (PPG) outcomes. Studies show that under varying lighting conditions, the accuracy of heart rate (HR) and pulse transit time (PTT) extracted from video-based PPG can be affected. Utilizing ambient light as a reference can reduce motion artifacts in PPG measurements without the need for additional hardware. Moreover, the intensity of ambient light can be monitored to ensure continuous physiological monitoring, with active lighting systems deployed when necessary for optimal cardiac measurements. Imaging PPG systems, whether high-performance cameras or webcam-based, demonstrate comparable physiological data acquisition, with ambient light intensity influencing the normalized plethysmographic signals. Therefore, ambient light levels play a crucial role in the accuracy and reliability of PPG measurements, impacting the detection of HR and PTT in various settings.
Any research about rust programming language in embedded devices?
5 answers
Research on Rust programming language in embedded devices has shown promising results. Rust offers memory safety, type safety, and modern language features for embedded systems. It has been noted that Rust can lead to more energy-efficient and faster implementations compared to C, particularly in applications like Fast Fourier Transform (FFT) on platforms like Raspberry Pi. Additionally, Rust's focus on safety makes it a suitable choice for developing secure operating systems for embedded devices, as seen in the creation of Tock, a secure operating system written entirely in Rust. Furthermore, Rust's potential in enhancing safety for multi-core applications in safety-critical domains like avionics has been explored, showcasing its ability to aid in the development of safe and efficient embedded systems.
Doppler radars for tracking small aerial objects
4 answers
Doppler radars play a crucial role in tracking small aerial objects like drones. Various studies have proposed innovative Doppler radar systems tailored for this purpose. One approach involves using continuous wave (CW) Doppler radar to provide early warning for small UASs, ensuring reliable detection with low false alarm rates. Another study introduces a CW bistatic Doppler radar scheme for high-resolution range detection of fast targets, enabling simultaneous range and velocity detection with high accuracy. Additionally, research focuses on utilizing micro-Doppler signatures to differentiate between UAVs and other objects, with experiments conducted using a high-frequency radar system for measuring micro-Doppler returns from UAV components. Furthermore, advancements in deep learning techniques have been explored for Micro Doppler Automatic Target Recognition (ATR), enhancing the detection and classification of small objects like drones based on radar signals.
How deep learning has been used to mitigate interference in 5G networks?
5 answers
Deep learning has been leveraged to mitigate interference in 5G networks through innovative approaches. One study focused on using a Convolutional Neural Network (CNN) model to classify 5G New Radio (NR) and radar altimeter signals, aiming to enhance aviation safety by detecting harmful interference. Another research proposed a deep Q-learning (DQL) algorithm for power control to intelligently mitigate air-ground interference in cellular-connected UAVs within 5G networks, outperforming traditional methods like Fixed Power Allocation (FPA) and tabular Q-learning (TQL). Additionally, continual learning in deep neural networks has been explored to address transmission impairments in optical communication systems, showing improved training accuracy and flexibility for dispersion and nonlinearity mitigation in long-haul transmission systems. These studies collectively demonstrate the efficacy of deep learning in combating interference challenges within 5G networks.
What are the most common methods and cost effective for measuring soil moisture levels?
5 answers
The most common methods for measuring soil moisture levels include direct techniques like the gravimetric method and indirect methods such as ground-penetrating radar and remote sensing. Low-cost sensors, both resistive and capacitive, are gaining popularity among farmers for soil moisture monitoring due to their cost-effectiveness. One cost-effective approach is the use of millimeter-wave (mmWave) radar-based methods, which offer non-contact soil moisture sensing with a mean absolute error of 2.2% volumetric water content (VWC) using a single Commercial-Off-The-Shelf (COTS) mmWave radar. Additionally, automated soil moisture systems utilizing low-cost sensors like SKU:SEN0193 provide medium accuracy at a very low acquisition cost, enabling spatial monitoring through multiple-point measurements, making them suitable for projects with budget limitations and medium accuracy requirements.
How to calculate power available within WEC device width?
4 answers
To calculate the power available within a Wave Energy Converter (WEC) device width, various methodologies and factors need to be considered. One approach involves assessing the power produced by the WEC under multi-directional wave conditions, which can impact the device's performance. Additionally, a novel net power assessment methodology incorporating upper bounds based on stroke, radiation power limit, and gross incident wave power can provide insights into the potential wave power that can be harnessed by different sized WECs at various ocean sites. Furthermore, analyzing the power delivered from a WEC using conventional frequency transfer functions and a "wave-by-wave" basis can offer comparisons and insights into power extraction from random sea states. Lastly, utilizing complex site-specific sea states in wave tank model testing can accurately estimate the total power output of a WEC, emphasizing the importance of realistic wave conditions for performance evaluation.
What are factors that affect the frequency discrimination?
4 answers
Frequency discrimination can be influenced by various factors such as memory for pitch, musical training, utilization of temporal fine-structure cues, place and temporal mechanisms, sensory processing in developmental language disorders like dyslexia, and effects of repetitions within the stimulus set. Memory for pitch and musical training can impact frequency discrimination thresholds in children, with musically trained children showing potentially smaller developmental effects. The role of place and temporal mechanisms in auditory frequency discrimination at 2 kHz suggests a dependence on temporal cues, except for high-rate frequency modulation detection. In dyslexia, group differences in frequency discrimination tasks may reflect sensory processing deficits, influenced by participant variability and task design. Additionally, the effects of local and global context on discrimination thresholds highlight distinct contributions to performance, with global context having a larger impact. The representation of vibration frequency in the somatosensory system varies across cortical regions, influenced by vibration velocity and temporal structure.
What is 3d radiation imasing?
5 answers
Three-dimensional (3D) radiation imaging involves advanced techniques for visualizing radiation sources in three dimensions. Various methods have been proposed, such as utilizing scattered radiation information for emission location and dose distribution determination, rearranging radar echoes to achieve high-resolution 3D imaging, reconstructing radiation source distribution using multiple detector viewpoints and SLAM technology, and employing real-time gamma-ray mapping for radiological material detection and localization. These approaches enable precise mapping of radioactive sources in real-time, aiding in applications like disaster monitoring, environmental security, nuclear safety, and decommissioning activities. Additionally, the development of compact radiation imaging devices utilizing stereoscopic principles further enhances the accuracy and efficiency of 3D radiation imaging systems.
Feature detection relate to cognitive psychology?
5 answers
Feature detection is a significant aspect in cognitive psychology, as evidenced by various research studies. Studies have shown that feature detectors can enhance listener performance in auditory discrimination tasks involving frequency modulation (FM). Furthermore, the cognitive response of subjects can be evaluated through EEG signals while watching video clips, extracting features related to evoked activity and subjects' interests. In the realm of dementia detection, visual features like eye blink rate and head turn rate have been utilized to identify neurodegenerative disorders, mild cognitive impairment, and functional memory disorders, showcasing the importance of feature analysis in cognitive assessment. Additionally, cognitive analysis of musical features in natural listening states has been explored, revealing the cognitive mechanisms of the brain for acoustic features and providing insights for music computing and composition.
What is the mathematical formula for amplitude demodulation in Matlab?
4 answers
The mathematical formula for amplitude demodulation in Matlab involves constructing an M × N Hankel matrix based on discrete samples, performing Singular Value Decomposition (SVD) on the matrix, and then using the first two singular values to estimate the amplitude information of the acquired signal. This method eliminates the need for reference signals, ensuring synchronization is unnecessary, and provides high Signal-to-Noise Ratio (SNR) even with non-integrity period sampling. Additionally, SVD itself acts as a filter, eliminating the need for extra low-pass filters in the signal conditioning module. The proposed approach demonstrates excellent demodulation accuracy and robust performance through numerical simulations and experiments, showcasing its effectiveness in providing reliable amplitude demodulation in Matlab.
How accurate is google earth mapping?
5 answers
Google Earth mapping accuracy varies based on the specific application and methodology used. Studies have shown high accuracy levels in mapping built-up areas when combining Synthetic Aperture Radar (SAR) data of Sentinel-1 and Multispectral Instrument (MSI) images of Sentinel-2 through Google Earth Engine (GEE) platform, achieving an overall accuracy of 97%. Additionally, the use of bidirectional reflectance distribution function (BRDF) signatures captured by multi-angle observation data has shown moderate improvements in land cover classification accuracy, with an overall validation accuracy increase of up to 4.9%. Furthermore, in mapping alpine grassland aboveground biomass, machine learning models like deep neural network (DNN) have demonstrated high accuracy, with DNN outperforming other models with an R2 of 0.818. These findings collectively suggest that Google Earth mapping can be highly accurate when utilizing advanced techniques and data sources.