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:Device Free Sensing/WiFi ensing/ Ultrasonic sensing :
Device Free (Wi-Fi) Sensing:
Device-free human activity recognition based on wireless signal is becoming a vital underpinning for various emerging applications such as healthcare, assisted living ,smart transportation, home automation, elderly people monitoring, and many other applications . WiFi-based non-contact approaches are gaining popularity due to the ubiquity and non-invasiveness. Passive human activity recognition (HAR) systems, in which no sensors are attached to the subject, provide great potentials compared to conventional systems. This new sensing technology has been made possible by analysing the Channel State Information (CSI) data of the Wi-Fi communication channel. This CSI data captures multipath effects, and phase shifts during the signal propagation from transmitter to receiver device. Wi-Fi signals are distorted by physical objects, including individuals moving around the home. These disruptions are picked up by Wi-Fi sensing technology and analysed by machine learning and deep learning algorithms to determine the cause. The attributes like amplitude, phase, and frequency response are examined over time for detection, prediction and estimation. The changes changes in the Channel State Information (CSI) values of the exchanged wireless packets carried by OFDM subcarriers helps in recognizing human activities and indoor localization problems. CSI can accurately mirror physical changes in the wireless channel, CSI analysis has become a valuable resource to many wireless sensing applications based on the opportunistic use of Wi-Fi signals.
Channel state information (CSI) includes specific indicators such as carrier signal strength, amplitude, phase, and signal delay. These indicators reveal the signal scattering, reflection, and power attenuation phenomena that occur with the carrier as the transmission distance changes. It can be used to measure the channel status of the wireless network in Wi-Fi communication. By analyzing and studying the changes in CSI, we can conversely speculate on the changes in the physical environment that cause the changes in the channel state, that is, to achieve non-contact intelligent sensing. CSI is extremely sensitive to environmental changes. In addition to perceiving environmental changes caused by large movements such as walking and running of people or animals, it can also capture subtle movements caused by small movements such as breathing and chewing of people or animals in a static environment.
Acoustic/Ultrasonic Sensing:
Acoustic waves are a type of wave that travels at the speed of sound. It travels by compressing and decompressing the medium it is traveling though. Ultrasonic waves are acoustic waves that are above 20KHz, which is beyond the human audible range.
The speed of sound in air is affected by changes in some physical conditions such as temperature, pressure, humidity, etc. Non-contact ultrasonic sensor used speed of sound to calculate the distance to the obstacle/object/surface etc. Any changes in the air medium (environmental parameters such as temperature and relative humidity gradient through medium will affect the accuracy of the sensor measurement. Therefore machine learning approach can be adopted to compensate the effect and increases the accuracy.
Applications of Ultrasonic sensors:
1) Majors applications: Obstacle Detection, Level Measurement, Distance Measurement, Industrial, Automotive, Healthcare and Consumer Electronics
2) Ultrasonic echolocation can also be used as a type of sensor to help aerial drones avoid obstacles so they can be sent into dangerous and difficult-to-reach locations.
3) Researchers from the University of Bristol have shown that by controlling and focusing sound waves from an array of ultrasound sources can create enough force to lift a bead-sized object off the ground. (Reference: Nature Communications)
4) Potential medical application of ultrasound is to enable blind people to “see” in a similar way to how bats do using the principle of echolocation. Rather than detecting reflected light waves to see objects, bats send out ultrasound waves and use the reflected sound to work out where things are. These echoes can provide information about the size and location of that object.
5) Ultrasound potentially safe, effective way to kill bacteria: High-power ultrasound, currently used for cell disruption, particle size reduction, welding and vaporization, has been shown to be 99.99 percent effective in killing bacterial spores after only 30 seconds of non-contact exposure in experiments conducted by researchers at Penn State and Ultran Labs, Boalsburg, Pa.
7) Contact and non-contact ultrasonic measurement in the food industry: a review
8) The nondestructive and contact-less monitoring of fermentation process using ultrasound reduces the risk of contamination. A remarkable correlation was found between the ultrasonic velocity and the bacterial catabolism. These results show the great potential of this non-invasive technique for monitoring biotechnological processes.
9) Non-contact monitoring of Respiration rate or Breathe rate.
Sound Classification:
Audio classifications can be many types such as — Acoustic Data Classification, Music classification, Natural Language Classification, and Environmental Sound Classification.
Audio classification is the process of analyzing audio recordings and categorizing them. Audio classification has numerous applications in the field of AI such as chatbots, automated voice translators, virtual assistants, music genre identification, and text to speech applications.
Sound classification is a growing area of research with numerous real world applications. Environmental Sound Classification (ESC), which is dedicated mainly to identify specific sound events, such as identifying dog barking, gunshots, and air conditioning sounds, has received increasing attention. The study result has been used in many practical applications, including robotic hearing, smart home, audio monitoring system, soundscape assessment and so on. Compared with regular and structured sounds such as speech and music, the environmental sound has neither static time patterns like melodies or rhythms nor semantic sequences like phonemes. Hence, it is difficult to find universal features that can represent various temporal patterns. Besides, the environmental sound contains a lot of noise and some sounds unrelated to the sound event, which lead to complicated composition structure with variability, diversity, and unstructured characteristics.
To deal with the above problems, various signal processing methods and machine learning techniques have been used for ESC tasks.
One use of acoustic data classification is the building and maintaining of sound libraries for audio multimedia. It also plays a role in ecosystem monitoring. One example of this is the estimation of the abundance of fish in a particular part of the ocean based on their acoustic data.
References:
2- Audio Classification: Environmental sounds classification
Sound Source Localization / Acoustic Source Localization:
Sound localization is the process of determining the location of a sound source. Sound localization deals with finding the source of sound with respect to an array of microphones.In practice, sound source localization is done using two type of cues, these are: binaural and monaural. Binaural cues are determined by using differences in sound signals reaching at the two ears . This difference is calculated using either time to intensity of the incident sound signal. Monaural cues are measured through the angle of incidence of the sound signal on the ear. The ability to distinguish and identify particular sounds from the surrounding noise is an important aspect of normal auditory system.
This localization problem receives immense importance by researchers from the field of medicine, robotics and signal processing.
Applications:
1-Hearing aid for the disabled people
2- Audio surveillance, teleconferencing, improved speech recognition and speech enhancement
3-Domestic and military applications.
4- Automatic identification of target and its location.
5- In the field of robotics
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