Sensor array in the context of Autofocus

Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The digital signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency. In digital electronics, a digital signal is represented as a pulse train, which is typically generated by the switching of a transistor.

Digital signal processing and analog signal processing are subfields of signal processing. DSP applications include audio and speech processing, sonar, radar and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, data compression, video coding, audio coding, image compression, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others.

In signal processing, direction of arrival (DOA) denotes the direction from which usually a propagating wave arrives at a point, where usually a set of sensors are located. These set of sensors forms what is called a sensor array. Often there is the associated technique of beamforming which is estimating the signal from a given direction. Various engineering problems addressed in the associated literature are:

• Find the direction relative to the array where the sound source is located
• Direction of different sound sources around you are also located by you using a process similar to those used by the algorithms in the literature
• Radio telescopes use these techniques to look at a certain location in the sky
• Recently beamforming has also been used in radio frequency (RF) applications such as wireless communication. Compared with the spatial diversity techniques, beamforming is preferred in terms of complexity. On the other hand, beamforming in general has much lower data rates. In multiple access channels (code-division multiple access (CDMA), frequency-division multiple access (FDMA), time-division multiple access (TDMA)), beamforming is necessary and sufficient
• Various techniques for calculating the direction of arrival, such as angle of arrival (AoA), time difference of arrival (TDOA), frequency difference of arrival (FDOA), or other similar associated techniques.
• Limitations on the accuracy of estimation of direction of arrival signals in digital antenna arrays are associated with jitter ADC and DAC.

Advanced sophisticated techniques perform joint direction of arrival and time of arrival (ToA) estimation to allow a more accurate localization of a node. This also has the merit of localizing more targets with less antenna resources. Indeed, it is well-known in the array processing community that, generally speaking, one can resolve ${\displaystyle P}$ targets via ${\displaystyle N>P}$ antennas. When JADE (joint angle and delay) estimation is employed, one can go beyond this limit.

Machine olfaction is the automated simulation of the sense of smell. An emerging application in modern engineering, it involves the use of robots or other automated systems to analyze air-borne chemicals. Such an apparatus is often called an electronic nose or e-nose. The development of machine olfaction is complicated by the fact that e-nose devices to date have responded to a limited number of chemicals, whereas odors are produced by unique sets of (potentially numerous) odorant compounds. The technology, though still in the early stages of development, promises many applications, such as:quality control in food processing, detection and diagnosis in medicine, detection of drugs, explosives and other dangerous or illegal substances, disaster response, and environmental monitoring.

One type of proposed machine olfaction technology is via gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds. However, a critical element in the development of these instruments is pattern analysis, and the successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation. Another challenge in current research on machine olfaction is the need to predict or estimate the sensor response to aroma mixtures. Some pattern recognition problems in machine olfaction such as odor classification and odor localization can be solved by using time series kernel methods.