SENSORS IN MACHINE LEARNING
ABSTRACT
Sensors are essential components of any modern device in the 21st century.
Practically, all modern systems with even modest complexity will rely on at least
one sensor, and many will have dozens of embedded sensors. These types of
sensors are found in diverse practical applications such as medical diagnostics,
sustaining human health and wellbeing environmental monitoring, civil
engineering, agriculture farming, among many others.
The fundamental function of any sensor is to detect and provide accurate
information of its sensing target, and depending on the type of sensor, the outcome
of the measurements may come in different forms such as electrical voltage signals
from a gas sensor, or digital images captured from a complementary metal-oxidesemiconductor (CMOS) image sensor array in the camera of a microscope.
Recent advancements and major breakthroughs in machine learning (ML)
technologies in the past decade have made it possible to collect, analyze, and
interpret an unprecedented amount of sensory information. A new era for “smart”
sensor systems is emerging that change the way that conventional sensor systems
are used to understand the world. Smart sensor systems have taken advantage of
classic and emerging ML algorithms and modern computer hardware to create
sophisticated “smart” models that are tailored specifically for sensing applications
Afrin Imtiaz Baig
2KE18CS004
CAMERA MOUNTING VEHICLE
ABSTRACT
Camera Mounting systems are used to connect a lens to a camera, ensuring both
good focus and image stability. Unmanned vehicles such as UAVs (unmanned
aerial vehicles), drones, UGVs (unmanned ground vehicles) and ROVs (remotely
operated vehicles) may use a wide variety of cameras to capture different kinds of
imagery, such as photos, thermal images, multispectral images for applications
such as precision agriculture, drone is an unmanned aircraft. Essentially, a drone is
a flying robot that can be remotely controlled or fly autonomously using softwarecontrolled flight plans in its embedded systems, that work in conjunction with
onboard sensors and a global positioning system (GPS).UAVs were most often
associated with the military. They were initially used for anti-aircraft target
practice, intelligence gathering and, more controversially, as weapons platforms.
Afrin Imtiaz Baig
2KE18CS004
SPECTRAL IMAGING
ABSTRACT
Spectral imaging is imaging that uses multiple bands across the electromagnetic
spectrum. While an ordinary camera captures light across three wavelength bands
in the visible spectrum, red, green, and blue (RGB), spectral imaging encompasses
a wide variety of techniques that go beyond RGB. Spectral imaging may use the
infrared, the visible spectrum, the ultraviolet, or some combination of the above. It
may include the acquisition of image data in visible and non-visible bands
simultaneously. It is also possible to capture hundreds of wavelength bands for
each pixel in an image.
Multispectral imaging captures a small number of spectral bands, typically three to
fifteen, through the use of varying filters and illumination. Many off-the-shelf
RGB cameras will detect a small amount of Near-Infrared (NIR) light, an infraredpassing filter may be used on the camera to ensure that visible light is blocked and
only NIR is captured in the image. Industrial, military, and scientific work,
however, uses sensors built for the purpose.
Hyper spectral imaging is another subcategory of spectral imaging, which
combines spectroscopy and digital photography. In hyper spectral imaging, a
complete spectrum or some spectral information is collected at every pixel in
an image plane. A hyper spectral camera uses special hardware to capture hundreds
of wavelength bands for each pixel, hyper spectral images are often represented as
an image cube.
Applications of spectral imaging include art conservation, astronomy, solar
physics, planet logy and Earth remote sensing
Afrin Imtiaz Baig
2KE18CS004