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Source publication
Purpose
The purpose of this paper is to acquaint a wide audience of readers with some of the unique remote sensing and navigation capabilities of animals.
Design/methodology/approach
Biomimetic comparison of remote sensors evolved by animals and sensors designed by man. The study and comparison includes thermal infrared sensors used by snakes, ech...
Context in source publication
Context 1
... of novel technologies through the distillation of principles and techniques learned from the study of biological systems (Bar-Cohen, 2006; Bleckmann et al. , 2004; Bonser and Vincent, 2007; Lenau et al. , 2008; Shu et al. , 2011; Stroble et al. , 2009). Biomimetics can be applied to sensor design in a number of ways: by emulating the structure of natural sensing organs; by emulating function, e.g. of human senses; by emulating behavioural aspects; or by using naturally occurring biological structures in novel ways (Benyus, 1997; Bogue, 2009; Helms et al. , 2009). The objective of this paper is to acquaint a wide audience of readers with some of the unique remote sensing and navigation capabilities of certain animal species. The author also hopes that this will generate additional interest among scientists and engineers in designing sensor systems based on the biomimetic approach. The remote sensing techniques covered in this paper are purposely confined to electromagnetic (EM) and acoustic waves, based on the definition “The detection and use of emitted and reflected EM or acoustic energy to detect and measure the physical and biological properties of distant objects or animals”. For instance, the ability of moths to smell odors at a distance or electrosensory systems of electric eels are beyond the scope of this article. Most remote sensors use EM waves or acoustic waves to detect, measure or image features of interest. Since the waves are sinusoidal, they can be defined by their wavelength and frequency. EM waves used in remote sensing cover a wide range of wavelengths, including visible, infrared and microwaves. The basic units for measuring wavelengths are micrometers ( m m) and centimeters (cm). As shown in Figure 1, the visible spectrum extends from 0.4 to 0.7 m m, the thermal infrared band used is centered at 10 m m, and microwaves have wavelengths in the centimeter range, including radar bands from 3 to 30 cm. EM waves travel through air (or vacuum) with the speed of light, while acoustic waves move more slowly. Figure 1 shows that the visible spectral region can be conveniently subdivided into blue (0.4-0.5 m m), green (0.5-0.6 m m) and red bands (0.6- 0.7 m m). At longer wavelengths beyond the visible are the near- infrared, thermal infrared and microwave spectral regions (Lillesand and Kiefer, 1994; Purkis and Klemas, 2011). Remote sensors can be classified by wavelength or application. They can include imagers (mappers), radiometers, spectrometers or distance rangers. Imagers produce a two-dimensional picture of the features viewed. They include film or digital cameras, multi-spectral scanners and radar mappers. Radiometers provide very accurate measurements of signal strength in a few spectral bands, as done by thermal infrared radiometers. Spectrometers measure the spectral distribution of a signal, and range detectors, such as lidars and radar altimeters, measure bathymetry and ground/sea elevation (Jensen, 2007; Klemas, 2009; Martin, 2004). Some sensors are passive, such as cameras that image solar reflected light, or thermal infrared sensors that map the temperature of heat-emitting landscapes. Other sensors are active, such as radar, lidar, and acoustic echolocators. They provide their own power pulses which reflect from selected targets. The fascination with rattlesnakes is based on fear of their legendary aggressiveness and deadly venom. Rattlesnakes can be found in most Southwestern states of the USA. They tend to avoid contact with humans and wide-open spaces that offer little protection from predators, spending their time under low-growing shrubs, debris and rocks. They are most active during the warmer times of the year and many of them are nocturnal and use their venom to capture and partially digest their prey. Although their threats to humans are exaggerated, rattlesnakes are interesting for several reasons. One of them is that their venom, a toxic saliva, is among the most complex substances known: a mixture of enzymes unique to pit vipers that destroys blood or paralyzes the nerves. Furthermore, their fangs are like moving hypodermic syringes. When rattlesnakes strike, their fangs are rotated by muscular contraction to an erect position. The fangs are hollow, which allows the snake to inject venom through the tooth into its victim, which saves energy that would otherwise be needed to subdue its prey (Ivanyi, 2006). The other unique feature that rattlesnakes and other pit vipers have is their remarkable ability to detect thermal infrared waves between 5 and 30 m m by means of sensory organs in their upper jaws. They can detect heat from a candle flame 10 m away. Heat given off by their warm-blooded prey, such as rodents and birds, in the form of thermal infrared EM waves, creates a heat image “seen” by the snake. The heat sensors are also used by rattlesnakes to find cool places to hide from the scorching desert sun (Bakken et al. , 2003; Krochmal and Bakken, 2012). As shown in Figure 2, located on each side of the snake’s head near the eyes is a pit organ that is highly effective in detecting differences in temperature. At short ranges, rattlesnakes can detect temperature differences as small as 0.2 8 C. The pit organ is essentially a pinhole infrared camera with a temperature-sensitive membrane suspended near its back. There are about 40 £ 40 sensory cells on the membrane and the field of view is about 100 8 wide, which implies that input to the organ could be represented in the brain with a resolution of about 2.5 8 . Since the radiation flux entering the organ must be large enough to quickly detect moving prey, the aperture of the infrared organ is wide, about 1 mm, and comparable to the organ depth. Thus, the incoming radiation from a point source does not strike a point-like region of the membrane, as in an ideal pin-hole camera, but rather a large disc-shaped region, resulting in distanded and blurry images on the membrane (Fang, 2010; Sichert et al. , 2006). Scientists have been puzzled how the snake can identify its prey in darkness with an angular precision of about 5 8 using such a poorly focused infrared input. It seems that the heat images are integrated with visual ones in the brain, which has been trained during daytime to interpret the heat images in relation to visual ones. Specifically, the information from the infrared system, combined with input from the “normal” visual system, allows formation of a neural map in the brain’s optic tectum. Bimodal neurons in the rattlesnake’s optic tectum, which receive sensory input from the eye retina and from the infrared-sensing pit organ, engage in highly nonlinear cross-modality interactions. This map is sharp enough to serve as a topological representation of the outside world (Newman and Hartline, 1981; Sichert et al. , 2006). The resulting image is accurate enough for hunting prey and distinguishing predator from prey even in total darkness. Thus, despite its poor spatial resolution, the heat sensor information is interpreted by the brain to estimate the size, position, distance and movement of the prey (Ivanyi, 2006; Schwarzschild, 2006). This enables the snake to strike vulnerable body parts of the prey or predator with remarkable precision. Even more interesting is the fact that some of the prey use countermeasures to confuse the snake. Ground squirrels are one type of prey hunted by rattlesnakes. Infrared videos show that ground squirrels’ tails, which are normally cooler than their bodies, heat up during bouts with rattlesnakes. By pumping warm blood into their tails, the squirrels divert the snake’s attention so it strikes at the tail, minimizing the injury to the squirrel. The ground squirrels have evolved blood proteins that partially neutralize rattlesnake venom, so an adult usually does not die from a bite. By whipping their heated tails back and forth the squirrels may also intimidate the snake to keep it away from their pups. In contrast, the ground squirrel tails do not warm up during similar taunting of gopher snakes, which do not have infrared sensors (Milius, 2004a; Minkel, 2007; Rundus et al. , 2007; Yokoyama et al. , 2011). There also are other animals, such as vampire bats and insects, that use infrared remote sensing. For instance forest fire beetles use their infrared sensors to detect forest fires at distances of tens of kilometers (Schmitz and Bleckman, 1998; Schmitz et al. , 2000). The reason the beetles fly towards the conflagration while everybody else is leaving, is because their eggs are laid in the still warm wood, and the emerging larvae feed on the wood, which has had its toxins destroyed by the fire (Schmitz and Anke, 2008). The infrared-detecting beetle Melanophila acuminate has one IR-responsive organ on either side of the thorax and each contains about 70 individual receptors called sensilla which are located at the bottom of a small pit with dimensions of about 0.3 £ 0.15 mm. These are essentially thermo-mechanical devices which rely on the incident IR to cause the expansion of an entrapped fluid which triggers a response from a mechanically sensitive cell (Bogue, 2009). Other beetles have a sensory plate in front of the first pair of legs covered with nervous tissue. The plate is located above a cavity and connected to the rest of the body, producing the equivalent of a bolometer, an instrument we employ as infrared detectors. It also seems likely that some sort of infrared image is formed in the brain of some beetles (Schmitz and Anke, 2008). To navigate and hunt for insects in complete darkness, bats have developed over millions of years an acoustic remote sensing system that surpasses any built by man. It is so sophisticated that they can fly at night at great speeds avoiding all obstacles in their path and can detect and track tiny insects from several meters away. The bat’s larynx produces ultrasonic sound, at frequencies between 10 and 150 kHz (vibrations per second). In ...
Citations
... Examples include the three pigments in the visual receptors, which only pick up 3 ranges of wavelengths (from 370 to 730 nm) out of the whole vibratory spectrum-what we perceive as variations of blue, green, and yellow colors (Kolb, 2011). Other species have a slightly broader perceptual sensitivity, with birds and certain insects sensitive to light vibrations in the ultraviolet range, and reptiles and fish perceive vibrations in the infrared range, both higher and lower than human visual perceptual limits (Klemas, 2013). ...
... The ants use remote, active, collective sensing to probe their surroundings. Remote sensing is extremely common in the biological world (Klemas, 2013). Primary examples are the use of sight, olfaction, hearing, and vibration (Hill, 2001;Klärner and Barth, 1982). ...
The cognitive abilities of biological organisms only make sense in the context of their environment. Here, we study longhorn crazy ant collective navigation skills within the context of a semi-natural, randomized environment. Mapping this biological setting into the ‘Ant-in-a-Labyrinth’ framework which studies physical transport through disordered media allows us to formulate precise links between the statistics of environmental challenges and the ants’ collective navigation abilities. We show that, in this environment, the ants use their numbers to collectively extend their sensing range. Although this extension is moderate, it nevertheless allows for extremely fast traversal times that overshadow known physical solutions to the ‘Ant-in-a-Labyrinth’ problem. To explain this large payoff, we use percolation theory and prove that whenever the labyrinth is solvable, a logarithmically small sensing range suffices for extreme speedup. Overall, our work demonstrates the potential advantages of group living and collective cognition in increasing a species’ habitable range.
... Behavioral studies also suggest that real raptors have their highest visual acuity along the LOS of the deep foveae. In addition, birds have sharper vision than humans and can see in certain spectral bands, like the ultraviolet (UV), which humans cannot [4]. This is due to their significantly larger number and types of retinal photoreceptive "cones" located at the back of the eye for enhanced color perception and significantly larger number of retinal photoreceptive "cell rods" for enhanced visual acuity. ...
Foveated sight as observed in some raptor eyes is a motivation for artificial imaging systems requiring both wide fields of view as well as specific embedded regions of higher resolution. These foveated optical imaging systems are applicable to many acquisition and tracking tasks and as such are often required to be relatively portable and operate in real-time. Two approaches to achieve foveation have been explored in the past: optical system design and back-end data processing. In this paper, these previous works are compiled and used to build a framework for analyzing and designing practical foveated imaging systems. While each approach (physical control of optical distortion within the lens design process, and post-processing image re-sampling) has its own pros and cons, it is concluded that a combination of both techniques will further spur the development of more versatile, flexible, and adaptable foveated imaging systems in the future.
Kompetensi Dasar 3.6 Kelas IX memberi tantangan bagi guru sains untuk mengelaborasi konsep sains secara terpadu. Pada kompetensi dasar tersebut konsep fisika tentang elektromagnetisme digabungkan dengan konsep biologi tentang sistem navigasi. Melalui studi literatur terhadap berbagai jurnal ilmiah, artikel ini bertujuan untuk mengkaji bagaimana konsep listrik dan magnet memiliki peran penting dalam peristiwa pergerakan/navigasi beberapa jenis hewan. Kajian ini juga diperluas terhadap hewan-hewan yang melakukan navigasi menggunakan gelombang bunyi. Secara lebih terperinci, tujuan artikel ini adalah untuk menganalisis bagaimana mekanisme berbagai jenis hewan melakukan migrasi dikaitkan dengan beberapa konsep fisika berupa kelistrikan, kemagnetan, dan gelombang bunyi.
Basic Competence 3.6 Class IX presents a challenge for science teachers to elaborate science concepts in an integrated manner. In these basic competencies, the physics concept of electromagnetism is combined with the biological concept of navigation systems. Through literature studies of various scientific journals, this article aims to examine how the concepts of electricity and magnetism have an important role in the movement/navigation of several types of animals. This study was also extended to animals that navigate using sound waves. In more detail, the purpose of this article is to analyze how the mechanism by which various types of animals migrate is related to several physical concepts in the form of electricity, magnetism, and sound waves.
The cognitive abilities of biological organisms only make sense in the context of their environment. Here, we study longhorn crazy ant collective navigation skills within the context of a semi-natural, randomized environment. Mapping this biological setting into the 'Ant-in-a-Labyrinth' framework which studies physical transport through disordered media allows us to formulate precise links between the statistics of environmental challenges and the ants' collective navigation abilities. We show that, in this environment, the ants use their numbers to collectively extend their sensing range. Although this extension is moderate, it nevertheless allows for extremely fast traversal times that overshadow known physical solutions to the 'Ant-in-a-Labyrinth' problem. To explain this large payoff, we use percolation theory and prove that whenever the labyrinth is solvable, a logarithmically small sensing range suffices for extreme speedup. Overall, our work demonstrates the potential advantages of group living and collective cognition in increasing a species' habitable range.
The cognitive abilities of biological organisms only make sense in the context of their environment. Here, we study longhorn crazy ant collective navigation skills within the context of a semi-natural, randomized environment. Mapping this biological setting into the 'Ant-in-a-Labyrinth' framework which studies physical transport through disordered media allows us to formulate precise links between the statistics of environmental challenges and the ants' collective navigation abilities. We show that, in this environment, the ants use their numbers to collectively extend their sensing range. Although this extension is moderate, it nevertheless allows for extremely fast traversal times that overshadow known physical solutions to the 'Ant-in-a-Labyrinth' problem. To explain this large payoff, we use percolation theory and prove that whenever the labyrinth is solvable, a logarithmically small sensing range suffices for extreme speedup. Overall, our work demonstrates the potential advantages of group living and collective cognition in increasing a species' habitable range.
This book narrates the development of various biomimetic microelectromechanical systems (MEMS) sensors, such as pressure, flow, acceleration, chemical, and tactile sensors, that are inspired by sensing phenomena that exist in marine life. The research described in this book is multi-faceted and combines the expertise and understanding from diverse fields, including biomimetics, microfabrication, sensor engineering, MEMS design, nanotechnology, and material science. A series of chapters examine the design and fabrication of MEMS sensors that function on piezoresistive, piezoelectric, strain gauge, and chemical sensing principles. By translating nature-based engineering solutions to artificial man-made technology, we can find innovative solutions to critical problems.
Animals
, for survival rely heavily on their sensing capabilities. May it be catching a prey, escaping from a predator, finding partners for reproduction or being aware of surrounding environment, senses have a very important role to play. It would not be an exaggeration to say that for a species to sustain itself and thrive through the evolutionary process, ‘sensing’ is probably the most decisive factor.
Purpose
This paper aims to propose a noise-robust method to estimate the frequency of the reflective echo to reduce the negative effects of noise and improve the accuracy and resolution of a resonant surface acoustic wave (SAW) sensor.
Design/methodology/approach
The proposed approach exploits the singular value decomposition to obtain the frequency information of a SAW response signal and overcome the noise influences.
Findings
Compared with the commonly used Fourier transform (FT) method, the accuracy and resolution improvement of the proposed method used in the SAW sensor is validated.
Originality/value
The system using the proposed method delivers lesser standard deviation, that is, delivers higher performance than the conventional system using the fast FT method.