Paper on Bionics


The perpetual hiatus between man and machine was curtailed by the advent of Bionics.

Transcendental scientists had undergone several vicissitudes in topics relevant to study of bionics. For past many years, making human like machines has been a dream of mankind. Today the experts from the field of engineering, ophthalmology, surgery and biology are working together to develop artificial organs. The crux of the topic stresses on learning from nature to inspire independent technological development.

Concepts  of  Bionics  , its classification , application of bionics pertaining to real life has  been elaborated  along with the pros and cons  in the present paper .Up to now the  physically challenged people  would interact with nature just by thoughts, now this virtuality was turned to reality by Bionics. 

The points which need retrospection are:
1) How can be Bionics made odd man out compared to other technologies?
2) Can the devices be user friendly?
3) Can the  future generations adapt this technology?
 4) How does the mankind depend Cyborg  as armours in wars?


-The Science On Edge                                                                                                                                         
              Bionics- ‘comes from biological electronics and denotes an effort to use biological design  principles to create novel technological devices and to create mechanical substitutes for extension of biological organs’. Technically speaking: replenishment or enhancement of organs or other body parts by mechanical versions. A less common and may be more recent meaning of term “bionics” refers to merging of organism and machine.      
                                                                           Fig 1

              In a more specific meaning, it is a ‘creativity technique’ that tries to use biological prototypes to get ideas for ‘engineering’ solutions which is motivated by the fact that ‘biological’  organisms and their organs have been well optimized by ‘evolutions’.

              In various fields of present technology bionics plays a vital role:-
              Engineering include  radar , echolocation of bats, non-stick coating imitating lotus effect and many more…In the field of computer science , bionic approach has produced cybernetics, artificial neural networks, swarm intelligence. In the field of medicine, bionics emphasizes on animal locomotion!
·                   How Basilisk lizards walk on water?
·                   How penguins minimize drag?
·                   How insects manage to remain airborne?
  In the field of Electronics, the advent of Bionics (‘Bio’ + ‘Electronics’)has improved the standards of a) nano tubes b) the myriad of micro- electro mechanical devices (MEM’s) constructed with technology derived from the silicon chip industry.


Evolution of bionics
                   The genealogy of Biological Engineering, spanning over a period of at least four millennia, shows that Biological Engineering is descended from the intersections of various disciplines that originate from three ancient pillars of knowledge, including the practices of engineering, medicine and the discipline of philosophy.                                               
                                                                    Fig 2             

Often bionics approach accentuates imitation of biological structures rather than mere implementation of the same function. One reason for growing interest in bionics is the fabrication methods are much more sophisticated than they are used to be. Because  of innovations in modern technical fields, it is possible to plan and construct complicated structures in  molecular level. As the performance gap between biological structures and our mechanical analogs shortens, engineers may feel increasingly encouraged to seek and adapt design concepts from nature and are beginning to develop a fabrication tool kit sophisticated enough to capture  their salient  features .  

Bionics tries to use biological prototypes innovatively to get ideas for engineering solutions which is motivated by the fact that biological organisms and their organs have been well optimized by evolution and further simulating it.

From the above outcomes, the evolutionary phenomenon of bionics had made its impact in multifaceted fields.

                Classification of  bionics:-

            Five Function Bionic Classification System [FFBCS]
          The five function bionic classification system is illustrated below:-

Fig .3

The Five Function Bionic Classification System {FFBCS} classifies a bionic device or tissue into four levels

Ø        0= no function
Ø        1 =  below normal biologic function
Ø        2 = normal biologic function
Ø        3 = above normal biologic function
For example,
            A “S2/M2/I2/P1/G0” bionic device provides: equal to normal biological functionality for body structure movement and information transfer; less than normal biological function for power source and distribution; and no ability for growth or self repair.
Present new Subject: Application Of The Classification System:-

            Now consider the application of five-function bionic classification system (FFDCS) through a bionic human limb. specifically let us suppose the arm hand: provides skeletal strength equal to that of a human limb; some, but not all, of the range of motion of a human arm hand; some ability to transmit messages from the brain to the arm/hand for the movement, but not as complete as that of a natural arm hand; power (rechargeable) that is not connected to as biological power within the body and no ability to grow or self-repair. This bionic limb would be classified as “S2/M1/I1/P1/G0”.
Fig 4.a                                                           Fig 4.b

              As a second application, we consider bionic eye. As a second application, we consider a bionic eye that that converts light into neural impulses and thus restores sight (but to a limited extent) is capable of normal eye movement as directed by the brain, and is powered by the biochemical processes that power biological cells. Such a bionic eye would be classified as “S0/M2/I1/P2/G0”. 
Let us now consider a completely hypothetical application -- a chip with a neural-silicon interface that allows enhancement of human information processing or storage. Let us further assume that it is powered by a battery that must be recharged. Such a device would be classified “S0/M0/I3/P1/G0”.                                 
Fig 5

The above figure illustrates a bionic eye.

          Biological vision  utilizes  massively parallel analog processors , usually called nerve cells which combine output  to extract from the  visual field  such details as  edges, local contrast , and movement .
          In order to have a closer and elaborated look at the bionic eye it is mandatory for us to have minimum knowledge of “prostheses” [a replacement for a part of the body]. Learning “prostheses” will make the understanding of bionic logic easier.             


                          The eye is an extraordinary molecular computer. When we see with normal vision, the light waves of the world descend upon the sheet of photoreceptors--100 million to 200 million rods and cones, depending on which estimate you accept--at the back of the eye. At this point, the light wave is translated into an electrical signal. Surprisingly, the translated signal is then shunted back. Through the retina's sheets, moving from neuron to neuron and subjected to a bewildering sequence of spatial and temporal distortions. At one point, the signal is compressed; at another, a delay of tens of milliseconds may occur before it is passed on in parallel to many other neuronal signals.

          At each point, the signal's fate depends on interactions with other neurons, some doing their own parallel shuttling and processing of neighboring signals, some handling new rafts of photons impinging on the photoreceptors.                           

                                                                      Fig 6
                            Eventually, the signal is gathered up and sent through the retina again and along the optic nerve to the back of the head for subsequent processing. A sense of the complexity of the processing at each neuronal layer is on display in the sequence of neural images shown here. The data came from a biologically functioning salamander retina (broadly analogous to a human one) and was processed by computer. A human face triggers activity in the nerve cells in each layer of the retina. The first frame of each film strip shows the ambient activity; the second and third show what happens just after movement starts and about a quarter of a second later, respectively. The functional activity begins at the very top. After the photoreceptors react to the reflectance of the face, the image is averaged in space and time by the horizontal cells; then their feedback reduces the photoreceptor’s activity, yielding the sharp image at the cone terminals. In the bipolar-cell sheet, some neurons (red) respond to the bright side of a boundary between light and dark areas, and others (green) respond to the dark side of the boundary. Bipolar-cell terminals reveal only an eye blink in frame 2, because they are inhibited by the narrow field cells. Simultaneously, the widely ramifying (W) cells receive input because of a change in visual space and broadcast over a wide area.
                  The activity is summed at the ganglion cells, which are driven by the bipolar but inhibited by the W-cell broadcast. At this level, where a signal is passed to the brain, frame 2 briefly reveals the arrival of the image, which is then turned off by the W cells in frame 3.

            In 1970’s a great deal of research was being done involving connecting sensory prostheses with the nerve cells of the body. One problem at that time was the only way to pick up a signal was to impale the nerve tissue, thus destroying the nerve. The advent of silicon receptor has solved this problem, and machine to nerve connections are now a possibility nevertheless research of the 1970’s has been crucial to the understanding of the nerves system and development of a bionic body.
                Experiments showed that muscles control the human body in a highly complicated manner. There are two connections in the central nervous system(CNS).The first is through gamma cells (which controls motion ) or the other being directly through alpha cells (fast , rough motion ) . In principle we know how the system works. A signal is sent to the CNS through golgi organs present in the tendon and through spindles. However this knowledge is difficult to utilize in making connections to a muscle system due to a large number of nerve fibers which control a single muscle.

   The function of prostheses can be made analogous to the simple controlling of a dog. In case of a prosthetic limb only some of these signals sent by the brain need to be picked up in order to tell the mechanized arm to function.           

                      The field of science concerned with processes of communication and control (especially the comparison of these processes in biological and artificial systems).


A human being whose body has been taken over in whole or in part by electromechanical devices.   The cyborg becomes a starting metaphor for exploring ways of breaking down the nature/culture binary. This line of thought is known as cyborg theory.
                            According to some definitions of the term, the metaphysical and physical attachments humanity has  with even the most basic technologies have already made us cyborgs. In a  typical example, a human fitted  with a heart pacemaker might be considered a cyborg, since s/he is incapable of surviving without the mechanical part.  

          Cybernetics is a theory of the communication and control of regulatory feedback. The term cybernetics stems from the Greek Κυβερνήτης (meaning steersman, governor, pilot, or rudder). Cybernetics is the discipline that studies communication and control in living beings and in the machines built by humans. A more philosophical definition, suggested in 1958 by Louis Couffignal, one of the pioneers of cybernetics in the 1930s, considers cybernetics as "the art of assuring efficiency of action".

Fig 7
                            In scholarly terms, cybernetics is the study of systems and control in an abstracted sense —  that is,  it is not grounded in any empirical field. Its emphasis is on the functional relations that hold  between the different parts of a system. These include in particular the transfer of information, and the circular relations that define feedback, self-organization, and autopsies. The main innovation  brought about by cybernetics is an understanding of goal-directedness or purpose as a negative feedback which minimizes the deviation between the perceived situation and the desired situation (goal).

                The artificial neuron is another name for the Threshold Logic Unit originally proposed by
Warren Culloch and Walter Pitts in 1940. It is the basic building block of the artificial neural network,
simulating a biological neuron(illustrated below). It receives one or more inputs and produces an
output based on the calculation of the sum of the inputs using a simple non-linear function as a threshold or step function which is usually a sigmoid. A neural network is an interconnected group of neurons. The prime examples are biological neural networks, especially the human brain. In modern usage the term most often refers to artificial neural networks (ANN), or neural nets for short, and this is the sense that is used in the rest of this article.

              The artificial neurons are highly interconnected in a large single-layer or multi-layer neural
network, the information processing performed in this way may be crudely summarised as follows: signals (action-potentials) appear at the unit's inputs (synapses).The weighted signals are now summed to produce an overall unit activation. If this activation exceeds a certain threshold the unit produces an
output response, usually 1 or 0 or 1 and -1.

                                                                      Fig 8
                      Mathematically, For a given artificial neuron, let there be n inputs with signals x1 through xn and weights w1 through wn. The signals are Boolean valued, i.e. they take on the values `1' or `0' only.(This  allows their relation to digital logic circuits to be discussed).
              The activation u is given by
                         u = \sum_{j=1}^n w_j x_j
       The output y is then given by determining if the activation meets a specified threshold θ. The  
             "signal"is sent, i.e. the output is set to one, if the activation meets the threshold.

                      y = \left\{ \begin{matrix} 1 & \mbox{if }u \ge \theta \\ 0 & \mbox{if }u < \theta \end{matrix} \right.
            SWARM INTELLIGENCE:-  
                                                                                   Fig 9

Ants "swarming" in a P2P network
Swarm intelligence (SI) is an artificial intelligence technique based around the study of
collective behavior in decentralized, self-organized, systems. Such systems are typically made up of a population of simple agents interacting locally with one another and with their environment. Although there is normally no centralized control structure dictating how individual agents should behave, local interactions between such agents often lead to the emergence of global behavior. Examples of systems like this can be found in nature, including ant colonies, bird flocking, animal herding, bacteria molding and fish schooling.

                The probably most successful swarm intelligence techniques developed up to now are Ant
Colony Optimization (ACO) and Particle Swarm Optimization (PSO). ACO is a met heuristic (other examples of met heuristics are simulated annealing, tab search, evolutionary computation, and so on) that can be used to find approximate solutions to difficult combinatorial optimization problems. In ACO artificial ants build solutions by moving on the problem graph and they, mimicking real ants, deposit artificial pheromone on the graph in such a way that future artificial ants can build better solutions. ACO has been successfully applied to an impressive number of optimization problems. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each time step. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large number  of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.

Real life uses                        
              Thus the approach of Bionics is motivated bay the fact that biological solutions will always be optimized by evolutionary force  from the above instances.

Bionics has been used to develop audiovisual equipment based on human eye and ear function.    Though the technology that produce Bionic implants is still in a primitive stage, yet some Bionics items already exist, the best known being the cochlear implant, a device for deaf people. They can even work better than natural ears at background noise filtering.

The modus operandi of cochlear implant is briefly illustrated below in Fig 10(a) (b) (c):-

1. Outer Ear
             The visible outer portion and ear canal funnels sound inward.
2. Middle Ear
            The eardrum and three tiny bones vibrate from sound waves.
3. Inner Ear
           The fluid-filled cochlea contains thousands of tiny sound receptors called hair cells. The hair cells sway with sound waves in the fluid filled space.
4. Hearing Nerve
          Thousands of little nerve pathways transmit sound information from the hair cells up to the hearing center of the brain.
                                     Though the branch of Bionics in the present day is highly sophisticated and explicitly used by the many fields of modern technology. It has both pros and cons concerning with the application in real life.


Ø        The development of dirt and water repellent paint using lotus effect.
Ø        Bionics seeks to transcend our biological nature by replacing biological parts with artificial parts(“deflesh”) or by translating the human mind into information in a computer(“uploading”).
Ø        Because of innovations in Bionics many  devices of advanced technologies came into light.

        For eg : nano tubes, micro-electromechanical devices(MEM’S)and integrated circuits (IC’s) vis-à-vis animal locomotion and cognition
Ø        The perpetual hiatus between man and machine was diluted by the advent of Bionics.
Ø        The latest cochlear device from Advance Bionics with hi-resolution lead to the  fabrication of audiovisual devices like Bionic eye and ear.
Ø        Continued research in neuroscience and bioengineering lead to the improvement in man-machine interfaces and functional replacements.
Ø        Dextra artificial limb is the first to let a person use existing nerve pathways to control individual computer-driven mechanical fingers. For  eg :Bionic legs allow to rum more than 67 mph and make great leaps.
Ø        Bionics  are the common elements of science fiction, movies like Six Million Dollar Man Terminator epitomizes it.
Ø        Study of bionics  reveals medical ultra sound imaging imitating echolocation  of bats which is useful for deaf people who can hear by echo.  


                      It is tempting to conclude that bionic vision devices are on our collective doorstep, both for robotic systems operating on their own in self guided vehicles. Continued research in neuroscience and bioengineering lead to the improvement in man-machine interfaces and functional replacement. Advance Bionic  components may be with in our grasp, by putting them all together in a  real life will probably remain the stuff of sci-fi.
                       By  the year 2010 there shall be an ample supply of volunteers waiting to be the first human with bionic eyes. Attaining to such an established state is perhaps most pressing  challenge that now faces biological engineering, if it to survive and flourish in the new century and into the future.

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  January 2005
[3]   A.K. Jain , L. Hong and R. Bolle, April 2005, On-Line  Fingerprint Verification, IEEE Trans, on  Pattern Analysis  and Machine Intelligence, Vol. 19. No.4, April 1997.
[4]   A.K. Jain , L. Hong , S. Pankanti , and R. Bolle, September, 2005, An Identify- Authentication System Using Fingerprints, Proceedings of the IEEE, Vol. 85, No. 9, September 2005 .
[5] M.FaundezZanurry, Vulnerability of Biometric security systems.Proceedings of IEEE Vol.36,No 10, December 2005.
[6] David Maltoni,Dario Maio, Anil K Jain and Salil Prabhakar,Hand book of Finger print Recognition ,Springer professional.

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