Wavelets are functions that satisfy certain mathematical requirements and are used in representing data or other functions. This idea is not new. Approximation using superposition of functions has existed since the early 1800's, when Joseph Fourier discovered that he could superpose sines and cosines to represent other functions. However, in wavelet analysis, the scale that we use to look at data plays a special role. Wavelet algorithms process data at different scales or resolutions. If we look at a signal with a large "window," we would notice gross features. Similarly, if we look at a signal with a small "window," we would notice small features. The result in wavelet analysis is to see both the forest and the trees, so to speak.

This makes wavelets interesting and useful. For many decades, scientists have wanted more appropriate functions than the sines and cosines which comprise the bases of Fourier analysis, to approximate choppy signals. By their definition, these functions are non-local (and stretch out to infinity). They therefore do a very poor job in approximating sharp spikes. But with wavelet analysis, we can use approximating functions that are contained neatly in finite domains. Wavelets are well-suited for approximating data with sharp discontinuities.

The wavelet analysis procedure is to adopt a wavelet prototype function, called an analyzing wavelet or mother wavelet. Temporal analysis is performed with a contracted, high-frequency version of the prototype wavelet, while frequency analysis is performed with a dilated, low-frequency version of the same wavelet. Because the original signal or function can be represented in terms of a wavelet expansion (using coefficients in a linear combination of the wavelet functions), data operations can be performed using just the corresponding wavelet coefficients. And if you further choose the best wavelets adapted to your data, or truncate the coefficients below a threshold, your data is sparsely represented. This sparse coding makes wavelets an excellent tool in the field of data compression.

Other applied fields that are making use of wavelets include astronomy, acoustics, nuclear engineering, sub-band coding, signal and image processing, neurophysiology, music, magnetic resonance imaging, speech discrimination, optics, fractals, turbulence, earthquake-prediction, radar, human vision, and pure mathematics applications such as solving partial differential equations.

In the history of mathematics, wavelet analysis shows many different origins. Much of the work was performed in the 1930s, and, at the time, the separate efforts did not appear to be parts of a coherent theory.

Pre-1930

Before 1930, the main branch of mathematics leading to wavelets began with Joseph Fourier (1807) with his theories of frequency analysis, now often referred to as Fourier synthesis. He asserted that any -periodic function f(x) is the sum

of its Fourier series. The coefficients , , and are calculated by

Fourier's assertion played an essential role in the evolution of the ideas mathematicians had about the functions. He opened up the door to a new functional universe.

After 1807, by exploring the meaning of functions, Fourier series convergence, and orthogonal systems, mathematicians gradually were led from their previous notion of frequency analysis to the notion of scale analysis. That is, analyzing f(x) by creating mathematical structures that vary in scale. How? Construct a function, shift it by some amount, and change its scale. Apply that structure in approximating a signal. Now repeat the procedure. Take that basic structure, shift it, and scale it again. Apply it to the same signal to get a new approximation. And so on. It turns out that this sort of scale analysis is less sensitive to noise because it measures the average fluctuations of the signal at different scales.

The first mention of wavelets appeared in an appendix to the thesis of A. Haar (1909). One property of the Haar wavelet is that it has compact support, which means that it vanishes outside of a finite interval. Unfortunately, Haar wavelets are not continuously differentiable which somewhat limits their applications.

The 1930s

By using a scale-varying basis function called the Haar basis function (more on this later) Paul Levy, a 1930s physicist, investigated Brownian motion, a type of random signal. He found the Haar basis function superior to the Fourier basis functions for studying small complicated details in the Brownian motion.

Another 1930s research effort by Littlewood, Paley, and Stein involved computing the energy of a function f(x):

The computation produced different results if the energy was concentrated around a few points or distributed over a larger interval. This result disturbed the scientists because it indicated that energy might not be conserved. The researchers discovered a function that can vary in scale and can conserve energy when computing the functional energy. Their work provided David Marr with an effective algorithm for numerical image processing using wavelets in the early 1980s.

1960-1980

Post-1980

• 2 cups water
• 1 ounce marijuana (fresh leaves and flowers of a female plant preferred)
• 4 cups warm milk
• 2 tablespoons blanched and chopped almonds
• 1/8 teaspoon garam masala [a mixture of cloves, cinnamon, and cardamom]
• 1/4 teaspoon powdered ginger
• 1/2 to 1 teaspoon rosewater
• 1 cup sugar

1. Bring the water to a rapid boil and pour into a clean teapot.
2. Remove any seeds or twigs from the marijuana, add it to the teapot and cover.
3. Let this brew for about 7 minutes.
4. Now strain the water and marijuana through a piece of muslin cloth, collect the water and save.
5. Take the leaves and flowers and squeeze between your hands to extract any liquid that remains.
6. Add this to the water.
7. Place the leaves and flowers in a mortar and add 2 teaspoons warm milk.
8. Slowly but firmly grind the milk and leaves together.
9. Gather up the marijuana and squeeze out as much milk as you can.
10. Repeat this process until you have used about 1/2 cup of milk (about 4 to 5 times).
11. Collect all the milk that has been extracted and place in a bowl.
12. By this time the marijuana will have turned into a pulpy mass.
13. Add the chopped almonds and some more warm milk.
14. Grind this in the mortar until a fine paste is formed.
15. Squeeze this paste and collect the extract as before.
16. Repeat a few more times until all that is left are some fibers and nut meal.
17. Discard the residue.
18. Combine all the liquids that have been collected, including the water the marijuana was brewed in.
19. Add to this the garam masala, dried ginger and rosewater.
20. Add the sugar and remaining milk.
21. Chill, serve, and enjoy.

• 250 gms Besan (gram flour)
• 200 gms Potatoes
• 200 gms Cauliflower
• 150 gms Onions
• 100 gms Spinach
• 200 gms Brinjal
• 10 gms Bhang seed powder
• 2 gms Soda-bicarb
• 5 gms Ajwain
• 5 gms Pomegranate seed powder
• Salt to taste
• Oil for deep-frying

1. Wash and peel all the vegetables.
2. Sieve besan, soda-bicarb and salt together.
3. Add bhang seed powder, ajwain, red chili powder, and pomegranate seed powder.
4. Add water according to requirement and make thick batter.
5. Heat oil in a deep-frying pan.
6. Dip the peeled vegetables in the batter.
7. Deep-fry on medium fire till golden brown.
8. Pakoras are ready to serve.

• When God took my blood pressure, He saw I was low in tenderness.
• When he read my temperature, the thermometer registered 40º of anxiety
• He ran an electrocardiogram and found that I needed several ‘love bypasses’ since my arteries were blocked with loneliness and could not provide for an empty heart.
• I went to orthopedics, because I could not walk by my brother’s side and I could not hug my friends, since I had fractured myself when tripping with envy.
• He also found I was shortsighted, since I could not see beyond the shortcomings of my brothers and sisters.
• When I complained about deafness, the diagnosis was that I had stopped listening to God’s voice talking to me on a daily basis.

• Every morning, take a full glass of gratitude
• When getting to work,take one spoon of peace
• Every hour, take one pill of patience, one cup of brotherhood and one glass of humility
• When getting home,take one dose of love
• When getting to bed, Take two tablets of clear conscience

Though many of us remember the eye-scanner identification device used in James Bond films in the 1980s, the technology remained a fiction until recently. These have become a more of a common scenario in Tom Cruise movies or something which had made the writers of movies like National Treasure keep a computer-geek character as a side kick for Nicholas Cage to help him break into high security areas with ease. These things have become so popular that even our Indian cinema which is considered to be a decade or two behind Hollywood films have now incorporated multimodal biometric like the advanced form of retinal and vein (heart) scan used by Rakesh Roshan. Thus biometric identification is no longer a fiction. It is a fact!!

Three factors can be used for security: something you know (a password or a PIN), something you have (a smart token or an access card), and something you are (biometric). Biometrics can be used alone or in conjunction with one of the other factors to strengthen the security check. Biometric technology has advantages over both of the other factors in that the user does not need to remember anything or possess a physical token in order to be identified. Tokens and cards can be lost, and passwords and PINs can be forgotten or compromised. A biometric is susceptible only to forgery, which can be extremely difficult, depending on the biometric.

Biometric technologies use the concept of “something you are” which provides airtight security by identifying an individual based on the physiological and/or behavioral characteristics identification.  Biometrics recognition systems include fingerprinting, palm prints, hand geometry, nailbed identification, facial recognition, DNA, earlobes, dental scan, thermal body features, vein, iris, retinal scan, etc the list is endless. Behavioral characteristics used for identification are signature dynamics, gait, keyboard dynamics, gesture, and voice recognition. Iris and retina scans which have been found to be among the most accurate ones. Moreover along with these the multimodal techniques of using the combination of two or more biometrics together for identification has now become popular. Presently we have a new addition: our heart’s electrical activity i.e. the Electrocardiogram (ECG).

Recently, there were efforts to explore the feasibility of using ECG as a new biometric measure for human identification. Along with so many biometric technologies our ECG which was being used for decades for analyzing patients and predicting the condition of their heart and also for prescribing medication, appears to be just an addition to the long list.  So from being a tool for clinical diagnosis and an active research area in the past two decades we now have researchers all over the world starting from USA and Canada to Taiwan and Thailand working in a new area with the same old ECG.

Proposal for the possibility of using ECG as a new biometrics modality for human identity recognition is not even a decade old. The validity of using ECG for biometric recognition is supported by the fact that the physiological and geometrical differences of the heart in different individuals display certain uniqueness in their ECG signals. Human individuals present different patterns in their ECG regarding wave shape, amplitude, different ECG wave intervals like PT (L, P, Q, R, S, T are the name of different waves present in ECG signal so based on them we have different intervals), due to difference in the physical conditions of the heart.

The permanence characteristic of ECG pulses of a person when studied by noting that the similarities of healthy subject’s pulses at different time intervals, suggest the distinctiveness and stability of ECG as a biometrics modality. Further, ECG signal is a life indicator, and can be used as a tool for liveness detection (because if u want to enter a secure area based on some other fragile biometric security like finger print you can easily fake it or even use the finger of an authorized person when he is in unconscious or dead). Comparing with other biometric traits, the ECG of a human is more universal, and difficult to be falsified by using fraudulent methods. An ECG-based biometric recognition system can find wide applications in physical access control, medical records management, as well as government and forensic application.

To build an efficient human identification system, the extraction of features that can truly represent the distinctive characteristics of a person is a challenging problem. Many proposed methods for ECG-based identity recognition use attributes that are temporal and amplitude distances between detected fiducial points. But, focusing on only a few fiducial points, the representation of discriminant characteristics of ECG signal might be inadequate. Moreover, their methods rely heavily on the accurate localization of wave boundaries, which is generally very difficult.

Thus if we have a systematic analysis for ECG-based biometric recognition which is an analytic-based method that combines temporal and amplitude features then it can be really a popular biometric. The analytic features should capture local information in a heartbeat signal and as such, the performance of this method should depend on the accuracy of fiducial points detected and discriminant power of the features. So we can go for an appearance-based feature extraction method which should capture the holistic patterns in a heartbeat signal, and then only the detection of the peak is necessary. This would be easier since R corresponds to the highest and sharpest peak in a heartbeat. To utilize the complementary characteristics of different types of features properly and improve the recognition accuracy, a hierarchical scheme for the integration of analytic and appearance attributes may be used.  Further we can go for some novel methods that do not require any waveform detection. Instead estimating and comparing the significant coefficients of transforms (like Fourier or Discrete Cosine) of the autocorrelated heartbeat signals.

So finally it will not be far when we will come near access points or entrances with the left and right door having knobs acting as ECG electrodes, and we would be able to open the door if the ECG collected by the knobs have the features matching those in the database of authorized personnel!!

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual i.e. “something you are”. Iris recognition is regarded as the most reliable and accurate biometric identification system available after retinal scan or any other multi modal biometric which in their own respect have different constraints. Most commercial iris recognition systems
use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition rates. We here are trying to develop a system of human recognition by iris image processing but in our own way using a database of 2000 iris images of 50 persons with 20 images of each eye provided by the University of Bath iris image processing group. Then implement a recognition process based on correlation of edge detected iris patterns and check for match & mismatch.

Multiple personalities occur when the identity of a person dissociates to the extent that they have separate existences with their own "identities, life histories, and enduring patterns of perceiving, thinking about and relating to the environment which is distinct from the habitual personality's mode of being in the world. Multiple Personality Disorder (MPD) is the name that remains in the International Statistical Classification of Diseases and Related Health Problems. It is presently known as Dissociative Identity Disorder (DID), as defined by the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR), is a mental condition whereby a single individual evidences two or more distinct identities or personalities, each with its own pattern of perceiving and interacting with the environment.

The diagnosis requires that at least two personalities routinely take control of the individual's behavior and that there is associated memory loss (which can be caused due to Alzheimer's disease; Parkinson's disease; Huntington's disease; Chemotherapy; concussion or even stress related activities are another factor which can result in memory loss) that goes beyond normal forgetfulness, often referred to as losing time or acute Dissociative Amnesia.

This condition is not an equivalent for schizophrenia (DSM-IV Schizophrenia and Other Psychotic Disorders), as is a common misconception. The term schizophrenia comes from root words for "split mind," but refers more to a fracture in the normal functioning of the brain than the personality.

Dissociation is a demonstrated symptom of several psychiatric disorders, including Borderline Personality Disorder (DSM-IV Personality Disorders), Post-traumatic stress disorder (DSM-IV Anxiety Disorders), and Complex Post Traumatic Stress Disorder, to name a few.

As a diagnosis, DID remains controversial. For many years DID was regarded as a North American phenomenon with the bulk of the literature still arising there. However, research demonstrates a lack of consensus belief in the validity of DID amongst North American psychiatrists. Practitioners who do accept DID as a valid disorder have produced an extensive literature with some of the more recent papers originating outside North America. Criticism of the diagnosis continues, with Piper and Merskey describing it as a culture bound and often iatrogenic condition which they believe is in decline.

One of the primary reasons for the ongoing recategorization of this condition is that there were once so few documented cases (research in 1944 showed only less than 100) of what was once referred to as multiple personality. Dissociation is recognized as a symptomatic presentation in response to trauma, extreme emotional stress, and, as noted, in association with emotional dysregulation and Borderline Personality Disorder. Often regarded as a dynamic sub-symptomatology, it has become more frequent as an ancillary diagnosis, rather than a primary diagnosis.

 

Mr. Madhusudhan Mishra

The world around us contains many beautiful phenomena. In the outdoors we can experience amazing sunsets; moonlight scattered through clouds, early morning fog at lake, underwater sunbeams, and much more. If we move inside we can see flames in the fireplace, light focused through a glass of cognac onto a table, translucent appearance of an orchid, steam rising from a cup of coffee, the soft flickering from a candle flame. The list is endless.

Today we can simulate these phenomena with computers. The creation of realistic looking synthetic images has reached a state that makes it possible to simulate almost any phenomena. We can render images of sunsets and sunrises using computers without having to wait for a clear day and right time. We cannot yet reproduce the same experience as being outside observing sunset, but this is mainly a limitation of display devices available, and not a limitation of the underlying global illumination algorithms.

In computer graphics, photon mapping is a global illumination algorithm developed by Henrik Wann Jensen that solves the rendering equation. Rays from the light source and rays from the camera are traced independently until some termination criterion is met, and then they are connected in a second step to produce a luminance value. It is used to realistically simulate the interaction of light with different objects. Specifically, it is capable of simulating the refraction of light through a transparent substance such as glass or water, diffuse interreflection between illuminated objects, and some of the effects caused by particulate matter such as smoke or water vapor.

The desired effects of the refraction of light through a transparent medium are called caustics. A caustic is a pattern of light that is focused on a surface after having had the original path of light rays bent by an intermediate surface. For example, as light rays pass through a glass of wine sitting on a table and the liquid it contains, they are refracted and focused on the table the glass is standing on. The wine also changes the pattern and color of the light.

With photon mapping, light packets called photons are sent out into the scene from the light source. Whenever a photon intersects with a surface, the intersection point, incoming direction, and energy of the photon are stored in a cache called the photon map. After intersecting the surface, a new direction for the photon is selected using the surface's bidirectional reflectance distribution function (BRDF). There are two methods for determining when a photon should stop bouncing. The first is to decrease the energy of the photon at each intersection point. Using this method, when a photon's energy reaches a predetermined low-energy threshold, the bouncing stops. The second method uses a Monte Carlo method technique called Russian roulette. In this method, at each intersection the photon has a certain likelihood of continuing to bounce with the scene, and that likelihood decreases with each bounce.

The photon map is then used during rendering to estimate the density of photons accumulated, as an estimate for radiance, thus the photon map data structure should be optimized for the k-nearest neighbor algorithm, as the spatial distribution of the k nearest photons are used for the estimation. Therefore, Jensen's implementation uses kd-trees for the photon map.

To avoid emitting unneeded photons, the initial direction of the outgoing photons is often constrained. Instead of simply sending out photons in random directions, they are sent in the direction of a known object that is a desired photon manipulator to either focus or diffuse the light. There are many other refinements that can be made to the algorithm: for example, choosing the amount of photons to send, and where and in what pattern to send them.

Photon mapping is generally a preprocess and is carried out before the main rendering of the image. Often the photon map is stored on disk for later use. Once the actual rendering has started, each intersection of an object and a ray is tested to see if it is within a certain range of one or more stored photons, and if so, the energy of the photons is added to the energy calculated using a standard illumination equation. The slowest part of the algorithm is searching the photon map for the nearest photons to the point being illuminated.

Although photon mapping was designed to work primarily with ray tracers, it can also be used with scanline renderers. Thus particularly in the case of entertainment industry where movies often make extensive use of computer generated special effects that seamlessly integrate with the real filmed footage. Moreover computer games are presenting increasingly realistic worlds in real time getting closer to the dream of virtual reality. Outside entertainment, synthetic photorealistic images are used in design, architecture, hospitals, education, advertising, and more. These areas also use non-photorealistic images (such as technical illustrations), but the “final product” is most often presented using photorealistic rendering. This is often most natural visualization technique since it is what we are used to seeing.

One feature of liquid crystals is that they're affected by electric current. A particular sort of nematic liquid crystal, called twisted nematicslight passage. (TN), is naturally twisted. Applying an electric current to these liquid crystals will untwist them to varying degrees, depending on the current's voltage. LCDs use these liquid crystals because they react predictably to electric current in such a way as to control

Liquid Crystal Types
Most liquid crystal molecules are rod-shaped and are broadly categorized as either thermotropic or lyotropic.

hermotropic liquid crystals will react to changes in temperature or, in some cases, pressure. The reaction of lyotropic liquid crystals, which are used in the manufacture of soaps and detergents, depends on the type of solvent they are mixed with. Thermotropic liquid crystals are either isotropic or nematic. The key difference is that the molecules in isotropic liquid crystal substances are random in their arrangement, while nematics have a definite order or pattern.

The orientation of the molecules in the nematic phase is based on the director. The director can be anything from a magnetic field to a surface that has microscopic grooves in it. In the nematic phase, liquid crystals can be further classified by the way molecules orient themselves in respect to one another. Smectic, the most common arrangement, creates layers of molecules. There are many variations of the smectic phase, such as smectic C, in which the molecules in each layer tilt at an angle from the previous layer. Another common phase is cholesteric, also known as chiral nematic. In this phase, the molecules twist slightly from one layer to the next, resulting in a spiral formation.

Ferroelectric liquid crystals (FLCs) use liquid crystal substances that have chiral molecules in a smectic C type of arrangement because the spiral nature of these molecules allows the microsecond switching response time that make FLCs particularly suited to advanced displays. Surface-stabilized ferroelectric liquid crystals (SSFLCs) apply controlled pressure through the use of a glass plate, suppressing the spiral of the molecules to make the switching even more rapid.