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Study Objective: Based on updated guidelines and expressed patient needs, we created a multidisciplinary clinic including endocrinology, gynecology/adolescent medicine, dermatology, psychology, and nutrition to provide comprehensive care to adolescent girls with polycystic ovary syndrome (PCOS). We describe the patient population presenting to this clinic, and prescribing patterns when a multidisciplinary approach is used. Design: Retrospective chart review. Setting: Tertiary care hospital. Participants: Female patients, aged 11-24 years, presenting for initial assessment in a multidisciplinary PCOS clinic. Interventions: None. Main Outcome Measures: Medical history, physical examination findings, laboratory measurements and prescribed therapies. Results: A total of 92 patients seen from 2014 to 2018 are described (age 15.9 years, range 11-24 years, body mass index 35.6 kg/m2, range 19.9-53.5). Metabolic syndrome features were common: 26% had a prediabetes hemoglobin A1c (>5.6%), 83% had a high-density lipoprotein (HDL) 120 mm Hg, and 43% had an alanine aminotransferase level of >30 U/L. Dermatologic findings included acne 93%, hirsutism 38%, acanthosis nigricans 85%, hidradenitis suppurativa 16%, and androgenic alopecia 2%. Of the patients, 33% had a diagnosis of depression or anxiety, 16% of patients had a diagnosis of obstructive sleep apnea, and an additional 59% had symptoms warranting a sleep study The most commonly prescribed medications were topical acne preparations (62%), followed by estrogen-containing hormonal therapy (56%) and metformin (40%). Conclusion: In adolescents with PCOS and obesity, metabolic, dermatologic, and psychologic co-morbidities are common. The use of a multidisciplinary clinic model including dermatology in addition to endocrinology, gynecology, psychology, and lifestyle experts provides care for most aspects of PCOS.

Introduction/Objective Adenomatoid odontogenic tumor (AOT) is an uncommon benign odontogenic tumor representing 3 to 7% of all odontogenic tumors. This tumor was first reported as adeno-ameloblastoma by Bernier and Tiecke in 1950 as it was initially assumed to be a type of ameloblastoma. In 1969, Philipsen and Bern proposed the term adenomatoid odontogenic tumor which was subsequently adopted by WHO and became the accepted terminology for this tumor. AOT is classified as a tumor of odontogenic epithelium but occasionally abnormal hard tissues consistent with dentinoid material may also be present as part of the tumor. AOT tends to occur in younger patients and 50% of the cases are diagnosed in teenagers. It occurs twice as commonly in females and frequently involves the anterior maxilla. Radiographically, AOT can appear as a radiolucent or mixed lucent-opaque lesion and may be associated with an impacted tooth. Methods/Case Report We report two unusually large expansile lesions of AOT that presented in the mandible. In the first case, the tumor presented as an expansile radiolucent lesion involving the right posterior mandible in a 32 year old female. In the second case, the tumor presented as an expansile mixed lucent-opaque lesion involving the left anterior mandible in a 21 year old female. The clinical presentation, radiographic and imaging findings, histopathologic features and treatment of these two cases will be discussed. The recommended treatment for AOT is surgical excision. The prognosis is good as this tumor seldom recurs after excision. Results (if a Case Study enter NA) NA Conclusion AOT is considered to be a non-aggressive, non-invasive and slow growing benign neoplasm. It is usually discovered on routine radiographic examination as the lesion is usually small and asymptomatic at the time of diagnosis but occasional cases that are larger in size have been reported in the literature.

In the chapter, The z-transform, the z-transform is presented. The z-transform is developed starting from the DTFT as a generalization of the Fourier analysis. Extending the set of basis signals at other than the unit-circle, results in the capability to analyze a large set of unbounded signals, which is very useful in stability analysis of systems. Examples of deriving the z-transform of useful signals are given. The properties of z-transform, which make the analysis of signals and systems much simpler, are presented. While the inverse z-transform is defined in terms of contour integration, in practice, the much simpler partial fraction and long-division methods are used. A number of examples of finding the inverse z-transform are presented. The transfer function, which is the ratio of the transform of the output and that of input, is presented with examples. The pole-zero characterization of systems is presented and the stability criterion of systems is given.

In this chapter, Discrete Fourier Transform, the most often used version of Fourier analysis, the DFT, and its inverse are derived with appropriate examples. The DFT, as in all versions of Fourier analysis, represents an arbitrary amplitude profile signal in terms of sinusoidal or equivalent complex exponentials. This transform is all important in practical signal and system analysis, since it approximates all other versions of Fourier analysis and has fast algorithms for its implementation. Further, it computes essential operations, such as convolution and correlation, faster than alternative methods. The criterion of representation of signals is the least squares error, which is practical with advantageous. The matrix formulation of the DFT is often used in its computation. The properties of the DFT are very useful in the analysis of signals, as in the case of all transforms. As in the case of the orthogonal transforms, the power of a signal can be computed in its DFT representation also.

An electrical filter modifies the spectrum of the signal in a desired way. In this chapter, Finite Impulse Response Filters, one type of filter called the FIR filter is designed and analyzed. The FIR filters can be designed easily with linear-phase response, which is desired in a variety of applications such as image processing. Two major types of methods are used in designing FIR filters. In the window method, the impulse response of the desired ideal filter is first found. Then, it is truncated by a suitable window and shifted to make it a practically realizable filter. The design is presented with appropriate examples. Another design method uses an optimum algorithm. This algorithm can design filters with arbitrary frequency response. Further, for the same specification, this method yields the least number of filter coefficients compared with other methods of design. The design is presented with appropriate examples. MATLABⓇ programs are provided for this type of design in the book’s web site.

In this chapter, Infinite Impulse Response Filters, another type of filter called the IIR filter is designed and analyzed. This type of filters requires less number of coefficients for the same specification. However, it is susceptible to instability. This type of filters is developed from the basic resistor–capacitor lowpass filter for clear understanding of the filtering process. The idea is that the transfer functions of families of analog filter have been designed with different characteristics. These transfer functions can be transformed to those of digital IIR filters. The bilinear transformation is most often used in designing digital IIR filters. While the range of the frequency of the components of continuous signals is infinite, the frequency range is restricted in digital filters due to sampling. This creates a problem called warping. Still, practical filters can be designed using the transformation method. In this chapter, examples of design of lowpass, highpass, bandpass, and bandreject filters are presented.

In this chapter, quantization effects, the quantization errors due to the limitation of wordlength in digital filters and DFT algorithms, are analyzed. As the noise signal due to quantization is of random nature, the quantization noise is modeled using probability theory. Some assumptions are made to make the model relatively simpler one. The analysis is carried out to find the adequate wordlength that will make the digital filter work with a high signal-to-noise ratio. The DFT algorithm reduces the noise of the previous stage by the scaling factor, and the resulting noise is much smaller at the output. In the digital filters, coefficient quantization results in changing the frequency response due to the movement of poles and zeros of the transfer function. The round-off noise due to truncation or rounding in multiplication operations reduces the signal-to-noise ratio at the output. Further, the assumed linear noise model becomes sufficiently nonlinear to produce spontaneous oscillations called limit cycles. All the unwanted effects due to quantization get reduced as the wordlength is increased. Although the models indicate the effects due to quantization to a good extent, the best analysis of these effects is by simulation.

In this chapter, discrete-time Fourier transform, the version of the Fourier analysis that is more suitable for theoretical analysis of signals and systems, is presented. It is derived starting with the DFT definition, resulting in the DTFT and its inverse. In the time domain, the input to the DTFT is infinite length samples of discrete signals. The corresponding transform, in the frequency domain, is a periodic continuous spectrum. Although it is usually presented assuming a sampling interval of 1 s, it can be easily extended for any sampling interval. The DFT is the samples of the DTFT spectrum at equal intervals on the unit circle. The properties of the DTFT are presented, which are very useful in the analysis of signals and systems. The transfer function concept is the ratio of the DTFT of the output of a system to the DTFT of the input applied. Using the transfer function, the zero-state output of systems is computed for specific systems. The Hilbert transform, which is useful in developing complex signals with one-sided spectrum, is presented. The digital differentiator, which approximates the derivative, is described. Finally, the approximation of the DTFT and its inverse by the DFT and IDFT is presented with examples.

In this chapter, Multirate Digital Signal Processing, the basic principles of multirate digital signal processing are presented. In certain applications, changing the sampling rate to suit the task turns out to be advantageous. In addition to adder, multiplier and delay units, two new components, the downsampler and upsampler, are required in multirate signal processing. Downsampling is discarding a set of samples of a signal to reduce the sampling rate. Upsampling increases the sampling rate of a signal by interpolation. Appropriate filtering of the downsampled or upsampled signal results, respectively, in decimated or interpolated signal. The upsampling and downsampling can be implemented by polyphase filters more efficiently. Some of the applications of multirate systems are sampling rate converters, narrowband filter design, and the digital wavelet transform. All the basic operations and the applications are presented by suitable examples.

A pair of terminals, which are external terminal points for currents to enter or leave a circuit, is called a port. Elements, such as a resistor with two terminals, are one-port circuit. A two-port circuit has two separate ports for input and output. Several currents flow through circuit elements in a circuit and, consequently, voltages appear at their terminals. But, not all of them are interest in applications. Modeling a circuit by a two-port circuit reduces the current variables to two. The two currents and the corresponding voltages are related by nodal and loop analysis. A two-port circuit may have dependent sources but no independent source.

The sinusoidal waveform, at a given frequency, is characterized by its amplitude and phase. In a single-phase source we studied so far, it is assumed that a sinusoidal wave is the input. However, keeping the amplitude the same, a source with three sinusoidal waveforms differing in equal amount of phase turns out to be better for electric-power generation, transmission and distribution systems, and in various other practical applications. The resulting circuits exhibit symmetry both with respect to geometrical structure and element values. Circuits with three sources differing in phase by 120∘ and of the same amplitude, called three-phase circuits, are mostly used in electrical power systems.

The circuit elements in circuits, we analyzed thus far, are conductively coupled. Conductors are used to connect elements. Circuit elements can also be connected through magnetic fields, called magnetically coupled. An inductor is basically a conducting coil. The induction of a voltage in another inductance by the current flowing in an inductance is mutual induction. A flywheel is a mechanical device that stores rotational energy and returns it to the system. Similarly, an inductor stores magnetic energy and returns it to the system. It does not dissipate energy like a resistor. While a resistor is required to limit the current in the circuit, an inductor smooths the flow of current.

Transform means change in form. For example, in finding the derivative of the product of two functions, we use the product rule and transform the problem into an easier one. Multiplication operation is more difficult than addition operation. By expressing the numbers in logarithmic form, we reduce the multiplication operation into an addition.

This chapter, Steady-state power, deals with computation of the steady-state power in AC circuits. Expressions for the energy dissipated by resistors and stored by capacitors and inductors are derived in both the time- and frequency-domains. Power factor is defined and the methods to improve it are described. The relation between the stored energy and the reactive power is clearly pointed out. A number of examples of AC circuit analysis are presented with clear explanation of all the steps involved from problem formulation to finding the solution. Supplementary programs for verifying the analytically obtained results by coding are available.

Background: In this study we investigate the incidence of corneal complications of extra capsular cataract extraction (ECCE) and manual small incision cataract surgery (SICS). We study the various etiological factors leading to these complications and also to highlight various prophylactic intra and post-operative measures to reduce these complications.Methods: Cataract surgery was conducted on 100 patients at Meenakshi Medical College Hospital and Research Institute. Patients were randomly divided into two groups. Group A of 50 patients were subjected to ECCE with posterior chamber intraocular lenses (PCIOL) and group B of 50 patients to small incision cataract surgery with PCIOL.Results: On the first post-operative day, 5 post-operative cases developed corneal edema (10%) in ECCE and 4 cases (8%) in SICS. Striate keratitis developed in 4 cases of ECCE (8%) and 3 SICS (6%). Nearly 62% patients who underwent ECCE accepted cylinder between 0.25-0.75 D while there were 52% cases in SICS.Conclusions: In this study we conclude that with various advances in cataract surgery the incidence of corneal complication have reduced and have helped in early visual rehabilitation and minimization of post-operative astigmatism by careful selection of incision type and location for MSICS.

The frequency-domain analysis of signals and systems is efficient, in practice, due to the availability of fast algorithms for the computation of the DFT. While the DFT is defined for any length, practically efficient algorithms are available only for fast computation of the N-point DFT with N an integral power of 2. The algorithms are based on the classical divide-and-conquer strategy of developing fast algorithms. A N-point DFT is recursively decomposed into half-length DFTs, until the DFT becomes trivial. The DFTs of the smaller transforms are combined to form the DFT of the input data. The decomposition can start from the time-domain end or the frequency-domain end. The first type is called decimation-in-time (DIT) algorithms. The second type is called decimation-in-frequency (DIF) algorithms. These algorithms are developed assuming that the data is complex-valued. They can be tailored to suit real-valued data. A set of algorithms are derived and examples of computing the DFT are given.

First, the real exponential is used to explain the concept of transform in reducing the multiplication operation into much simpler addition operation. Then, the complex exponential with a pure imaginary exponent is introduced and the relation between the exponential and sinusoidal signals is established. It is pointed out that the DFT gives a complex exponential polynomial representation of a signal. The orthogonality property of the complex exponentials is used to derive the definitions of the DFT and the IDFT. The least squares error criterion of signal representation is established. Several examples of computing the DFT and IDFT are presented using the matrix form of the DFT and the IDFT. An image processing application concludes the chapter.

The FT version of the Fourier analysis is presented. FT is the most general version of the Fourier analysis and it is capable of representing all types of signals. However, it is mostly used to represent continuous aperiodic signals by continuous aperiodic spectra. The FT is derived starting from the FS definition. Examples of finding the FT of signals are given. Properties of the FT are presented next with examples. Then, the representation of all types of signals by the FT is described. The aliasing effect is presented from the frequency-domain point of view. Typical applications of the FT are given. The numerical approximation of the FT by the DFT concludes the chapter.

The aliasing, leakage, and picket-fence effects are addressed. The aliasing effect, due to a low sampling frequency, in representing a continuous signal by its samples is dealt. A model for the truncation effect, due to chopping off long duration data, is derived. The characteristics of rectangular, triangular, Hann, and Hamming windows in reducing the leakage and smearing effects due to truncation are studied. Avoiding the picket-fence effect by zero-padding signals to get adequate spectral resolution is finally presented.

Fast computation of the 1-D and 2-D linear convolution and correlation operations by using the DFT is presented. Implementing the convolution of long sequences using the overlap–save method along with the DFT is explained. Some applications, using the DFT to implement the convolution and correlation operations, in image processing and digital communications applications are presented in detail.

The 2-D DFT is presented as two sets of 1-D DFTs. Examples of the 2-D DFT are given. The formal definitions of 2-D DFT and IDFT are presented. The computation of the 2-D DFT by the row–column method is described with examples. Finally, a comprehensive coverage of the properties of the 2-D DFT is presented with examples.

The following DFT properties are presented with examples: linearity, periodicity, time shifting, frequency shifting, time-reversal, duality, convolution, correlation, upsampling, zero padding, symmetry, and Parseval’s theorem.

Although the transform domain processing is essential, as the images naturally occur in the spatial domain, image enhancement in the spatial domain is presented first. Point operations, histogram processing, and neighborhood operations are presented. The convolution operation, along with the Fourier analysis, is essential for any form of signal processing. Therefore, the 1-D and 2-D convolution operations are introduced. Linear and nonlinear filtering of images is described next.

Human vision is more sensitive to color than gray levels. Therefore, color image processing is important, although it requires more memory to store and longer execution times to process. There are different color models, and each one is suitable for some application. In the RGB model, a color image is expressed in terms of the intensities of its red, green, and blue components. In the HSI model, the intensity component is separated from the color components. This model can use the algorithms for gray level images. Some of the processing are based on those of gray level images, and some are exclusive to color images.