19.1 Building Image Classifier with CNN

The convolutional neural network (CNN) is the perfect choice for building an image classifier system. In this chapter, we will implement various CNN classifiers in Python by considering various case studies.

19.2 Dog–Cat Classifier

Consider an image classification problem that involves identifying photos as either containing a cat or dog. Our task is to develop a CNN model that can identify the object in the image as a dog or cat. The CNN model will be trained on a dataset of images containing a dog or cat, and then this trained model will be used for classification. Once the model is built, it can be used as a template to build other models for predicting the class of any image on the trained dataset. It means we can use the same model to classify the tumors by training them on medical images.

Preparing the Dataset

To build a dog–cat classifier, we will use 11000 manually annotated photos of dogs and cats, where 5500 photos are of dogs, and the remaining ones are of cats. This is the partial dataset that has been created from the dogs-vs-cats dataset available at Kaggle. The link for this dataset is given below.

https://www.kaggle.com/competitions/dogs-vs-cats/data

This full dataset contains 37500 images (24000 in the train and 13500 in the test folders), which must be manually segregated into dog and cat folders. Since the dataset is huge and segregation is time-consuming, we took 10000 images (5000 images of dogs and 5000 images of cats) in the train folder and 1000 images in the test folder (500 images of dogs and 500 images of cats).

12.1 Introduction to Clustering

In machine learning (ML), labeling the data is one of the crucial tasks. But sometimes, we do not have the labeled data. Even though the data is not labeled, we can still analyze it using clustering techniques. As you know, algorithms in ML are broadly classified as supervised and unsupervised techniques. In the case of supervised learning, the input data points/examples are labeled, while in unsupervised learning, the input data points/examples are not labeled. Cluster analysis, also called clustering, comes under unsupervised learning. Here, the input data points are not labeled. Clustering is the most popular technique in unsupervised learning.

Clustering is defined as grouping the input data points into various clusters/groups based on their similarity.

A cluster contains objects that are more similar to each other. In other words, during cluster analysis, the data is grouped into classes or clusters, so that records within a cluster (intra-cluster) have high similarity with one another but have high dissimilarities in comparison to objects in other clusters (inter-cluster).

The clustering algorithm aims to minimize the intra-cluster distance and maximize the inter-cluster distance, as shown in Figure 12.1.

An example of clustering is shown in Figure 12.2. Here, records in the input have different shapes. Here, we only have the input data without any label of shape. After applying the clustering algorithm, they were classified into three types of clusters. Here, the clustering algorithm considers the dimensions of the object and its color as the input features. Records whose features are highly similar are gathered to form a single cluster. In this case, we get three clusters representing three types of records, i.e., it clusters rhombus, circle, and triangle separately, as shown in Figure 12.2.

Learning Outcomes

After reading this chapter, the reader will be able to

• Understand the distribution of energy density in a black body radiation as a function of wavelength and temperature

• Derive classical laws of black body radiation such as Wien distribution law and Rayleigh–Jeans law

• Get an idea about the development of quantum theory of radiation

• Understand Planck's quanta postulates and explain the black body radiation spectrum

• Derive Planck's law of black body radiation

• Verify Planck's law of black body radiation experimentally

• Derive Wien distribution law and Rayleigh–Jeans law and explain ultraviolet catastrophe from Planck's law of black body radiation

• Determine the temperature of cosmic microwave background radiation using Planck's law of black body radiation

• Solve numerical problems and multiple choice questions on black body Radiation

11.1 Introduction

Figure 11.1 The whole electromagnetic spectrum. Thermal radiation ranges in frequency from the shortest infrared rays through the visible-light spectrum to the longest ultraviolet rays.

Radiation emitted from the surface of a heated source is known as thermal radiation. In this process, thermal energy is spread out in all directions in the form of electromagnetic radiation and travels directly to its point of absorption at the speed of light. It does not require an intervening medium for its propagation. The wavelength of thermal radiation ranges from the longest infrared rays through the visible-light spectrum to the shortest ultraviolet rays. Such electromagnetic spectrum is shown in Figure 11.1 as a function of frequency. The distribution of radiant energy with their corresponding intensities within various ranges of wavelengths is governed by the temperature of the emitting surface .

In the Korean drama My Liberation Notes (Netflix, 2022), written by Park Hay-Young, three office workers sit in the human resources (HR) manager's room. They have been asked to meet with the HR manager to address a specific issue related to the company's HR policy. As part of a neoliberal workplace well-being initiative, the company encourages employees to join a club to explore their hobbies and other interests. According to its institutional logic, if employees are allowed to pursue their personal interests at the workplace, it will make them ‘happy’ and creative, eventually leading to greater productivity. The HR manager regularly emails employees about various clubs, such as photography, hiking, and pottery to encourage them to choose a club.

Three colleagues from different departments receive regular club invitations via email, but none of them find the clubs interesting enough to join. To them, the exercise seems absurd, especially given their challenges, such as the high cost of living, normalized overwork, and dignity violations in the workplace. The HR manager's attempts to persuade them to join a club seem meaningless and futile in the face of their existential crisis. They are tired of the monotony of their lives, which limits their hope and possibilities. Consequently, they frequently reject club suggestions. Eventually, the HR manager asks them to meet her in the office and they provide vague answers, but as they leave, they realize they can create their own club to avoid the pressure of joining one. They name it the ‘Liberation Club’, whose objective is to journal their existential struggles to overcome personal and social reifications.

The Poona Pact, 1932, was a watershed moment in the history of Dalit politics. Nearly a century later, it remains the subject of debate and discussion. A definite setback to the independent mobilization of the Depressed Classes, the Poona Pact deprived them of the historic right to a separate electorate with a double vote granted by the British government. This chapter seeks to describe and analyse the stance taken by Periyar and his Self-Respect Movement (SRM) towards what B. R. Ambedkar described as ‘a mean deal’ (Ambedkar, 2014 [1994], p. 40).1

The pact was signed at a time when the Indian National Congress was in the ascendant and had demonstrated its all-India character and strength through a series of mass agitations. In response to its rise, in south India, the non-Brahmin castes had mobilized under the Justice Party and Periyar's SRM. At the all-India level, the Depressed Classes had become a force to be reckoned with under the leadership of Ambedkar. Both Periyar and Ambedkar viewed the Congress primarily as a formation that represented the Brahmins and Hindu upper castes.

To understand the position taken by Periyar on the Depressed Classes’ question, we need to trace the emergence of Depressed Class consciousness and the formation of political organizations representing the interests of Depressed Classes in south India—a group that Eleanor Zelliot describes as ‘the other [apart from that of western India] politically vocal group of Untouchables’, the largest in terms of numbers in any region of India then (2013, p. 115). Even though the political demands of the Depressed Classes coalesced only at the time of the Simon Commission (1928), their roots can be traced back much further.

18.1 Image Recognition

Using a convolutional neural network (CNN), technological development in image recognition has revolutionized far beyond our imagination. Let us consider the comic scene shown in Figure 18.1, as it provides interesting insights into the development of image recognition and depicts a decade-back possible scenario. Here, a manager asks his computer programmer to “Develop an app which can check whether the user is in a national park or not, when he clicks some photo!” Being an easy and feasible task, the computer programmer responds that the task is merely of few hours. But, the manager's curiosity goes up, and he asks the programmer further to check whether the image is of a bird or not? Surprisingly, the programmer responds, “I need a research team and five years for this task.”

This surprised the manager as he expected it to be an easy task. But the programmer who has a knack in the field knows that it is one of the complex problems to be addressed in computer science.

In the last decade, we have provided solutions to many complex problems in the field of computers. But, for the last 50 years, we have been struggling to solve the problems in image recognition. However, thanks to the efforts of researchers and computer scientists across the globe, we can solve these problems now. Even a three-year-old child can identify a bird's photo, but identifying a way by which computers can do the same task was not a cake-walk; hence, it took almost 50 years!

We have finally found a promising approach for object recognition using deep CNN in recent years. In this chapter, we will discuss the working principle and concepts of CNN, a deep neural network approach to solving the problem of image recognition.

Classification of Large Signal Amplifiers

The amplifiers studied so far are small signal amplifiers, where the magnitude of the input signal is small, and the main aim is to amplify either voltage or current with minimum distortion. However, in many applications like control, communication, and power conversion, a large amount of power, sometimes exceeding tens of kW, is to be handled by transistors and other semiconductor devices. In that case, the employed amplifiers are called power amplifiers or large signal amplifiers, where output signals, voltage and current, are large in magnitude.

Based on the type of circuit configuration like CE, CB, and CC, and the location of the quiescent point on the output characteristics, power amplifiers are classified as class A, class B, class AB, class C, and D, E, and F. Each class has its advantages and limitations, which will be discussed along with their circuits and operation. Class D is used very little, and classes E and F are rarely used, so only A, B, and C types of amplifiers will form part of this study, and their classification criterion is mentioned next.

Class A Amplifier: In class A operation, an amplifier is so biased that its operating point is almost in the middle of the output characteristics. The magnitude of the input signal is such that the amplifier operates over its full linear region of the characteristics, but without any clipping of the input signal. So, the output is the amplified replica of the input signal with minimal distortion. However, class A operation works with poor power conversion efficiency; the theoretical maximum power conversion efficiency from DC input to AC output is from 25–50%.

Concept of Negative Feedback

In a feedback system, a signal that is proportional to the output is fed back to the input. It may happen unintentionally or be done intentionally. When the feedback signal adds to the input signal, it is called positive feedback, and when the input signal gets subtracted from the feedback signal, it becomes negative feedback.

Positive feedback is mostly used for the realization of oscillators, whereas negative feedback is used to stabilize the gain of amplifiers against a variation in transistor parameters, supply voltage, and temperature etc.The study in this chapter is limited to negative feedback only, which is primarily used to improve any one of the four types of amplifiers given in the next section, such that the amplifiers become as close to ideal as possible. However, certain conditions are required that help achieve the objective. For example, a primary amplifier is needed to have a very high gain in the forward direction, minimum reverse transmission, which normally happens as a property of the transistors used. Appropriate negative feedback connection and minimum effect of loading due to the feedback network on the main amplifier circuit are also very important.

The above mentioned term appropriate negative feedback needs a bit of explanation. In the voltage and current amplifiers, variables at the input and output are the same, hence there is no problem as such while feeding a part of the output to input.

4A Chemical Potential

Very often, the term “chemical potential” is not well understood by the students. After studying thermal physics and statistical mechanics for several times, students are still in a lot of confusion about the meaning of the term “chemical potential”. This quantity is represented by the letter 𝜇. Typically, students learn the definition of 𝜇, its properties, its derivation in some simple cases, and its consequences, and work out numerical problems on it. Still, students ask the question: “What is the chemical potential?” and “What does it actually mean?” Attempts are made in this appendix to clarify the meaning of this physical quantity 𝜇 with some simple examples.

The concept of chemical potential has appeared first in the classical works of J. W. Gibbs. Since then, it has become actually a subtle concept in thermodynamics and statistical mechanics. It is not easy to grasp the meaning and significance of chemical potential 𝜇, like thermodynamic concepts such as temperature 𝑇 , internal energy 𝐸, or even entropy 𝑆. In fact, chemical potential 𝜇 has acquired a reputation as a concept not easy to grasp even for the experienced physicist. Chemical potential was introduced by Gibbs within the context of an extensive exposition on the foundations of statistical mechanics. In his exposition, Gibbs considered a grand canonical ensemble of systems in which the exchange of particles occurs with the surroundings. In this description, the chemical potential 𝜇 appears as a constant required for a necessary closure to the corresponding set of equations. Thus, a fundamental connection with thermodynamics is achieved by observing that the unknown constant 𝜇 is indeed related to standard thermodynamic functions like the Helmholtz free energy 𝐹 = 𝑈 − 𝑇 𝑆 or the Gibbs thermodynamic potential 𝐺 = 𝐹 + 𝑃 𝑉 through their first derivatives. 𝜇, in fact, appeared as a conjugate variable to volume V. 4A.1 Comments about chemical potential

We are familiar with the term potential used in mechanical and electrical system. A capacity factor is associated with each potential term. For example, in a mechanical system, mass is the capacity factor associated with the gravitational potential 𝑔(ℎ2 − ℎ1), where ℎ1 and ℎ2 are the corresponding heights, and the gravitational work done is given by 𝑚𝑔(ℎ2 − ℎ1).

One of the most honoured figures in the state of Tamil Nadu, arguably home to the highest number of temples in India, is an atheist who profaned the gods. E. V. Ramasamy (1879–1973), popularly called ‘Periyar’ (the Great One), was a rationalist and radical social reformer. A household name in the region and the central figure of the Dravidian movement, he is best known for his polemics against religion, fervent propagation of atheism, support for proportional representation for backward and scheduled castes, and demand for political autonomy for south Indian states. His opposition to the caste system and the oppression of women are exemplified in his writings and speeches spanning over five decades. One of the first things that Dravida Munnetra Kazhagam (DMK) leader M. K. Stalin did on assuming office as chief minister of Tamil Nadu in 2021 was to declare Periyar's birth anniversary (17 September) as ‘Social Justice Day’—underscoring his reputation as a crusader for social justice.

In 2018, statues of Periyar were vandalized across Tamil Nadu, reportedly by Hindu right-wing activists. His statues outside temples, bearing the inscription ‘There is no god, there is no god, there is no god at all. He who invented god is a fool. He who propagates god is a scoundrel. He who worships god is a barbarian’ have been an eyesore for the Hindu right, and its leaders have been promising to have them removed.

12.1 INTRODUCTION [LO1]

There are in general two approaches to solving equilibrium problems in solid mechanics: Eulerian and Lagrangian. The first approach deals with vectors such as force and moments, and considers the static equilibrium and compatibility equations to solve the problems. In the second approach, scalars such as work and energy are used, and here solutions to problems are based on the principle of conservation of energy. There are many situations where the second approach is more advantageous, and here some powerful methods, such as the method of virtual work, based on this approach, are used.

Eulerian and Lagrangian approaches to solving solid mechanics problems are much more involved. However, here we have chosen to describe these in a simplified manner, which is suitable as a prologue to the present discussion on energy methods.

In mechanics, energy is defined as the capacity to do work, and this may exist in different forms. We are concerned here with elastic strain energy, which is a form of potential energy stored in a body on which some work is done by externally applied forces. Here it is assumed that the material remains elastic when work has been done so that all the energy is recoverable and no permanent deformation occurs. This means that strain energy U = work done. If the load is applied gradually in straining, the material load–extension graph is as shown in Figure 12.1, and we may write U = ½ Pδ.

The hatched portion of the load–extension graph represents the strain energy and the unhatched portion ABD represents the complementary energy that is utilized in some advanced energy methods of solution.

List of Experiments

• 1. Determine the energy band gap of N-type Germanium (Ge) semiconductor using the four-probe method.

• 2. Verify Stefan's fourth power law for black body radiation and determine the exponent of temperature.

• 3. Study of thermoelectricity: Determine thermopower of copper-constantan thermocouple.

• 4. Study the variation of magnetic field with distance along the axis of current-carrying coil and then estimate the radius of the coil.

• 5. Study of Carrey Foster bridge: Determine resistance per unit length of the bridge wire and of a given unknown resistance.

• 6. Determine specific charge (charge to mass ratio; e/m) for electron.

• 7. Study of tangent galvanometer: Determine reduction factor and horizontal component of the earth's magnetic field.

• 8. Determine the wavelength of sodium light using Newton's rings method.

• 9. Determine the concentration of sugar solution using half-shade polarimeter.

• 10. Determine the wavelength of spectral lines of mercury (for violet, green, yellow-1, and yellow-2) using plane transmission grating.

• 11. Determine the charge sensitivity and ballistic constant of a ballistic galvanometer.

• 12. Determine the wavelength of spectral lines of hydrogen and hence the value of Rydberg constant.

• 13. Draw the V-I characteristic of light-emitting diode (LED) and determine the value of Planck's constant.

Experiment 1: Band Gap of Semiconductor

Objective: To determine the energy band gap of n-type Ge-semiconductor using four-probe method.

Apparatus Used: The following apparatus are used in this experiment:

• 1. Probe arrangement (shown in Figure 14.1).

• 2. n-type Ge-crystal chip with nonconducting base.

• 3. Oven with temperature up to 200°C.

• 4. A constant current generator power supply.

• 5. Power source for the oven.

• 6. Thermometer.

Learning Outcomes

After reading this chapter, the reader will be able to

• Demonstrate the meaning of reversible, irreversible, and quasi-static processes used in thermodynamics

• Explain heat engines, and their efficiency and indicator diagram

• Formulate the second law of thermodynamics and apply it to various thermodynamic processes

• Demonstrate an idea about entropy and its variation in various thermodynamic processes

• State and compare various statements of the second law of thermodynamics

• Elucidate the thermodynamic scale of temperature and its equivalence to the perfect gas scale

• Explain the principle of increase of entropy

• Understand the third law of thermodynamics and explain the significance of unattainability of absolute zero

• Solve numerical problems and multiple choice questions on the second law of thermodynamics

9.1 Introduction

The first law of thermodynamics states that only those processes can occur in nature in which the law of conservation of energy holds good. But our daily experience shows that this cannot be the only restriction imposed by nature, because there are many possible thermodynamic processes that conserve energy but do not occur in nature. For example, when two objects are in thermal contact with each other, the heat never flows from the colder object to the warmer one, even though this is not forbidden by the first law of thermodynamics. This simple example indicates that there are some other basic principles in thermodynamics that must be responsible for controlling the behavior of natural processes. One such basic principle is contained in the formulation of the second law of thermodynamics.

This principle limits the use of energy within a source and elucidates that energy cannot be arbitrarily passed from one object to another, just as heat cannot be transferred from a colder object to a hotter one without doing any external work. Similarly, cream cannot be separated from coffee without a chemical process that changes the physical characteristics of the system or its surroundings. Further, the internal energy stored in the air cannot be used to propel a car, or the energy of the ocean cannot be used to run a ship without disturbing something (surroundings) around that object.

Introduction

Classification, characteristics, and basic design methods of certain types of networks that perform filtering action on the basis of the frequency of signals are briefly discussed in this chapter. The filters, which used only passive elements, and known as passive filters, were the only kind of filters in earlier days. Passive filters are still in use in many specific cases but have been replaced by active filters (using at least one active device) in a majority of applications. One essential reason for the changeover from the passive filters to the active filters was the inability of the realization of practically feasible inductors in integrated circuit (IC) form over a large frequency range of operation. Hence, structures that replaced (simulated) inductors employing resistance, capacitance, and op-amp were synonymous with the active filters, and these were called active RC filters. The usage of op-amps is still dominant, but other active devices are also used in a big way.

Another important approach to analog filter realization has emerged in the form of switched capacitor (SC) circuits. An important feature of the SC circuits is that it uses only capacitors, op-amps, and electronic switches. Consequently, performance parameters of the circuit depend on capacitor ratios and switching frequency. It is to be noted that very small value capacitances can be used, resulting in consuming less chip area, and better practical results as capacitors in ratio form can be fabricated with much less tolerance.

7.1 Introduction to Multiple Linear Regression

In simple linear regression, we have one dependent variable and only one independent variable. As we discussed in the previous chapter, the stipend of a research scholar is dependent on his years of research experience.

But most of the time, the dependent variable is influenced by more than one independent variable. When the dependent variable depends on more than one independent variable, it is known as multiple linear regression.

Figure 7.1 indicates the difference between simple and multiple regressions mathematically.

In Figure 7.1(b), b0 is the constant while x1, x2, and xn are independent variables on which the dependent variable y depends. You can point out that mathematically multiple linear regression is derived similarly to simple linear regression. The major difference is that in multiple linear regression, we have multiple independent variables, as it is from x1 to xn instead of only one independent variable, x1, in the case of simple linear regression. It is also important to note that we have b1 to bn as the coefficients of these independent variables, respectively.

The price prediction of a house can be viewed as a multiple linear regression problem, where factors such as plot size, number of bedrooms, and location play a significant role in determining the price. Unlike simple linear regression, which relies solely on plot size, multiple linear regression considers various features to accurately estimate the house price.

Let us understand this concept further with a real-life example. Consider a case where a venture capitalist has hired a data scientist to analyze different companies’ data to predict their profit. Identifying the company having maximum profit will help the venture capitalist to select the company he could invest in the near future to earn maximum profit.