Periyar's writings on women were at the heart of his commitment to a radical concept of freedom. Periyar is known most not only for his atheism and radical critique of religion (Manoharan, 2022a) but also for his commitment and contribution to anti-caste thought and politics (Manoharan, 2020; 2022b). However, crucial, perhaps even central, to Periyar's politics of Self-Respect was his approach to the women's question. In this chapter, we discuss how Periyar's approach to the women's question was grounded not only in a rights-based discourse, but also in a freedom-based discourse; not just freedom from patriarchy, but also sexual freedom in a radically libertarian sense. More importantly, Periyar argued that freedom for women took priority over freedom from colonialism, and challenged patriarchal tendencies within Indian nationalism.

Scholars engaged with feminist politics have looked at the critical importance given to the women's question and gender in the Self-Respect Movement (SRM). In their readings on gender politics in India, Anandhi and Velayuthan (2010) highlight the ‘limitations in theory itself in dealing with diversities and subalternity’ and argue that in a scenario where gender intersects with caste and class, the theory and methods used ‘should generate knowledge from the margins’. While feminist scholars such as Uma Chakravarti (2018) and Sharmila Rege (2013) have discussed the intersections of caste and patriarchy, others who have studied the Periyarist politics of gender—Anandhi (1991), Geetha (1998), and Hodges (2005)—have meticulously captured what we very broadly call Self-Respect perspectives and made important contributions to the study of women’s politics of and from the margins of Tamil Nadu.

16.1 Introduction to Artificial Neural Network

Neural networks and deep learning are the buzzwords in modern-day computer science. And, if you think that these are the latest entrants in this field, you probably have a misconception. Neural networks have been around for quite some time, and they have only started picking up now, putting up a huge positive impact on computer science.

Artificial neural network (ANN) was invented in the 1960s and 1970s. It became a part of common tech talks, and people started thinking that this machine learning (ML) technique would solve all the complex problems that were challenging the researchers during that time. But sooner, the hopes and expectations died off over the next decade.

The decline could not be attributed to some loopholes in neural networks, but the major reason for the decline was the “technology” itself. The technology back then was not up to the right standard to facilitate neural networks as they needed a lot of data for training and huge computation resources for building the model. During that time, both data and computing power were scarce. Hence, the resulting neural network remained only on paper rather than taking centerstage of the machine to solve some real-world problems.

Later on, at the beginning of the 21st century, we saw a lot of improvements in storage techniques resulting in reduced cost per gigabyte of storage. Humanity witnessed a huge rise in big data due to the Internet boom and smartphones.

‘Periyar had hatred towards the Brahmins and preached violence against them.’ ‘Periyar favoured the powerful among the non-Brahmin castes.’ ‘Periyar sidelined the Dalits.’ These are the three main accusations against Periyar by his critics on the issue of caste. In an earlier paper (Manoharan, 2020), I have questioned the last two criticisms. In this chapter, I will address the first. Periyar was opposed to casteism in all its forms. In India, he identified the dominant form of casteism to be Brahminism, a ritual birth-based social hierarchy that derived legitimacy from scriptures, practices, traditions, and values associated with Hinduism and had material consequences. This led Periyar to be vehement in his criticism of the castes that were scripturally considered the highest, the Brahmins, and most sympathetic to the castes that were considered to be the lowest, the ‘untouchables’. He understood that caste had a secular–material dimension as well, which was interconnected to the ideological–ritual dimension.

Working in the historical context that he did in Tamil Nadu, Periyar's approach to caste identified three broad social categories—the Brahmins, the Dalits,1 and the ‘Shudras’. His primary target of criticism was the first, the Brahmins. This led to counter-accusations that he was unfairly targeting only one community for casteism. But as I have discussed earlier (Manoharan, 2022), he often challenged the non-Brahmins for internalizing casteism, for subscribing to notions of hierarchy over others, and for the lack of an egalitarian spirit.

learning Outcomes

After reading this chapter, the reader will be able to

• Understand the meaning of three processes of heat flow: conduction, convection, and radiation

• Know about thermal conductivity, diffusivity, and steady-state condition of a thermal conductor

• Derive Fourier's one-dimensional heat flow equation and solve it in the steady state

• Derive the mathematical expression for the temperature distribution in a lagged bar

• Derive the amount of heat flow in a cylindrical and a spherical thermal conductor

• Solve numerical problems and multiple choice questions on the process of conduction of heat

6.1 Introduction

Heat is the thermal energy transferred between different substances that are maintained at different temperatures. This energy is always transferred from the hotter object (which is maintained at a higher temperature) to the colder one (which is maintained at a lower temperature). Heat is the energy arising due to the movement of atoms and molecules that are continuously moving around, hitting each other and other objects. This motion is faster for the molecules with a largeramount of energy than the molecules with a smaller amount of energy that causes the former to have more heat. Transfer of heat continues until both objects attain the same temperature or the same speed. This transfer of heat depends upon the nature of the material property determined by a parameter known as thermal conductivity or coefficient of thermal conduction. This parameter helps us to understand the concept of transfer of thermal energy from a hotter to a colder body, to differentiate various objects in terms of the thermal property, and to determine the amount of heat conducted from the hotter to the colder region of an object. The transfer of thermal energy occurs in several situations:

• When there exists a difference in temperature between an object and its surroundings,

• When there exists a difference in temperature between two objects in contact with each other, and

• When there exists a temperature gradient within the same object.

Introduction

Statistical mechanics bridges the gaps between the laws of thermodynamics and the internal structure of the matter. Some examples are as follows:

• 1. Assembly of atoms in gaseous or liquid helium.

• 2. Assembly of water molecules in solid, liquid, or vapor state.

• 3. Assembly of free electrons in metal.

The behavior of all these abovementioned assemblies is totally different in different phases. Therefore, it is most significant to relate the macroscopic behavior of the system to its microscopic structure.

In this mechanics, most probable behavior of assembly are studied instead of individual particle interactions or behavior.

The behavior of assembly that is repeated a maximum time is known as most probable behavior.

hase Space

Six coordinates can fully characterize the state of any system:

• 1. Three for describing the position x, y, z and three for momentum Px, Py, Pz.

• 2. The combined position and momentum space (x, y, z, Px, Py, Pz) is called phase space.

• 3. The momentum space represents the energy of state,

For a system of N particles, there exists 3N position coordinates and 3N momentum coordinates. A single particle in phase space is known as a phase point, and the space occupied by it is known as µ-space.

olume Element ofµ-Space

4. Consider a particle having the position and momentum coordinates in the range.

13.1 Implementation of k-means Clustering and Hierarchical Clustering

In the previous chapter, we discussed various clustering algorithms. We learned that clustering algorithms are broadly classified into partitioning methods, hierarchical methods, and density-based methods. The k-means clustering algorithm follows partitioning method; agglomerative and divisive algorithms follow the hierarchical method, while DBSCAN is based on density-based clustering methods.

In this chapter, we will implement each of these algorithms by considering various case studies by following a step-by-step approach. You are advised to perform all these steps on your own on the mentioned databases stated in this chapter.

The k-means algorithm is considered a partitioning method and an unsupervised machine learning (ML) algorithm used to identify clusters of data items in a dataset. It is one of the most prominent ML algorithms, and its implementation in Python is quite straightforward. This chapter will consider three case studies, i.e., customers shopping in the mall dataset, the U.S. arrests dataset, and a popular Iris dataset. We will understand the significance of k-means clustering techniques to implement it in Python through these case studies. Along with the clustering of data items, we will also discuss the ways to find out the optimal number of clusters. To compare the results of the k-means algorithm, we will also implement hierarchical clustering for these problems.

We will kick-start the implementation of the k-means algorithm in Spyder IDE using the following steps.

• Step 1: Importing the libraries and the dataset—The dataset for the respective case study would be downloaded, and then the required libraries would be imported.

• Step 2: Finding the optimal number of clusters—We will find the optimal number of clusters by the elbow method for the given dataset.

• Step 3: Fitting k-means to the dataset—A k-means model will be prepared by training the model over the acquired dataset.

• Step 4: Visualizing the clusters—The clusters formed by the k-means model would then be visualized in the form of scatter plots.

Introduction

Humans have had a lengthy history of understanding electricity and magnetism. The tangible characteristics of light have also been studied. But in contrast to optics, electricity and magnetism—now known as electromagnetics—have been believed to be governed by different physical laws. This makes sense because optical physics as it was previously understood by humans differs significantly from the physics of electricity and magnetism. For instance, the ancient Greeks and Asians were aware of lode stone between 600 and 400 BC. Since 200 BC, China has been using the compass. The Greeks described static energy as early as 400 BC. But these oddities had no real effect until the invention of telegraphy. The voltaic cell or galvanic cell was created by Luigi Galvani and Alesandro Volta in the late 1700s, which led to the development of telegraphy. It quickly became clear that information could be transmitted using just two wires attached to a voltaic cell. The development of telegraphy was therefore prompted by this potential by the early 1800s. To learn more about the characteristics of electricity and magnetism, Andre-Marie Ampere (1823) and Michael Faraday (1838) conducted tests. Ampere's law and Faraday's law are consequently called after them. In order to comprehend telegraphy better, Kirchhoff voltage and current rules were also established in 1845. The data transmission mechanism was not well comprehended despite these laws. The cause of the data transmission signal's distortion was unknown. The ideal signal would alternate between ones and zeros, but the digital signal quickly lost its shape along a data transmission line.

Learning Outcomes

After reading this chapter, the reader will be able to

• Express the meaning of sphere of influence and collision frequency

• Derive the distribution function for the free paths among the molecules and demonstrate the concept of mean free path

• Calculate the expression for mean free path following Clausius and Maxwell

• Derive the expression for pressure exerted by a gas using the survival equation

• Calculate the expressions for viscosity, thermal conductivity, and diffusion coefficient of a gaseous system

• Demonstrate Brownian motion with its characteristics and calculate the mean square displacement of a particle executing Brownian motion

• State the idea of a random walk problem

• Solve numerical problems and multiple choice questions on the mean free path, viscosity, thermal conduction, diffusion, Brownian motion, and random walk

4.1 Introduction

The molecules of an ideal gas are considered as randomly moving point particles. From the concept of kinetic theory of gases (KTG), it is well established that even at room temperature, such point molecules of the ideal gas move at very large speeds. The average value of this speed can be determined assuming that the molecules obey Maxwell's speed distribution law and is given by the following expression

Operational Amplifiers: Introduction

An operational amplifier (op-amp) is a very prominent active device used in analog integrated circuit (IC) design. Prominence is due to the widespread and diverse areas of applications of the op-amps as its parameters are very close to ideal in a certain range of operating frequencies. Apart from basic arithmetic operations such as addition, multiplication, and integration, op-amps are also widely employed as amplifiers, wave shaping circuits, active filters, log/anti-logarithmic amplifiers, nonlinear function generators, and in analog-to digital and digital-to-analog conversion, and so on.

Figure 8.1(a) shows a pin connection diagram of the most commonly used type-741 op-amp; it needs a dual power supply, has two terminals for inverting and non-inverting inputs, one terminal for the output, and three terminals without any connections for simple applications. Dual op-amps and quad op-amp ICs with matching characteristics are also available.

Op-amp is essentially a high-gain differential amplifier (DA) that can be shown in its simplest form as represented in Figure 8.1(b). The output voltage of the op-amp is the difference between the two input voltages multiplied by the high-gain factor A, so the output voltage is expressed as:

The differential gain A is frequency dependent in a practical op-amp. Therefore, as a first approximation, it is represented by a single-pole roll-off model given below.

Semiconductor Diodes

A semiconductor diode is a two-terminal device. Ideally, the diode behaves as a short circuit in one direction for current flow; it is called the forward direction, and the same diode behaves as an open circuit for the current flow in the opposite direction, which is called the reverse direction.

Ideal Silicon p–n Junction Diode

One of the most important characteristics of an ideal diode is that it behaves as an ideal switch, and the switching action is controlled by the direction of the current that flows in one direction only. Figure 1.1(a) shows a symbol for an ideal diode as a switch, where the arrow is in the forward direction. It also shows the positive direction of the current and voltage drop across the diode. Figure 1.1(b) shows the v, i characteristics of the ideal diode, which conducts only in one direction. The names assigned to the diode terminals are anode and cathode. The direction of the forward current in the diode is from the anode to the cathode inside the diode.

Fabricating an ideal diode in practice is not possible. Still, its idealized model in Figure 1.1(b) serves as a good approximation of a practical diode for basic analysis purposes.

At an early stage, diodes were realized using vacuum tubes with filament inside. However, now solid-state diodes are fabricated using semiconductors.

Introduction

Figure 10.1 shows a simple block diagram of a typical analog signal processing system. The first step in the development of an analog signal processing system is to divide the given specifications into analog and digital parts. Based on the advantages in VLSI technology, a variety of signal processing systems have been developed and will continue to be developed with the format of Figure 10.1. One such example is the advent of sampled data technique and MOS technology, which enabled the fabrication of a general signal processor. However, analog-sampled techniques moved onward from MOS technology toward CMOS technology, as it was highly suitable for combining analog and digital systems.

In most of the practical cases, input signals are of analog types like speech signals, sensor output, radar signals, and so on. The first block in Figure 10.1 is a pre-processing block, which usually consists of analog filters, sample and hold process, and an analog-to-digital converter (ADC). Depending on the nature of the input signal, the input block may require signal processing with strict speed and accurate specifications. After the conversion of analog signals to digital signals, the next block is mostly a microprocessor. The advantage of using a microprocessor is that its function can easily be controlled and modified. Post-processing is done in the final stage, wherein the signal is converted back to the analog form in most cases, and this stage uses digital-to-analog converter (DAC) and some filtering. Proper interfacing is required between the three building blocks of Figure 10.1; placement of the the inter facing is indicated symbolically by the arrow heads.

Frequency Response

Some amplifier circuits employing either BJT or FET were analyzed in Chapter 2. Almost all the circuits contained blocking and bypass capacitors in addition to the biasing components, load resistance, and so on. However, these external capacitors and the internal parasitic capacitors of the transistor were considered absent during the analysis because either dc analysis was done or for the simplification in the analysis. There was no mention of the practical limit on the device parameters or on the components used. It does not mean that the obtained expressions for the voltage gain, current gain, and input and output resistance were wrong. These expressions are very important and relevant, and for most of the operations of amplifiers, these expressions are to be employed. However, there is something more, which is also very important, and that remains to be studied.

When the internal device capacitances or the intentionally connected capacitance or load impedance having reactive components are considered, the amplifier gain becomes a complex number, A–θ, instead of a real number. A significant point is that both the gain A and phase angle –θ depend on the input signal frequency, and the gain magnitude decreases at low and at comparatively high signal frequencies; amplification remains almost constant in the mid-frequency range. The frequency response characteristics of an amplifier are the plot of gain and phase with frequency.