Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Intrinsically conducting polymers (ICP) and conductive fillers incorporated conductive polymer-based composites (CPC) greatly facilitate the research in electromagnetic interference (EMI) s...

Electromagnetic Interference Shielding Polymers and Nanocomposites - A Review

Conducting polymer composites of polypyrrole/yttrium oxide (PPy/Y2O3) were synthesized byin situ polymerization of pyrrole with Y2O3 using FeCl3 as an oxidant. The Y2O3 is varied in five different weight percentages of PPy in PPy/Y2O3 composites. The synthesized polymer composites are characterized by infrared and X-ray diffraction techniques. The surface morphology of the composite is studied by scanning electron microscopy. The glass transition temperature of the polymer and its composite is discussed by DSC. Electrical conductivity of the compressed pellets depends on the concentration of Y2O3 in PPy. The frequency dependent a.c. conductivity reveals that the Y2O3 concentration in PPy is responsible for the variation of conductivity of the composites. Frequency dependent dielectric constant at room temperature for different composites are due to interfacial space charge (Maxwell Wagner) polarization leading to the large value of dielectric constant. Frequency dependent dielectric loss, as well as variation of dielectric loss as a function of mass percentage of Y2O3 is also presented and discussed.

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Synthesis, characterization and a.c. conductivity of polypyrrole/Y 2 O 3 composites

A Text-book Of Quantitative Inorganic Analysis Ed.3rd

Thank you very much for reading advanced digital signal processing and noise reduction. Maybe you have knowledge that, people have search hundreds times for their chosen books like this advanced digital signal processing and noise reduction, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their laptop.

Advanced Digital Signal Processing And Noise Reduction

In situ polymerization of aniline was carried out in the presence of fly ash (FA) to synthesize polyaniline/ fly ash (PANI/FA) composites. The PANI/FA composites have been synthesized with various compositions (15, 20, 30 and 40 wt%) of FA in PANI. The composites, thus synthesized have been characterized by infrared spectroscopy and X-ray diffraction. The morphology of these samples was studied by scanning electron microscopy. Further the a.c. conductivity of these composites have been investigated in the frequency range 102–106 Hz. The presence of polarons and bipolarons are made responsible for frequency dependence of a.c. conductivity in these composites. The Cole-Cole plots indicate clear shift in the distribution of relaxation times as the wt% of FA in PANI changes. These composites show almost symmetric semicircles of Cole-Cole plots indicating the Debye-type relaxation in their polarization response.

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Synthesis, characterization and low frequency a.c. conduction of polyaniline/fly ash composites

Conducting polyaniline/γ-Fe2O3 (PANI/FE) composites have been synthesized using an in situ deposition technique by placing fine-graded γ-Fe2O3 in a polymerization mixture of aniline. The composites are characterized by using scanning electron microscopy (SEM), X-ray diffraction (XRD) and infrared (IR) spectroscopy. The electrical properties such as d.c. and a.c. conductivities are studied by sandwitching the pellets of these composites between the silver electrodes. It is observed that the conductivity increases up to a composition of 20 wt.% of γ-Fe2O3 in polyaniline and decreases thereafter. The initial increase in conductivity is attributed to the extended chain length of polyaniline, where polarons possess sufficient energy to hop between favourable sites. Beyond 20 wt.% of γ-Fe2O3 in polyaniline, the blocking of charge carrier hop occurs, reducing conductivity values. The magnetic properties such as hysteresis characteristics and normalized a.c. susceptibility are also measured, which show a strong dependence on content of γ-Fe2O3 in polyaniline. Because of superparamagnetic behaviour of these composites, they may find extensive technological applications, especially for absorbing and shielding applications in microwave frequencies.

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Synthesis, characterization and magnetic properties of polyaniline/γ-Fe2O3 composites

Polyaniline/zirconium oxide (PANI/ZrO2) nanocomposites have been synthesized by incorporating ZrO2 nanoparticles into the PANI matrix via liquid–liquid interfacial polymerization method. The composite formation and structural changes in PANI/ZrO2 nanocomposites were investigated by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR). PXRD pattern of PANI/ZrO2 nanocomposites exhibited sharp and well-defined peaks of monoclinic phase of ZrO2 in PANI matrix. SEM images of the composites showed that ZrO2 nanoparticles were dispersed in the PANI matrix. The FT-IR analysis revealed that there was strong interaction between PANI and ZrO2. AC conductivity and dielectric properties of the nanocomposites were studied in the frequency range, 50–106 Hz. AC conductivity of the nanocomposites obeyed the power law indicating the universal behaviour of disordered media. The nanocomposites showed high dielectric constant in the order of 104, which could be related to dielectric relaxation phenomenon. Further, the materials were checked for their supercapacitance performance by using cyclic voltammetry (CV), chronopotentiometry (CP) and electrochemical impedance spectroscopy (EIS). Among the synthesized nanocomposites, PANI/ZrO2-25 wt.% showed a higher specific capacitance of 341 F g−1 at 2 m Vs−1 and good cyclic stability with capacitance retention of about 88% even after 500 charge–discharge cycles.

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Synthesis of polyaniline/ZrO2 nanocomposites and their performance in AC conductivity and electrochemical supercapacitance

Conducting Polypyrrole-lead titanate (PPy/PbTiO3) composites have been prepared by in situ deposition technique by placing different wt.% of fine grade powder of PbTiO3 (10, 20, 30, 40, and 50%) during polymerization of pyrrole. The composites formed were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA), and these data indicate that PbTiO3 particles are dominating with an increase in crystallinity as well as thermal stability of the composites. The results on the low frequency dielectric studies which are obtained in the form of pressed pellet state are interpreted in terms of Maxwell Wagner polarization, which are responsible for the dielectric relaxation mechanism and frequency dependence of conductivity.

Preparation, Characterization and Low Frequency a.c. Conduction of Polypyrrole-Lead Titanate Composites

The synthesis and characterization of first row transition metal complexes of the 1, 4(2'-hydroxyphenyl-1-yl) di-imino azine {1,4(2'HPDA)} are reported. The complexes have been characterized by elemental analysis, molar conductance, magnetic studies, IR, 1H NMR and UV-visible studies. They have the stoichiometry of the type [M{1,4(2'HPDA)}2 2H2O ] and [M'L2] where M= Mn(II), Fe(III), Co(II), Ni(II) and Cu(II), and M' = ZrO(II), VO(II), Zn(II), Cd(II), and Hg(II). The antibacterial and antifungal activity of the metal complexes has been investigated. Both ligand and complexes have shown good antibacterial and antifungal activity.

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M. Revanasiddappa

Papers

Synthesis, characterization and low frequency a.c. conduction of polyaniline/fly ash composites

Synthesis, characterization and magnetic properties of polyaniline/γ-Fe2O3 composites

Synthesis of polyaniline/ZrO2 nanocomposites and their performance in AC conductivity and electrochemical supercapacitance

Preparation, Characterization and Low Frequency a.c. Conduction of Polypyrrole-Lead Titanate Composites

Transition Metal Complexes of 1, 4(2'-Hydroxyphenyl-1-yl) di-imino azine: Synthesis, Characterization and Antimicrobial Studies