Observation of Size-Dependent Electron-Phonon Scattering and Temperature-Dependent Photoluminescence Quenching in Triangular-Shaped Silver Nanoparticles

Research studies on plasmonic properties of triangular-shaped silver nanoparticles might lead to several interesting applications. However, in this work, triangular-shaped silver nanoparticles have been synthesized by simple solvothermal technique and reported the effect of size on the electron-phonon scattering in the synthesized materials by analyzing their temperature-dependent photoluminescence (PL) emission characteristics. It has been observed that total integrated PL emission intensity is quenched by 33 % with the increase in temperature from 278 to 323 K. The observed decrease in PL emission intensity has been ascribed to the increase of electron-phonon scattering rate with the increase in temperature. The values of electron-phonon coupling strength (S) for synthesized samples have been evaluated by theoretical fitting of the experimentally obtained PL emission data. Smaller sized triangular nanoparticle has been found to exhibit stronger temperature dependence in PL emission, which strongly suggests that smaller sized triangular silver nanostructures have better electron-phonon coupling.

Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.

A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.