230 s room-temperature storage time and 1.14 eV hole localization energy in In0.5Ga0.5As quantum dots on a GaAs interlayer in GaP with an AlP barrier

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Appl. Phys. Lett. 106, 042102 (2015) and may be found at https://doi.org/10.1063/1.4906994.A GaP n+p-diode containing In0.5Ga0.5As quantum dots (QDs) and an AlP barrier is characterized electrically, together with two reference samples: a simple n+p-diode and an n+p-diode with AlP barrier. Localization energy, capture cross-section, and storage time for holes in the QDs are determined using deep-level transient spectroscopy. The localization energy is 1.14(±0.04) eV, yielding a storage time at room temperature of 230(±60) s, which marks an improvement of 2 orders of magnitude compared to the former record value in QDs. Alternative material systems are proposed for still higher localization energies and longer storage times

Off-line handwritten signature recognition by wavelet entropy and neural network

Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%

Materials for Future Quantum Dot-Based Memories

The present paper investigates the current status of the storage times in self-organized QDs, surveying a variety of heterostructures advantageous for strong electron and/or hole confinement. Experimental data for the electronic properties, such as localization energies and capture cross-sections, are listed. Based on the theory of thermal emission of carriers from QDs, we extrapolate the values for materials that would increase the storage time at room temperature to more than millions of years. For electron storage, GaSb/AlSb, GaN/AlN, and InAs/AlSb are proposed. For hole storage, GaSb/Al0.9Ga0.1As, GaSb/GaP, and GaSb/AlP are promising candidates

Room-Temperature Hysteresis in a Hole-Based Quantum Dot Memory Structure

We demonstrate a memory effect in self-assembled InAs/Al0.9Ga0.1As quantum dots (QDs) near room temperature. The QD layer is embedded into a modulation-doped field-effect transistor (MODFET) which allows to charge and discharge the QDs and read out the logic state of the QDs. The hole storage times in the QDs decrease from seconds at 200 K down to milliseconds at room temperature

Public Transit Ridership and Car-Oriented Cities: The Case of the Dallas Region

U.S. cities have invested large amounts of sums on public transit and urban rail in the last few decades, but the transit usage in most of these car-oriented cities is very low, and previous efforts to increase ridership have been mostly fruitless. This research examines the factors affecting transit ridership in a large car-oriented metropolitan setting and uses the Dallas region in the United States as a case for the study to identify factors that could help in increasing ridership. Most previous studies of transit ridership have not included many of the variables thought to influence transit ridership. Therefore, the disparities among the findings of empirical research completed to date point to the necessity for further study. This study addresses these shortcomings by exploring multiple factors, measuring population, technology, geography, and socioeconomic characteristics

Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions

An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD), logarithmic difference signal ratio (LDSR), and correlation coefficient (CC). The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN) environment, where the recognition rate was 81.48% for 5 dB

The Use of LPC and Wavelet Transform for Influenza Disease Modeling

In this paper, we investigated the modeling of the pathological features of the influenza disease on the human speech. The presented work is novel research based on a real database and a new combination of previously used methods, discrete wavelet transform (DWT) and linear prediction coding (LPC). Three verification system experiments, Normal/Influenza, Smokers/Influenza, and Normal/Smokers, were studied. For testing the proposed pathological system, several classification scores were calculated for the recorded database, from which we can see that the proposed method achieved very high scores, particularly for the Normal with Influenza verification system. The performance of the proposed system was also compared with other published recognition systems. The experiments of these schemes show that the proposed method is superior