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%

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

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

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

Application of wavelet transform for PDZ domain classification.

PDZ domains have been identified as part of an array of signaling proteins that are often unrelated, except for the well-conserved structural PDZ domain they contain. These domains have been linked to many disease processes including common Avian influenza, as well as very rare conditions such as Fraser and Usher syndromes. Historically, based on the interactions and the nature of bonds they form, PDZ domains have most often been classified into one of three classes (class I, class II and others - class III), that is directly dependent on their binding partner. In this study, we report on three unique feature extraction approaches based on the bigram and trigram occurrence and existence rearrangements within the domain's primary amino acid sequences in assisting PDZ domain classification. Wavelet packet transform (WPT) and Shannon entropy denoted by wavelet entropy (WE) feature extraction methods were proposed. Using 115 unique human and mouse PDZ domains, the existence rearrangement approach yielded a high recognition rate (78.34%), which outperformed our occurrence rearrangements based method. The recognition rate was (81.41%) with validation technique. The method reported for PDZ domain classification from primary sequences proved to be an encouraging approach for obtaining consistent classification results. We anticipate that by increasing the database size, we can further improve feature extraction and correct classification