On-chip template training system and image processing applications using iterative annealing on ACE16k chip

Cellular neural networks proved to be a useful parallel computing system for image processing applications. Cellular neural networks (CNNs) constitute a class of recurrent and locally coupled arrays of identical cells. The connectivity among the cells is determined by a set of parameters called templates. CNN templates are the key parameters to perform a desired task. One of the challenging problems in designing templates is to find the optimal template that functions appropriately for the solution of the intended problem. In this paper, we have implemented the Iterative Annealing Optimization Method on the analog CNN chip to find an optimum template by training a randomly selected initial template. We have been able to show that the proposed system is efficient to find the suitable template for some specific image processing applications.     

Robustness and stability measures for scheduling: single-machine environment

This paper addresses the issue of finding robust and stable schedules with respect to random disruptions. Specifically, two surrogate measures for robustness and stability are developed. The proposed surrogate measures, which consider both busy and repair time distributions, are embedded in a tabu-search-based scheduling algorithm, which generates schedules in a single-machine environment subject to machine breakdowns. The performance of the proposed scheduling algorithm and the surrogate measures are tested under a wide range of experimental conditions. The results indicate that one of the proposed surrogate measures performs better than existing methods for the total tardiness and total flowtime criteria in a periodic scheduling environment. A comprehensive bibliography is also presented.     

A transmission line fault locator based on Elman recurrent networks

In this paper, a transmission line fault location model which is based on an Elman recurrent network (ERN) has been presented for balanced and unbalanced short circuit faults. All fault situations with different inception times are implemented on a 380-kV prototype power system. Wavelet transform (WT) is used for selecting distinctive features about the faulty signals. The system has the advantages of utilizing single-end measurements, using both voltage and current signals. ERN is able to determine the fault location occurred on transmission line rapidly and correctly as an important alternative to standard feedforward back propagation networks (FFNs) and radial basis functions (RBFs) neural networks.     

Unitary and non-unitary manipulations of qubit-bath entanglement: non-Markov qubit cooling

Initialization of quantum logic operations makes it imperative to cool down the information-carrying qubits as much and as fast as possible, so as to purify their state, or at least their ensemble average. Yet, the limit on the speed of existing cooling schemes is either the duration of the qubit equilibration with its bath or the decay time of an auxiliary state to one of the qubit states. Here we show that highly-frequent phase-shifts or measurements of the state of thermalized qubits can be designed to affect the qubit-bath entanglement so that the qubits undergo cooling, well before they re-equilibrate with the bath and without resorting to auxiliary states. These processes can be used in principally novel, advantageous, cooling schemes to assist quantum logic operations.     

Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition

The aim of this paper is to estimate the fault location on transmission lines quickly and accurately. The faulty current and voltage signals obtained from a simulation are decomposed by wavelet packet transform (WPT). The extracted features are applied to artificial neural network (ANN) for estimating fault location. As data sets increase in size, their analysis become more complicated and time consuming. The energy and entropy criterion are applied to wavelet packet coefficients to decrease the size of feature vectors. The test results of ANN demonstrate that the applying of energy criterion to current signals after WPT is a very powerful and reliable method for reducing data sets in size and hence estimating fault locations on transmission lines quickly and accurately.     

Drug/nondrug classification using Support Vector Machines with various feature selection strategies

In conjunction with the advance in computer technology, virtual screening of small molecules has been started to use in drug discovery. Since there are thousands of compounds in early-phase of drug discovery, a fast classification method, which can distinguish between active and inactive molecules, can be used for screening large compound collections. In this study, we used Support Vector Machines (SVM) for this type of classification task. SVM is a powerful classification tool that is becoming increasingly popular in various machine-learning applications. The data sets consist of 631 compounds for training set and 216 compounds for a separate test set. In data pre-processing step, the Pearson's correlation coefficient used as a filter to eliminate redundant features. After application of the correlation filter, a single SVM has been applied to this reduced data set. Moreover, we have investigated the performance of SVM with different feature selection strategies, including SVM–Recursive Feature Elimination, Wrapper Method and Subset Selection. All feature selection methods generally represent better performance than a single SVM while Subset Selection outperforms other feature selection methods. We have tested SVM as a classification tool in a real-life drug discovery problem and our results revealed that it could be a useful method for classification task in early-phase of drug discovery.     

Developing centrifugal spun thermally cross-linked gelatin based fibrous biomats for antibacterial wound dressing applications

Fibrous materials obtained from natural polymers, such as gelatin, have been used in medical applications due to their biocompatibility and biodegradability. Herein, free-standing durable fibrous gelatin biomats with antibacterial activity were developed via a simple, low cost and fast production route, centrifugal spinning, and subsequent thermal crosslinking. After a series of preliminary experiments, droplet−/bead-free porous biomats with fine fibers, 3.41 ± 1.8 μm in diameter, were fabricated. Subsequently, antimicrobial biomats were produced by adding AgNO₃ into the production solution. X-ray diffractometer and energy dispersive X-ray results showed Ag NPs existing as AgCl in the biomats, which could be attributed to chemical reaction between the Ag NPs and residual Cl in the impure gelatin. Later, both the neat-gelatin and Ag-gelatin biomats were thermally crosslinked at 170°C to gain stability against water. Although the Ag addition reduced ultimate tensile strength by half, from 881 to 495 kPa, the crosslinked biomats were robust enough to be used for wound dressing applications. They were also found to be highly breathable, with the air permeability of 256 and 81.2 mm/s, respectively. The biomats showed antibacterial activity against Escherichia coli and Staphylococcus aureus bacteria. The results show that the free-standing fibrous-gelatin-based biomats produced is applicable for wound dressing applications.     

Efficient and Thermally Stable Wide Bandgap Perovskite Solar Cells by Dual-Source Vacuum Deposition

Wide bandgap perovskites are being widely studied in view of their potential applications in tandem devices and other semitransparent photovoltaics. Vacuum deposition of perovskite thin films is advantageous as it allows the fabrication of multilayer devices, fine control over thickness and purity, and it can be upscaled to meet production needs. However, the vacuum processing of multicomponent perovskites (typically used to achieve wide bandgaps) is not straightforward, because one needs to simultaneously control several thermal sources during the deposition. Here a simplified dual-source vacuum deposition method to obtain wide bandgap perovskite films is shown. The solar cells obtained with these materials have similar or even larger efficiency as those including multiple A-cations, but are much more thermally stable, up to 3500 h at 85 °C for a perovskite with a bandgap of 1.64 eV. With optimized thickness, record efficiency of >19% and semitransparent devices with stabilized power output in excess of 17% are achieved.