A nanodisk array connected with a fin field-effect transistor is fabricated and analyzed for spiking neural network applications. This nanodevice performs weighted sums in the time domain using rising slopes of responses triggered by input spike pulses. The nanodisk arrays, which act as a resistance of several giga-ohms, are fabricated using a self-assembly bio-nano-template technique. Weighted sums are achieved with an energy dissipation on the order of 1 fJ, where the number of inputs can be more than one hundred. This amount of energy is several orders of magnitude lower than that of conventional digital processors.

This paper introduces a time-domain weighted-sum calculation operation based on a spiking neuron model, and discusses a resistance-capacitance circuit that performs a calculation operation assumed to be realized in CMOS VLSI technology. A nanodevice that executes this calculation is also presented. The calculation circuit is useful for extremely low power operation. This operation uses the rising slopes of post-synaptic potentials triggered by input spike pulses. In the time-domain calculation circuit, the energy dissipation is independent of the resistance, and only depends on the capacitance and voltages. However, the time constant, which is the product of the resistance and capacitance, should be relatively large to guarantee the calculation resolution, and therefore the resistance should be at the giga-ohms levels. The nanodevice consists of a nanodisk array connected with a fin field-effect transistor. Nanodisk arrays can be fabricated using a self-assembly bio-nano-template technique, and they act as resistors with resistance levels of several giga-ohms. A weighted sum can be achieved with an energy dissipation on the order of 1 fJ, with a number of inputs that can be more than 100. This amount of energy is several orders of magnitude lower than that of conventional digital processors.

We have proposed a motion detection model, CA3–GU–CA1 (CGC) model, inspired by hippocampal function. The CGC model treats edges extracted from monocular image sequences, and detects motion of the edges on segmented 2D maps without image matching. In this paper, we propose an FPGA implementation of the CGC model, in order to achieve low power processing toward practical use. Then, we propose an obstacle detection algorithm using time-to-collision (TTC) based edge grouping. We have evaluated the performance of motion and obstacle detection by using artificial and real image sequences. The results show that the CGC model can achieve high detection rate in complicated situations, and can achieve accurate detection when using a high frame-rate. The proposed obstacle-detection algorithm can detect dangerous objects moving across based on a novel TTC estimation algorithm. Both motion detection and obstacle detection parts can operate at more than 1000 fps. The CGC model can also operate with a power dissipation of about 1.4 W based on the FPGA implementation.

In the implementation of spiking neuron models, which can achieve realistic neuron operation, generation of post-synaptic potentials (PSPs) is an essential function. We have already proposed a new nanodisk array structure for generating PSPs using delay in electron hopping among nanodisks. Generated PSPs have fluctuation caused by stochastic electron movement. Noise or fluctuation is effectively used in neural processing. In this paper, we review our proposed structure and show fluctuation controllability based on single-electron circuit simulation.

In this paper we present a methodology to use Gabor response features for real-time visual road environment classification. Processing Gabor filters using hardware solely dedicated to this task enables improved real-time texture classification. Using such hardware enables us to successfully extract Gabor feature information for a four-class road environment classification problem. We used summary histogram as an intermediate level of texture representation prior to final classification. Overall we obtain a maximally correct classification circa 98%, outperforming prior work in the field.

We propose a motion detection model, which is suitable for higher speed operation than the video rate, inspired by the neuronal propagation in the hippocampus in the brain. The model detects motion of edges, which are extracted from monocular image sequences, on specified 2D maps without image matching. We introduce gating units into a CA3-CA1 model, where CA3 and CA1 are the names of hippocampal regions. We use the function of gating units to reduce mismatching for applying our model in complicated situations. We also propose a map-division method to achieve accurate detection. We have evaluated the performance of the proposed model by using artificial and real image sequences. The results show that the proposed model can run up to 1.0 ms/frame if using a resolution of 64 x 60 units division of 320 x 240 pixels image. The detection rate of moving edges is achieved about 99% under a complicated situation. We have also verified that the proposed model can achieve accurate detection of approaching objects at high frame rate (> 100 fps), which is better than conventional models, provided we can obtain accurate positions of image features and filter out the origins of false positive results in the post-processing.

This study demonstrated a new approach of odorous gas treatment in a wet scrubbing-oxidation system with in situ generation of ferrate(VI), in which gaseous CH3SH can be quickly absorbed by aqueous alkaline solution and rapidly oxidized by liquid ferrate(VI) generated through electrochemical synthesis in situ. In this study, the electrochemical generation of ferrate(VI) in aqueous NaOH solution was studied and the experiments demonstrated that a maximum current efficiency to generate ferrate(VI) occurred at 14M NaOH concentration, while an applied current density of 14.06mAcm−2 was applied. Then the self-decomposition of ferrate(VI) in such strong alkaline solutions was studied, and the results showed that ferrate(VI) behaved more stable in the stronger alkaline solution. Furthermore, the reactivity of ferrate(VI) with CH3SH in this highly-concentrated NaOH solution was investigated under different reaction conditions as the first time. The experimental results confirmed that CH3SH can be fully oxidized by ferrate(VI) to sulphate ion as a final product. The second-order reaction model is suitable to describe the kinetics of CH3SH reaction with ferrate(VI) in the strong alkaline solution. Meanwhile, stoichiometry of ferrate(VI) reaction with CH3SH in aqueous solution was determined with a minimum molar ratio of 2.20:1 (Fe(VI):CH3SH) to destruct CH3SH effectively and a higher molar ratio of 4.53:1 to convert CH3SH to sulphate ion completely. The experiments also demonstrated that the NaOH concentration in aqueous solution would be a key parameter and the best performance of CH3SH removal was achieved at the optimum NaOH concentration of 6M under our experimental conditions due to an optimum balance between the oxidation potential of ferrate(VI) and its generation rate in this wet scrubbing-oxidation system.

Sulfur-containing compounds are one kind of representative odorant from sewage, wastewater treatment plants and associated with various industries. In this study, the on-line electrogenerated ferrate (VI) technique was used to remove gaseous methyl mercaptan (CH3SH) for odor control. The second-order reaction kinetics of CH3SH oxidation with ferrate was identified and meanwhile, the stability of ferrate in high alkaline medium was investigated for the first time. The influencing factors including alkalinity, current density and co-existing ions on the ferrate formation were also investigated with details in this study. Experimental results show that for the ferrate formation, the 14 mol dm(-3) NaOH is the optimal alkalinity at 4.5 mA cm(-2); increasing current density and decreasing the SO42- concentration are helpful for the ferrate production. This fundamental research provides essential understanding for developing the on-line electrochemical ferrate process for gaseous sulfur-containing compound degradation in odor control. (C) 2012 Elsevier Ltd. All rights reserved.

It is well known that the structural disorder of photocatalyst nanopowder usually leads to the enhanced scattering of free electrons, and thus reduces the electron mobility; in contrast, an ordered and interconnected nanostructure favors to improve electron transport, then achieving the higher photocatalytic efficiency. Therefore, in this study, a highly ordered and coaxial WO3/TiO2 nanostructure, consisting of nanotubes and nanorods, was prepared by a sol–gel template technique. The obtained photocatalysts were characterized by FE-SEM, XRD, UV–vis DRS and PL measurement. These photocatalysts exhibited a strong photoresponse in the visible region and a low PL emission. Their photoactivity was evaluated by means of the degradation of 2,3-dichlorophenol (2,3-DCP) under visible light irradiation. Experimental results show that while there was no obvious degradation of 2,3-DCP with the TiO2 nanotubes as photocatalysts, ∼53% of degradation degree in 300min was obtained with the WO3/TiO2 nanocomposites. This significant activity should be attributed to the role of WO3 loaded in TiO2 nanotubes. The WO3 loading not only facilitates the effective separation of photogenerated carriers occurring on the TiO2 surface, but also enhances the surface hydroxyl groups and surface acidity, thus improving the overall photocatalytic performance.

Coupled Markov random field (MRF) models have been proposed so far for visual image processing. These are classified into boundary- and region-based models, where hidden variables that determine the interaction between the units corresponding to image pixels are given by line and label processes, respectively. In this paper, we have investigated a region-based coupled MRF model with phase variables as the hidden variables, and have modified the model for applications to coarse image-region segmentation by replacing some nonlinear functions in the update equations of the intensity and label processes with piecewise linear (PWL) functions. Using PWL functions facilitates VLSI implementation of coupled MRF models, and also makes their performance improved. We have verified that the modified region-based MRF model is superior to the resistive-fuse network, which is one of the boundary-based MRF models. We also propose an improvement of the modified region-based MRF model by introducing a new parameter, and evaluate the model performance in coarse image region segmentation.

In this paper, the effects of pH and various inorganic anions (Cl-, NO3-, H2PO4- and SO42-) on the visible photocatalytic activity of WO3/TiO2 nanocomposite films for the degradation of 2,3-dichlorophenol (2,3-DCP) were studied. Experimental evidences have indicated that the lower pH values, the higher visible activity of WO3/TiO2 catalyst. The presence of cl(-), NO3- and H2PO4- has a minor effect on the degradation of 2,3-DCP, while SO(4)(2-)could perform a strong inhibition effect. These results reflected that the degradation of 2,3-DCP with WO3/TiO2 photocatalyst under visible light irradiation mainly took place on the interface of organic compounds/catalyst surface, not in the bulk solution.

We propose a motion detection model inspired by hippocampal function and its FPGA implementation. The proposed model detects the motion of edges extracted from monocular image sequences. The motion is detected on segmented 2D maps without image matching, which allows the model to operate with higher speed than the video rate. We introduce gating units into our original CA3-CA1 model to improve the detection rate, where CA3 and CA1 are the names of hippocampal regions. We have evaluated the performance of our model by using artificial and real image sequences. The results show that the proposed model can achieve high detection rate. We have implemented the model into an FPGA, by which we can achieve motion detection within 1.0 msec/frame with power dissipation of about 1.4 W when 64 × 60 segmented blocks are used for 320 × 240 pixel images.

Titanium dioxide (TiO2) nanotubes have been reported one decade ago and have proven to be of a great interest in photocatalytic water splitting, as well as gas sensing and anti-bacterial/cancer treatment. This paper presents an overview on general preparation approaches (chemical treatment, template method and anodic oxidation) of tubular TiO2 nanoarchitectures and their characterization. Current applications of the nanotubes as photocatalysts are also reviewed.

Spiking neuron models, which simplify the biological neuron function, have attracted much attention recently in the fields of computational neuroscience and artificial neural networks. In these models, generation of post-synaptic potentials (PSPs) is an essential function. In this paper, we propose a new nanodevice structure using a nanodisk array connected to a MOSFET for spiking neuron models. The structure generates PSPs by taking advantage of the delay in electron hopping movement among nanodisks. The results of single-electron circuit simulation demonstrate the controllability of PSP shapes by a control gate placed over the nanodisk array.

In this work, the homogeneous and heterogeneous degradations of diphenamid (DPA) in aqueous solution were conducted by direct photolysis with UVC (254nm) and by photocatalysis with TiO2/UVA (350nm), and the experimental results were compared. It was found that the homogeneous photolysis by UVC irradiation alone was quite efficient to degrade DPA up to 100% after 360min, but was very inefficient to mineralize its intermediates in terms of dissolved organic carbon reduction of only 8%. In contrast, the heterogeneous photocatalysis with TiO2/UVA showed relatively a lower degree of DPA degradation (51%), but a higher degree of its mineralization (11%) after 360min. These results reveal that the photocatalysis process has relatively poor selectivity to degrade different compounds including various intermediates from the DPA degradation, which is beneficial to its mineralization. In addition, over 20 intermediates were identified by LC–MS and 1H NMR analyses. Based on the identified intermediates, the reaction mechanisms and the detailed pathways of the DPA degradation by photolysis and photocatalysis were proposed, and are presented in this paper.