Fuzzy-neuro LVQ and its comparison with fuzzy algorithm LVQ in artificial odor discrimination system

The human sensory test is often used for obtaining the sensory quantities of odors, however, the fluctuation of results due to the expert's condition can cause discrepancies among panelists. Authors have studied the artificial odor discrimination system using a quartz resonator sensor and a back-propagation neural network as the recognition system, however, the unknown category of odor is always recognized as the known category of odor. In this paper, a kind of fuzzy algorithm for learning vector quantization (LVQ) is developed and used as a pattern classifier. In this type of fuzzy LVQ, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistics of the measurement error directly. During learning, the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated by shifting the central position of the fuzzy reference vector toward or away from the input vector, and by modifying its fuzziness. Two types of fuzziness modifications are used, i.e., a constant modification factor and a variable modification factor. This type of fuzzy-neuro (FN) LVQ is different in nature from fuzzy algorithm (FA) LVQ, and in this paper, the performance of FNLVQ network is compared with that of FALVQ in an artificial odor recognition system. Experimental results show that both FALVQ and FNLVQ could provide high recognition probability in determining various known categories of odors, however, the FNLVQ neural system has the ability to recognize the unknown category of odor that could not be recognized by the FALVQ neural system.     

Performance Optimization of Parallel‐Like Ternary Organic Solar Cells through Simultaneous Improvement in Charge Generation and Transport

Ternary organic photovoltaic (OPV) devices with multiple light‐absorbing active materials have emerged as an efficient strategy for realizing further improvements in the power conversion efficiency (PCE) without building complex multijunction structures. However, the third component often acts as recombination centers and, hence, the optimization of ternary blend morphology poses a major challenge to improving the PCE of these devices. In this work, the performance of OPVs is enhanced through the morphological modification of nonfullerene acceptor (NFA)‐containing binary active layers. This modification is achieved by incorporating fullerenes into the layers. The uniformly dispersed fullerenes are sufficiently continuous and successfully mediate the ordering of NFA without charge or energy transfer. Owing to the simultaneous improvement in the charge generation and extraction, the PCE (12.1%) of these parallel‐linked ternary devices is considerably higher than those of the corresponding binary devices (9.95% and 7.78%). Moreover, the additional energy loss of the ternary device is minimized, compared with that of the NFA‐based binary device, due to the judicious control of the effective donor:acceptor composition of the ternary blends.     

High‐Efficiency Photovoltaic Devices using Trap‐Controlled Quantum‐Dot Ink prepared via Phase‐Transfer Exchange

Colloidal‐quantum‐dot (CQD) photovoltaic devices are promising candidates for low‐cost power sources owing to their low‐temperature solution processability and bandgap tunability. A power conversion efficiency (PCE) of >10% is achieved for these devices; however, there are several remaining obstacles to their commercialization, including their high energy loss due to surface trap states and the complexity of the multiple‐step CQD‐layer‐deposition process. Herein, high‐efficiency photovoltaic devices prepared with CQD‐ink using a phase‐transfer‐exchange (PTE) method are reported. Using CQD‐ink, the fabrication of active layers by single‐step coating and the suppression of surface trap states are achieved simultaneously. The CQD‐ink photovoltaic devices achieve much higher PCEs (10.15% with a certified PCE of 9.61%) than the control devices (7.85%) owing to improved charge drift and diffusion. Notably, the CQD‐ink devices show much lower energy loss than other reported high‐efficiency CQD devices. This result reveals that the PTE method is an effective strategy for controlling trap states in CQDs.     

Analysis of reflectance spectra of tropical seagrass species and their value for mapping using multispectral satellite images

ABSTRACT

Mapping of the distribution of individual seagrass species is essential for any attempts to manage seagrass ecosystems. It is therefore important to understand how the spectra of different seagrass species vary, in order to establish their unique absorption features and how these can be utilised for mapping by making use of remote-sensing images. This paper presents measurements of the reflectance spectra between 400 and 900 nm for nine tropical species of seagrass. Continuum removal and multispectral resampling procedures were applied to the spectra. Dendrogram analysis was carried out to identify species clustering as the basis for a mapping scheme. Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) approaches were employed for the classification of seagrass species using WorldView-2 images and measured spectra as the input endmember. Classification Tree Analysis (CTA) and an image segmentation approach using CTA (Object-Based Image Analysis – OBIA) were performed as a means of comparison. The results indicate that the absorption features and overall shape of the spectra for all seagrass species are relatively similar, and implied that the major differences are attributable to the absolute reflectance values. Consequently, SAM and SID produced results of low accuracy (<30%), whereas, CTA and OBIA delivered results exhibiting higher accuracy (60–92%). The use of a spectral-based classification algorithm was ineffective for the classification and mapping of seagrass species using multispectral images. The utilisation of absolute reflectance values was beneficial for the classification of seagrass species having similar spectral shape.     

Robust MIMO H∞ Integral-Backstepping PID Controller for Hovering Control of Unmanned Model Helicopter

The problem of stabilization of a model helicopter in a hover configuration subject to parametric uncertainty and external disturbances is addressed. Multiinput multioutput (MIMO) proportional-integral-derivative (PID) control law is reformulated into a full-state feedback control law to synthesize the controller by using robust H∞ control theory. In full-state feedback representation, PID control has implicit integral-backstepping structure. Therefore a new parameter, ρ, can be introduced that acts on the derivative of the control signal. The parameters of MIMO PID controller are then obtained with solving the algebraic Riccati equation with selecting the values of ρ and γ. Model helicopter simulation is carried out to verify the performance of the proposed controller to stabilize the uncertain helicopter model and to suppress external disturbances.     

The Influence of Chemical Treatments on Cantala Fiber Properties and Interfacial Bonding of Cantala Fiber/Recycled High Density Polyethylene (rHDPE)

The influence of chemical treatments on the properties of cantala fiber as well as on the quality of the interfacial bonding of cantala fiber/rHDPE was investigated. The fibers were treated with alkali, silane, and a combination of both. The results showed that the loss of hemicellulose and lignin after the alkali treatment, and the presence of a silane layer on the fiber surface after the silane or alkali-silane treatment, improved the thermal stability, surface energy, and IFSS. The highest surface energy of 45.37 mN/m was obtained during the alkali treatment (NF12). The alkali-silane treatment with 0.75% wt of silane (NSF075) gave the highest thermal stability and IFSS value.     

Dynamic swarm particle for fast motion vehicle tracking

Nowadays, the broad availability of cameras and embedded systems makes the application of computer vision very promising as a supporting technology for intelligent transportation systems, particularly in the field of vehicle tracking. Although there are several existing trackers, the limitation of using low‐cost cameras, besides the relatively low processing power in embedded systems, makes most of these trackers useless. For the tracker to work under those conditions, the video frame rate must be reduced to decrease the burden on computation. However, doing this will make the vehicle seem to move faster on the observer's side. This phenomenon is called the fast motion challenge. This paper proposes a tracker called dynamic swarm particle (DSP), which solves the challenge. The term particle refers to the particle filter, while the term swarm refers to particle swarm optimization (PSO). The fundamental concept of our method is to exploit the continuity of vehicle dynamic motions by creating dynamic models based on PSO. Based on the experiments, DSP achieves a precision of 0.896 and success rate of 0.755. These results are better than those obtained by several other benchmark trackers.     

Exploiting non‐orthogonal multiple access in downlink coordinated multipoint transmission with the presence of imperfect channel state information

In this paper, the impact of imperfect channel state information (CSI) on a downlink coordinated multipoint (CoMP) transmission system with non‐orthogonal multiple access (NOMA) is investigated since perfect knowledge of a channel cannot be guaranteed in practice. Furthermore, the channel estimation error is applied to estimate the channel information wherein its a priori of variance is assumed to be known. The impact of the number of coordinated base stations (BSs) on downlink CoMP NOMA is investigated. Users are classified into one of two groups according to their position within the cell, namely, cell‐center user (CCU) and cell‐edge user (CEU). In this paper, ergodic capacity and sum capacity for both CCU and CEU are derived as closed forms. In addition, various experiments are conducted with different parameters such as SNR, error variance, and power allocation to show their impact on the CoMP method. The results show that CoMP NOMA outperforms the CoMP orthogonal multiple access (OMA) wherein the condition of the channel impacts the performance of CoMP NOMA less. It is worth noting that a higher number of coordinated BSs enhances the total capacity of CoMP NOMA. Finally, the performance analysis is validated due to the close accordance between the analytical and simulation results.     

Tunable Fluorescence from Dye‐Modified DNA‐Assembled Plasmonic Nanocube Arrays

Colloidal crystal engineering with DNA on template‐confined surfaces is used to prepare arrays of nanocube‐based plasmonic antennas and deliberately place dyes with sub‐nm precision into their hotspots, on the DNA bonds that confine the cubes to the underlying gold substrate. This combined top‐down and bottom‐up approach provides independent control over both the plasmonic gap and photonic lattice modes of the surface‐confined particle assemblies and allows for the tuning of the interactions between the excited dyes and plasmonically active antennas. Furthermore, the gap mode of the antennas can be modified in situ by utilizing the solvent‐dependent structure of the DNA bonds. This is studied by placing two dyes, with different emission wavelengths, under the nanocubes and recording their solvent‐dependent emission. It is shown that dye emission not only depends upon the in‐plane structure of the antennas but also the size of the gap, which is regulated with solvent. Importantly, this approach allows for the systematic understanding of the relationship between nanoscale architecture and plasmonically coupled dye emission, and points toward the use of colloidal crystal engineering with DNA to create stimuli responsive architectures, which can find use in chemical sensing and tunable light sources.