A Study of feature extraction algorithms for optical flow tracking

Sparse optical flow algorithms, such as the Lucas-Kanade approach, provide more robustness to noise than dense optical flow algorithms and are the preferred approach in many scenarios. Sparse optical flow algorithms estimate the displacement for a selected number of pixels in the image. These pixels can be chosen randomly. However, pixels in regions with more variance between the neighbours will produce more reliable displacement estimates. The selected pixel locations should therefore be chosen wisely. In this study, the suitability of Harris corners, Shi-Tomasi's “Good features to track", SIFT and SURF interest point extractors, Canny edges, and random pixel selection for the purpose of frame-by-frame tracking using a pyramidical Lucas-Kanade algorithm is investigated. The evaluation considers the important factors of processing time, feature count, and feature trackability in indoor and outdoor scenarios using ground vehicles and unmanned aerial vehicles, and for the purpose of visual odometry estimation.

Mission path planning of aerial survey tasks using hybrid game evolutionary algorithm

The work presented in this report is aimed to implement a cost-effective offline mission path planner for aerial inspection tasks of large linear infrastructures. Like most real-world optimisation problems, mission path planning involves a number of objectives which ideally should be minimised simultaneously. Understandably, the objectives of a practical optimisation problem are conflicting each other and the minimisation of one of them necessarily implies the impossibility to minimise the other ones. This leads to the need to find a set of optimal solutions for the problem; once such a set of available options is produced, the mission planning problem is reduced to a decision making problem for the mission specialists, who will choose the solution which best fit the requirements of the mission. The goal of this work is then to develop a Multi-Objective optimisation tool able to provide the mission specialists a set of optimal solutions for the inspection task amongst which the final trajectory will be chosen, given the environment data, the mission requirements and the definition of the objectives to minimise. All the possible optimal solutions of a Multi-Objective optimisation problem are said to form the Pareto-optimal front of the problem. For any of the Pareto-optimal solutions, it is impossible to improve one objective without worsening at least another one. Amongst a set of Pareto-optimal solutions, no solution is absolutely better than another and the final choice must be a trade-off of the objectives of the problem. Multi-Objective Evolutionary Algorithms (MOEAs) are recognised to be a convenient method for exploring the Pareto-optimal front of Multi-Objective optimization problems. Their efficiency is due to their parallelism architecture which allows to find several optimal solutions at each time

Engineering the mechanical properties of graphene nanotube hybrid structures through structural modulation

The excellent multi-functional properties of carbon nanotube (CNT) and graphene have enabled them as appealing building blocks to construct 3D carbon-based nanomaterials or nanostructures. The recently reported graphene nanotube hybrid structure (GNHS) is one of the representatives of such nanostructures. This work investigated the relationships between the mechanical properties of the GNHS and its structure basing on large-scale molecular dynamics simulations. It is found that increasing the length of the constituent CNTs, the GNHS will have a higher Young’s modulus and yield strength. Whereas, no strong correlation is found between the number of graphene layers and Young’s modulus and yield strength, though more graphene layers intends to lead to a higher yield strain. In the meanwhile, the presences of multi-wall CNTs are found to greatly strengthen the hybrid structure. Generally, the hybrid structures exhibit a brittle behavior and the failure initiates from the connecting regions between CNT and graphene. More interestingly, affluent formations of monoatomic chains and rings are found at the fracture region. This study provides an in-depth understanding of the mechanical performance of the GNHSs while varying their structures, which will shed lights on the design and also the applications of the carbon-based nanostructures.

Representation of facial expression categories in continuous arousal-valence space: Feature and correlation

Representation of facial expressions using continuous dimensions has shown to be inherently more expressive and psychologically meaningful than using categorized emotions, and thus has gained increasing attention over recent years. Many sub-problems have arisen in this new field that remain only partially understood. A comparison of the regression performance of different texture and geometric features and investigation of the correlations between continuous dimensional axes and basic categorized emotions are two of these. This paper presents empirical studies addressing these problems, and it reports results from an evaluation of different methods for detecting spontaneous facial expressions within the arousal-valence dimensional space (AV). The evaluation compares the performance of texture features (SIFT, Gabor, LBP) against geometric features (FAP-based distances), and the fusion of the two. It also compares the prediction of arousal and valence, obtained using the best fusion method, to the corresponding ground truths. Spatial distribution, shift, similarity, and correlation are considered for the six basic categorized emotions (i.e. anger, disgust, fear, happiness, sadness, surprise). Using the NVIE database, results show that the fusion of LBP and FAP features performs the best. The results from the NVIE and FEEDTUM databases reveal novel findings about the correlations of arousal and valence dimensions to each of six basic emotion categories.

Automated power semiconductor switching performance feature extraction from experimental double-pulse waveform data

Double-pulse tests are commonly used as a method for assessing the switching performance of power semiconductor switches in a clamped inductive switching application. Data generated from these tests are typically in the form of sampled waveform data captured using an oscilloscope. In cases where it is of interest to explore a multi-dimensional parameter space and corresponding result space it is necessary to reduce the data into key performance metrics via feature extraction. This paper presents techniques for the extraction of switching performance metrics from sampled double-pulse waveform data. The reported techniques are applied to experimental data from characterisation of a cascode gate drive circuit applied to power MOSFETs.

Hybrid chalcogenide nanoparticles : 2D-WS2 nanocrystals inside nested WS2 fullerenes

The MOCVD assisted formation of nested WS2 inorganic fullerenes (IF-WS2) was performed by enhancing surface diffusion with iodine, and fullerene growth was monitored by taking TEM snapshots of intermediate products. The internal structure of the core-shell nanoparticles was studied using scanning electron microscopy (SEM) after cross-cutting with a focused ion beam (FIB). Lamellar reaction intermediates were found occluded in the fullerene particles. In contrast to carbon fullerenes, layered metal chalcogenides prefer the formation of planar, plate-like structures where the dangling bonds at the edges are stabilized by excess S atoms. The effects of the reaction and annealing temperatures on the composition and morphology of the final product were investigated, and the strength of the WS2 shell was measured by intermittent contact-mode AFM. The encapsulated lamellar structures inside the hollow spheres may lead to enhanced tribological activities.

A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics

Partial shading and rapidly changing irradiance conditions significantly impact on the performance of photovoltaic (PV) systems. These impacts are particularly severe in tropical regions where the climatic conditions result in very large and rapid changes in irradiance. In this paper, a hybrid maximum power point (MPP) tracking (MPPT) technique for PV systems operating under partially shaded conditions witapid irradiance change is proposed. It combines a conventional MPPT and an artificial neural network (ANN)-based MPPT. A low cost method is proposed to predict the global MPP region when expensive irradiance sensors are not available or are not justifiable for cost reasons. It samples the operating point on the stairs of I–V curve and uses a combination of the measured current value at each stair to predict the global MPP region. The conventional MPPT is then used to search within the classified region to get the global MPP. The effectiveness of the proposed MPPT is demonstrated using both simulations and an experimental setup. Experimental comparisons with four existing MPPTs are performed. The results show that the proposed MPPT produces more energy than the other techniques and can effectively track the global MPP with a fast tracking speed under various shading patterns.

Hybrid CuTCNQ/AgTCNQ metal-organic charge transfer complexes via galvanic replacement vs. corrosion recrystallization

This study reports a hybrid of two metal-organic semiconductors that are based on organic charge transfer complexes of 7,7,8,8-tetracyanoquinodimethane (TCNQ). It is shown that the spontaneous reaction between semiconducting microrods of CuTCNQ with Ag+ ions leads to the formation of a CuTCNQ/AgTCNQ hybrid, both in aqueous solution and acetonitrile, albeit with completely different reaction mechanisms. In an aqueous environment, the reaction proceeds by a complex galvanic replacement (GR) mechanism, wherein in addition to AgTCNQ nanowires, Ag0 nanoparticles and Cu(OH)2 crystals decorate the surface of CuTCNQ microrods. Conversely, in acetonitrile, a GR mechanism is found to be thermodynamically unfavorable and instead a corrosion-recrystallization mechanism leads to the decoration of CuTCNQ microrods with AgTCNQ nanoplates, resulting in a pure CuTCNQ/AgTCNQ hybrid metal-organic charge transfer complex. While hybrids of two different inorganic semiconductors are regularly reported, this report pioneers the formation of a hybrid involving two metal-organic semiconductors that will expand the scope of TCNQ-based charge transfer complexes for improved catalysis, sensing, electronics and biological applications.

A hybrid quality function deployment and cybernetic analytic network process model for project manager selection

Identifying appropriate decision criteria and making optimal decisions in a structured way is a complex process. This paper presents an approach for doing this in the form of a hybrid Quality Function Deployment (QFD) and Cybernetic Analytic Network Process (CANP) model for project manager selection. This involves the use of QFD to translate the owner's project management expectations into selection criteria and the CANP to weight the expectations and selection criteria. The supermatrix approach then prioritises the candidates with respect to the overall decision-making goal. A case study is used to demonstrate the use of the model in selecting a renovation project manager. This involves the development of 18 selection criteria in response to the owner's three main expectations of time, cost and quality.

An evaluation of the neocognitron

We describe a sequence of experiments investigating the strengths and limitations of Fukushima's neocognitron as a handwritten digit classifier. Using the results of these experiments as a foundation, we propose and evaluate improvements to Fukushima's original network in an effort to obtain higher recognition performance. The neocognitron's performance is shown to be strongly dependent on the choice of selectivity parameters and we present two methods to adjust these variables. Performance of the network under the more effective of the two new selectivity adjustment techniques suggests that the network fails to exploit the features that distinguish different classes of input data. To avoid this shortcoming, the network's final layer cells were replaced by a nonlinear classifier (a multilayer perceptron) to create a hybrid architecture. Tests of Fukushima's original system and the novel systems proposed in this paper suggest that it may be difficult for the neocognitron to achieve the performance of existing digit classifiers due to its reliance upon the supervisor's choice of selectivity parameters and training data. These findings pertain to Fukushima's implementation of the system and should not be seen as diminishing the practical significance of the concept of hierarchical feature extraction embodied in the neocognitron. © 1997 IEEE.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Feature selection using expected attainable discrimination

We propose expected attainable discrimination (EAD) as a measure to select discrete valued features for reliable discrimination between two classes of data. EAD is an average of the area under the ROC curves obtained when a simple histogram probability density model is trained and tested on many random partitions of a data set. EAD can be incorporated into various stepwise search methods to determine promising subsets of features, particularly when misclassification costs are difficult or impossible to specify. Experimental application to the problem of risk prediction in pregnancy is described.