Are object detection assessment criteria ready for maritime computer vision?

Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently. The maritime environment offers its own unique requirements and challenges. Assessment of the quality of detections is a fundamental need in computer vision. However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting. Thus, a large body of related work in computer vision appears inapplicable to the maritime setting at the first sight. We discuss the problem of defining assessment metrics suitable for maritime computer vision. We consider new bottom edge proximity metrics as assessment metrics for maritime computer vision. These metrics indicate that existing computer vision approaches are indeed promising for maritime computer vision and can play a foundational role in the emerging field of maritime computer vision

Similarity Based Neuro-fuzzy System for Rainfall-runoff Modeling in an Urban Tropical Catchment

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Real Time Neural Fuzzy System for Rainfall-Runoff Modeling

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Near-Infrared Quantum Dots For Bioimaging And Targeting Applications

<p>Luminescent semiconductor nanocrystals or quantum dots (QDs) offer attractive characteristics as a new class of fluorescent probes for molecular, cellular and in vivo imaging. While traditional cadmium-containing QDs have been widely used in biomedical research, diagnostics, and drug delivery, the cytotoxicity arising from the release of Cd2+ ions caused by the degradation of the surface coating is deemed to be a shortfall of cadmium-based QDs for long-term cellular and in vivo imaging. Here we report a direct synthesis of silver-doped zinc selenide QDs in water with near-infrared tunable fluorescence emissions, coinciding with the biological window of transmission to offer high signal-to-noise for fluorescence imaging of cells and small animals. Glutathione, which carries both carboxyl and amino groups, serves as a stabilizing ligand and offers the flexibility of decorating the surface of the QDs with moieties such as proteins, peptides and DNA. The cytotoxicity of the as-synthesized QDs was evaluated on macrophage (RAW 264.7) cells and human mesenschymal stem cells using MTS cell viability assay. The results indicated that the silver- doped ZnSe QDs possess low cytotoxicity. In vivo biodistribution study shows that these bare QDs are different from conventional QDs, it traversed through systemic route and could accumulate in the stomach of nude mice. These QDs were conjugated to monoclonal CD44v6 antibody and tested with human gastric adenocarcinoma cell line (AGS). The results indicated the feasibility of modifying the surface properties of these QDs for efficient targeting applications. The QDs were also conjugated to heparin and used to formulate nanocomplexes with chitosan to encapsulate tumor necrosis factor-alpha. Quantitative imaging analysis revealed in vivo trafficking kinetics of the nanocomplexes to the lymph nodes after subcutaneous administration into nude mice. This study demonstrates the potential of incorporation of near-infrared-emitting QDs in nanocarrier drug delivery that allows in vivo trafficking of the biodistriution events and will be of greatly improve the development new drug nanocarrier formulations.</p>Dissertatio

ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal

Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden layer. EEG data are collected from 57 healthy participants from eight locations while subjected to audio-visual stimuli. Discrimination of emotions from EEG is investigated based on valence and arousal levels. The accuracy of the proposed neural network is compared with various feature extraction methods and feedforward learning algorithms. The results showed that the highest accuracy is achieved when using the proposed neural network with a type of radial basis function

Rough-fuzzy rule interpolation

AbstractFuzzy rule interpolation forms an important approach for performing inference with systems comprising sparse rule bases. Even when a given observation has no overlap with the antecedent values of any existing rules, fuzzy rule interpolation may still derive a useful conclusion. Unfortunately, very little of the existing work on fuzzy rule interpolation can conjunctively handle more than one form of uncertainty in the rules or observations. In particular, the difficulty in defining the required precise-valued membership functions for the fuzzy sets that are used in conventional fuzzy rule interpolation techniques significantly restricts their application. In this paper, a novel rough-fuzzy approach is proposed in an attempt to address such difficulties. The proposed approach allows the representation, handling and utilisation of different levels of uncertainty in knowledge. This allows transformation-based fuzzy rule interpolation techniques to model and harness additional uncertain information in order to implement an effective fuzzy interpolative reasoning system. Final conclusions are derived by performing rough-fuzzy interpolation over this representation. The effectiveness of the approach is illustrated by a practical application to the prediction of diarrhoeal disease rates in remote villages. It is further evaluated against a range of other benchmark case studies. The experimental results confirm the efficacy of the proposed work

Dynamic portfolio rebalancing through reinforcement learning

Portfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on providing an alternative view in maximising portfolio returns using Reinforcement Learning (RL) by considering dynamic risks appropriate to market conditions through dynamic portfolio rebalancing. The proposed algorithm is able to improve portfolio management by introducing the dynamic rebalancing of portfolios with vigorous risk through an RL agent. This is done while accounting for market conditions, asset diversifications, risk and returns in the global financial market. Studies have been performed in this paper to explore four types of methods with variations in fully portfolio rebalancing and gradual portfolio rebalancing, which combine with and without the use of the Long Short-Term Memory (LSTM) model to predict stock prices for adjusting the technical indicator centring. Performances of the four methods have been evaluated and compared using three constructed financial portfolios, including one portfolio with global market index assets with different risk levels, and two portfolios with uncorrelated stock assets from different sectors and risk levels. Observed from the experiment results, the proposed RL agent for gradual portfolio rebalancing with the LSTM model on price prediction outperforms the other three methods, as well as returns of individual assets in these three portfolios. The improvements of the returns using the RL agent for gradual rebalancing with prediction model are achieved at about 27.9–93.4% over those of the full rebalancing without prediction model. It has demonstrated the ability to dynamically adjust portfolio compositions according to the market trends, risks and returns of the global indices and stock assets

Dynamic portfolio rebalancing with lag-optimised trading indicators using SeroFAM and genetic algorithms

Some common technical indicators, such as moving average convergence divergence (MACD), relative strength index (RSI), and MACD histogram (MACDH) are used in technical analyses and stock trading. However, some of them are lagging indicators, affecting the effectiveness in the stock trading and portfolio management. A forecasted MACDH (fMACDH) indicator for predicting next day price by a neuro-fuzzy network, Self-reorganizing Fuzzy Associative Machine (SeroFAM) which has been reported in the prior research work. In order to further reduce the lagging effect, two trading indicators are proposed in this paper: the optimised fMACDH indicator and the fMACDH-fRSI indicator. The optimised fMACDH indicator is derived to extend price forecasting to 1-5 days ahead as the prediction depth, using 1-5 days of historical price data as the input depth. The fMACDH-fRSI indicator is derived by combining the optimized fMACDH indicator and the forecasted RSI (fRSI) indicator. A genetic algorithm (GA) and the fitness functions are designed with the SeroFAM in this paper, which are utilised for optimising parameters of these two proposed indicators. Experiments have been conducted to evaluate and benchmark of the proposed trading indicators optimised by the GA. Two rule-based portfolio rebalancing algorithms are then proposed using the optimised fMACDH trading indicator tuned by the GA: the Tactical Buy and Hold (TBH) and the Rule-Based Business Cycle (RBBC) portfolio rebalancing algorithms. The TBH algorithm takes advantage of relative differences in risk levels to perform rebalancing during trend reversals. The RBBC portfolio rebalancing algorithm takes advantage of the offsets between the business cycles of different market sectors. Experiments have been conducted to evaluate the performance of both algorithms using two sets of portfolios consisting of different assets. The TBH portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by about 26% - 27%; as well outperforms the Buy and Hold strategy by 5% - 40%. The RBBC portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by 54% - 55%; it also outperforms 12 out of the 13 assets with the Buy and Hold strategy, by an average performance of about 166%. The results are highly encouraging with consistent performances achieved in dynamic portfolio rebalancing

A novel framework for making dominant point detection methods non-parametric

Most dominant point detection methods require heuristically chosen control parameters. One of the commonly used control parameter is maximum deviation. This paper uses a theoretical bound of the maximum deviation of pixels obtained by digitization of a line segment for constructing a general framework to make most dominant point detection methods non-parametric. The derived analytical bound of the maximum deviation can be used as a natural bench mark for the line fitting algorithms and thus dominant point detection methods can be made parameter-independent and non-heuristic. Most methods can easily incorporate the bound. This is demonstrated using three categorically different dominant point detection methods. Such non-parametric approach retains the characteristics of the digital curve while providing good fitting performance and compression ratio for all the three methods using a variety of digital, non-digital, and noisy curves