Estimating the number of neurons in multi-neuronal spike trains

A common way of studying the relationship between neural activity and behavior is through the analysis of neuronal spike trains that are recorded using one or more electrodes implanted in the brain. Each spike train typically contains spikes generated by multiple neurons. A natural question that arises is "what is the number of neurons $\nu$ generating the spike train?"; This article proposes a method-of-moments technique for estimating $\nu$. This technique estimates the noise nonparametrically using data from the silent region of the spike train and it applies to isolated spikes with a possibly small, but nonnegligible, presence of overlapping spikes. Conditions are established in which the resulting estimator for $\nu$ is shown to be strongly consistent. To gauge its finite sample performance, the technique is applied to simulated spike trains as well as to actual neuronal spike train data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS371 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Vision-based neural network classifiers and their applications

A thesis submitted for the degree of Doctor of Philosophy of University of LutonVisual inspection of defects is an important part of quality assurance in many fields of production. It plays a very useful role in industrial applications in order to relieve human inspectors and improve the inspection accuracy and hence increasing productivity. Research has previously been done in defect classification of wood veneers using techniques such as neural networks, and a certain degree of success has been achieved. However, to improve results in tenus of both classification accuracy and running time are necessary if the techniques are to be widely adopted in industry, which has motivated this research. This research presents a method using rough sets based neural network with fuzzy input (RNNFI). Variable precision rough set (VPRS) method is proposed to remove redundant features utilising the characteristics of VPRS for data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable foml for input to an improved BP neural network classifier. The improved BP neural network classifier is improved in three aspects: additional momentum, self-adaptive learning rates and dynamic error segmenting. Finally, to further consummate the classifier, a uniform design CUD) approach is introduced to optimise the key parameters because UD can generate a minimal set of uniform and representative design points scattered within the experiment domain. Optimal factor settings are achieved using a response surface (RSM) model and the nonlinear quadratic programming algorithm (NLPQL). Experiments have shown that the hybrid method is capable of classifying the defects of wood veneers with a fast convergence speed and high classification accuracy, comparing with other methods such as a neural network with fuzzy input and a rough sets based neural network. The research has demonstrated a methodology for visual inspection of defects, especially for situations where there is a large amount of data and a fast running speed is required. It is expected that this method can be applied to automatic visual inspection for production lines of other products such as ceramic tiles and strip steel

A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

Profitability and Data-Snooping Tests of Four Technical Trade Strategies for Cryptocurrency Pair BTC/USDT and ETH/USDT in Cryptocurrency Markets During 2022–2023

We provide a comprehensive investigation into the profitability of technical trading methods applied to the cryptocurrency pairs BTC/USDT and ETH/USDT. By employing rigorous evaluations and incremental examinations, we address the pervasive issue of data-snooping bias that often plagues the evaluation of trading strategies. Our empirical results indicate the lack of profitable technical trading strategies in both the analysis sample and prediction sample periods, even after rigorous adjustments for data snooping. These findings highlight the difficulties associated with selecting profitable technical trading strategies in the dynamic and volatile cryptocurrency market. Market participants, including individual traders, institutional investors, and regulatory bodies, should take note of our findings when making investment decisions based on technical analysis

The impact of digital finance on energy total factor productivity

In the context of modern scientific and technological revolution and industrial transformation, the development of digital finance is conducive to improving the ecological environment and the energy total factor productivity (TFP). In this paper, DEA cross-efficiency model is used to measure the energy TFP, and moment estimation method is used to empirically test the influence and heterogeneity of digital finance on energy TFP, and then the intermediary model is constructed to study and analyze the influence mechanism based on the sample data of 30 provinces in the Chinese Mainland from 2011 to 2018. The following research findings are obtained. First, the development of digital finance has played a significant role in improving energy TFP. Second, spatial heterogeneity exists in the process of digital finance affecting energy TFP improvement, that is, digital finance has a more obvious improvement effect on the energy TFP in central and western China than in eastern China. Third, digital finance can affect energy TFP through technological innovation; that is, the improvement of regional technological innovation is an important transmission mechanism for digital finance to affect energy TFP

Video Fire Detection Algorithm using Multi-Feature Fusion

At present, the moving target detection and flame characteristics extraction almost become the most important parts in majority of video fire detection systems. Through the above two-part study, a new fire features detection method is presented in precise moving target area. That is, using the improved background difference method and flame features (such as the color and uniformity, Wavelet energy, stroboscopic and contour features) to detect fire. Experiments show that this method can improve theaccuracy and anti-interference ability of fire detection. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.334

Dynamic analysis of a Leslie-Gower predator-prey model with the fear effect and nonlinear harvesting

In this paper, we investigate the stability and bifurcation of a Leslie-Gower predator-prey model with a fear effect and nonlinear harvesting. We discuss the existence and stability of equilibria, and show that the unique equilibrium is a cusp of codimension three. Moreover, we show that saddle-node bifurcation and Bogdanov-Takens bifurcation can occur. Also, the system undergoes a degenerate Hopf bifurcation and has two limit cycles (i.e., the inner one is stable and the outer is unstable), which implies the bistable phenomenon. We conclude that the large amount of fear and prey harvesting are detrimental to the survival of the prey and predator

Practical Distributed Control for VTOL UAVs to Pass a Tunnel

Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible to amateur and commercial users alike. An air traffic management (ATM) system is needed to help ensure that this newest entrant into the skies does not collide with others. In an ATM, airspace can be composed of airways, intersections and nodes. In this paper, for simplicity, distributed coordinating the motions of Vertical TakeOff and Landing (VTOL) UAVs to pass an airway is focused. This is formulated as a tunnel passing problem, which includes passing a tunnel, inter-agent collision avoidance and keeping within the tunnel. Lyapunov-like functions are designed elaborately, and formal analysis based on invariant set theorem is made to show that all UAVs can pass the tunnel without getting trapped, avoid collision and keep within the tunnel. What is more, by the proposed distributed control, a VTOL UAV can keep away from another VTOL UAV or return back to the tunnel as soon as possible, once it enters into the safety area of another or has a collision with the tunnel during it is passing the tunnel. Simulations and experiments are carried out to show the effectiveness of the proposed method and the comparison with other methods