Temporal Detection Methods Research Articles

Akaluc/AkaLumine bioluminescence system enables highly sensitive, non-invasive and temporal monitoring of gene expression in Drosophila

Bioluminescence generated by luciferase and luciferin has been extensively used in biological research. However, detecting signals from deep tissues in vivo poses a challenge to traditional methods. To overcome this, the Akaluc and AkaLumine bioluminescent systems were developed, resulting in improved signal detection. We evaluate the potential of Akaluc/AkaLumine in Drosophila melanogaster to establish a highly sensitive, non-invasive, and temporal detection method for gene expression. Our results show that oral administration of AkaLumine to flies expressing Akaluc provided a higher luminescence signal than Luc/D-luciferin, with no observed harmful effects on flies. The Akaluc/AkaLumine system allows for monitoring of dynamic temporal changes in gene expression. Additionally, using the Akaluc fusion gene allows for mRNA splicing monitoring. Our findings indicate that the Akaluc/AkaLumine system is a powerful bioluminescence tool for analyzing gene expression in deep tissues and small numbers of cells in Drosophila.

Dynamic temporal ADS-B data attack detection based on sHDP-HMM

For the next generation air traffic surveillance, ADS-B is becoming the primary method to obtain more accurate data with wide coverage, which establishes the foundation for automatic and intelligent air traffic management system. However, ADS-B is designed without sufficient security considerations, transmitting with plain text without integrity and authentication validations. Thus, ADS-B data is in face of various attack threats, which may cause disruptions on system availability and reliability. To eliminate effects of attack behaviours, attack detection is in demand to avoid attack data injecting into decision making flow. Based on hidden Markov model with sticky hierarchical Dirichlet process, the dynamic temporal detection method is proposed to detect multiple attack patterns. Taking advantage of multiple attribute data, the dimensions of data are reduced to one dimension to set up feature sequences. With sticky hierarchical Dirichlet process, the parameters are obtained for hidden Markov model dynamically. Utilizing hidden Markov model, the generative model is established to predict hidden states of ADS-B data sequence. By analysing the contextual deviation information on hidden state sequences, the attack behaviours are discriminated to determine the attack data. By experiments on real ADS-B data, the feasibility and accuracy of the proposed method are validated.

Action temporal detection method based on confidence curve analysis

Action temporal detection is a derivative task of action recognition which needs researchers to predict temporal intervals and specific categories in untrimmed videos. Aiming at the problem of too many proposed segments and insufficient filtering effect in multi-stage networks, we propose an action temporal detection method using confidence curve analysis to generate proposal segments. Fixed step window sliding is adopted to generate candidate segments in a video, and we adjust a training mode in segment network. The proposal segments are generated by analyzing the confidence curve of candidate segments, finally proposal segments are input into localization network to classify and adjust confidence level. Extensive experiments performed on THUMOS2014 benchmark show that the proposed method performs significantly better than the original muti-stage convolutional network that mAP increase from 19.0% to 26.4% with 252% accelerating.

Mathematical Morphology Based Moving Object Detection Algorithm under Complicated Background

In many applications of modern world, moving object detection is an important work. Especially, the detection of moving object under complicated background has more difficult. On the basis of introduction of conventional detection methods such as temporal difference detection (TDD) and background subtraction detection, the paper proposes a new mathematical morphology based moving object detection algorithm in view of complicated background. The proposed algorithm utilizes the opening and closing operations to process the temporal differenced image. Then, it compares the processed image with the background differenced image. The experimental results show that the proposed algorithm can be able to detect the moving object effectively under complicatedly external environment and has a higher detection precision in contrast with the conventional temporal detection method..

A novel spatial–temporal detection method of dim infrared moving small target

Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial–temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time–space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background.

How does improved forecasting benefit detection? An application to biosurveillance

While many methods have been proposed for detecting disease outbreaks from pre-diagnostic data, their performance is usually not well understood. We argue that most existing temporal detection methods for biosurveillance can be characterized as a forecasting component coupled with a monitoring/detection component.In this paper, we describe the effect of forecast accuracy on detection performance. Quantifying this effect allows one to measure the benefits of improved forecasting and determine when it is worth improving a forecast method’s precision at the cost of robustness or simplicity. We quantify the effect of forecast accuracy on detection metrics under different scenarios and investigate the effect when standard assumptions are violated. We illustrate our results by examining performance on authentic biosurveillance data.

Detection of focal epileptiform events in the EEG by spatio-temporal dipole clustering

ObjectiveMethods for the detection of epileptiform events can be broadly divided into two main categories: temporal detection methods that exploit the EEG’s temporal characteristics, and spatial detection methods that base detection on the results of an implicit or explicit source analysis. We describe how the framework of a spatial detection method was extended to improve its performance by including temporal information. This results in a method that provides (i) automated localization of an epileptogenic focus and (ii) detection of focal epileptiform events in an EEG recording. For the detection, only one threshold value needs to be set. MethodsThe method comprises five consecutive steps: (1) dipole source analysis in a moving window, (2) automatic selection of focal brain activity, (3) dipole clustering to arrive at the identification of the epileptiform cluster, (4) derivation of a spatio-temporal template of the epileptiform activity, and (5) template matching. Routine EEG recordings from eight paediatric patients with focal epilepsy were labelled independently by two experts. The method was evaluated in terms of (i) ability to identify the epileptic focus, (ii) validity of the derived template, and (iii) detection performance. The clustering performance was evaluated using a leave-one-out cross validation. Detection performance was evaluated using Precision-Recall curves and compared to the performance of two temporal (mimetic and wavelet based) and one spatial (dipole analysis based) detection methods. ResultsThe method succeeded in identifying the epileptogenic focus in seven of the eight recordings. For these recordings, the mean distance between the epileptic focus estimated by the method and the region indicated by the labelling of the experts was 8mm. Except for two EEG recordings where the dipole clustering step failed, the derived template corresponded to the epileptiform activity marked by the experts. Over the eight EEGs, the method showed a mean sensitivity and selectivity of 92 and 77%, respectively. ConclusionsThe method allows automated localization of the epileptogenic focus and shows good agreement with the region indicated by the labelling of the experts. If the dipole clustering step is successful, the method allows a detection of the focal epileptiform events, and gave a detection performance comparable or better to that of the other methods. SignificanceThe identification and quantification of epileptiform events is of considerable importance in the diagnosis of epilepsy. Our method allows the automatic identification of the epileptic focus, which is of value in epilepsy surgery. The method can also be used as an offline exploration tool for focal EEG activity, displaying the dipole clusters and corresponding time series.