Regression analysis for paths inference in a novel Proton CT system

In this work, we analyse the proton paths inference for the construction of CT imagery based on a new proton CT proton system, which can record multiple proton paths/residual energies. Based on the recorded paths of multiple protons, every proton path is inferred. The inferred proton paths can then be used for the residual energies detection and CT imagery construction for analyzing a specific tissue. Different regression methods (linear regression and Gaussian process regression models) are exploited for the path inference of every proton in this work. The studies on a recorded proton trajectories dataset show that the Gaussian process regression method achieves better accuracies for the path inference, from both path assignment accuracy and root mean square errors (RMSEs) studies

A new Gaussian mixture method with exactly exploiting the negative information for GMTI radar tracking in a low-observable environment

This paper investigates the problem of ground vehicle tracking with a Ground Moving Target Indicator (GMTI) radar. In practice, the movement of ground vehicles may involve several different manoeuvring types (acceleration, deceleration, standstill, etc.). Consequently, the GMTI radar may lose measurements when the radial velocity of the ground vehicle is below a threshold when it stops, i.e. falling into the Doppler blind region. Besides, there will be false alarms in low-observable environments where there exist high noises interferences. In this paper, we develop a novel algorithm for the GMTI tracking in a low-observable environment with false alarms while exactly incorporating the ‘negative information’ (i.e., the target is likely to stop when no measurements are recorded) based on the Bayesian inference framework. For the Bayesian inference implementation, the Gaussian mixture approximation method is adopted to approximate related distributions, while different filtering algorithms (including both extended Kalman filter and its generalization for interval-censored measurements) are applied for updating the Gaussian mixture components. Target state estimation can be directly obtained through the Gaussian mixture model for the GMTI tracking at every time instance. We have compared the developed method with other state-of-the-art ones and the simulation results show that the proposed method substantially outperforms the existing methods for the GMTI tracking problem

Deep learning based prediction on greenhouse crop yield combined TCN and RNN

Funding: This research was supported as part of SMARTGREEN, an Interreg project supported by the North Sea Programme of the European Regional Development Fund of the European Union.Peer reviewedPublisher PD

On the supersoluble hypercentre of a finite group

[EN] We give some sufficient conditions for a normal p-subgroup P of a finite group G to have every G-chief factor below it cyclic. The S-permutability of some p-subgroups of O^p(G)plays an important role. Some known results can be reproved and some others appear as corollaries of our main theorems.A. Ballester-Bolinches and R. Esteban-Romero have been supported by the Grant MTM2014-54707-C3-1-P from the Ministerio de Economia y Competitividad, Spain, and FEDER, European Union. A. Ballester-Bolinches and Y. Li have been supported by a project from the National Natural Science Foundation of China (NSFC, No. 11271085) and a project of Natural Science Foundation of Guangdong Province (No. 2015A030313791). L. Miao thanks the China Scholarship Council and for its financial support and the Department of Mathematics of the University of Valencia for its hospitality.Miao, L.; Ballester-Bolinches, A.; Esteban Romero, R.; Li, Y. (2017). On the supersoluble hypercentre of a finite group. Monatshefte für Mathematik. 184(4):641-648. https://doi.org/10.1007/s00605-016-0987-9S6416481844Ballester-Bolinches, A., Esteban-Romero, R., Asaad, M.: Products of finite groups. In: de Gruyter Expositions in Mathematics, vol. 53. Walter de Gruyter, Berlin (2010). doi: 10.1515/9783110220612Ballester-Bolinches, A., Esteban-Romero, R., Qiao, S.H.: A note on a result of Guo and Isaacs about $$p$$ p -supersolubility of finite groups. Arch. Math. (Basel) 106, 501–506 (2016). doi: 10.1007/s00013-016-0901-7Berkovich, Y., Isaacs, I.M.: $$p$$ p -Groups stabilizing certain subgroups. J. Algebra 414, 82–94 (2014). doi: 10.1016/j.jalgebra.2014.04.026Chen, X., Guo, W., Skiba, A.N.: Some conditions under which a finite group belongs to a Baer-local formation. Commun. Algebra 42(10), 4188–4203 (2014). doi: 10.1080/00927872.2013.806519Doerk, K., Hawkes, T.: Finite soluble groups. In: De Gruyter Expositions in Mathematics, vol. 4. Walter de Gruyter, Berlin (1992). doi: 10.1515/9783110870138Gorenstein, D.: Finite Groups. Chelsea, New York (1980)Guo, Y., Isaacs, I.M.: Conditions on $$p$$ p -supersolvability. Arch. Math. (Basel) 105, 215–222 (2015). doi: 10.1007/s00013-015-0803-0Huppert, B.: Endliche Gruppen I. In: Grund. Math. Wiss., vol. 134. Springer, Berlin (1967)Isaacs, I.M.: Semipermutable $$\pi$$ π -subgroups. Arch. Math. (Basel) 102, 1–6 (2014). doi: 10.1007/s00013-013-0604-2Li, Y., Qiao, S., Su, N., Wang, Y.: On weakly $$s$$ s -semipermutable subgroups of finite groups. J. Algebra 371, 250–261 (2012). doi: 10.1016/j.jalgebra.2012.06.025Su, N., Li, Y., Wang, Y.: The weakly $$s$$ s -supplemented property of finite groups. Chin. J. Contemp. Math. 35(4), 1–12 (2014). doi: 10.16205/j.cnki.cama.2015.0010Wang, L., Wang, Y.: On $$s$$ s -subgroups of finite groups. Commun. Algebra 34, 143–149 (2006). doi: 10.1080/0092787050034608

Multiple model ballistic missile tracking with state-dependent transitions and Gaussian particle filtering

This paper proposes a new method for tracking the entire trajectory of a ballistic missile from launch to impact on the ground. Multiple state models are used to represent the different ballistic missile dynamics in three flight phases: boost, coast and reentry. In particular, the transition probabilities between state models are represented in a state-dependent way by utilising domain knowledge. Based on this modelling system and radar measurements, a state-dependent interacting multiple model approach based on Gaussian particle filtering is developed to accurately estimate information describing the ballistic missile such as the phase of flight, position, velocity and relevant missile parameters. Comprehensive numerical simulation studies show that the proposed method outperforms the traditional multiple model approaches for ballistic missile tracking

Enhancing Privacy with Optical Element Design for Fall Detection

Falling poses significant risks, especially for the geriatric population. In this study, we introduce an innovative approach to privacy-preserving fall detection using computer vision. Our technique leverages a deep neural network (DNN) to accurately identify falling events in input images, while simultaneously prioritizing privacy through the implementation of an optical element. The experimental results establish that our proposed method outperforms alternative hardware and software-based privacy-preserving approaches in terms of encryption level and accuracy. These results are derived from an extensive dataset encompassing diverse falling scenarios

Enhancing Fall Detection Accuracy with a Transfer Learning-Aided Transformer Model using Computer Vision

Falls are a significant health concern for older adults globally, and prompt identification is critical to providing necessary healthcare support. Our study proposes a new fall detection method using computer vision based on modern deep learning techniques. Our approach involves training a transformer model on a large 2D pose dataset for general action recognition, followed by transfer learning. Specifically, we freeze the first few layers of the trained transformer model and train only the last two layers for fall detection. Our experimental results demonstrate that our proposed method outperforms both classical machine learning and deep learning approaches in fall/non-fall classification. Overall, our study suggests that our proposed methodology could be a valuable tool for identifying falls

Studies of evolutionary algorithms for the reduced Tomgro model calibration for modelling tomato yields

The reduced Tomgro model is one of the popular biophysical models, which can reflect the actual growth process and model the yields of tomato-based on environmental parameters in a greenhouse. It is commonly integrated with the greenhouse environmental control system for optimally controlling environmental parameters to maximize the tomato growth/yields under acceptable energy consumption. In this work, we compare three mainstream evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and differential evolutionary (DE)) for calibrating the reduced Tomgro model, to model the tomato mature fruit dry matter (DM) weights. Different evolutionary algorithms have been applied to calibrate 14 key parameters of the reduced Tomgro model. And the performance of the calibrated Tomgro models based on different evolutionary algorithms has been evaluated based on three datasets obtained from a real tomato grower, with each dataset containing greenhouse environmental parameters (e.g., carbon dioxide concentration, temperature, photosynthetically active radiation (PAR)) and tomato yield information at a particular greenhouse for one year. Multiple metrics (root mean square errors (RMSEs), relative root mean square errors (r-RSMEs), and mean average errors (MAEs)) between actual DM weights and model-simulated ones for all three datasets, are used to validate the performance of calibrated reduced Tomgro model

A Novel Camera Based Approach for Automatic Expiry Date Detection and Recognition on Food Packages

There is abundant of information on food packages, which include the food name, the expiry date and the ingredients. These information, especially the expiry date needs to be coded correctly before the products can be released into the market/supply chains. Failure of printing the correct expiry date can lead to both the health issues to the public and financial issues for recalling product back and even reimbursement. In this paper, we develop an automatic system that can achieve the expiry date region detection and recognition in an efficient and effective way. A deep neural network (DNN) based approach is firstly applied to find the expiry date region on the food package. The date characters are then extracted and recognized through the image processing and machine learning methods from the expiry date region. The system is the first camera based automatic system for recognizing expiry date on food packages. And the results tested on different types of food packages show that the system can achieve good performance on both detection and recognition of the expiry date

A novel unified deep neural networks methodology for use by date recognition in retail food package image

There exist various types of information on retail food packages, including use by date, food product name and so on. The correct coding of use by dates on food packages is vitally important for avoiding potential health risks to customers caused by erroneous mislabelling of use by dates. It is extremely tedious and laborious to check the use by dates coding manually by a human operator, which is prone to generate errors thus an automatic system for validating the correctness of the coding of use by dates is needed. In order to construct such a system, firstly it needs to correctly automatic recognise use by dates on food packages. In this work, we propose a novel dual deep neural networks based methodology for automatic recognition of use by dates in food package photos recorded by a camera, which is a combination of two networks: a fully convolutional network (FCN) for use by date ROI detection and a convolutional recurrent neuron network (CRNN) for date character recognition. The proposed methodology is the first attempt to apply deep learning for automatic use by date recognition. From comprehensive experimental evaluations, it is shown that the proposed method can achieve high accuracies in use by date recognition (more than 95% on our testing dataset), given food package images with varying lighting conditions, poor printing quality and varied textual/pictorial contents collected from multiple real retailer sites