Wireless sensor network for cattle monitoring system

This paper describes a cost effective Wireless Sensor Network (WSN) technology for monitoring the health of dairy cows. By monitoring and understanding the cow individual and herd behaviour, farmers can potentially identify the onset of illness, lameness or other undesirable health conditions. However, the WSN implementation needs to cope with various technical challenges before it can be suitably and routinely applied in cow management. This paper discusses results concerning data transportation (i.e. mobility) from the cow mounted sensory devices

Preliminary design and optimization of toroidally-wound limited angle servo motor based on a generalized magnetic circuit model

This paper proposes a new generalized equivalent magnetic circuit model for the preliminary design of a toroidally-wound limited angle servo motor (LASM). In the model, the magnetic networks are formulated as a function of the pole number and geometric dimensions. Nonlinear saturation effect of the ferromagnetic material is also taken into consideration. A multi-objective optimization function involving the torque requirement, the mass, the time constant, and magnetic saturations of ferromagnetic material is introduced. Based on the proposed model, six design cases with different objectives have been carried by the particle swarm optimization (PSO) method. The comparisons of different optimization cases demonstrate the effectiveness and computation efficiency of the proposed method, and hence its suitability in preliminary design. Moreover, the generalized model can be readily applied in the other electromagnetic modelling

The impact of physical conditions on network connectivity in wireless sensor network

In Wireless Sensor Networks, end-to-end routing paths need to be established when nodes want to communicate with the desired destination. For nodes assumed to be static, many routing protocols such as Directed Diffusion have been proposed to meet this requirement efficiently. The performance of such routing protocols is relative to the given network connectivity. This paper addresses mobile sensor nodes taking into account the diversity of scattered node density and investigates how physical conditions impact on network connectivity which in turn influences routing performance. Three analysis metrics: path availability, path duration, and interavailable path time are proposed to quantify the impact of different physical conditions on network connectivity. Simulation results show that the network connectivity varies significantly as a function of different physical conditions

Bio-medical application on predicting systolic blood pressure using neural networks

This paper presents a new study based on artificial neural network, which is a typical technique for processing big data, for the prediction of systolic blood pressure by correlated factors (gender, serum cholesterol, fasting blood sugar and electrocardiography signal). Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the bio-medical prediction system. The database of raw data is divided into two parts: 80% for training the neural network and the remaining 20% for testing the performance. The experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This novel method of predicting systolic blood pressure contributes to giving early warnings to adults who may not take regular blood pressure measurements. Also, as it is known that an isolated blood pressure measurement is sometimes not very accurate due to the daily fluctuation, our predictor can provide another reference value to the medical staff.published_or_final_versio

Predicting systolic blood pressure using machine learning

In this paper, a new study based on machine learning technique, specifically artificial neural network, is investigated to predict the systolic blood pressure by correlated variables (BMI, age, exercise, alcohol, smoke level etc.). The raw data are split into two parts, 80% for training the machine and the remaining 20% for testing the performance. Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the prediction system. Based on a database with 498 people, the probabilities of the absolute difference between the measured and predicted value of systolic blood pressure under 10mm Hg are 51.9% for men and 52.5% for women using the back-propagation neural network With the same input variables and network status, the corresponding results based on the radial basis function network are 51.8% and 49.9% for men and women respectively. This novel method of predicting systolic blood pressure contributes to giving early warnings to young and middle-aged people who may not take regular blood pressure measurements. Also, as it is known an isolated blood pressure measurement is sometimes not very accurate due to the daily fluctuation, our predictor can provide another reference value to the medical staff. Our experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. © 2014 IEEE.published_or_final_versio

A Prediction Model of Blood Pressure for Telemedicine

This paper presents a new study based on a machine learning technique, specifically an artificial neural network, for predicting systolic blood pressure through the correlation of variables (age, BMI, exercise level, alcohol consumption level, smoking status, stress level, and salt intake level). The study was carried out using a database containing a variety of variables/factors. Each database of raw data was split into two parts: one part for training the neural network and the remaining part for testing the performance of the network. Two neural network algorithms, back-propagation and radial basis function, were used to construct and validate the prediction system. According to the experiment, the accuracy of our predictions of systolic blood pressure values exceeded 90%. Our experimental results show that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This new method of predicting systolic blood pressure helps to give an early warning to adults, who may not get regular blood pressure measurements that their blood pressure might be at an unhealthy level. Also, because an isolated measurement of blood pressure is not always very accurate due to daily fluctuations, our predictor can provide the predicted value as another figure for medical staff to refer to.postprin

Evaluation of radiation-induced changes to parotid glands following conventional radiotherapy in patients with nasopharygneal carcinoma

Objectives: Xerostomia is a common post-radiotherapy (post-RT) complication in nasopharyngeal carcinoma (NPC) patients. This study evaluated the relation of post-RT parotid gland changes with the dose received. Methods: Data from 18 NPC patients treated by radiotherapy between 1997 and 2001 were collected. Parotid gland volumes were measured and compared between their pre-RT and post-RT CT images; both sets of CT were conducted with the same scanning protocol. Doppler ultrasound was used to assess the haemodynamic condition of the glands after radiotherapy. Doppler ultrasound results were compared against 18 agematched normal participants. A questionnaire was used to evaluate the patients'comments of xerostomia condition. Radiotherapy treatment plans of the participants were retrieved from the Eclipse treatment planning system from which the radiation doses delivered to the parotid glands were estimated. The correlations of parotid gland doses and the post-RT changes were evaluated. Results: The post-RT parotid glands were significantly smaller (p<0.001) than the pre-RT ones. They also demonstrated lower vascular velocity, resistive and pulsatility indices (p<0.05) than normal participants. The degree of volume shrinkage and subjective severity of xerostomia demonstrated dose dependence, but such dependence was not definite in the haemodynamic changes. Conclusion: It was possible to predict the gland volume change and subjective severity of xerostomia based on the dose to the parotid glands for NPC patients. However, such prediction was not effective for the vascular changes. The damage to the gland was long lasting and had significant effects on the patients' quality of life. © 2011 The British Institute of Radiology.link_to_subscribed_fulltex

Inverse planning in three-dimensional conformal and intensity-modulated radiotherapy of mid-thoracic oesophageal cancer

The aim of this study is to demonstrate the use of inverse planning in three-dimensional conformal radiation therapy (3DCRT) of oesophageal cancer patients and to evaluate its dosimetric results by comparing them with forward planning of 3DCRT and inverse planning of intensity-modulated radiotherapy (IMRT). For each of the 15 oesophageal cancer patients in this study, the forward 3DCRT, inverse 3DCRT and inverse IMRT plans were produced using the FOCUS treatment planning system. The dosimetric results and the planner's time associated with each of the treatment plans were recorded for comparison. The inverse 3DCRT plans showed similar dosimetric results to the forward plans in the planning target volume (PTV) and organs at risk (OARs). However, they were inferior to that of the IMRT plans in terms of tumour control probability and target dose conformity. Furthermore, the inverse 3DCRT plans were less effective in reducing the percentage lung volume receiving a dose below 25 Gy when compared with the IMRT plans. The inverse 3DCRT plans delivered a similar heart dose as in the forward plans, but higher dose than the IMRT plans. The inverse 3DCRT plans significantly reduced the operator's time by 2.5 fold relative to the forward plans. In conclusion, inverse planning for 3DCRT is a reasonable alternative to the forward planning for oesophageal cancer patients with reduction of the operator's time. However, IMRT has the better potential to allow further dose escalation and improvement of tumour control. © 2004 The British Institute of Radiology.link_to_OA_fulltex

Multi-objective optimal design of a toroidally wound radial-flux halbach permanent magnet array limited angle torque motor

This paper presents the modeling, optimization and validation of a toroidally wound radial-flux Halbach array permanent magnet limited angle torque motor. A fully parameterized and flexible equivalent magnetic circuit model of the proposed motor, which is arranged in matrix form by means of Kirchhoff’s current laws for computational efficiency and ease of extension, is introduced for preliminary design. To optimize the design, a multi-objective optimization process is carried out and the multi-objective particle swarm optimization method is used to obtain the Pareto front of the desired objectives. An approved solution in Pareto front is selected and validated by finite element analysis method. A prototype based on the final design is built and tested. The experiment results further underpin the effectiveness of the proposed design and optimization approach