WSN and RFID integration to support intelligent monitoring in smart buildings using hybrid intelligent decision support systems

The real time monitoring of environment context aware activities is becoming a standard in the service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt reaction to potential hazards identified at an early stage to engage appropriate control actions. This requires capturing real time data to process locally at the device level or communicate to backend systems for real time decision making. This research examines the wireless sensor network and radio frequency identification technology integration in smart homes to support advanced safety systems deployed upstream to safety and emergency response. These systems are based on the use of hybrid intelligent decision support systems configured in a multi-distributed architecture enabled by the wireless communication of detection and tracking data to support intelligent real-time monitoring in smart buildings. This paper introduces first the concept of wireless sensor network and radio frequency identification technology integration showing the various options for the task distribution between radio frequency identification and hybrid intelligent decision support systems. This integration is then illustrated in a multi-distributed system architecture to identify motion and control access in a smart building using a room capacity model for occupancy and evacuation, access rights and a navigation map automatically generated by the system. The solution shown in the case study is based on a virtual layout of the smart building which is implemented using the capabilities of the building information model and hybrid intelligent decision support system.The Saudi High Education Ministry and Brunel University (UK

Measuring and analysing vibration motors in insoles via accelerometers

Purpose: Falling is a major public health concern among elderly people, and they often cause serious injuries1,2. They most frequently occur during walking and are associated with the chronic deterioration in the neuromuscular and sensory systems, as well as with ankle muscle weakness and lower endurance of these muscles to fatigue1,3. Vibrating insoles, providing a subsensory mechanical noise signal to the plantar side of the feet, may improve balance in healthy young and older people and in patients with stroke or diabetic neuropathy4. The object of this study is to find the most suitable vibrator to put into the insole which can effectively improve the balance control of the elderlies. Method: We choose three different vibration actuators (micro vibration motor, brushless motor and eccentric motor) with two different weights on the insole. First, we put three same motors and two accelerometers on the insole, as shown in Figure1, then attach another layer on both side of the insole. Second, connect the motors to the power supply and the accelerometer to NI PXI-1033 spectrum analyzer which is used to collect the accelerometers' data. At last, using Fast Fourier Transform (FFT) to analyze and compare the results to see which motor is the most stable and suitable to put into the insole. Results & Discussion: The results showed that the most stable one is the brushless motor. The reason why the frequency is stable is that the relationship between voltage and frequency is linear, and the error is small through continuous measurements. On the other hand, when a person weight 55 kg stands on the insole, the frequency isn't affected by the weight. These two results appear very similar to each other, as shown in Figure 2. According to the result, we use the brushless motor to be our vibrator in the insole, and hope this will help the elderlies improve their balance control ability more efficiency

An intelligent manufacturing system for heat treatment scheduling

This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

NO Abatement using Microwave Micro Plasma Generated using Granular Activated Carbon

Abatement of NO using microwave micro-plasma is presented in this paper. The micro-plasma is generated using granular activated carbon (GAC) particles of size (size 2-3mm) in loosely fluidised bed in microwave filed operated at 2.45GHz. A single mode microwave cavity reactor (SMMCR) was constructed and microwave was injected through another slotted waveguide in a sandwiched manner. COMSOL Multiphysics software was used to investigate the microwave electric field and the power density within the SMMCR. Gas mixture of air and 500 ppm NO (in N2) at the flow rate of 2 l/min was passed through a quartz tube centered within SMMCR while the supplied microwave power was very low 10-80 W and NO reduction was greater than 98%. The mass of GAC used for generating the plasma was 5g. When air is mixed with NO (in N2), the efficiency of NOx reduction achieved vary greatly with respect to the supplied microwave energy and behavior has become complex and is not predictable. The gas analyzer (testo 350) was used to measure the gas (NO, NO2, CO and O2) concentration and temperature

Efficient architectures and power modelling of multiresolution analysis algorithms on FPGA

In the past two decades, there has been huge amount of interest in Multiresolution Analysis Algorithms (MAAs) and their applications. Processing some of their applications such as medical imaging are computationally intensive, power hungry and requires large amount of memory which cause a high demand for efficient algorithm implementation, low power architecture and acceleration. Recently, some MAAs such as Finite Ridgelet Transform (FRIT) Haar Wavelet Transform (HWT) are became very popular and they are suitable for a number of image processing applications such as detection of line singularities and contiguous edges, edge detection (useful for compression and feature detection), medical image denoising and segmentation. Efficient hardware implementation and acceleration of these algorithms particularly when addressing large problems are becoming very chal-lenging and consume lot of power which leads to a number of issues including mobility, reliability concerns. To overcome the computation problems, Field Programmable Gate Arrays (FPGAs) are the technology of choice for accelerating computationally intensive applications due to their high performance. Addressing the power issue requires optimi- sation and awareness at all level of abstractions in the design flow. The most important achievements of the work presented in this thesis are summarised here. Two factorisation methodologies for HWT which are called HWT Factorisation Method1 and (HWTFM1) and HWT Factorasation Method2 (HWTFM2) have been explored to increase number of zeros and reduce hardware resources. In addition, two novel efficient and optimised architectures for proposed methodologies based on Distributed Arithmetic (DA) principles have been proposed. The evaluation of the architectural results have shown that the proposed architectures results have reduced the arithmetics calculation (additions/subtractions) by 33% and 25% respectively compared to direct implementa-tion of HWT and outperformed existing results in place. The proposed HWTFM2 is implemented on advanced and low power FPGA devices using Handel-C language. The FPGAs implementation results have outperformed other existing results in terms of area and maximum frequency. In addition, a novel efficient architecture for Finite Radon Trans-form (FRAT) has also been proposed. The proposed architecture is integrated with the developed HWT architecture to build an optimised architecture for FRIT. Strategies such as parallelism and pipelining have been deployed at the architectural level for efficient im-plementation on different FPGA devices. The proposed FRIT architecture performance has been evaluated and the results outperformed some other existing architecture in place. Both FRAT and FRIT architectures have been implemented on FPGAs using Handel-C language. The evaluation of both architectures have shown that the obtained results out-performed existing results in place by almost 10% in terms of frequency and area. The proposed architectures are also applied on image data (256 £ 256) and their Peak Signal to Noise Ratio (PSNR) is evaluated for quality purposes. Two architectures for cyclic convolution based on systolic array using parallelism and pipelining which can be used as the main building block for the proposed FRIT architec-ture have been proposed. The first proposed architecture is a linear systolic array with pipelining process and the second architecture is a systolic array with parallel process. The second architecture reduces the number of registers by 42% compare to first architec-ture and both architectures outperformed other existing results in place. The proposed pipelined architecture has been implemented on different FPGA devices with vector size (N) 4,8,16,32 and word-length (W=8). The implementation results have shown a signifi-cant improvement and outperformed other existing results in place. Ultimately, an in-depth evaluation of a high level power macromodelling technique for design space exploration and characterisation of custom IP cores for FPGAs, called func-tional level power modelling approach have been presented. The mathematical techniques that form the basis of the proposed power modeling has been validated by a range of custom IP cores. The proposed power modelling is scalable, platform independent and compares favorably with existing approaches. A hybrid, top-down design flow paradigm integrating functional level power modelling with commercially available design tools for systematic optimisation of IP cores has also been developed. The in-depth evaluation of this tool enables us to observe the behavior of different custom IP cores in terms of power consumption and accuracy using different design methodologies and arithmetic techniques on virous FPGA platforms. Based on the results achieved, the proposed model accuracy is almost 99% true for all IP core's Dynamic Power (DP) components.EThOS - Electronic Theses Online ServiceThomas Gerald Gray Charitable TrustGBUnited Kingdo

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Nonlinear and conventional biosignal analyses applied to tilt table test for evaluating autonomic nervous system and autoregulation

Copyright © Tseng et al.; Licensee Bentham Open. This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.Tilt table test (TTT) is a standard examination for patients with suspected autonomic nervous system (ANS) dysfunction or uncertain causes of syncope. Currently, the analytical method based on blood pressure (BP) or heart rate (HR) changes during the TTT is linear but normal physiological modulations of BP and HR are thought to be predominately nonlinear. Therefore, this study consists of two parts: the first part is analyzing the HR during TTT which is compared to three methods to distinguish normal controls and subjects with ANS dysfunction. The first method is power spectrum density (PSD), while the second method is detrended fluctuation analysis (DFA), and the third method is multiscale entropy (MSE) to calculate the complexity of system. The second part of the study is to analyze BP and cerebral blood flow velocity (CBFV) changes during TTT. Two measures were used to compare the results, namely correlation coefficient analysis (nMxa) and MSE. The first part of this study has concluded that the ratio of the low frequency power to total power of PSD, and MSE methods are better than DFA to distinguish the difference between normal controls and patients groups. While in the second part, the nMxa of the three stages moving average window is better than the nMxa with all three stages together. Furthermore the analysis of BP data using MSE is better than CBFV data.The Stroke Center and Department of Neurology, National Taiwan University, National Science Council in Taiwan, and the Center for Dynamical Biomarkers and Translational Medicine, National Central University, which is sponsored by National Science Council and Min-Sheng General Hospital Taoyuan

An assessment of pulse transit time for detecting heavy blood loss during surgical operation

Copyright @ Wang et al.; Licensee Bentham Open. This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.The main contribution of this paper is the use of non-invasive measurements such as electrocardiogram (ECG) and photoplethysmographic (PPG) pulse oximetry waveforms to develop a new physiological signal analysis technique for detecting blood loss during surgical operation. Urological surgery cases were considered as the control group due to its generality, and cardiac surgery as experimental group since it involves blood loss and water supply. Results show that the control group has the tendency of a reduction of the pulse transient time (PTT), and this indicates an increment in the blood flow velocity changes from slow to fast. While for the experimental group, the PTT indicates high values during blood loss, and low values during water supply. Statistical analysis shows considerable differences (i.e., P <0.05) between both groups leading to the conclusion that PTT could be a good indicator for monitoring patients' blood loss during a surgical operation.The National Science Council (NSC) of Taiwan and the Centre for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan

NO Abatement using Microwave Micro Plasma Generated with Granular Activated Carbon

The micro-plasma is generated using granular activated carbon (GAC) particles of size (2-3mm) in loosely fluidized bed in a microwave cavity operated at 2.45GHz. A single mode microwave cavity reactor (SMMCR) was constructed and microwave was injected through another slotted single mode waveguide in a sandwiched manner. COMSOL Multiphysics software was used to investigate the microwave electric field and the power density within the SMMCR. Gas mixture of air and 500 ppm NO (in N2) at the flow rate of 2 l/min was passed through a quartz tube centered within the SMMCR while the supplied microwave power was very low 10-80 W and corresponding NO reduction was greater than 98%. The mass of GAC used for generating the plasma was 5g. The efficiency of NO reduction is found to be 24.84 g(NO2)/kWh. When air is mixed with NO (in N2), the efficiency of NOx reduction achieved vary greatly with respect to the supplied microwave energy and behavior has become complex and is not predictable, which needs further investigation. A gas analyzer (testo 350) was used to measure the gas (NO, NO2, CO and O2) concentration and temperature.Marine Exhaust Gas Treatment System (MAGS) grant reference number 42471-295209

Quasi-Periodicities Detection Using Phase-Rectified Signal Averaging in EEG Signals as a Depth of Anesthesia Monitor

Phase-rectified signal averaging (PRSA) has been known to be a useful method to detect periodicities in non-stationary biological signals. Determination of quasi-periodicities in electroencephalogram (EEG) is a candidate for quantifying the changes of depth of anesthesia (DOA). In this paper, DOA monitoring capacity of periodicities detected using PRSA were quantified by assessing EEG signals collected from 56 patients during surgery. The method is compared to sample entropy (SampEn), detrended fluctuation analysis (DFA) and permutation entropy (PE). The performance of quasi-periodicities defined by acceleration capacity (AC) and deceleration capacity (DC) was tested using the area under the receiver operating characteristic curve (AUC) and Pearson correlation coefficient. During the surgery, a significant difference (p < 0.05) in the quasi-periodicities was observed among three different stages under general anesthesia. There is a larger mean AUC and correlation coefficient of quasi-periodicities compared to SampEn, DFA and PE using expert assessment of conscious level (EACL) and bispectral index (BIS) as the gold standard, respectively. Quasi-periodicities detected using PRSA in EEG signals are powerful monitor of DOA and perform more accurate and robust results compared to SampEn, DFA and PE. The results do provide a valuable reference to researchers in the filed of clinical applications.10.13039/501100003711-Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by the Ministry of Science and Technology; 10.13039/501100001809-National Natural Science Foundation of China