DEVELOPING A BIOSENSOR WITH APPLYING KALMAN FILTER AND NEURAL NETWORK TO ANALYZE DATA FOR FUSARIUM DETECTION

Early detection of Fusarium is arduous and highly desired as the detection assists in protecting crops from the harmful potential of plant pathogens which affect the quality and quantity of agriculture products. The thesis work concentrates on searching an approach for Fusarium spore detection and developing portable, reliable and affordable Fusarium detection device. The system can also promptly and continuously sample and sense the presence of the fungus spores in the air. From the investigation of the Fusarium oxysporum Chlamydospores by ATR-FTIR spectroscopy, a distinct infrared spectrum of the Chlamydospore was collected. There are two typical infrared wavelengths can be used for Fusarium detection: 1054cm-1 (9.48µm) and 1642cm-1 (6.08µm). Infrared (IR) light is a form of electromagnetic wave which its wavelength ranges from around 0.75µm to 1000µm and it is invisible to human eyes. To be familiar with the light concepts and quantities, it is necessary to start working with the visible light which is also a form of electromagnetic wave with the wavelength range from about 0.3µm to 0.75µm. A visible spectrometer, which automatically corrects data error caused by unstable light, was built. By using the Kalman filter algorithm, Matlab simulations and training program, the appropriate coefficients to apply to the Kalman corrector were found. The experiments proved that the corrector in the visible spectrometer can reduce the data error in the spectra at the order of 10 times. From the knowledge of working with the visible spectrometer and visible light, the task of searching the detecting approach and building the device in the IR spectrum was reconsidered. The Fusarium detection device was successfully built. Among other components to build the device, there are two essential thermopiles and one infrared light source. The infrared light source emits an IR spectrum from 2µm to 22µm. The two thermopiles working on the IR wavelengths of λ1=6.09±0.06µm and λ2=9.49±0.44µm are used for Fusarium spore detection analysis. The Beer-Lambert assists in quantifying the number of spores in the sample. The group distinction coefficient supports in distinguishing the Fusarium spore from other particulates in the experiments (pollen, turmeric, and starch). Pollen was chosen as it is often present in crop fields, and the other two samples were chosen as they help to verify the work of the system. The group distinction coefficients of Fusarium (1.14±0.15), pollen (0.13±0.11), turmeric (0.79±0.07) and starch (0.94±0.07) are distinct from each other. The size of Fusarium spore is from about 10µm to 70µm. To mitigate the influence of the other particulates, such as pollens or dust which their sizes are not in the above range, a bandpass particle filter consists of a cyclone separator and a high voltage trap were designed and built. The particles with the sizes not in the interested range are eliminated by the filter. From simulations by the COMSOL Multiphysics and experiments, the particle filter proves that it works well with the assigned particle size range. The filter is useful as it helps to sample a certain size range which contains the interested bio objects. As other electronic devices, the Fusarium detection device encountered several common types of noises (thermal noise, burst noise, and background noise). These noises along with the thermopile signals are amplified by the amplifiers. These amplifiers have high gain coefficients to amplify weak signals in nV to µV in magnitude. These noises depend on the operating conditions such as power supplies or environment temperature. If the operating conditions can be monitored, the information of the conditions can be used to correct the error data. To perform the correction task, the neural network was selected. To make a NN working, it requires sufficient data to train. In this research, the training data were collected in one week to record as much as possible working conditions. In addition to the thermopile data, the training data also included the environment temperature and the 5V and 9V voltage-regulator data. Then, the trained NN was applied to fix error data. The contribution of this NN method is the use of operating conditions to fix error data. Although the errors in the data can be corrected well by the trained NN, several other problems still exist. In the samples of Fusarium, starch, pollen, and turmeric, the group-distinction coefficients of Fusarium and starch are very similar. To distinguish better the samples with similar group distinction coefficients, the existing Fusarium detection device was upgraded with a broadband thermopile. The extra thermopile was used along with λ1 and λ2 thermopiles to analyze the reflecting IR light of the samples. To pre-process the thermal noise and burst noise, an adaptive and cognitive Kalman algorithm was proposed. Burst noise is expressed in the form of outliers in the thermopile data. To detect these outliers, a mechanism of using first-order and second-order discrete differentiation of the data and correcting the burst noise and thermal noise was introduced. To study the effectiveness of this pre-processing, the pre-processed data and raw data were applied in the NN training. The main stopping parameters in the training are the number of epochs, absolute mean error, and entropy. The pre-processed data and the trained NN were used for distinguishing samples. The three-thermopile Fusarium detection device led to a use of a validation area to distinguish the samples with similar group-distinction coefficients. The results prove that the use of three thermopiles works very well. The research provides a comprehensive approach of designing system, particulate sampling, particulate filtering, signal processing, and sample distinguishing. The results from the experiments prove that the proposed approach can detect not only Fusarium but also many other different bio-objects. For further work from this research, the Fusarium detection apparatus should be tested in the crop fields infected by Fusarium spores. The outcomes of the research can be applied in other areas such as food safety and human living or hospital environment to detect not only Fusarium spores but the other pathogens, spores, and mold

Monte Carlo Simulation of Correction Factors for Neutron Activation Foils

In the conventional neutron activation analysis, the elemental concentrations are normally determined from the comparison ratios between the measured specific activities of the sample and the standard reference material. An advantage in the comparison ratio method is that the systematic error due to neutron self-shielding and multi-scattering effects is canceled out, and the correction factors can be ignored but the preparation of reference standards to match the same conditions with those of various samples is the main difficulty. In the modern trend of neutron activation analysis, the K0-standardization method has been developed and applied in almost all the NAA laboratories. An important research work in the procedure under this method is the characteristic information regarding the neutron source, such as thermal and epithermal neutron fluxes, and epithermal spectrum shape-factor. These neutron spectrum parameters are experimentally determined by using the activation foils, in which the corrections for all neutron effects cause systematic errors should be taken into account. Using the MCNP5 code, a well-known Monte Carlo simulation program, the results of correction factors of thermal, epithermal and resonance neutron self-shielding factors for Au, Co, Mn, W activation foils are presented in this chapter

SECURITY CAPABILITY ANALYSIS OF COGNITIVE RADIO NETWORK WITH SECONDARY USER CAPABLE OF JAMMING AND SELF-POWERING

This paper investigates a cognitive radio network where a secondary sender assists a primarytransmitter in relaying primary information to a primary receiver and also transmits its own information toa secondary recipient. This sender is capable of jamming to protect secondary and/or primary informationagainst an eavesdropper and self-powering by harvesting radio frequency energy of primary signals.Security capability of both secondary and primary networks are analyzed in terms of secrecy outageprobability. Numerous results corroborate the proposed analysis which serves as a design guidelineto quickly assess and optimize security performance. More importantly, security capability trade-offbetween secondary and primary networks can be totally controlled with appropriate selection of systemparameters

Designing Songs for Teaching and Learning English: A Literature Review

This paper presents the theoretical backgrounds covering the theories of material design, which is useful for understanding of the effective material and the steps to produce it, and mentioning the theories of learning, which include foci on the language, the learners, and learning process. The paper aslo addresses a relationship among factors that contribute to teaching and learning process by using songs as English language teaching material. The authors review the benefits of songs and music under the lights of the cognitive, linguistic, and pedagogical levels through the material design model suggested by Hutchinson and Waters (1987). This paper contributes to the understanding of designing songs as a tool for teaching and learning English. Keywords: Songs, Music, Material design, Cognitive process, Motivation A material design model DOI: 10.7176/JLLL/61-10 Publication date:October 31st 201

Relative Positional Encoding for Speech Recognition and Direct Translation

Transformer models are powerful sequence-to-sequence architectures that are capable of directly mapping speech inputs to transcriptions or translations. However, the mechanism for modeling positions in this model was tailored for text modeling, and thus is less ideal for acoustic inputs. In this work, we adapt the relative position encoding scheme to the Speech Transformer, where the key addition is relative distance between input states in the self-attention network. As a result, the network can better adapt to the variable distributions present in speech data. Our experiments show that our resulting model achieves the best recognition result on the Switchboard benchmark in the non-augmentation condition, and the best published result in the MuST-C speech translation benchmark. We also show that this model is able to better utilize synthetic data than the Transformer, and adapts better to variable sentence segmentation quality for speech translation.Comment: Submitted to Interspeech 202

Secrecy performance of underlay cooperative cognitive network using non-orthogonal multiple access with opportunistic relay selection

In this paper, an underlay cooperative cognitive network using a non-orthogonal multiple access (UCCN-NOMA) system is investigated, in which the intermediate multiple relays help to decode and forward two signals x1 and x2 from a source node to two users D-1 and D-2, respectively, under wiretapping of an eavesdropper (E). We study the best relay selection strategies by three types of relay selection criteria: the first and second best relay selection is based on the maximum channel gain of the links Ri-D1, Ri-D-2, respectively; the third one is to ensure a minimum value of the channel gains from the Ri-E link. We analyze and evaluate the secrecy performances of the transmissions x1 and x2 from the source node to the destination nodes D-1, D-2, respectively, in the proposed UCCN-NOMA system in terms of the secrecy outage probabilities (SOPs) over Rayleigh fading channels. Simulation and analysis results are presented as follows. The results of the (sum) secrecy outage probability show that proposed scheme can realize the maximal diversity gain. The security of the system is very good when eavesdropper node E is far from the source and cooperative relay. Finally, the theoretical analyses are verified by performing Monte Carlo simulations.Web of Science113art. no. 38

Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold Algorithm

In order to address optimization problems, artificial intelligence (AI) is employed in the construction industry, which aids in the growth and popularization of AI. This study utilizes a Hybrid algorithm called Adaptive Selection Slime Mold Algorithm (ASSMA), which combines the Tournament Selection (TS) and Slime Mould Algorithm (SMA) to address the four-factor optimization problem in projects. This combination will improve the original algorithm's performance, speed up result finding and achieve good convergence via Pareto Front. Hence, efficient resource management must be comprehended in order to optimize the time, cost, quality and environmental impact trade-off (TCQE). Case studies are used to illustrate the capabilities of the new model, and ASSMA results are compared to those of the data envelopment analysis (DEA) method used by the previous researcher. To improve the suggested model's superiority and effectiveness, it is compared to the multiple-target swarm algorithm (MOPSO), multi-objective artificial bee colony (MOABC) and non-dominant sort genetic algorithm (NSGA-II). Based on the overall results, it is clear that the ASSMA model illustrates diversification and offers a robust and convincing optimal solution for readers to understand the potential of the proposed model