Search results for: EEG signals classification

Authors: Olalekan Lawrence Ojo

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Designing microwave terrestrial propagation networks requires a thorough evaluation of the severity of multipath fading, especially at frequencies below 10 GHz. In nations like Nigeria, without a large enough databases to support the existing empirical models, the mistakes in the prediction technique intended for the evaluation may be severe. The need for higher bandwidth for various satellite applications makes the investigation of the effects of radio refractivity, fading due to multipath, and Geoclimatic factors on satellite propagation links more important. One of the key elements to take into account for the best functioning of microwave frequencies is the clear air effects. This work has taken into account the geographical distribution of radio refractivity and fades depth over a number of stations in Nigeria. Data from five locations in Nigeria—Akure, Enugu, Jos, Minna, and Sokoto—based on five-year (2017–2021) measurement methods of atmospheric pressure, relative, and humidity temperature—at two levels (ground surface and 100 m heights)—are studied to deduced their effects on signals propagated through a µwave communication links. The assessments included considerations for µwave communication systems as well as the impacts of the dry and wet components of radio refractivity, the effects of the fade depth at various frequencies, and a 20 km link distance. The results demonstrate that the percentage occurrence of the dry terms dominated the radio refractivity constituent at the surface level, contributing a minimum of about 78% and a maximum of about 92%, while at heights of 100 meters, the percentage occurrence of the dry terms dominated the radio refractivity constituent, contributing a minimum of about 79% and a maximum of about 92%. The spatial distribution reveals that, regardless of height, the country's tropical rainforest (TRF) and freshwater swampy mangrove (FWSM) regions reported the greatest values of radio refractivity. The statistical estimate shows that fading values can differ by as much as 1.5 dB, especially near the TRF and FWSM coastlines, even during clear air conditions. The current findings will be helpful for budgeting Earth-space microwave links, particularly for the rollout of Nigeria's 5G and 6G projected microcellular networks.

Keywords: fade depth, geoclimatic factor, refractivity, refractivity gradient

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Authors: Mst Ilme Faridatul, Bo Wu

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Multi-temporal urban land cover mapping is of paramount importance for monitoring urban sprawl and managing the ecological environment. For diversified urban activities, it is challenging to map land covers in a complex urban environment. Spectral indices have proved to be effective for mapping urban land covers. To improve multi-temporal urban land cover classification and mapping, we evaluate the performance of three spectral indices, e.g. modified normalized difference bare-land index (MNDBI), tasseled cap water and vegetation index (TCWVI) and shadow index (ShDI). The MNDBI is developed to evaluate its performance of enhancing urban impervious areas by separating bare lands. A tasseled cap index, TCWVI is developed to evaluate its competence to detect vegetation and water simultaneously. The ShDI is developed to maximize the spectral difference between shadows of skyscrapers and water and enhance water detection. First, this paper presents a comparative analysis of three spectral indices using Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM) and Operational Land Imager (OLI) data. Second, optimized thresholds of the spectral indices are imputed to classify land covers, and finally, their performance of enhancing multi-temporal urban land cover mapping is assessed. The results indicate that the spectral indices are competent to enhance multi-temporal urban land cover mapping and achieves an overall classification accuracy of 93-96%.

Keywords: land cover, mapping, multi-temporal, spectral indices

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Authors: R. Ananthanarayanan, S. B. Nakula, D. R. Seenivasagam, J. Naua, B. Sharma

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Product packaging is a critical component of production, trade, and marketing, playing numerous vital roles that often go unnoticed by consumers. Packaging is essential for maintaining the shelf life, quality assurance, and safety of both manufactured and agricultural products. For example, harvested produce or processed foods can quickly lose quality and freshness, making secure packaging crucial for preservation and safety throughout the food supply chain. In Fiji, agricultural packaging has primarily been managed by local companies for international trade, with gradual advancements in these practices. To further enhance the industry’s performance, this study examines the challenges and constraints hindering the optimization of agricultural packaging practices in Fiji. The study utilizes Multi-Criteria Decision Making (MCDM) tools, specifically Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). ISM analyzes the hierarchical structure of barriers, categorizing them from the least to the most influential, while MICMAC classifies barriers based on their driving and dependence power. This approach helps identify the interrelationships between barriers, providing valuable insights for policymakers and decision-makers to propose innovative solutions for sustainable development in the agricultural packaging sector, ultimately shaping the future of packaging practices in Fiji.

Keywords: agricultural packaging, barriers, ISM, MICMAC

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Authors: Usama Shahid, Muhammad Zunnurain Hussain

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This research paper presents a comprehensive analysis of a sentiment analysis approach that employs valence and arousal as its foundational pillars, in comparison to traditional techniques. Sentiment analysis is an indispensable task in natural language processing that involves the extraction of opinions and emotions from textual data. The valence and arousal dimensions, representing the intensity and positivity/negativity of emotions, respectively, enable the creation of four quadrants, each representing a specific emotional state. The study seeks to determine the impact of utilizing these quadrants to identify distinct emotional states on the accuracy and efficiency of sentiment analysis, in comparison to traditional techniques. The results reveal that the valence and arousal-based approach outperforms other approaches, particularly in identifying nuanced emotions that may be missed by conventional methods. The study's findings are crucial for applications such as social media monitoring and market research, where the accurate classification of emotions and opinions is paramount. Overall, this research highlights the potential of using valence and arousal as a framework for sentiment analysis and offers invaluable insights into the benefits of incorporating specific types of emotions into the analysis. These findings have significant implications for researchers and practitioners in the field of natural language processing, as they provide a basis for the development of more accurate and effective sentiment analysis tools.

Keywords: sentiment analysis, valence and arousal, emotional states, natural language processing, machine learning, text analysis, sentiment classification, opinion mining

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Authors: Jun Won Kim

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Objective: Theta-phase gamma-amplitude coupling (TGC) was used as a novel evidence-based tool to reflect the dysfunctional cortico-thalamic interaction in patients with schizophrenia. However, to our best knowledge, no studies have reported the diagnostic utility of the TGC in the resting-state electroencephalographic (EEG) of neuroleptic-naive patients with schizophrenia compared to healthy controls. Thus, the purpose of this EEG study was to understand the underlying mechanisms in patients with schizophrenia by comparing the TGC at rest between two groups and to evaluate the diagnostic utility of TGC. Method: The subjects included 90 patients with schizophrenia and 90 healthy controls. All patients were diagnosed with schizophrenia according to the criteria of Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) by two independent psychiatrists using semi-structured clinical interviews. Because patients were either drug-naïve (first episode) or had not been taking psychoactive drugs for one month before the study, we could exclude the influence of medications. Five frequency bands were defined for spectral analyses: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), beta (13.5–30 Hz), and gamma (30-80 Hz). The spectral power of the EEG data was calculated with fast Fourier Transformation using the 'spectrogram.m' function of the signal processing toolbox in Matlab. An analysis of covariance (ANCOVA) was performed to compare the TGC results between the groups, which were adjusted using a Bonferroni correction (P < 0.05/19 = 0.0026). Receiver operator characteristic (ROC) analysis was conducted to examine the discriminating ability of the TGC data for schizophrenia diagnosis. Results: The patients with schizophrenia showed a significant increase in the resting-state TGC at all electrodes. The delta, theta, slow alpha, fast alpha, and beta powers showed low accuracies of 62.2%, 58.4%, 56.9%, 60.9%, and 59.0%, respectively, in discriminating the patients with schizophrenia from the healthy controls. The ROC analysis performed on the TGC data generated the most accurate result among the EEG measures, displaying an overall classification accuracy of 92.5%. Conclusion: As TGC includes phase, which contains information about neuronal interactions from the EEG recording, TGC is expected to be useful for understanding the mechanisms the dysfunctional cortico-thalamic interaction in patients with schizophrenia. The resting-state TGC value was increased in the patients with schizophrenia compared to that in the healthy controls and had a higher discriminating ability than the other parameters. These findings may be related to the compensatory hyper-arousal patterns of the dysfunctional default-mode network (DMN) in schizophrenia. Further research exploring the association between TGC and medical or psychiatric conditions that may confound EEG signals will help clarify the potential utility of TGC.

Keywords: quantitative electroencephalography (QEEG), theta-phase gamma-amplitude coupling (TGC), schizophrenia, diagnostic utility

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Authors: Rai Sachindra Prasad

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Thought is physical force. This has been well recognized but hardly translated visually or otherwise in the sense of its transfer from one individual to another. In the present world of chaos and disorder with yawning gaps between right and wrong thinking individuals, if it is possible to transfer the right thoughts to replace the wrong ones it would indeed be a great achievement in the present situation of the world which is torn with violence with dangerous thoughts of individuals. Moreover, such a possibility would completely remove the barrier of language between two persons, which at times proves to be a great obstacle in realizing a desired purpose. If a proper instrumentation scheme containing appropriate transducers and electronics is designed and implemented to realize this thought ransfer phenomenon, this would prove to be extremely useful when properly used. Considering the advancements already made in recording the nerve impulses in the brain, which are electrical events of very short durations that move along the axon, it is conceivable that this may be used to good effect in implementing the scheme. In such a proposition one shoud consider the roles played by pineal body, pituitary gland and ‘association’ areas. Pioneer students of brain have thought that associations or connections between sensory input and motor output were made in these areas. It is currently believed that rather than being regions of simple sensory-motor connections, the association areas process and integrate sensory information relayed to them from the primary sensory areas of the cortex and from the thalamus, after the information has been processed, it may be sent to motor areas to be acted upon. Again, even though the role played by pineal body is not known fully to neurologists its interconnection with pituitary gland is a matter of great significance to the ‘Rishis’ and; Seers’ s described in Vedas and Puranas- the ancient Holy books of Hindus. If the pineal body is activated through meditation it would control the pituitary gland thereby the individual’s thoughts and acts. Thus, if thoughts can be picked up by special transducers, these can be connected to suitable electronics circuitry to amplify the signals. These signals in the form of electromagnetic waves can then be transmitted using modems for long distance transmission and eventually received by or passed on to a subject of interest through another set of electronics circuit and devices.

Keywords: modems, pituitary gland, pineal body, thought transfer

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Authors: Rohit Shrivastava, Stefan Luding

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A mechanical wave is propagation of vibration with transfer of energy and momentum. Studying the energy as well as spectral energy characteristics of a propagating wave through disordered granular media can assist in understanding the overall properties of wave propagation through inhomogeneous materials like soil. The study of these properties is aimed at modeling wave propagation for oil, mineral or gas exploration (seismic prospecting) or non-destructive testing for the study of internal structure of solids. The study of Energy content (Kinetic, Potential and Total Energy) of a pulse propagating through an idealized one-dimensional discrete particle system like a mass disordered granular chain can assist in understanding the energy attenuation due to disorder as a function of propagation distance. The spectral analysis of the energy signal can assist in understanding dispersion as well as attenuation due to scattering in different frequencies (scattering attenuation). The selection of one-dimensional granular chain also helps in studying only the P-wave attributes of the wave and removing the influence of shear or rotational waves. Granular chains with different mass distributions have been studied, by randomly selecting masses from normal, binary and uniform distributions and the standard deviation of the distribution is considered as the disorder parameter, higher standard deviation means higher disorder and lower standard deviation means lower disorder. For obtaining macroscopic/continuum properties, ensemble averaging has been used. Interpreting information from a Total Energy signal turned out to be much easier in comparison to displacement, velocity or acceleration signals of the wave, hence, indicating a better analysis method for wave propagation through granular materials. Increasing disorder leads to faster attenuation of the signal and decreases the Energy of higher frequency signals transmitted, but at the same time the energy of spatially localized high frequencies also increases. An ordered granular chain exhibits ballistic propagation of energy whereas, a disordered granular chain exhibits diffusive like propagation, which eventually becomes localized at long periods of time.

Keywords: discrete elements, energy attenuation, mass disorder, granular chain, spectral energy, wave propagation

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Authors: H. Arzani, Z. Arzani

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Every piece of rangeland has a different set of physical and biological characteristics. This requires the manager to synthesis various information for regular monitoring to define changes trend to get wright decision for sustainable management. So range managers need to use computerized technologies to monitor rangeland, and select. The best management practices. There are four examples of computerized technologies that can benefit sustainable management: (1) Photographic method for cover measurement: The method was tested in different vegetation communities in semi humid and arid regions. Interpretation of pictures of quadrats was done using Arc View software. Data analysis was done by SPSS software using paired t test. Based on the results, generally, photographic method can be used to measure ground cover in most vegetation communities. (2) GPS application for corresponding ground samples and satellite pixels: In two provinces of Tehran and Markazi, six reference points were selected and in each point, eight GPS models were tested. Significant relation among GPS model, time and location with accuracy of estimated coordinates was found. After selection of suitable method, in Markazi province coordinates of plots along four transects in each 6 sites of rangelands was recorded. The best time of GPS application was in the morning hours, Etrex Vista had less error than other models, and a significant relation among GPS model, time and location with accuracy of estimated coordinates was found. (3) Application of satellite data for rangeland monitoring: Focusing on the long term variation of vegetation parameters such as vegetation cover and production is essential. Our study in grass and shrub lands showed that there were significant correlations between quantitative vegetation characteristics and satellite data. So it is possible to monitor rangeland vegetation using digital data for sustainable utilization. (4) Rangeland suitability classification with GIS: Range suitability assessment can facilitate sustainable management planning. Three sub-models of sensitivity to erosion, water suitability and forage production out puts were entered to final range suitability classification model. GIS was facilitate classification of range suitability and produced suitability maps for sheep grazing. Generally digital computers assist range managers to interpret, modify, calibrate or integrating information for correct management.

Keywords: computer, GPS, GIS, remote sensing, photographic method, monitoring, rangeland ecosystem, management, suitability, sheep grazing

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Authors: L. Benbaouche, H. E. , F. Krim

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In this paper we propose real-time control of grid-connected single phase inverter, which is flexible and efficient. The first step is devoted to the study and design of the controller through simulation, conducted by the LabVIEW software on the computer 'host'. The second step is running the application from PXI 'target'. LabVIEW software, combined with NI-DAQmx, gives the tools to easily build applications using the digital to analog converter to generate the PWM control signals. Experimental results show that the effectiveness of LabVIEW software applied to power electronics.

Keywords: real-time control, labview, inverter, PWM

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Authors: Johnson Rotimi Oluremi, A. Adedayo Adegbola, A. Samson Adediran, O. Solomon Oladapo

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Spent engine oil contains heavy metals and polycyclic aromatic hydrocarbons which contribute to chronic health hazards, poor soil aeration, immobilisation of nutrients and lowering of pH in soil. It affects geotechnical properties of lateritic soil thereby constituting geotechnical and foundation problems. This study is therefore based on the stabilization of spent engine oil (SEO) contaminated lateritic soil using cement kiln dust (CKD) as a mean of restoring it to its pristine state. Geotechnical tests which include sieve analysis, atterberg limit, compaction, California bearing ratio and unconfined compressive strength tests were carried out on the natural, SEO contaminated and CKD stabilized SEO contaminated lateritic soil samples. The natural soil classified as A-2-7 (2) by AASHTO classification and GC according to the Unified Soil Classification System changed to A-4 non-plastic soil due to SEO contaminated even under the influence of CKD it remained unchanged. However, the maximum dry density (MDD) of the SEO contaminated soil increased while the optimum moisture content (OMC) behaved vice versa with the increase in the percentages of CKD. Similarly, the bearing strength of the stabilized SEO contaminated soil measured by California Bearing Ratio (CBR) increased with percentage increment in CKD. In conclusion, spent engine oil has a detrimental effect on the geotechnical properties of the lateritic soil sample but which can be remediated using 10% CKD as a stand alone admixture in stabilizing spent engine oil contaminated soil.

Keywords: spent engine oil, lateritic soil, cement kiln dust, stabilization, compaction, unconfined compressive strength

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Authors: Rohit Gupta, Ravinder Agarwal

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Surface electromyographic (sEMG) signal has the potential to identify the human activities and intention. This potential is further exploited to control the artificial limbs using the sEMG signal from residual limbs of amputees. The paper deals with the development of multichannel cost efficient sEMG signal interface for research application, along with evaluation of proposed class dependent statistical approach of the feature selection method. The sEMG signal acquisition interface was developed using ADS1298 of Texas Instruments, which is a front-end interface integrated circuit for ECG application. Further, the sEMG signal is recorded from two lower limb muscles for three locomotions namely: Plane Walk (PW), Stair Ascending (SA), Stair Descending (SD). A class dependent statistical approach is proposed for feature selection and also its performance is compared with 12 preexisting feature vectors. To make the study more extensive, performance of five different types of classifiers are compared. The outcome of the current piece of work proves the suitability of the proposed feature selection algorithm for locomotion recognition, as compared to other existing feature vectors. The SVM Classifier is found as the outperformed classifier among compared classifiers with an average recognition accuracy of 97.40%. Feature vector selection emerges as the most dominant factor affecting the classification performance as it holds 51.51% of the total variance in classification accuracy. The results demonstrate the potentials of the developed sEMG signal acquisition interface along with the proposed feature selection algorithm.

Keywords: classifiers, feature selection, locomotion, sEMG

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Authors: Mofizul Islam Awwal

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Due to globalization, industries are rapidly growing throughout the world which leads to many manufacturing organizations. But recently, service industries are beginning to emerge in large numbers almost in all parts of the world including some developing countries. In this context, organizations need to have strong competitive advantage over their rivals to achieve their strategic business goals. Manufacturing industries are adopting many methods and techniques in order to achieve such competitive edge. Over the last decades, manufacturing industries have been successfully practicing lean concept to optimize their production lines. Due to its huge success in manufacturing context, lean has made its way into the service industry. Very little importance has been addressed to service in the area of operations management. Service industries are far behind than manufacturing industries in terms of operations improvement. It will be a hectic job to transfer the lean concept from production floor to service back/front office which will obviously yield possible improvement. Service processes are not as visible as production processes and can be very complex. Lack of research in this area made it quite difficult for service industries as there are no standardized frameworks for successfully implementing lean concept in service organization. The purpose of this research paper is to capture the present scenario of service industry in terms of lean implementation. Thorough analysis of past literature will be done on the applicability and understanding of lean in service structure. Classification of research papers will be done and critical factors will be unveiled for implementing lean in service industry to achieve operational excellence.

Keywords: lean service, lean literature classification, lean implementation, service industry, service excellence

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Authors: Sandeep Thakur, Ismail Mondal, Phani Bhusan Ghosh, Papita Das, Tarun Kumar De

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Sundarbans Reserve forest of India has been undergoing major transformations in the recent past owing to population pressure and related changes. This has brought about major changes in the spatial landscape of the region especially in the western parts. This study attempts to assess the impacts of the Landcover changes on the mangrove habitats. Time series imageries of Landsat were used to analyze the Normalized Differential Vegetation Index (NDVI) patterns over the western parts of Indian Sundarbans forest in order to assess the heath of the mangroves in the region. The images were subjected to Land use Land cover (LULC) classification using sub-pixel classification techniques in ERDAS Imagine software and the changes were mapped. The spatial proliferation of aquaculture farms during the study period was also mapped. A multivariate regression analysis was carried out between the obtained NDVI values and the LULC classes. Similarly, the observed meteorological data sets (time series rainfall and minimum and maximum temperature) were also statistically correlated for regression. The study demonstrated the application of NDVI in assessing the environmental status of mangroves as the relationship between the changes in the environmental variables and the remote sensing based indices felicitate an efficient evaluation of environmental variables, which can be used in the coastal zone monitoring and development processes.

Keywords: aquaculture farms, LULC, Mangrove, NDVI

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Authors: Shankha Sanyal, Archi Banerjee, Sayan Biswas, Sourya Sengupta, Sayan Nag, Ranjan Sengupta, Dipak Ghosh

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The term ‘gestalt’ has been widely used in the field of psychology which defined the perception of human mind to group any object not in part but as a 'unified' whole. Music, in general, is polyphonic - i.e. a combination of a number of pure tones (frequencies) mixed together in a manner that sounds harmonious. The study of human brain response due to different frequency groups of the acoustic signal can give us an excellent insight regarding the neural and functional architecture of brain functions. Hence, the study of music cognition using neuro-biosensors is becoming a rapidly emerging field of research. In this work, we have tried to analyze the effect of different frequency bands of music on the various frequency rhythms of human brain obtained from EEG data. Four widely popular Rabindrasangeet clips were subjected to Wavelet Transform method for extracting five resonant frequency bands from the original music signal. These frequency bands were initially analyzed with Detrended/Adaptive Fractal analysis (DFA/AFA) methods. A listening test was conducted on a pool of 100 respondents to assess the frequency band in which the music becomes non-recognizable. Next, these resonant frequency bands were presented to 20 subjects as auditory stimulus and EEG signals recorded simultaneously in 19 different locations of the brain. The recorded EEG signals were noise cleaned and subjected again to DFA/AFA technique on the alpha, theta and gamma frequency range. Thus, we obtained the scaling exponents from the two methods in alpha, theta and gamma EEG rhythms corresponding to different frequency bands of music. From the analysis of music signal, it is seen that loss of recognition is proportional to the loss of long range correlation in the signal. From the EEG signal analysis, we obtain frequency specific arousal based response in different lobes of brain as well as in specific EEG bands corresponding to musical stimuli. In this way, we look to identify a specific frequency band beyond which the music becomes non-recognizable and below which in spite of the absence of other bands the music is perceivable to the audience. This revelation can be of immense importance when it comes to the field of cognitive music therapy and researchers of creativity.

Keywords: AFA, DFA, EEG, gestalt in music, Hurst exponent

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Authors: Wenqi Liu, Reginald Bailey

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This study explores an advanced approach to enhancing B2B sales forecasting by integrating machine learning models with a rule-based decision framework. The methodology begins with the development of a machine learning classification model to predict conversion likelihood, aiming to improve accuracy over traditional methods like logistic regression. The classification model's effectiveness is measured using metrics such as accuracy, precision, recall, and F1 score, alongside a feature importance analysis to identify key predictors. Following this, a machine learning regression model is used to forecast sales value, with the objective of reducing mean absolute error (MAE) compared to linear regression techniques. The regression model's performance is assessed using MAE, root mean square error (RMSE), and R-squared metrics, emphasizing feature contribution to the prediction. To bridge the gap between predictive analytics and decision-making, a rule-based decision model is introduced that prioritizes customers based on predefined thresholds for conversion probability and predicted sales value. This approach significantly enhances customer prioritization and improves overall sales performance by increasing conversion rates and optimizing revenue generation. The findings suggest that this combined framework offers a practical, data-driven solution for sales teams, facilitating more strategic decision-making in B2B environments.

Keywords: sales forecasting, machine learning, rule-based decision model, customer prioritization, predictive analytics

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Authors: H. G. C. P. Dinesh, G. Tharshini, M. P. B. Ekanayake, G. M. R. I. Godaliyadda

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This paper presents a new spatial feature extraction method based on principle component analysis (PCA) and Fisher Discernment Analysis (FDA) for facial expression recognition. It not only extracts reliable features for classification, but also reduces the feature space dimensions of pattern samples. In this method, first each gray scale image is considered in its entirety as the measurement matrix. Then, principle components (PCs) of row vectors of this matrix and variance of these row vectors along PCs are estimated. Therefore, this method would ensure the preservation of spatial information of the facial image. Afterwards, by incorporating the spectral information of the eigen-filters derived from the PCs, a feature vector was constructed, for a given image. Finally, FDA was used to define a set of basis in a reduced dimension subspace such that the optimal clustering is achieved. The method of FDA defines an inter-class scatter matrix and intra-class scatter matrix to enhance the compactness of each cluster while maximizing the distance between cluster marginal points. In order to matching the test image with the training set, a cosine similarity based Bayesian classification was used. The proposed method was tested on the Cohn-Kanade database and JAFFE database. It was observed that the proposed method which incorporates spatial information to construct an optimal feature space outperforms the standard PCA and FDA based methods.

Keywords: facial expression recognition, principle component analysis (PCA), fisher discernment analysis (FDA), eigen-filter, cosine similarity, bayesian classifier, f-measure

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Chayma Bahar, Chokri Baccouch, Hedi Sakli, Nizar Sakli

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Harvesting and transport of optical and radiofrequency signals are a topical subject with multiple challenges. In this paper, we present a Optical RECTENNA system. We propose here a hybrid system solar cell antenna for 5G mobile communications networks. Thus, we propose rectifying circuit. A parametric study is done to follow the influence of load resistance and input power on Optical RECTENNA system performance. Thus, we propose a solar cell antenna structure in the frequency band of future 5G standard in 2.45 GHz bands.

Keywords: antenna, IoT, optical rectenna, solar cell

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Authors: Chi-Chang Lin, Jian-Rong Wu, Jiun-Yan Chiu

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Myopia, ubiquitous symptom that is necessary to correct the eyesight by optical lens struggles many people for their daily life. Recent years, younger people raise interesting on using contact lens because of its convenience and aesthetics. In clinical, the risk of eye infections increases owing to the behavior of incorrectly using contact lens unsupervised cleaning which raising the infection risk of cornea, named ocular keratitis. In order to overcome the identification needs, new detection or analysis method with rapid and more accurate identification for clinical microorganism is importantly needed. In our study, we take advantage of Raman spectroscopy having unique fingerprint for different functional groups as the distinct and fast examination tool on microorganism. As we know, Raman scatting signals are normally too weak for the detection, especially in biological field. Here, we applied special SERS enhancement substrates to generate higher Raman signals. SERS filter we designed in this article that prepared by deposition of silver nanoparticles directly onto cellulose filter surface and suspension nanoparticles - gold nanostars (AuNSs) also be introduced together to achieve better enhancement for lower concentration analyte (i.e., various bacteria). Research targets also focusing on studying the shape effect of synthetic AuNSs, needle-like surface morphology may possible creates more hot-spot for getting higher SERS enhance ability. We utilized new designed SERS technology to distinguish the bacteria from ocular keratitis under strain level, and specific Raman and SERS fingerprint were grouped under pattern recognition process. We reported a new method combined different SERS substrates can be applied for clinical microorganism detection under strain level with simple, rapid preparation and low cost. Our presenting SERS technology not only shows the great potential for clinical bacteria detection but also can be used for environmental pollution and food safety analysis.

Keywords: bacteria, gold nanostars, Raman spectroscopy surface-enhanced Raman scattering filter

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Authors: Rahul Thakur, Onkar Chandel

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Red Hat is the world leader in providing open source software and solutions. A global enterprise, like Red Hat, has unique issues of connecting employees with content because of distributed offices, multiple teams spread across geographies, multiple languages, and different cultures. Employees, of a global company, create content that is distributed across departments, teams, regions, and countries. This makes finding the best content difficult since owners keep iterating on the existing content. When employees are unable to find the content, they end up creating it once again and in the process duplicating existing material and effort. Also, employees may not find the relevant content and spend time reviewing obsolete duplicate, or irrelevant content. On an average, a person spends 15 minutes/day in failed searches that might result in missed business opportunities, employee frustration, and substandard deliverables. Red Hat Knowledge Management Office (KMO) applied 'open source strategy' to solve the above problems. Under the Open Source Strategy, decisions are taken collectively. The strategy aims at accomplishing common goals with the help of communities. The objectives of this initiative were to save employees' time, get them authentic content, improve their content search experience, avoid duplicate content creation, provide context based search, improve analytics, improve content management workflows, automate content classification, and automate content upload. This session will describe open source strategy, its applicability in content management, challenges, recommended solutions, and outcome.

Keywords: content classification, content management, knowledge management, open source

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Yoshinori Wakatake

Abstract:

Many defects after the release of software packages are caused due to omissions of sufficient test items in test specifications. Poor test specifications are detected by manual review, which imposes a high human load. The prevention of omissions depends on the end-user awareness of test specification writers. If test specifications were written while envisioning the behavior of end-users, the number of omissions in test items would be greatly reduced. The paper pays attention to the point that writers who can achieve it differ from those who cannot in not only the description richness but also their gaze information. It proposes a method to estimate the degree of user-awareness of writers through the analysis of their gaze information when writing test specifications. We conduct an experiment to obtain the gaze information of a writer of the test specifications. Test specifications are automatically classified using gaze information. In this method, a Random Forest model is constructed for the classification. The classification is highly accurate. By looking at the explanatory variables which turn out to be important variables, we know behavioral features to distinguish test specifications of high quality from others. It is confirmed they are pupil diameter size and the number and the duration of blinks. The paper also investigates test specifications automatically classified with gaze information to discuss features in their writing ways in each quality level. The proposed method enables us to automatically classify test specifications. It also prevents test item omissions, because it reveals writing features that test specifications of high quality should satisfy.

Keywords: blink, eye tracking, gaze information, pupil diameter, quality improvement, specification document, user-awareness

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Authors: Mohd Iz'aan Paiz Bin Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah Yusof

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Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.

Keywords: classification, feature extraction, fuzzy, inspection system, image analysis, macroscopic images

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Authors: Cheng-Yu Tsai, Wen-Te Liu, Shin-Mei Hsu, Yin-Tzu Lin, Chi Wu

Abstract:

Background: Obstructive Sleep apnea syndrome (OSAS) is a common respiratory disorder during sleep. In addition, Body parameters were identified high predictive importance for OSAS severity. However, the effects of body parameters on OSAS severity remain unclear. Objective: In this study, the objective is to establish a prediction model for OSAS by using body parameters and investigate the effects of body parameters in OSAS. Methodologies: Severity was quantified as the polysomnography and the mean hourly number of greater than 3% dips in oxygen saturation during examination in a hospital in New Taipei City (Taiwan). Four levels of OSAS severity were classified by the apnea and hypopnea index (AHI) with American Academy of Sleep Medicine (AASM) guideline. Body parameters, including neck circumference, waist size, and body mass index (BMI) were obtained from questionnaire. Next, dividing the collecting subjects into two groups: training and testing groups. The training group was used to establish the random forest (RF) to predicting, and test group was used to evaluated the accuracy of classification. Results: There were 3330 subjects recruited in this study, whom had been done polysomnography for evaluating severity for OSAS. A RF of 1000 trees achieved correctly classified 79.94 % of test cases. When further evaluated on the test cohort, RF showed the waist and BMI as the high import factors in OSAS. Conclusion It is possible to provide patient with prescreening by body parameters which can pre-evaluate the health risks.

Keywords: apnea and hypopnea index, Body parameters, obstructive sleep apnea syndrome, Random Forest

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Authors: Mohsen Maraoui

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In the aim of natural language processing applications improvement, such as information retrieval, machine translation, lexical disambiguation, we focus on statistical approach to semantic indexing for multilingual text documents based on conceptual network formalism. We propose to use this formalism as an indexing language to represent the descriptive concepts and their weighting. These concepts represent the content of the document. Our contribution is based on two steps. In the first step, we propose the extraction of index terms using the multilingual lexical resource Euro WordNet (EWN). In the second step, we pass from the representation of index terms to the representation of index concepts through conceptual network formalism. This network is generated using the EWN resource and pass by a classification step based on association rules model (in attempt to discover the non-taxonomic relations or contextual relations between the concepts of a document). These relations are latent relations buried in the text and carried by the semantic context of the co-occurrence of concepts in the document. Our proposed indexing approach can be applied to text documents in various languages because it is based on a linguistic method adapted to the language through a multilingual thesaurus. Next, we apply the same statistical process regardless of the language in order to extract the significant concepts and their associated weights. We prove that the proposed indexing approach provides encouraging results.

Keywords: concept extraction, conceptual network formalism, fuzzy association rules, multilingual thesaurus, semantic indexing

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Authors: Yingisani Chabalala, Elhadi Adam, Khalid Adem Ali

Abstract:

Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapped spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit tree mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well-suited for accurate smallholder fruit plantation mapping.

Keywords: smallholder agriculture, fruit trees, data fusion, precision agriculture

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Authors: Khalid Al Mutairi, Mashhor Mansor, Magdy El-Bana, Asyraf Mansor, Saud AL-Rowaily

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Urban expansion and the associated increase in anthropogenic pressures have led to a great loss of the Red Sea’s biodiversity. Floristic composition, diversity, and environmental controls were investigated for 210 relive's on twenty coral islands of Farasan in the Red Sea, Saudi Arabia. Multivariate statistical analyses for classification (Cluster Analysis), ordination (Detrended Correspondence Analysis (DCA), and Redundancy Analysis (RDA) were employed to identify vegetation types and their relevance to the underlying environmental gradients. A total of 191 flowering plants belonging to 53 families and 129 genera were recorded. Geophytes and chamaephytes were the main life forms in the saline habitats, whereas therophytes and hemicryptophytes dominated the sandy formations and coral rocks. The cluster analysis and DCA ordination identified twelve vegetation groups that linked to five main habitats with definite floristic composition and environmental characteristics. The constrained RDA with Monte Carlo permutation tests revealed that elevation and soil salinity were the main environmental factors explaining the vegetation distributions. These results indicate that the flora of the study archipelago represents a phytogeographical linkage between Africa and Saharo-Arabian landscape functional elements. These findings should guide conservation and management efforts to maintain species diversity, which is threatened by anthropogenic activities and invasion by the exotic invasive tree Prosopis juliflora (Sw.) DC.

Keywords: biodiversity, classification, conservation, ordination, Red Sea

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Authors: Meenal Surawar, Rajashree Kotharkar

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Urban Heat Island (UHI) is found more pronounced as a prominent urban environmental concern in developing cities. To study the UHI effect in the Indian context, the Nagpur urban area has been explored in this paper using Landsat 7 ETM+ satellite images through Remote Sensing and GIS techniques. This paper intends to study the effect of LU/LC pattern on daytime Land Surface Temperature (LST) variation, contributing UHI formation within the Nagpur Urban area. Supervised LU/LC area classification was carried to study urban Change detection using ENVI 5. Change detection has been studied by carrying Normalized Difference Vegetation Index (NDVI) to understand the proportion of vegetative cover with respect to built-up ratio. Detection of spectral radiance from the thermal band of satellite images was processed to calibrate LST. Specific representative areas on the basis of urban built-up and vegetation classification were selected for observation of point LST. The entire Nagpur urban area shows that, as building density increases with decrease in vegetation cover, LST increases, thereby causing the UHI effect. UHI intensity has gradually increased by 0.7°C from 2000 to 2006; however, a drastic increase has been observed with difference of 1.8°C during the period 2006 to 2013. Within the Nagpur urban area, the UHI effect was formed due to increase in building density and decrease in vegetative cover.

Keywords: land use/land cover, land surface temperature, remote sensing, urban heat island

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Authors: Juliana A. B. Silva, Caio H. T. L. Albuquerque, Leonardo L. dos Santos, Cristiane K. Oliveira, Ivani Malvestiti, Fernando Hallwass, Ricardo L. Longo

Abstract:

Complexes with lanthanide ions have been extensively studied due to their applications as luminescent, magnetic and catalytic materials as molecular or extended crystals, thin films, glasses, polymeric matrices, ionic liquids, and in solution. NMR chemical shift data in solution have been reported and suggest fluxional structures in a wide range of coordination compounds with rare earth ions. However, the fluxional mechanisms for these compounds are still not established. This structural fluxionality may affect the photophysical, catalytic and magnetic properties in solution. Thus, understanding the structural interconversion mechanisms may aid the design of coordination compounds with, for instance, improved (electro)luminescence, catalytic and magnetic behaviors. The [Eu(btfa)₃bipy] complex, where btfa= 4,4,4-trifluoro-1-phenyl-1,3-butanedionate and bipy= 2,2’-bipiridyl, has a well-defined X-ray crystallographic structure and preliminary 1H NMR data suggested a structural fluxionality. Thus, we have investigated a series of coordination compounds with lanthanide ions [Ln(btfa)₃L], where Ln = La, Eu, Gd or Yb and L= bipy or phen (phen=1,10-phenanthroline) using a combined theoretical-experimental approach. These complexes were synthesized and fully characterized, and detailed NMR measurements were obtained. They were also studied by quantum chemical computational methods (DFT-PBE0). The aim was to determine the relevant factors in the structure of these compounds that favor or not the fluxional behavior. Measurements of the 1H NMR signals at variable temperature in CD₂Cl₂ of the [Eu(btfa)₃L] complexes suggest that these compounds have a fluxional structure, because the crystal structure has non-equivalent btfa ligands that should lead to non-equivalent hydrogen atoms and thus to more signals in the NMR spectra than those obtained at room temperature, where all hydrogen atoms of the btfa ligands are equivalent, and phen ligand has an effective vertical symmetry plane. For the [Eu(btfa)₃bipy] complex, the broadening of the signals at –70°C provides a lower bound for the coalescence temperature, which indicates the energy barriers involved in the structural interconversion mechanisms are quite small. These barriers and, consequently, the coalescence temperature are dependent upon the radii of the lanthanide ion as well as to their paramagnetic effects. The PBE0 calculated structures are in very good agreement with the crystallographic data and, for the [Eu(btfa)₃bipy] complex, this method provided several distinct structures with almost the same energy. However, the energy barrier for structural interconversion via dissociative pathways were found to be quite high and could not explain the experimental observations. Whereas the pseudo-rotation pathways, involving the btfa and bipy ligands, have very small activation barriers, in excellent agreement with the NMR data. The results also showed an increase in the activation barrier along the lanthanide series due to the decrease of the ionic radii and consequent increase of the steric effects. TD-DFT calculations showed a dependence of the ligand donor state energy with different structures of the complex [Eu(btfa)₃phen], which can affect the energy transfer rates and the luminescence. The energy required to promote the structural fluxionality may also enhance the luminescence quenching in solution. These results can aid in the design of more luminescent compounds and more efficient devices.

Keywords: computational chemistry, lanthanide-based compounds, NMR, structural fluxionality

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Authors: Yang Xinyu, GongCong, Hu Changjuan

Abstract:

Stairway spaces are a crucial component of the pathway systems and vertical transportation networks in mountain city parks. These spaces are closely integrated with the undulating terrain of mountain environments, resulting in continuously changing spatial conditions that can significantly influence participants' behavioral characteristics, thereby affecting their perception. EEG signals, which have been proven to reflect various non-attentive physiological activities in the brain, are widely used in studies related to stress recovery effects and emotional perception. Existing research predominantly examines the impact of spatial characteristics and landscape elements of trails and greenways in plain cities on participants' perception, utilizing EEG signals in laboratory-simulated environments. These studies have preliminarily revealed the relationship between spatial environments and perception preferences. However, on-site ergonomics research in mountain environments remains relatively underdeveloped. To address this gap, the Stairway spaces in Pipashan Park, Chongqing, were selected as the research object. Wearable hydrogel EEG devices were employed to monitor participants' EEG data in real environments, and a Generalized Linear Mixed Model (GLMM) was constructed to explore differences in participants' perception under different paths and modes of movement, as well as the impact of visual and auditory environmental elements within each path on their perception. The model analysis results indicate significant differences in EEG data across different paths and movement modes. Additionally, typical mountainous spatial characteristics, such as openness, green view index, and elevation difference, are identified as key factors influencing participants' EEG data. Higher levels of natural sound and green view index were shown to effectively alleviate participants' stress perception in mountain stairway spaces. The findings reveal the intrinsic connections between environment, behavior, and perception in stairway spaces of mountain city parks, providing a theoretical basis for optimizing the design of stairway spaces in mountain cities.

Keywords: audio-visual perception, EEG monitoring, mountain city park, real environment, stairway space

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