Search results for: PQ signal filtering

Authors: Kumar Kanudkuri, A. Sandhya

Abstract:

Typically, in a MRI clinical setting, there are several protocols run, each indicated for a specific anatomy and disease condition. However, these protocols or parameters within them can change over time due to changes to the recommendations by the physician groups or updates in the software or by the availability of new technologies. Most of the time, the changes are performed by the MRI technologist to account for either time, coverage, physiological, or Specific Absorbtion Rate (SAR ) reasons. However, giving properly guidelines to MRI technologist is important so that they do not change the parameters that negatively impact the image quality. Typically a standard American College of Radiology (ACR) MRI phantom is used for Quality Control (QC) in order to guarantee that the primary objectives of MRI are met. The visual evaluation of quality depends on the operator/reviewer and might change amongst operators as well as for the same operator at various times. Therefore, overcoming these constraints is essential for a more impartial evaluation of quality. This makes quantitative estimation of image quality (IQ) metrics for MRI quality control is very important. So in order to solve this problem, we proposed that there is a need for a robust, open-source, and automated MRI image control tool. The Designed and developed an automatic analysis tool for measuring MRI image quality (IQ) metrics like Signal to Noise Ratio (SNR), Signal to Noise Ratio Uniformity (SNRU), Visual Information Fidelity (VIF), Feature Similarity (FSIM), Gray level co-occurrence matrix (GLCM), slice thickness accuracy, slice position accuracy, High contrast spatial resolution) provided good accuracy assessment. A standardized quality report has generated that incorporates metrics that impact diagnostic quality.

Keywords: ACR MRI phantom, MRI image quality metrics, SNRU, VIF, FSIM, GLCM, slice thickness accuracy, slice position accuracy

Procedia PDF Downloads 154

Authors: James Rigor Camacho, Wansu Lim

Abstract:

Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

Procedia PDF Downloads 102

Authors: William L. Whitehouse, Louisa H. Y. Lo, Andrew B. Kinghorn, Simon C. C. Shiu, Julian. A. Tanner

Abstract:

C-reactive protein (CRP) is an acute-phase reactant and sensitive indicator for sepsis and other life-threatening pathologies, including systemic inflammatory response syndrome (SIRS). Currently, clinical turn-around times for established CRP detection methods take between 30 minutes to hours or even days from centralized laboratories. Here, we report the development of an electrochemical biosensor using redox probe-tagged DNA aptamers functionalized onto cheap, commercially available screen-printed electrodes. Binding-induced conformational switching of the CRP-targeting aptamer induces a specific and selective signal-ON event, which enables single-step and reagentless detection of CRP in as little as 1 minute. The aptasensor dynamic range spans 5-1000nM (R=0.97) or 5-500nM (R=0.99) in 50% diluted human serum, with a LOD of 3nM, corresponding to 2-orders of magnitude sensitivity under the clinically relevant cut-off for CRP. The sensor is stable for up to one week and can be reused numerous times, as judged from repeated real-time dosing and dose-response assays. By decoupling binding events from the signal induction mechanism, structure-switching electrochemical aptamer-based sensors (SS-EABs) provide considerable advantages over their adsorption-based counterparts. Our work expands on the retinue of such sensors reported in the literature and is the first instance of an SS-EAB for reagentless CRP detection. We hope this study can inspire further investigations into the suitability of SS-EABs for diagnostics, which will aid translational R&D toward fully realized devices aimed at point-of-care applications or for use more broadly by the public.

Keywords: structure-switching, C-reactive protein, electrochemical, biosensor, aptasensor.

Procedia PDF Downloads 57

Authors: M. G. Mostafa, Manjushree Chowdhury, Tapan Kumar Biswas, , Ananda Kumar Saha

Abstract:

This study focused on effluents characterization and treatment process to reduce of toxicity from tannery effluents. Tanning industry is one of the oldest industries in the world. It is typically characterized as pollutants generated industries which produce wide varieties of high strength toxic chemicals. The study was conducted during the year 2008 to 2009 and the tannery effluents were collected three times in a year from the outlet of some selected leather industries located in Hagaribagh industrial zone Dhaka, Bangladesh. The analysis results of the raw effluents reveal that the effluents were yellowish-brown color, having basic pH, very high value of BOD5¬¬, COD, TDS, TSS, TS, and high concentrations of Cr, Na, SO42-, Cl- and other organic and inorganic constituents. The tannery effluents were treated with various doses of FeCl3 after settling and a subsequent filtration through sand-stone. The study observed that coagulant (FeCl3) 150 mg/L dose around neutral pH showed the best removal efficiency for major physico-chemical parameters. The analysis results of illustrate that the most of the physical and chemical parameters were found well below the prescribed permissible limits for effluent discharged. The study suggests that tannery effluents could be treated by a combined process consisting of settling, filtering and coagulating with FeCl3.

Keywords: characterization, effluent, tannery, treatment

Procedia PDF Downloads 444

Authors: Sujata Kulkarni, Udhav Bhosle, Vijaykumar T.

Abstract:

Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.

Keywords: Carl STA/LTA, features extraction, real time, dataset, machine learning, seismic detection

Procedia PDF Downloads 115

Authors: Bruno R. A. Oliveira, Italo V. V. Braga, Jonas P. Reges, Luiz P. O. Santos, Sidney C. Duarte, Emilson R. R. Melo, Auzuir R. Alexandria

Abstract:

This project aims to building a controlled greenhouse, or for better understanding, a structure where one can maintain a given range of temperature values (°C) coming from radiation emitted by an incandescent light, as previously defined, characterizing as a kind of on-off control and a differential, which is the plotting of temperature versus time graphs assisted by MATLAB software via serial communication. That way it is possible to connect the stove with a computer and monitor parameters. In the control, it was performed using a PIC 16F877A microprocessor which enabled convert analog signals to digital, perform serial communication with the IC MAX232 and enable signal transistors. The language used in the PIC's management is Basic. There are also a cooling system realized by two coolers 12V distributed in lateral structure, being used for venting and the other for exhaust air. To find out existing temperature inside is used LM35DZ sensor. Other mechanism used in the greenhouse construction was comprised of a reed switch and a magnet; their function is in recognition of the door position where a signal is sent to a buzzer when the door is open. Beyond it exist LEDs that help to identify the operation which the stove is located. To facilitate human-machine communication is employed an LCD display that tells real-time temperature and other information. The average range of design operating without any major problems, taking into account the limitations of the construction material and structure of electrical current conduction, is approximately 65 to 70 ° C. The project is efficient in these conditions, that is, when you wish to get information from a given material to be tested at temperatures not as high. With the implementation of the greenhouse automation, facilitating the temperature control and the development of a structure that encourages correct environment for the most diverse applications.

Keywords: greenhouse, microcontroller, temperature, control, MATLAB

Procedia PDF Downloads 399

Authors: J. Alvarez, J. Pimienta, R. Sarmiento

Abstract:

Diode lasers working in free mode present different shifting and broadening determined by external factors such as temperature, current or mechanical vibrations, and they are not more useful in applications such as spectroscopy, metrology, and cooling of atoms, among others. Different configurations can reduce the spectral width of a laser; one of the most effective is to extend the optical resonator of the laser diode and use optical feedback either with the help of a partially reflective mirror or with a diffraction grating; this latter configuration is not only allowed to reduce the spectral width of the laser line but also to coarsely adjust its working wavelength, within a wide range typically ~ 10nm by slightly varying the angle of the diffraction grating. Two settings are commonly used for this purpose, the Littrow configuration and the Littmann Metcalf. In this paper, we present the design, construction, and characterization of a compact extended laser cavity in Littrow configuration. The designed cavity is compact and was machined on an aluminum block using computer numerical control (CNC); it has a mass of only 380 g. The design was tested on laser diodes with different wavelengths, 650nm, 780nm, and 795 nm, but can be equally efficient at other wavelengths. This report details the results obtained from the extended cavity working at a wavelength of 780 nm, with an output power of around 35mW and a line width of less than 1Mhz. The cavity was used to observe the spectrum of the corresponding Rubidium D2 line. By modulating the current and with the help of phase detection techniques, a dispersion signal with an excellent signal-to-noise ratio was generated that allowed the stabilization of the laser to a transition of the hyperfine structure of Rubidium with an integral proportional controller (PI) circuit made with precision operational amplifiers.

Keywords: Littrow, Littman-Metcalf, line width, laser stabilization, hyperfine structure

Procedia PDF Downloads 218

Authors: Abdelkader Harrouz, Omar Harrouz, Ali Benatiallah

Abstract:

This paper is to present the essential definitions, roles and characteristics of automation of metering system. We discuss measurement, data acquisition and metrological control of a signal sensor from dynamic metering system. After that, we present control of instruments of metering system of fluid with more detailed discussions to the reference standards.

Keywords: communication, metering, computer, sensor

Procedia PDF Downloads 546

Authors: Jean-Pierre Dubois, Jihad S. Daba, H. Karam, J. Abdallah

Abstract:

Tsunami early detection and warning systems have proved to be of ultimate importance, especially after the destructive tsunami that hit Japan in March 2012. Such systems are crucial to inform the authorities of any risk of a tsunami and of the degree of its danger in order to make the right decision and notify the public of the actions they need to take to save their lives. The purpose of this research is to enhance existing tsunami detection and warning systems. We first propose an automated and miniaturized model of an early tsunami detection and warning system. The model for the operation of a tsunami warning system is simulated using the data acquisition toolbox of Matlab and measurements acquired from specified internet pages due to the lack of the required real-life sensors, both seismic and hydrologic, and building a graphical user interface for the system. In the second phase of this work, we implement various satellite image filtering schemes to enhance the acquired synthetic aperture radar images of the tsunami affected region that are masked by speckle noise. This enables us to conduct a post-tsunami damage extent study and calculate the percentage damage. We conclude by proposing improvements to the existing telecommunication infrastructure of existing warning tsunami systems using a migration to IP-based networks and fiber optics links.

Keywords: detection, GIS, GSN, GTS, GPS, speckle noise, synthetic aperture radar, tsunami, wiener filter

Procedia PDF Downloads 384

Authors: Hongquan Zhang

Abstract:

Inspired by endogenous protein motors, researchers have constructed various synthetic DNA motors based on the specificity and predictability of Watson-Crick base pairing. However, the application of DNA motors to signal amplification and biosensing is limited because of low mobility and difficulty in real-time monitoring of the walking process. The objective of our work was to construct a new type of DNA motor termed target-triggered DNA motors that can walk for hundreds of steps in response to a single target binding event. To improve the mobility and processivity of DNA motors, we used gold nanoparticles (AuNPs) as scaffolds to build high-density, three-dimensional tracks. Hundreds of track strands are conjugated to a single AuNP. To enable DNA motors to respond to specific protein and nucleic acid targets, we adapted the binding-induced DNA assembly into the design of the target-triggered DNA motors. In response to the binding of specific target molecules, DNA motors are activated to autonomously walk along AuNP, which is powered by a nicking endonuclease or DNAzyme-catalyzed cleavage of track strands. Each moving step restores the fluorescence of a dye molecule, enabling monitoring of the operation of DNA motors in real time. The motors can translate a single binding event into the generation of hundreds of oligonucleotides from a single nanoparticle. The motors have been applied to amplify the detection of proteins and nucleic acids in test tubes and live cells. The motors were able to detect low pM concentrations of specific protein and nucleic acid targets in homogeneous solutions without the need for separation. Target-triggered DNA motors are significant for broadening applications of DNA motors to molecular sensing, cell imagining, molecular interaction monitoring, and controlled delivery and release of therapeutics.

Keywords: biosensing, DNA motors, gold nanoparticles, signal amplification

Procedia PDF Downloads 78

Authors: Aziz Ikhlef

Abstract:

Most of multi-slices CT scanners are built with detectors composed of scintillator - photodiodes arrays. The photodiodes arrays are mainly based on front-illuminated technology for detectors under 64 slices and on back-illuminated photodiode for systems of 64 slices or more. The designs based on back-illuminated photodiodes were being investigated for CT machines to overcome the challenge of the higher number of runs and connection required in front-illuminated diodes. In backlit diodes, the electronic noise has already been improved because of the reduction of the load capacitance due to the routing reduction. This translated by a better image quality in low signal application, improving low dose imaging in large patient population. With the fast development of multi-detector-rows CT (MDCT) scanners and the increasing number of examinations, the clinical community has raised significant concerns on radiation dose received by the patient in both medical and regulatory community. In order to reduce individual exposure and in response to the recommendations of the International Commission on Radiological Protection (ICRP) which suggests that all exposures should be kept as low as reasonably achievable (ALARA), every manufacturer is trying to implement strategies and solutions to optimize dose efficiency and image quality based on x-ray emission and scanning parameters. The added demands on the CT detector performance also comes from the increased utilization of spectral CT or dual-energy CT in which projection data of two different tube potentials are collected. One of the approaches utilizes a technology called fast-kVp switching in which the tube voltage is switched between 80kVp and 140kVp in fraction of a millisecond. To reduce the cross-contamination of signals, the scintillator based detector temporal response has to be extremely fast to minimize the residual signal from previous samples. In addition, this paper will present an overview of detector technologies and image chain improvement which have been investigated in the last few years to improve the signal-noise ratio and the dose efficiency CT scanners in regular examinations and in energy discrimination techniques. Several parameters of the image chain in general and in the detector technology contribute in the optimization of the final image quality. We will go through the properties of the post-patient collimation to improve the scatter-to-primary ratio, the scintillator material properties such as light output, afterglow, primary speed, crosstalk to improve the spectral imaging, the photodiode design characteristics and the data acquisition system (DAS) to optimize for crosstalk, noise and temporal/spatial resolution.

Keywords: computed tomography, X-ray detector, medical imaging, image quality, artifacts

Procedia PDF Downloads 259

Authors: Sofia G. Meirinho, Luis G. Dias, António M. Peres, Lígia R. Rodrigues

Abstract:

The emerging development of electrochemical aptasen sors has enabled the easy and fast detection of protein biomarkers in standard and real samples. Biomarkers are produced by body organs or tumours and provide a measure of antigens on cell surfaces. When detected in high amounts in blood, they can be suggestive of tumour activity. These biomarkers are more often used to evaluate treatment effects or to assess the potential for metastatic disease in patients with established disease. Osteopontin (OPN) is a protein found in all body fluids and constitutes a possible biomarker because its overexpression has been related with breast cancer evolution and metastasis. Currently, biomarkers are commonly used for the development of diagnostic methods, allowing the detection of the disease in its initial stages. A previously described RNA aptamer was used in the current work to develop a simple and sensitive electrochemical aptasensor with high affinity for human OPN. The RNA aptamer was biotinylated and immobilized on a gold electrode by avidin-biotin interaction. The electrochemical signal generated from the aptamer–target molecule interaction was monitored electrochemically using cyclic voltammetry in the presence of [Fe (CN) 6]−3/− as a redox probe. The signal observed showed a current decrease due to the binding of OPN. The preliminary results showed that this aptasensor enables the detection of OPN in standard solutions, showing good selectivity towards the target in the presence of others interfering proteins such as bovine OPN and bovine serum albumin. The results gathered in the current work suggest that the proposed electrochemical aptasensor is a simple and sensitive detection tool for human OPN and so, may have future applications in cancer disease monitoring.

Keywords: osteopontin, aptamer, aptasensor, screen-printed electrode, cyclic voltammetry

Procedia PDF Downloads 424

Authors: S. H. Jang

Abstract:

Posterior reversible encephalopathy syndrome (PRES) is characterized by acute neurologic symptoms (visual loss, headache, altered mentality and seizures) and by typical imaging findings (bilateral subcortical and cortical edema with predominatly posterior distribution). Nephrotic syndrome is a syndrome comprising signs of proteinuria, hypoalbuminemia, and edema. It is well known that hypertension predispose patient with nephrotic syndrome to PRES. A 45-year old male was referred for suddenly developed vertigo, disequilibrium. He had previous history of nephrotic syndrome. His medical history included diabetes controlled with medication. He was hospitalized because of generalized edema a few days ago. His vital signs were stable. On neurologic examination, his mental state was alert. Horizontal nystagmus to right side on return to primary position was observed. He showed good grade motor weakness and ataxia in right upper and lower limbs without other sensory abnormality. Brain MRI showed increased signal intensity in FLAIR image, decreased signal intensity in T1 image and focal enhanced lesion in T1 contrast image at whole midbrain, pons and cerebellar peduncle symmetrically, which was compatible with vasogenic edema. Laboratory findings showed severe proteinuria and hypoalbuminemia. He was given intravenous dexamethasone and diuretics to reduce vasogenic edema and raise the intra-vascular osmotic pressure. Nystagmus, motor weakness and limb ataxia improved gradually over 2 weeks; He recovered without any neurologic symptom and sign. Follow-up MRI showed decreased vasogenic edema fairly. We report a case of brain stem PRES in normotensive, nephrotic syndrome patient.

Keywords: posterior reversible encephalopathy syndrome, MRI, nephrotic syndrome, vasogenic brain edema

Procedia PDF Downloads 268

Authors: Hadj Abd El Kader Benghenia, Fethi Bereksi Reguig

Abstract:

In clinical medicine, blood pressure, raised blood hemodynamic monitoring is rich pathophysiological information of cardiovascular system, of course described through factors such as: blood volume, arterial compliance and peripheral resistance. In this work, we are interested in analyzing these signals to propose a detection algorithm to delineate the different sequences and especially systolic blood pressure (SBP), diastolic blood pressure (DBP), and the wave and dicrotic to do their analysis in order to extract the cardiovascular parameters.

Keywords: blood pressure, SBP, DBP, detection algorithm

Procedia PDF Downloads 428

Authors: Walid A. Attia, Taher M. Bazan, Fawzy Eltohamy, Mahmoud Fathy

Abstract:

System performance evaluation is an essential stage in the design of high-resolution satellite telescopes prior to the development process. In this paper, a system performance evaluation of a very high-resolution satellite telescope is investigated. The evaluated system has a Korsch optical scheme design. This design has been discussed in another paper with respect to three-mirror anastigmat (TMA) scheme design and the former configuration showed better results. The investigated system is based on the Korsch optical design integrated with a time-delay and integration charge coupled device (TDI-CCD) sensor to achieve a ground sampling distance (GSD) of 25 cm. The key performance metrics considered are the spatial resolution, the signal to noise ratio (SNR) and the total modulation transfer function (MTF) of the system. In addition, the national image interpretability rating scale (NIIRS) metric is assessed to predict the image quality according to the modified general image quality equation (GIQE). Based on the orbital, optical and detector parameters, the estimated GSD is found to be 25 cm. The SNR has been analyzed at different illumination conditions of target albedos, sun and sensor angles. The system MTF has been computed including diffraction, aberration, optical manufacturing, smear and detector sampling as the main contributors for evaluation the MTF. Finally, the system performance evaluation results show that the computed MTF value is found to be around 0.08 at the Nyquist frequency, the SNR value was found to be 130 at albedo 0.2 with a nadir viewing angles and the predicted NIIRS is in the order of 6.5 which implies a very good system image quality.

Keywords: modulation transfer function, national image interpretability rating scale, signal to noise ratio, satellite telescope performance evaluation

Procedia PDF Downloads 380

Authors: Aziz Ikhlef

Abstract:

Most of multi-slices Computed Tomography (CT) scanners are built with detectors composed of scintillator - photodiodes arrays. The photodiodes arrays are mainly based on front-illuminated technology for detectors under 64 slices and on back-illuminated photodiode for systems of 64 slices or more. The designs based on back-illuminated photodiodes were being investigated for CT machines to overcome the challenge of the higher number of runs and connection required in front-illuminated diodes. In backlit diodes, the electronic noise has already been improved because of the reduction of the load capacitance due to the routing reduction. This is translated by a better image quality in low signal application, improving low dose imaging in large patient population. With the fast development of multi-detector-rows CT (MDCT) scanners and the increasing number of examinations, the clinical community has raised significant concerns on radiation dose received by the patient in both medical and regulatory community. In order to reduce individual exposure and in response to the recommendations of the International Commission on Radiological Protection (ICRP) which suggests that all exposures should be kept as low as reasonably achievable (ALARA), every manufacturer is trying to implement strategies and solutions to optimize dose efficiency and image quality based on x-ray emission and scanning parameters. The added demands on the CT detector performance also comes from the increased utilization of spectral CT or dual-energy CT in which projection data of two different tube potentials are collected. One of the approaches utilizes a technology called fast-kVp switching in which the tube voltage is switched between 80 kVp and 140 kVp in fraction of a millisecond. To reduce the cross-contamination of signals, the scintillator based detector temporal response has to be extremely fast to minimize the residual signal from previous samples. In addition, this paper will present an overview of detector technologies and image chain improvement which have been investigated in the last few years to improve the signal-noise ratio and the dose efficiency CT scanners in regular examinations and in energy discrimination techniques. Several parameters of the image chain in general and in the detector technology contribute in the optimization of the final image quality. We will go through the properties of the post-patient collimation to improve the scatter-to-primary ratio, the scintillator material properties such as light output, afterglow, primary speed, crosstalk to improve the spectral imaging, the photodiode design characteristics and the data acquisition system (DAS) to optimize for crosstalk, noise and temporal/spatial resolution.

Keywords: computed tomography, X-ray detector, medical imaging, image quality, artifacts

Procedia PDF Downloads 192

Authors: Aarnav Singh, Jatin Moolchandani

Abstract:

The proposed research work aims to build a query-based AI chatbot that can answer any question related to any topic. A chatbot is software that converses with users via text messages. In the proposed system, we aim to build a chatbot that generates a response based on the user’s query. For this, we use natural language processing to analyze the query and some set of texts to form a concise answer. The texts are obtained through web-scrapping and filtering all the credible sources from a web search. The objective of this project is to provide a chatbot that is able to provide simple and accurate answers without the user having to read through a large number of articles and websites. Creating an AI chatbot that can answer a variety of user questions on a variety of topics is the goal of the proposed research project. This chatbot uses natural language processing to comprehend user inquiries and provides succinct responses by examining a collection of writings that were scraped from the internet. The texts are carefully selected from reliable websites that are found via internet searches. This project aims to provide users with a chatbot that provides clear and precise responses, removing the need to go through several articles and web pages in great detail. In addition to exploring the reasons for their broad acceptance and their usefulness across many industries, this article offers an overview of the interest in chatbots throughout the world.

Keywords: Chatbot, Artificial Intelligence, natural language processing, web scrapping

Procedia PDF Downloads 58

Authors: Zhengyu Yan, Yan Yu, Jianqiu Chen

Abstract:

A novel sensing system has been designed for naphthalene detection based on the quenched fluorescence signal of CdS quantum dots. The fluorescence intensity of the system reduced significantly after adding CdS quantum dots to the water pollution model because of the fluorescent static quenching f mechanism. Herein, we have demonstrated the facile methodology can offer a convenient and low analysis cost with the recovery rate as 97.43%-103.2%, which has potential application prospect.

Keywords: CdS quantum dots, modification, detection, naphthalene

Procedia PDF Downloads 483

Authors: Abdulaziz Alakeel, Roaa Alamri, Abdulrahman Alomair, Mohammed Fouda

Abstract:

Introduction: Ibrutinib is a selective, potent, and irreversible small-molecule inhibitor of Bruton's tyrosine kinase (BTK). It forms a covalent bond with a cysteine residue (CYS-481) at the active site of Btk, leading to inhibition of Btk enzymatic activity. The drug is indicated to treat certain type of cancers such as mantle cell lymphoma (MCL), chronic lymphocytic leukaemia and Waldenström's macroglobulinaemia (WM). Cardiac failure is a condition referred to inability of heart muscle to pump adequate blood to human body organs. There are multiple types of cardiac failure including left and right-sided heart failure, systolic and diastolic heart failures. The aim of this review is to evaluate the risk of cardiac failure associated with the use of ibrutinib and to suggest regulatory recommendations if required. Methodology: Signal Detection team at the National Pharmacovigilance Center (NPC) of Saudi Food and Drug Authority (SFDA) performed a comprehensive signal review using its national database as well as the World Health Organization (WHO) database (VigiBase), to retrieve related information for assessing the causality between cardiac failure and ibrutinib. We used the WHO- Uppsala Monitoring Centre (UMC) criteria as standard for assessing the causality of the reported cases. Results: Case Review: The number of resulted cases for the combined drug/adverse drug reaction are 212 global ICSRs as of July 2020. The reviewers have selected and assessed the causality for the well-documented ICSRs with completeness scores of 0.9 and above (35 ICSRs); the value 1.0 presents the highest score for best-written ICSRs. Among the reviewed cases, more than half of them provides supportive association (four probable and 15 possible cases). Data Mining: The disproportionality of the observed and the expected reporting rate for drug/adverse drug reaction pair is estimated using information component (IC), a tool developed by WHO-UMC to measure the reporting ratio. Positive IC reflects higher statistical association while negative values indicates less statistical association, considering the null value equal to zero. The results of (IC=1.5) revealed a positive statistical association for the drug/ADR combination, which means “Ibrutinib” with “Cardiac Failure” have been observed more than expected when compared to other medications available in WHO database. Conclusion: Health regulators and health care professionals must be aware for the potential risk of cardiac failure associated with ibrutinib and the monitoring of any signs or symptoms in treated patients is essential. The weighted cumulative evidences identified from causality assessment of the reported cases and data mining are sufficient to support a causal association between ibrutinib and cardiac failure.

Keywords: cardiac failure, drug safety, ibrutinib, pharmacovigilance, signal detection

Procedia PDF Downloads 121

Authors: Maziar Noei

Abstract:

Calculation showed that when the nanosheet is doped by Si, the adsorption energy is about -85.62 to -87.43kcal/mol and also the amount of HOMO/LUMO energy gap (Eg) will reduce significantly. Boron nitride nanosheet is a suitable adsorbent for cyanogen and can be used in separation processes cyanogen. It seems that nanosheet (BNNS) is a suitable semiconductor after doping. The doped BNNS in the presence of cyanogens (C2N2) an electrical signal is generating directly and, therefore, can potentially be used for cyanogen sensors.

Keywords: nanosheet, DFT, cyanogen, sensors

Procedia PDF Downloads 275

Authors: Abderrahim Elmoataz

Abstract:

More and more contemporary applications involve data in the form of functions defined on irregular and topologically complicated domains (images, meshs, points clouds, networks, etc). Such data are not organized as familiar digital signals and images sampled on regular lattices. However, they can be conveniently represented as graphs where each vertex represents measured data and each edge represents a relationship (connectivity or certain affinities or interaction) between two vertices. Processing and analyzing these types of data is a major challenge for both image and machine learning communities. Hence, it is very important to transfer to graphs and networks many of the mathematical tools which were initially developed on usual Euclidean spaces and proven to be efficient for many inverse problems and applications dealing with usual image and signal domains. Historically, the main tools for the study of graphs or networks come from combinatorial and graph theory. In recent years there has been an increasing interest in the investigation of one of the major mathematical tools for signal and image analysis, which are Partial Differential Equations (PDEs) variational methods on graphs. The normalized p-laplacian operator has been recently introduced to model a stochastic game called tug-of-war-game with noise. Part interest of this class of operators arises from the fact that it includes, as particular case, the infinity Laplacian, the mean curvature operator and the traditionnal Laplacian operators which was extensiveley used to models and to solve problems in image processing. The purpose of this paper is to introduce and to study a new class of normalized p-Laplacian on graphs. The introduction is based on the extension of p-harmonious function introduced in as discrete approximation for both infinity Laplacian and p-Laplacian equations. Finally, we propose to use these operators as a framework for solving many inverse problems in image processing.

Keywords: normalized p-laplacian, image processing, stochastic game, inverse problems

Procedia PDF Downloads 506

Authors: Charles Chikwendu Okpala, Christopher Chukwutoo Ihueze

Abstract:

As a technique that reduces variation in a product by lessening the sensitivity of the design to sources of variation, rather than by controlling their sources, Taguchi Robust Design entails the designing of ideal goods, by developing a product that has minimal variance in its characteristics and also meets the desired exact performance. This paper examined the concept of the manufacturing approach and its application to brake pad product of an automotive parts manufacturing company. Although the firm claimed that only defects, excess inventory, and over-production were the few wastes that grossly affect their productivity and profitability, a careful study and analysis of their manufacturing processes with the application of Single Minute Exchange of Dies (SMED) tool showed that the waste of waiting is the fourth waste that bedevils the firm. The selection of the Taguchi L9 orthogonal array which is based on the four parameters and the three levels of variation for each parameter revealed that with a range of 2.17, that waiting is the major waste that the company must reduce in order to continue to be viable. Also, to enhance the company’s throughput and profitability, the wastes of over-production, excess inventory, and defects with ranges of 2.01, 1.46, and 0.82, ranking second, third, and fourth respectively must also be reduced to the barest minimum. After proposing -33.84 as the highest optimum Signal-to-Noise ratio to be maintained for the waste of waiting, the paper advocated for the adoption of all the tools and techniques of Lean Production System (LPS), and Continuous Improvement (CI), and concluded by recommending SMED in order to drastically reduce set up time which leads to unnecessary waiting.

Keywords: lean production system, single minute exchange of dies, signal to noise ratio, Taguchi robust design, waste

Procedia PDF Downloads 122

Authors: Ismail Abubakar, Hamid Mehrabi, Reg Morton

Abstract:

Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.

Keywords: modal analysis, artificial neural network, mode shape, natural frequencies, pattern recognition

Procedia PDF Downloads 148

Authors: Plessen Mogens Graf

Abstract:

Within the context of multivariate time series segmentation, this paper proposes a method inspired by a posteriori optimal trading. After a normalization step, time series are treated channelwise as surrogate stock prices that can be traded optimally a posteriori in a virtual portfolio holding either stock or cash. Linear transaction costs are interpreted as hyperparameters for noise filtering. Trading signals, as well as trading signals obtained on the reversed time series, are used for unsupervised channelwise labeling before a consensus over all channels is reached that determines the final segmentation time instants. The method is model-free such that no model prescriptions for segments are made. Benefits of proposed approach include simplicity, computational efficiency, and adaptability to a wide range of different shapes of time series. Performance is demonstrated on synthetic and real-world data, including a large-scale dataset comprising a multivariate time series of dimension 1000 and length 2709. Proposed method is compared to a popular model-based bottom-up approach fitting piecewise affine models and to a recent model-based top-down approach fitting Gaussian models and found to be consistently faster while producing more intuitive results in the sense of segmenting time series at peaks and valleys.

Keywords: time series segmentation, model-free, trading-inspired, multivariate data

Procedia PDF Downloads 130

Authors: Aqsa Jamil, Tamura Hiroshi, Katsuchi Hiroshi, Wang Jiaqi

Abstract:

The yield point represents the upper limit of forces which can be applied to a specimen without causing any permanent deformation. After yielding, the behavior of the specimen suddenly changes, including the possibility of cracking or buckling. So, the accumulation of damage or type of fracture changes depending on this condition. As it is difficult to accurately detect yield points of the several stress concentration points in structural steel specimens, an effort has been made in this research work to develop a convenient technique using thermography (temperature-based detection) during tensile tests for the precise detection of yield point initiation. To verify the applicability of thermography camera, tests were conducted under different loading conditions and measuring the deformation by installing various strain gauges and monitoring the surface temperature with the help of a thermography camera. The yield point of specimens was estimated with the help of temperature dip, which occurs due to the thermoelastic effect during the plastic deformation. The scattering of the data has been checked by performing a repeatability analysis. The effects of temperature imperfection and light source have been checked by carrying out the tests at daytime as well as midnight and by calculating the signal to noise ratio (SNR) of the noised data from the infrared thermography camera, it can be concluded that the camera is independent of testing time and the presence of a visible light source. Furthermore, a fully coupled thermal-stress analysis has been performed by using Abaqus/Standard exact implementation technique to validate the temperature profiles obtained from the thermography camera and to check the feasibility of numerical simulation for the prediction of results extracted with the help of the thermographic technique.

Keywords: signal to noise ratio, thermoelastic effect, thermography, yield point

Procedia PDF Downloads 100

Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

Abstract:

In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy.

Keywords: autoencoder, brainwave signal analysis, electroencephalogram, feature extraction, feature selection, optimization

Procedia PDF Downloads 110

Authors: Udayan Patankar, Shashwati Bhagat, Vilas Nitneware, Ants Koel

Abstract:

An ISM band power amplifier is a type of electronic amplifier used to convert a low-power radio-frequency signal into a larger signal of significant power, typically used for driving the antenna of a transmitter. Due to drastic changes in telecommunication generations may lead to the requirements of improvements. Rapid changes in communication lead to the wide implementation of WLAN technology for its excellent characteristics, such as high transmission speed, long communication distance, and high reliability. Many applications such as WLAN, Bluetooth, and ZigBee, etc. were evolved with 2.4GHz to 5 GHz ISM Band, in which the power amplifier (PA) is a key building block of RF transmitters. There are many manufacturing processes available to manufacture a power amplifier for desired power output, but the major problem they have faced is about the power it consumed for its proper working, as many of them are fabricated on the GaN HEMT, Bi COMS process. In this paper we present a CMOS Base two stage cascode design of power amplifier working on 2.4GHz ISM frequency band. To lower the costs and allow full integration of a complete System-on-Chip (SoC) we have chosen 0.13µm low power CMOS technology for design. While designing a power amplifier, it is a real task to achieve higher power efficiency with minimum resources. This design showcase the Multi biased Cascode methodology to implement a two-stage CMOS power amplifier using ADS and LTSpice simulating tool. Main source is maximum of 2.4V which is internally distributed into different biasing point VB driving and VB driven as required for distinct stages of two stage RF power amplifier. It shows maximum power added efficiency near about 70.195% whereas its Power added efficiency calculated at 1 dB compression point is 44.669 %. Biased MOSFET is used to reduce total dc current as this circuit is designed for different wireless applications comes under 2.4GHz ISM Band.

Keywords: RFIC, PAE, RF CMOS, impedance matching

Procedia PDF Downloads 217

Authors: N. Seemuang, T. McLeay, T. Slatter

Abstract:

Worn tools have a direct effect on the surface finish and part accuracy. Tool condition monitoring systems have been developed over a long period and used to avoid a loss of productivity resulting from using a worn tool. However, the majority of tool monitoring research has applied expensive sensing systems not suitable for production. In this work, the cutting sound in turning machine was studied using microphone. Machining trials using seven cutting conditions were conducted until the observable flank wear width (FWW) on the main cutting edge exceeded 0.4 mm. The cutting inserts were removed from the tool holder and the flank wear width was measured optically. A microphone with built-in preamplifier was used to record the machining sound of EN24 steel being face turned by a CNC lathe in a wet cutting condition using constant surface speed control. The sound was sampled at 50 kS/s and all sound signals recorded from microphone were transformed into the frequency domain by FFT in order to establish the frequency content in the audio signature that could be then used for tool condition monitoring. The extracted feature from audio signal was compared to the flank wear progression on the cutting inserts. The spectrogram reveals a promising feature, named as ‘spindle noise’, which emits from the main spindle motor of turning machine. The spindle noise frequency was detected at 5.86 kHz of regardless of cutting conditions used on this particular CNC lathe. Varying cutting speed and feed rate have an influence on the magnitude of power spectrum of spindle noise. The magnitude of spindle noise frequency alters in conjunction with the tool wear progression. The magnitude increases significantly in the transition state between steady-state wear and severe wear. This could be used as a warning signal to prepare for tool replacement or adapt cutting parameters to extend tool life.

Keywords: tool wear, flank wear, condition monitoring, spindle noise

Procedia PDF Downloads 329

Authors: Sofia M. Karadimitriou, Kostas Triantafyllopoulos, Timothy Heaton

Abstract:

Reduced dimension Dynamic Spatio-Temporal Models (DSTMs) jointly describe the spatial and temporal evolution of a function observed subject to noise. A basic state space model is adopted for the discrete temporal variation, while a continuous autoregressive structure describes the continuous spatial evolution. Application of such a DSTM relies upon the pre-selection of a suitable reduced set of basic functions and this can present a challenge in practice. In this talk, we propose an online estimation method for high dimensional spatio-temporal data based upon DSTM and we attempt to resolve this issue by allowing the basis to adapt to the observed data. Specifically, we present a wavelet decomposition in order to obtain a parsimonious approximation of the spatial continuous process. This parsimony can be achieved by placing a Laplace prior distribution on the wavelet coefficients. The aim of using the Laplace prior, is to filter wavelet coefficients with low contribution, and thus achieve the dimension reduction with significant computation savings. We then propose a Hierarchical Bayesian State Space model, for the estimation of which we offer an appropriate particle filter algorithm. The proposed methodology is illustrated using real environmental data.

Keywords: multidimensional Laplace prior, particle filtering, spatio-temporal modelling, wavelets

Procedia PDF Downloads 420

Authors: David P. Tantua, Abdulkader Helwan

Abstract:

Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

Procedia PDF Downloads 368