Search results for: feature noise

Authors: A. Benyahia, M. Zergoug, M. Amir, M. Fodil

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The main objective of this work is to enhance the Pulsed Eddy Current (PEC) response from the aluminum structure using signal processing. Cracks and metal loss in different structures cause changes in PEC response measurements. In this paper, time-frequency analysis is used to represent PEC response, which generates a large quantity of data and reduce the noise due to measurement. Power Spectral Density (PSD) after Wavelet Decomposition (PSD-WD) is proposed for defect detection. The experimental results demonstrate that the cracks in the surface can be extracted satisfactorily by the proposed methods. The validity of the proposed method is discussed.

Keywords: DT, pulsed eddy current, continuous wavelet transform, Mexican hat wavelet mother, defect detection, power spectral density.

Procedia PDF Downloads 237

Authors: Benjawan Rangsikamol, Chutimet Srinilta

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This research paper deals with extraction of Thai character features using the proposed histogram projection so as to improve the recognition performance. The process starts with transformation of image files into binary files before thinning. After character thinning, the skeletons are entered into the proposed extraction using histogram projection (horizontal and vertical) to extract unique features which are inputs of the subsequent recognition step. The recognition rate with the proposed extraction technique is as high as 97 percent since the technique works very well with the idiosyncrasies of Thai characters.

Keywords: character recognition, histogram projection, multilayer perceptron, Thai character features extraction

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Authors: Ajay Kadam, Ramesh Kagalkar

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The proposed research objective is to add to a framework for programmed recognition of sound. In this framework the real errand is to distinguish any information sound stream investigate it & anticipate the likelihood of diverse sounds show up in it. To create and industrially conveyed an adaptable sound web crawler a flexible sound search engine. The calculation is clamor and contortion safe, computationally productive, and hugely adaptable, equipped for rapidly recognizing a short portion of sound stream caught through a phone microphone in the presence of frontal area voices and other predominant commotion, and through voice codec pressure, out of a database of over accessible tracks. The algorithm utilizes a combinatorial hashed time-recurrence group of stars examination of the sound, yielding ordinary properties, for example, transparency, in which numerous tracks combined may each be distinguished.

Keywords: fingerprinting, pure tone, white noise, hash function

Procedia PDF Downloads 322

Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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Diabetic nephropathy (DN) is a major complication of diabetes, with fibrosis and propionate metabolism playing critical roles in its progression. Identifying biomarkers linked to these pathways may provide novel insights into DN diagnosis and treatment. This study aims to identify biomarkers associated with fibrosis and propionate metabolism in DN. Analyze the biological pathways and regulatory mechanisms of these biomarkers. Develop a machine learning model to predict DN-related biomarkers and validate their functional roles. Publicly available transcriptome datasets related to DN (GSE96804 and GSE104948) were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/gds), and 924 propionate metabolism-related genes (PMRGs) and 656 fibrosis-related genes (FRGs) were identified. The analysis began with the extraction of DN-differentially expressed genes (DN-DEGs) and propionate metabolism-related DEGs (PM-DEGs), followed by the intersection of these with fibrosis-related genes to identify key intersected genes. Instead of relying on traditional models, we employed a combination of deep neural networks (DNNs) and ensemble methods such as Gradient Boosting Machines (GBM) and XGBoost to enhance feature selection and biomarker discovery. Recursive feature elimination (RFE) was coupled with these advanced algorithms to refine the selection of the most critical biomarkers. Functional validation was conducted using convolutional neural networks (CNN) for gene set enrichment and immunoinfiltration analysis, revealing seven significant biomarkers—SLC37A4, ACOX2, GPD1, ACE2, SLC9A3, AGT, and PLG. These biomarkers are involved in critical biological processes such as fatty acid metabolism and glomerular development, providing a mechanistic link to DN progression. Furthermore, a TF–miRNA–mRNA regulatory network was constructed using natural language processing models to identify 8 transcription factors and 60 miRNAs that regulate these biomarkers, while a drug–gene interaction network revealed potential therapeutic targets such as UROKINASE–PLG and ATENOLOL–AGT. This integrative approach, leveraging deep learning and ensemble models, not only enhances the accuracy of biomarker discovery but also offers new perspectives on DN diagnosis and treatment, specifically targeting fibrosis and propionate metabolism pathways.

Keywords: diabetic nephropathy, deep neural networks, gradient boosting machines (GBM), XGBoost

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Authors: Grishma D. Solanki, Karshan Kandoriya

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In modern era of information age, digitalization has revolutionized like never before. Powerful computers, advanced photo editing software packages and high resolution capturing devices have made manipulation of digital images incredibly easy. As per as image forensics concerns, one of the most actively researched area are detection of copy move forgeries. Higher computational complexity is one of the major component of existing techniques to detect such tampering. Moreover, copy move forgery is usually performed in three steps. First, copying of a region in an image then pasting the same one in the same respective image and finally doing some post-processing like rotation, scaling, shift, noise, etc. Consequently, pseudo Zernike moment is used as a features extraction method for matching image blocks and as a primary factor on which performance of detection algorithms depends.

Keywords: copy-move image forgery, digital forensics, image forensics, image forgery

Procedia PDF Downloads 288

Authors: Jian-Ping Yu, Wen-Xiu Ma, Yong-Li Sun, Chaudry Masood Khalique

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In this paper, a 2d Toda equation is studied, which is a classical integrable system and plays a vital role in mathematics, physics and other areas. New lump-type solution is constructed by using the Hirota bilinear method. One interesting feature of this research is that this lump-type solutions possesses two types of multiple-lump-type waves, which are one- and two-lump-type waves. Moreover, the corresponding 3d plots, density plots and contour plots are given to show the dynamical features of the obtained multiple-lump-type solutions.

Keywords: 2d Toda equation, Hirota bilinear method, Lump-type solution, multiple-lump-type solution

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Authors: Akshay Satishrao Kawtikwar

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The effect of blast masses on structures is an essential aspect that need to be considered. This type of assault could be very horrifying, who where we take it into consideration in the course of the design system. While designing a building, now not only the wind and seismic masses however also the consequences of the blast have to be take into consideration. Blast load is the burden implemented to a structure form a blast wave that comes straight away after an explosion. A blast in or close to a constructing can reason catastrophic harm to the interior and exterior of the building, inner structural framework, wall collapsing, and so on. The most important feature of blast resistant construction is the ability to absorb blast energy without causing catastrophic failure of the structure as a whole. Construction materials in blastprotective structures must have ductility as well as strength.

Keywords: blast resistant design, blast load, explosion, ETABS

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Authors: Gerardo Hernández Sierra

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Today, we are exposed to different anxiogenic stimuli, some of those stimuli (such as traffic, noise, etc.) generates anxiety in people, being the anxiety a factor that can develop different disorders in people. Therefore, and to improve the quality of life of people it is necessary to find new and helpful tools according to the times we’re living to decrease their anxiety state. Moreover, video games are consolidated globally as a way of interactive entertainment characterized by being available to many people, being fun and easy to play. Even so, people reports that they like playing videogames because they decrease their stress (an anxiety detonator). This research will seek the effectiveness of some videogame genres to reduce the intensity of state anxiety in students. Using State Trait Anxiety Inventory (STAI) to do a monitoring of the levels of anxiety pre and post displayed the videogames.

Keywords: anxiety, state trait anxiety inventory (STAI), stress, videogames

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Authors: Seong Woo Kwak, Jung-Min Yang

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A strategy of fault diagnosis and tolerance for asynchronous sequential machines is discussed in this paper. With no synchronizing clock, it is difficult to diagnose an occurrence of permanent or stuck-in faults in the operation of asynchronous machines. In this paper, we present a fault detector comprised of a timer and a set of static functions to determine the occurrence of faults. In order to realize immediate fault tolerance, corrective control theory is applied to designing a dynamic feedback controller. Existence conditions for an appropriate controller and its construction algorithm are presented in terms of reachability of the machine and the feature of fault occurrences.

Keywords: asynchronous sequential machines, corrective control, fault diagnosis and tolerance, fault detector

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Authors: Garadi Ahmed, Boubakar S. Bouazza

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The Zero Cross-Correlation (C, w) code is a family of binary sequences of length C and constant Hamming-weight, the cross correlation between any two sequences equal zero. In this paper, we evaluate the performance of ZCC code based on Balanced Incomplete Block Design (BIBD) for Spectral Amplitude Coding Optical Code Division Multiple Access (SAC-OCDMA) system using direct detection. The BER obtained is better than 10-9 for five simultaneous users.

Keywords: spectral amplitude coding-optical code-division-multiple-access (SAC-OCDMA), phase induced intensity noise (PIIN), balanced incomplete block design (BIBD), zero cross-correlation (ZCC)

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Authors: Samabia Tehsin, Sumaira Kausar

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Due to the vast increase in the multimedia data in recent years, efficient and robust retrieval techniques are needed to retrieve and index images/ videos. Text embedded in the images can serve as the strong retrieval tool for images. This is the reason that text extraction is an area of research with increasing attention. English text extraction is the focus of many researchers but very less work has been done on other languages like Urdu. This paper is focusing on Urdu text extraction from video frames. This paper presents a text detection feature set, which has the ability to deal up with most of the problems connected with the text extraction process. To test the validity of the method, it is tested on Urdu news dataset, which gives promising results.

Keywords: caption text, content-based image retrieval, document analysis, text extraction

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Authors: Karim Matter, Heba Fayed, Ahmed Abd-Elaziz, Moustafa Hussein

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Due to the exponential growth of mobile devices and wireless services, a huge demand for radiofrequency has increased. The presence of several frequencies causes interference between cells, which must be minimized to get the lower Bit Error Rate (BER). For this reason, it is of great interest to use visible light communication (VLC). This paper suggests a VLC system that decreases the BER by applying a new LED distribution with a hexagonal shape using a Frequency Reuse (FR) concept to mitigate the interference between the reused frequencies inside the hexagonal shape. The BER is measured in two scenarios, Line of Sight (LoS) and Non-Line of Sight (Non-LoS), for each technique that we used. The recommended values of BER in the proposed model for Soft Frequency Reuse (SFR) in the case of Los at 4, 8, and 10 dB signal to noise ratio (SNR), are 3.6×10⁻⁶, 6.03×10⁻¹³, and 2.66×10⁻¹⁸, respectively.

Keywords: visible light communication (VLC), field of view (FoV), hexagonal array, frequency reuse

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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: Federico Garrido, Mostafa El Hayani, Ahmed Shams

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The exploration of the potential of artificial intelligence (AI) in architecture is still embryonic, however, its latent capacity to change design disciplines is significant. 'Synthetic Classism' is a research project that questions the underlying aspects of classically organized architecture not just in aesthetic terms but also from a geometrical and morphological point of view, intending to generate new architectural information using historical examples as source material. The main aim of this paper is to explore the uses of artificial intelligence and machine learning algorithms in architectural design while creating a coherent narrative to be contained within a design process. The purpose is twofold: on one hand, to develop and train machine learning algorithms to produce architectural information of small pavilions and on the other, to synthesize new information from previous architectural drawings. These algorithms intend to 'interpret' graphical information from each pavilion and then generate new information from it. The procedure, once these algorithms are trained, is the following: parting from a line profile, a synthetic 'front view' of a pavilion is generated, then using it as a source material, an isometric view is created from it, and finally, a top view is produced. Thanks to GAN algorithms, it is also possible to generate Front and Isometric views without any graphical input as well. The final intention of the research is to produce isometric views out of historical information, such as the pavilions from Sebastiano Serlio, James Gibbs, or John Soane. The idea is to create and interpret new information not just in terms of historical reconstruction but also to explore AI as a novel tool in the narrative of a creative design process. This research also challenges the idea of the role of algorithmic design associated with efficiency or fitness while embracing the possibility of a creative collaboration between artificial intelligence and a human designer. Hence the double feature of this research, both analytical and creative, first by synthesizing images based on a given dataset and then by generating new architectural information from historical references. We find that the possibility of creatively understand and manipulate historic (and synthetic) information will be a key feature in future innovative design processes. Finally, the main question that we propose is whether an AI could be used not just to create an original and innovative group of simple buildings but also to explore the possibility of fostering a novel architectural sensibility grounded on the specificities on the architectural dataset, either historic, human-made or synthetic.

Keywords: architecture, central pavilions, classicism, machine learning

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Authors: Robert Poenaru, Mihail Pleşa

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In this work, a solution that addresses the protection of pre-trained neural networks is developed: Singularization. This method involves applying permutations to the weight matrices of a pre-trained model, introducing a form of structured noise that obscures the original model’s architecture. These permutations make it difficult for an attacker to reconstruct the original model, even if the permuted weights are obtained. Experimental benchmarks indicate that the application of singularization has a profound impact on model performance, often degrading it to the point where retraining from scratch becomes necessary to recover functionality, which is particularly effective for securing intellectual property in neural networks. Moreover, unlike other approaches, singularization is lightweight and computationally efficient, which makes it well suited for resource-constrained environments. Our experiments also demonstrate that this technique performs efficiently in various image classification tasks, highlighting its broad applicability and practicality in real-world scenarios.

Keywords: machine learning, ANE, CNN, security

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Authors: Saba Ebrahimi, Hedieh Ashrafi

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Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

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Authors: H. Al-Hajeri

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The computation of the effectiveness of turbulence enhancement surface features, such as ribs as means of promoting mixing and hence heat transfer, has attracted the continued attention of the engineering community. In this study, the simulation of a three-dimensional cooling passage is carried out employing a number of turbulence models including Durbin model. The cooling passage consists of a square section duct whose upper and lower surfaces feature staggered cuboid ribs. The main objective of this paper is to provide comparisons of the performance of the v2-f model against other established turbulence models as implemented in the commercial CFD code Ansys Fluent. The present study demonstrates that the v2-f model can successfully capture the isothermal air flow phenomena in flow over obstacles.

Keywords: CFD, cooling passage, Durbin model, turbulence model

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Authors: Seung-Lock Seo

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This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults.

Keywords: data mining, process data, monitoring, safety, industrial processes

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Authors: Rashi Jain

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The LTE systems employ Orthogonal Frequency Division Multiplexing (OFDM) as the multiple access technology for the Downlink channels. For enhanced performance, accurate channel estimation is required. Various algorithms such as Least Squares (LS), Minimum Mean Square Error (MMSE) and Recursive Least Squares (RLS) can be employed for the purpose. The paper proposes channel estimation algorithm based on Kalman Filter for LTE-Downlink system. Using the frequency domain pilots, the initial channel response is obtained using the LS criterion. Then Kalman Filter is employed to track the channel variations in time-domain. To suppress the noise within a symbol, threshold processing is employed. The paper draws comparison between the LS, MMSE, RLS and Kalman filter for channel estimation. The parameters for evaluation are Bit Error Rate (BER), Mean Square Error (MSE) and run-time.

Keywords: LTE, channel estimation, OFDM, RLS, Kalman filter, threshold

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Authors: S. M. Giripunje, Mohit Kumar

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Acoustic energy that exists in our everyday life and environment have been overlooked as a green energy that can be extracted, generated, and consumed without any significant negative impact to the environment. The harvested energy can be used to enable new technology like wireless sensor networks. Technological developments in the realization of truly autonomous MEMS devices and energy storage systems have made acoustic energy harvesting (AEH) an increasingly viable technology. AEH is the process of converting high and continuous acoustic waves from the environment into electrical energy by using an acoustic transducer or resonator. AEH is not popular as other types of energy harvesting methods since sound waves have lower energy density and such energy can only be harvested in very noisy environment. However, the energy requirements for certain applications are also correspondingly low and also there is a necessity to observe the noise to reduce noise pollution. So the ability to reclaim acoustic energy and store it in a usable electrical form enables a novel means of supplying power to relatively low power devices. A quarter-wavelength straight-tube acoustic resonator as an acoustic energy harvester is introduced with polyvinylidene fluoride (PVDF) and PVDF doped with ZnO nanoparticles, piezoelectric cantilever beams placed inside the resonator. When the resonator is excited by an incident acoustic wave at its first acoustic eigen frequency, an amplified acoustic resonant standing wave is developed inside the resonator. The acoustic pressure gradient of the amplified standing wave then drives the vibration motion of the PVDF piezoelectric beams, generating electricity due to the direct piezoelectric effect. In order to maximize the amount of the harvested energy, each PVDF and PVDF-ZnO piezoelectric beam has been designed to have the same structural eigen frequency as the acoustic eigen frequency of the resonator. With a single PVDF beam placed inside the resonator, the harvested voltage and power become the maximum near the resonator tube open inlet where the largest acoustic pressure gradient vibrates the PVDF beam. As the beam is moved to the resonator tube closed end, the voltage and power gradually decrease due to the decreased acoustic pressure gradient. Multiple piezoelectric beams PVDF and PVDF-ZnO have been placed inside the resonator with two different configurations: the aligned and zigzag configurations. With the zigzag configuration which has the more open path for acoustic air particle motions, the significant increases in the harvested voltage and power have been observed. Due to the interruption of acoustic air particle motion caused by the beams, it is found that placing PVDF beams near the closed tube end is not beneficial. The total output voltage of the piezoelectric beams increases linearly as the incident sound pressure increases. This study therefore reveals that the proposed technique used to harvest sound wave energy has great potential of converting free energy into useful energy.

Keywords: acoustic energy, acoustic resonator, energy harvester, eigenfrequency, polyvinylidene fluoride (PVDF)

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Authors: Faisal Zaman, Ya Ping Wong, Boon Yian Ng

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Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this, we present a novel approach using modified kernel density estimation (KDE) technique with bilateral filtering to remove noisy points and outliers. First we present a method for estimating optimal bandwidth of multivariate KDE using particle swarm optimization technique which ensures the robust performance of density estimation. Then we use mean-shift algorithm to find the local maxima of the density estimation which gives the centroid of the clusters. Then we compute the distance of a certain point from the centroid. Points belong to outliers then removed by automatic thresholding scheme which yields an accurate and economical point surface. The experimental results show that our approach comparably robust and efficient.

Keywords: point preprocessing, outlier removal, surface reconstruction, kernel density estimation

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Authors: P. Naderi, M. N. Ouarzazi, S. C. Hirata, H. Ben Hamed, H. Beji

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The stability of mixed convection in a Newtonian fluid medium heated from below and cooled from above, also known as the Poiseuille-Rayleigh-Bénard problem, has been extensively investigated in the past decades. To our knowledge, mixed convection in porous media has received much less attention in the published literature. The present paper extends the mixed convection problem in porous media for the case of a viscoelastic fluid flow owing to its numerous environmental and industrial applications such as the extrusion of polymer fluids, solidification of liquid crystals, suspension solutions and petroleum activities. Without a superimposed through-flow, the natural convection problem of a viscoelastic fluid in a saturated porous medium has already been treated. The effects of the viscoelastic properties of the fluid on the linear and nonlinear dynamics of the thermoconvective instabilities have also been treated in this work. Consequently, the elasticity of the fluid can lead either to a Hopf bifurcation, giving rise to oscillatory structures in the strongly elastic regime, or to a stationary bifurcation in the weakly elastic regime. The objective of this work is to examine the influence of the main horizontal flow on the linear and characteristics of these two types of instabilities. Under the Boussinesq approximation and Darcy's law extended to a viscoelastic fluid, a temporal stability approach shows that the conditions for the appearance of longitudinal rolls are identical to those found in the absence of through-flow. For the general three-dimensional (3D) perturbations, a Squire transformation allows the deduction of the complex frequencies associated with the 3D problem using those obtained by solving the two-dimensional one. The numerical resolution of the eigenvalue problem concludes that the through-flow has a destabilizing effect and selects a convective configuration organized in purely transversal rolls which oscillate in time and propagate in the direction of the main flow. In addition, by using the mathematical formalism of absolute and convective instabilities, we study the nature of unstable three-dimensional disturbances. It is shown that for a non-vanishing through-flow, general three-dimensional instabilities are convectively unstable which means that in the absence of a continuous noise source these instabilities are drifted outside the porous medium, and no long-term pattern is observed. In contrast, purely transversal rolls may exhibit a transition to absolute instability regime and therefore affect the porous medium everywhere including in the absence of a noise source. The absolute instability threshold, the frequency and the wave number associated with purely transversal rolls are determined as a function of the Péclet number and the viscoelastic parameters. Results are discussed and compared to those obtained from laboratory experiments in the case of Newtonian fluids.

Keywords: instability, mixed convection, porous media, and viscoelastic fluid

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Authors: A. A. Hussen, I. N. Jleta

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Micro-electromechanical system (MEMS) accelerometers and gyroscopes are suitable for the inertial navigation system (INS) of many applications due to the low price, small dimensions and light weight. The main disadvantage in a comparison with classic sensors is a worse long term stability. The estimation accuracy is mostly affected by the time-dependent growth of inertial sensor errors, especially the stochastic errors. In order to eliminate negative effect of these random errors, they must be accurately modeled. Where the key is the successful implementation that depends on how well the noise statistics of the inertial sensors is selected. In this paper, the Allan variance technique will be used in modeling the stochastic errors of the inertial sensors. By performing a simple operation on the entire length of data, a characteristic curve is obtained whose inspection provides a systematic characterization of various random errors contained in the inertial-sensor output data.

Keywords: Allan variance, accelerometer, gyroscope, stochastic errors

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Authors: Abdesselam Babouri, Ahcène Lemzadmi, M Rahmane, B. Belhadi, N. Abouchi

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Medical monitoring aims at monitoring and remotely controlling the vital physiological parameters of the patient. The physiological sensors provide repetitive measurements of these parameters in the form of electrical signals that vary continuously over time. Various measures allow informing us about the health of the person's physiological data (weight, blood pressure, heart rate or specific to a disease), environmental conditions (temperature, humidity, light, noise level) and displacement and movements (physical efforts and the completion of major daily living activities). The collected data will allow monitoring the patient’s condition and alerting in case of modification. They are also used in the diagnosis and decision making on medical treatment and the health of the patient. This work presents the implementation of a monitoring system to be used for the control of physiological parameters.

Keywords: clinical monitoring, physiological parameters, biomedical sensors, personal health

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Authors: Violetta Parutis

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This qualitative study aims to answer the following research questions: i) What are the factors that students perceive as relevant to a) promoting and b) preventing good grades? ii) How does socio-economic status (SES) feature in those beliefs? We conducted in-depth interviews with 19 first- and second-year undergraduates of varying SES at a research-intensive university in the UK. The interviews yielded eight factors that students perceived as promoting and six perceived as preventing good grades. The findings suggested one significant difference between the beliefs of low and high SES students in that low SES students perceive themselves to be at a greater disadvantage to their peers while high SES students do not have such beliefs. This could have knock-on effects on their performance.

Keywords: social class, education, academic performance, students’ beliefs

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Authors: Mayar Almasri, Rosa Helmi, Rayana Enany

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This report describes the features of AutoCAD software released by Adobe. It explains how the program makes it easier for engineers and designers and reduces their time and effort spent using AutoCAD. Moreover, it highlights how AutoCAD works, how some of the commands used in it, such as Shortcut, make it easy to use, and features that make it accurate in measurements. The results of the report show that most users of this program are designers and engineers, but few people know about it and find it easy to use. They prefer to use it because it is easy to use, and the shortcut commands shorten a lot of time for them. The feature got a high rate and some suggestions for improving AutoCAD in Aperture, but it was a small percentage, and the highest percentage was that they didn't need to improve the program, and it was good.

Keywords: artificial intelligence, design, planning, commands, autodesk, dimensions

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Authors: Arnab Roy, P. S. Anil Kumar

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Hall bar magnetic field sensors based on planar hall effect were fabricated from permalloy (Ni¬80Fe20) thin films grown by pulsed laser ablation. As large as 400% planar Hall voltage change was observed for a magnetic field sweep within ±4 Oe, a value comparable with present day TMR sensors at room temperature. A very large planar Hall sensitivity of 1200 Ω/T was measured close to switching fields, which was not obtained so far apart from 2DEG Hall sensors. In summary, a highly sensitive low magnetic field sensor has been constructed which has the added advantage of simple architecture, good signal to noise ratio and robustness.

Keywords: planar hall effect, permalloy, NiFe, pulsed laser ablation, low magnetic field sensor, high sensitivity magnetic field sensor

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Authors: Golnush Masghati-Amoli, Paul Chin

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Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

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Authors: Mohammad Arif Sobhan Bhuiyan, Zainal Abidin Nordin, Mamun Bin Ibne Reaz

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A compact and power efficient high performance Voltage Controlled Oscillator (VCO) is a must in analog and digital circuits especially in the communication system, but the best trade-off among the performance parameters is a challenge for researchers. In this paper, a design of a compact 3-stage differential voltage controlled ring oscillator (VCRO) with low phase noise, low power and higher tuning bandwidth is proposed in 0.18 µm CMOS technology. The VCRO is designed with symmetric load and positive feedback techniques to achieve higher gain and minimum delay. The proposed VCRO can operate at tuning range of 3.9-5.0 GHz at 1.6 V supply voltage. The circuit consumes only 1.0757 mW of power and produces -129 dbc/Hz. The total active area of the proposed VCRO is only 11.74 x 37.73 µm2. Such a VCO can be the best choice for compact and low-power RF applications.

Keywords: CMOS, VCO, VCRO, oscillator

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Authors: Shubham Jaiswal

Abstract:

In the present article, a drive is taken to compute the solution of spatial fractional order advection-dispersion equation having source/sink term with given initial and boundary conditions. The equation is converted to a system of ordinary differential equations using second-kind shifted Chebyshev polynomials, which have finally been solved using finite difference method. The striking feature of the article is the fast transportation of solute concentration as and when the system approaches fractional order from standard order for specified values of the parameters of the system.

Keywords: spatial fractional order advection-dispersion equation, second-kind shifted Chebyshev polynomial, collocation method, conservative system, non-conservative system

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