Search results for: stochastic signals

Authors: Anjani Kumar Tiwari, Neelam Kumari, Anil Mishra

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The family of phosphodiesterases (PDEs) plays a critical role in control of the level, localization, and duration of intracellular 3’-5’-cyclic adenosine monophosphate (cAMP) and 3’-5’-cyclic guanosine monophosphate (cGMP) signals by specifically hydrolyzing these cyclic nucleotides. As the involvement of cyclic nucleotide second messengers in cell signaling and homeostasis is established, the regulation of these pathways in the brain by various PDE isoforms is an area of considerable interest, as they are involved in nearly all brain functions and in the etiology of neuropsychiatric diseases. The PDE10A isoform, isolated from different species and characterized regarding structure and function, has received much attention in recent years, particularly in the context of schizophrenia and Huntington’s disease, which are both related to a role of PDE10A in the regulation of striatal dopaminergic neurotransmission. Quinazoline analogue 1-(4-methoxyphenyl)-6,7-dimethoxyquinazoline, was evaluated as specific PET marker for phosphodiesterase (PDE) 10A. Here, we report the radiosynthesis of [11C]2 and the in vitro and in vivo evaluation of [11C]2 as a potential positron emission tomography (PET) radiotracer for imaging PDE10A in the central nervous system (CNS). The radiosynthesis of [11C]2 was achieved by O-methylation of the corresponding des-methyl precursor with [11C]methyl iodide. [11C]2 was obtained with ∼50% radiochemical yield. PET imaging studies in rat brain displayed initial specific uptake with very rapid clearance of [11C]2 from brain. Though [11C]2 is not an ideal radioligand for clinical imaging of PDE10A in the CNS. Modified analogue of quinazoline having a higher potency for inhibiting PDE10A and improved pharmacokinetic properties will be necessary for imaging this enzyme with PET.

Keywords: PDE10A, PET, radiotracer, quinazoline

Procedia PDF Downloads 186

Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson

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The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.

Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0

Procedia PDF Downloads 180

Authors: Lingerew Nebere Kassie, Ping-Yu Chang, Hsin-Hua Huang, , Chaw-Son Chen

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Transient electromagnetic (TEM) data plays a crucial role in hydrogeological and environmental applications, providing valuable insights into geological structures and resistivity variations. However, the presence of noise often hinders the interpretation and reliability of these data. Our study addresses this issue by utilizing a FASTSNAP system for the TEM survey, which operates at different modes (low, medium, and high) with continuous adjustments to discretization, gain, and current. We employ a denoising approach that processes the raw data obtained from each acquisition mode to improve signal quality and enhance data reliability. We use a signal-averaging technique for each mode, increasing the signal-to-noise ratio. Additionally, we utilize wavelet transform to suppress noise further while preserving the integrity of the underlying signals. This approach significantly improves the data quality, notably suppressing severe noise at late times. The resulting denoised data exhibits a substantially improved signal-to-noise ratio, leading to increased accuracy in parameter estimation. By effectively denoising TEM data, our study contributes to a more reliable interpretation and analysis of underground structures. Moreover, the proposed denoising approach can be seamlessly integrated into existing ground-based TEM data processing workflows, facilitating the extraction of meaningful information from noisy measurements and enhancing the overall quality and reliability of the acquired data.

Keywords: data quality, signal averaging, transient electromagnetic, wavelet transform

Procedia PDF Downloads 84

Authors: Marek Gancarz, Agnieszka Nawrocka, Robert Rusinek, Marcin Tadla

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Volatile organic compounds (VOCs) are the fungi metabolites in the gaseous form produced during improper storage of agricultural commodities (e.g. grain, food). The spoilt commodities produce a wide range of VOCs including alcohols, esters, aldehydes, ketones, alkanes, alkenes, furans, phenols etc. The characteristic VOCs and odours can be determined by using electronic nose (e-Nose) which contains a matrix of different kinds of sensors e.g. resistive sensors. The aim of the present studies was to determine relationship between response of the resistive sensors on the chosen volatiles and their concentration. According to the literature, it was chosen volatiles characteristic for the cereals: ethanol, 3-methyl-1-butanol and hexanal. Analysis of the sensor signals shows that a signal shape is different for the different substances. Moreover, each VOC signal gives information about a maximum of the normalized sensor response (R/Rmax), an impregnation time (tIM) and a cleaning time at half maximum of R/Rmax (tCL). These three parameters can be regarded as a ‘VOC fingerprint’. Seven resistive sensors (TGS2600-B00, TGS2602-B00, TGS2610-C00, TGS2611-C00, TGS2611-E00, TGS2612-D00, TGS2620-C00) produced by Figaro USA Inc., and one (AS-MLV-P2) produced by AMS AG, Austria were used. Two out of seven sensors (TGS2611-E00, TGS2612-D00) did not react to the chosen VOCs. The most responsive sensor was AS-MLV-P2. The research was supported by the National Centre for Research and Development (NCBR), Grant No. PBS2/A8/22/2013.

Keywords: agricultural commodities, organic compounds, resistive sensors, volatile

Procedia PDF Downloads 368

Authors: Sundara Subramanian Karuppasamy, Che Hua Yang

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In this work, the laser ultrasound technique has been used for analyzing and imaging the inner defects in metal blocks. To detect the defects in blocks, traditionally the researchers used piezoelectric transducers for the generation and reception of ultrasonic signals. These transducers can be configured into the sparse and phased array. But these two configurations have their drawbacks including the requirement of many transducers, time-consuming calculations, limited bandwidth, and provide confined image resolution. Here, we focus on the non-contact method for generating and receiving the ultrasound to examine the inner defects in aluminum blocks. A Q-switched pulsed laser has been used for the generation and the reception is done by using Laser Doppler Vibrometer (LDV). Based on the Doppler effect, LDV provides a rapid and high spatial resolution way for sensing ultrasonic waves. From the LDV, a series of scanning points are selected which serves as the phased array elements. The side-drilled hole of 10 mm diameter with a depth of 25 mm has been introduced and the defect is interrogated by the linear array of scanning points obtained from the LDV. With the aid of the Synthetic Aperture Focusing Technique (SAFT) algorithm, based on the time-shifting principle the inspected images are generated from the A-scan data acquired from the 1-D linear phased array elements. Thus the defect can be precisely detected with good resolution.

Keywords: laser ultrasonics, linear phased array, nondestructive testing, synthetic aperture focusing technique, ultrasonic imaging

Procedia PDF Downloads 133

Authors: M. Nadeem, Y. K. Chang, C. Diallo, U. Venkatadri, P. Havard, T. Nguyen-Quang

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Uniform distribution of agro-chemicals is highly important because there is a significant loss of agro-chemicals, for example from pesticide, during spraying due to non-uniformity of droplet and off-target drift. Improving the efficiency of spray pattern for different cropping systems would reduce energy, costs and to minimize environmental pollution. In this paper, we examine the water jet patterns in order to study the performance and uniformity of water distribution during the spraying process. We present a method to quantify the water amount from a sprayer jet by using the Particle Image Velocimetry (PIV) system. The results of the study will be used to optimize sprayer or nozzles design for chemical application. For this study, ten sets of images were acquired by using the following PIV system settings: double frame mode, trigger rate is 4 Hz, and time between pulsed signals is 500 µs. Each set of images contained different numbers of double-framed images: 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 at eight different pressures 25, 50, 75, 100, 125, 150, 175 and 200 kPa. The PIV images obtained were analysed using custom-made image processing software for droplets and volume calculations. The results showed good agreement of both manual and PIV measurements and suggested that the PIV technique coupled with image processing can be used for a precise quantification of flow through nozzles. The results also revealed that the method of measuring fluid flow through PIV is reliable and accurate for sprayer patterns.

Keywords: image processing, PIV, quantifying the water volume from nozzle, spraying pattern

Procedia PDF Downloads 237

Authors: Mohsen Azarmjoo, Mehdi Mehdizadeh Koupaei, Maryam Mehdizadeh Koupaei, Asghar Mahdlouei Azar

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The increasing prevalence of wireless networks in our daily lives has made them indispensable. The aim of this research is to investigate the management of information transfer in wireless networks and the integration of renewable solar energy resources in a residential building. The focus is on the transmission of electricity and information through wireless networks, as well as the utilization of sensors and wireless fire alarm systems. The research employs a descriptive approach to examine the transmission of electricity and information on a wireless network with electric and optical telephone lines. It also investigates the transmission of signals from sensors and wireless fire alarm systems via radio waves. The methodology includes a detailed analysis of security, comfort conditions, and costs related to the utilization of wireless networks and renewable solar energy resources. The study reveals that it is feasible to transmit electricity on a network cable using two pairs of network cables without the need for separate power cabling. Additionally, the integration of renewable solar energy systems in residential buildings can reduce dependence on traditional energy carriers. The use of sensors and wireless remote information processing can enhance the safety and efficiency of energy usage in buildings and the surrounding spaces.

Keywords: renewable energy, intelligentization, wireless sensors, fire alarm system

Procedia PDF Downloads 54

Authors: Bingxi Huang, Yiqing Li, Marek Morzynski, Bernd R. Noack

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We propose a Kriging-based global optimization method for active flow control with multiple actuation parameters. This method is designed to converge quickly and avoid getting trapped into local minima. We follow the model-free explorative gradient method (EGM) to alternate between explorative and exploitive steps. This facilitates a convergence similar to a gradient-based method and the parallel exploration of potentially better minima. In contrast to EGM, both kinds of steps are performed with Kriging surrogate model from the available data. The explorative step maximizes the expected improvement, i.e., favors regions of large uncertainty. The exploitive step identifies the best location of the cost function from the Kriging surrogate model for a subsequent weight-biased linear-gradient descent search method. To verify the effectiveness and robustness of the improved Kriging-based optimization method, we have examined several comparative test problems of varying dimensions with limited evaluation budgets. The results show that the proposed algorithm significantly outperforms some model-free optimization algorithms like genetic algorithm and differential evolution algorithm with a quicker convergence for a given budget. We have also performed direct numerical simulations of the fluidic pinball (N. Deng et al. 2020 J. Fluid Mech.) on three circular cylinders in equilateral-triangular arrangement immersed in an incoming flow at Re=100. The optimal cylinder rotations lead to 44.0% net drag power saving with 85.8% drag reduction and 41.8% actuation power. The optimal results for active flow control based on this configuration have achieved boat-tailing mechanism by employing Coanda forcing and wake stabilization by delaying separation and minimizing the wake region.

Keywords: direct numerical simulations, flow control, kriging, stochastic optimization, wake stabilization

Procedia PDF Downloads 106

Authors: Huawei Bai, Jianguo Yao, Fellow, IEEE

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Atrial fibrillation (AF) is the most common form of heart arrhythmia and is closely associated with mortality and morbidity in heart failure, stroke, and coronary artery disease. The development of single spot optical sensors enables widespread photoplethysmography (PPG) screening, especially for AF, since it represents a more convenient and noninvasive approach. To our knowledge, most existing studies based on public and unbalanced datasets can barely handle the multiple noises sources in the real world and, also, lack interpretability. In this paper, we construct a large- scale PPG dataset using measurements collected from PPG wrist- watch devices worn by volunteers and propose an attention-based two-stream heterogeneous neural network (TSHNN). The first stream is a hybrid neural network consisting of a three-layer one-dimensional convolutional neural network (1D-CNN) and two-layer attention- based bidirectional long short-term memory (Bi-LSTM) network to learn representations from temporally sampled signals. The second stream extracts latent representations from the PPG time-frequency spectrogram using a five-layer CNN. The outputs from both streams are fed into a fusion layer for the outcome. Visualization of the attention weights learned demonstrates the effectiveness of the attention mechanism against noise. The experimental results show that the TSHNN outperforms all the competitive baseline approaches and with 98.09% accuracy, achieves state-of-the-art performance.

Keywords: PPG wearables, atrial fibrillation, feature fusion, attention mechanism, hyber network

Procedia PDF Downloads 121

Authors: Jalal M. Abdallah

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There is a great problem in testing and investigations the reliability of different type of transformers insulation materials. It summarized in how to create and simulate the real conditions of working transformer and testing its insulation materials for Partial Discharge PD, typically as in the working mode. A lot of tests may give untrue results as the physical behavior of the insulation material differs under tests from its working condition. In this work, the real working conditions were simulated, and a large number of specimens have been tested. The investigations first stage, begin with choosing samples of different types of insulation materials (papers, pressboards, etc.). The second stage, the samples were dried in ovens at 105 C⁰and 0.01bar for 48 hours, and then impregnated with dried and gasless oil (the water content less than 6 ppm.) at 105 C⁰and 0.01bar for 48 hours, after so specimen cooling at room pressure and temperature for 24 hours. The third stage is investigating PD for the samples using ICM PD measuring device. After that, a continuous test on oil-impregnated insulation materials (paper, pressboards) was developed, and the phase resolved partial discharge pattern of PD signals was measured. The important of this work in providing the industrial sector with trusted high accurate measuring results based on real simulated working conditions. All the PD patterns (results) associated with a discharge produced in well-controlled laboratory condition. They compared with other previous and other laboratory results. In addition, the influence of different temperatures condition on the partial discharge activities was studied.

Keywords: transformers, insulation materials, voids, partial discharge

Procedia PDF Downloads 315

Authors: Han-Bin Park, Taesam Kang, SunKi Hong, Jeong Hoi Gu

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The piezoelectric material is being used widely for actuators due to its large power density with simple structure. It can generate a larger force than the conventional actuators with the same size. Furthermore, the response time of piezoelectric actuators is very short, and thus, it can be used for very fast system applications with compact size. To control the piezoelectric actuator, we need analog signal conditioning circuits as well as digital microcontrollers. Conventional microcontrollers are not equipped with analog parts and thus the control system becomes bulky compared with the small size of the piezoelectric devices. To overcome these weaknesses, we are developing one-chip micro controller that can handle analog and digital signals simultaneously using mixed signal FPGA technology. We used the SmartFusion™ FPGA device that integrates ARM®Cortex-M3, analog interface and FPGA fabric in a single chip and offering full customization. It gives more flexibility than traditional fixed-function microcontrollers with the excessive cost of soft processor cores on traditional FPGAs. In this paper we introduce the design of single chip controller using mixed signal FPGA, SmartFusion™[1] device. To demonstrate its performance, we implemented a PI controller for power driving circuit and a 5th order H-infinity controller for the system with piezoelectric actuator in the FPGA fabric. We also demonstrated the regulation of a power output and the operation speed of a 5th order H-infinity controller.

Keywords: mixed signal FPGA, PI control, piezoelectric actuator, SmartFusion™

Procedia PDF Downloads 520

Authors: Angela U. Makolo, Temitayo A. Olagunju

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The knowledge of signaling pathways is central to understanding the biological mechanisms of organisms since it has been identified that in eukaryotic organisms, the number of signaling pathways determines the number of ways the organism will react to external stimuli. Signaling pathways are studied using protein interaction networks constructed from protein-protein interaction data obtained using high throughput experimental procedures. However, these high throughput methods are known to produce very high rates of false positive and negative interactions. In order to construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed. A weighted interaction graph of the Saccharomyces cerevisiae (Baker’s Yeast) organism using the proteins as the nodes and interactions between them as edges was constructed. The weights were obtained using Bayesian probabilistic network to estimate the posterior probability of interaction between two proteins given the gene expression measurement as biological evidence. Only interactions above a threshold were accepted for the network model. A pathway was formalized as a simple path in the interaction network from a starting protein and an ending protein of interest. We were able to identify some pathway segments, one of which is a segment of the pathway that signals the start of the process of meiosis in S. cerevisiae.

Keywords: Bayesian networks, protein interaction networks, Saccharomyces cerevisiae, signalling pathways

Procedia PDF Downloads 542

Authors: Yana Xiao

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The invention of Triboelectric Nanogenerators (TENGs) provides an effective approach to the sustainable power of energy. Liquid-solid interfaces-based TENGs have been researched in virtue of less friction for harvesting energy from raindrops, rivers, and oceans in the form of water flows. However, TENGs based on droplets have rarely been investigated. In this study, we have proposed a new kind of droplet tube-based TENG (DT-TENG) with free-standing and reformative grating electrodes. Both straight and curved DT-TENGs were designed, fabricated, and evaluated, including straight tubes TENG with 27 electrodes and curved tubes TENG of 25cm radius curvature- at the inclination of 30°, 45° and 60° respectively. Different materials and hydrophobicity treatments for the tubes have also been studied, together with a discussion on the mechanism and applications of DT-TENGs. As different types of liquid discrepant energy performance, this kind of DT-TENG can be potentially used in laboratories to identify liquid or solvent. In addition, a smart fishing float is contrived, which can recognize different levels of movement speeds brought about by different weights and generate corresponding electric signals to remind the angler. The electric generation performance when using a PVC helix tube around a cylinder is similar in straight situations under the inclination of 45° in this experiment. This new structure changes the direction of a water drop or flows without losing kinetic energy, which makes utilizing Helix-Tube-TENG to harvest energy from different building morphologies possible.

Keywords: triboelectric nanogenerator, energy harvest, liquid tribomaterial, structure innovation

Procedia PDF Downloads 88

Authors: S. Aswath, Chinmaya Krishna Tilak, Amal Suresh, Ganesh Udupa

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There are technologies to control a humanoid robot in many ways. But the use of Electromyogram (EMG) electrodes has its own importance in setting up the control system. The EMG based control system helps to control robotic devices with more fidelity and precision. In this paper, development of an electromyogram based interface for human gesture recognition for the control of a humanoid robot is presented. To recognize control signs in the gestures, a single channel EMG sensor is positioned on the muscles of the human body. Instead of using a remote control unit, the humanoid robot is controlled by various gestures performed by the human. The EMG electrodes attached to the muscles generates an analog signal due to the effect of nerve impulses generated on moving muscles of the human being. The analog signals taken up from the muscles are supplied to a differential muscle sensor that processes the given signal to generate a signal suitable for the microcontroller to get the control over a humanoid robot. The signal from the differential muscle sensor is converted to a digital form using the ADC of the microcontroller and outputs its decision to the CM-530 humanoid robot controller through a Zigbee wireless interface. The output decision of the CM-530 processor is sent to a motor driver in order to control the servo motors in required direction for human like actions. This method for gaining control of a humanoid robot could be used for performing actions with more accuracy and ease. In addition, a study has been conducted to investigate the controllability and ease of use of the interface and the employed gestures.

Keywords: electromyogram, gesture, muscle sensor, humanoid robot, microcontroller, Zigbee

Procedia PDF Downloads 407

Authors: Julius M. Ndambuki, Gislar E. Kifanyi, Samuel N. Odai, Charles Gyamfi

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Conjunctive management of surface and groundwater resources is a challenging task due to the spatial and temporal variability nature of hydrology as well as hydrogeology of the water storage systems. Surface water-groundwater hydrogeology is highly uncertain; thus it is imperative that this uncertainty is explicitly accounted for, when managing water resources. Various methodologies have been developed and applied by researchers in an attempt to account for the uncertainty. For example, simulation-optimization models are often used for conjunctive water resources management. However, direct application of such an approach in which all realizations are considered at each iteration of the optimization process leads to a very expensive optimization in terms of computational time, particularly when the number of realizations is large. The aim of this paper, therefore, is to introduce and apply an efficient approach referred to as Retrospective Optimization Approximation (ROA) that can be used for optimizing conjunctive use of surface water and groundwater over a multiple hydrogeological model simulations. This work is based on stochastic simulation-optimization framework using a recently emerged technique of sample average approximation (SAA) which is a sampling based method implemented within the Retrospective Optimization Approximation (ROA) approach. The ROA approach solves and evaluates a sequence of generated optimization sub-problems in an increasing number of realizations (sample size). Response matrix technique was used for linking simulation model with optimization procedure. The k-means clustering sampling technique was used to map the realizations. The methodology is demonstrated through the application to a hypothetical example. In the example, the optimization sub-problems generated were solved and analysed using “Active-Set” core optimizer implemented under MATLAB 2014a environment. Through k-means clustering sampling technique, the ROA – Active Set procedure was able to arrive at a (nearly) converged maximum expected total optimal conjunctive water use withdrawal rate within a relatively few number of iterations (6 to 7 iterations). Results indicate that the ROA approach is a promising technique for optimizing conjunctive water use of surface water and groundwater withdrawal rates under hydrogeological uncertainty.

Keywords: conjunctive water management, retrospective optimization approximation approach, sample average approximation, uncertainty

Procedia PDF Downloads 231

Authors: Kun Chen, Yan Wang, Yangyu Zhao, Shufang Li, Lian Chen, Xiaoyue Guo, Jue Zhang, Jing Fang

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The coupling strength between uterine electromyography (UEMG) and Fetal heart rate (FHR) signals during peripartum reflects the fetal biophysical activities. Therefore, UEMG-FHR coupling characterization is instructive in assessing placenta function. This study introduced a physiological marker named elevated frequency of UEMG-FHR coupling (E-UFC) and explored its predictive value for pregnancies complicated by pre-eclampsia and small for gestational age (SGA). Placental insufficiency patients (n=12) and healthy volunteers (n=24) were recruited and participated. UEMG and FHR were recorded non-invasively by a trans-abdominal device in women at term with singleton pregnancy (32-37 weeks) from 10:00 pm to 8:00 am. The product of the wavelet coherence and the wavelet cross-spectral power between UEMG and FHR was used to weight these two effects in order to quantify the degree of the UEMG-FHR coupling. E-UFC was exacted from the resultant spectrogram by calculating the mean value of the high-coherence (r > 0.5) frequency band. Results showed the high-coherence between UEMG and FHR was observed in the frequency band (1/512-1/16Hz). In addition, E-UFC in placental insufficiency patients was weaker compared to healthy controls (p < 0.001) at group level. These findings suggested the proposed approach could be used to quantitatively characterize the fetal biophysical activities, which is beneficial for early detection of placental insufficiency and reduces the occurrence of adverse pregnancy.

Keywords: uterine electromyography, fetal heart rate, coupling analysis, wavelet analysis

Procedia PDF Downloads 202

Authors: Ngoc Tu Truong, Nuong T. Bui, Ben Rao, Ya L. Shen

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Quorum sensing is a phenomenon present in many gram-negative bacteria that allows bacterial communication and controlled expression of a large suite of genes through quorum sensing signals - N-acyl homoserine lactones (AHLs). In order to investigate the ability of the rhlR/rhlI quorum sensing system in Pseudomonas aeruginosa to express the ß-Galactosidase reporter gene, an engineered E. coli strain EpHL02, was genetically engineered. This engineered E. coli strain EpHL02 responded to the presence of the N-butanoyl homoserine lactone and N-hexanoyl homoserine lactone to express the ß-Galactosidase reporter gene at a concentration limit of 5x10⁻⁸ M. This was also found to be comparable to AHLs extraction from Serratia marcescens H31. Moreover, we examined this ability of this engineered E. coli strain for respond of AHLs from extractions of Pseudomonas aeruginosa ATCC9027. The results demonstrated that the rhlR/rhlI quorum sensing system can express the ß-Galactosidase reporter gene by using the N-butanoyl homoserine lactone, N-hexanoyl homoserine lactone and AHLs from extractions of Serratia marcescens H31 and Pseudomonas aeruginosa ATCC9027 in the engineered E. coli strain EpHL02.

Keywords: N-butanoyl homoserine lactone, C4-HSL, N-hexanoyl homoserine lactone, C6-HSL, Pseudomonas aeruginosa, quorum sensing, Serratia marcescens, ß-galactosidase reporter gene

Procedia PDF Downloads 305

Authors: Cristian Pauna

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With few steps away from the 2020, investments in financial markets is a common activity nowadays. In the electronic trading environment, the automated investment software has become a major part in the business intelligence system of any modern financial company. The investment decisions are assisted and/or made automatically by computers using mathematical algorithms today. The complexity of these algorithms requires computer assistance in the investment process. This paper will present several investment strategies that can be automated with algorithmic trading for Deutscher Aktienindex DAX30. It was found that, based on several price action mathematical models used for high-frequency trading some investment strategies can be optimized and improved for automated investments with good results. This paper will present the way to automate these investment decisions. Automated signals will be built using all of these strategies. Three major types of investment strategies were found in this study. The types are separated by the target length and by the exit strategy used. The exit decisions will be also automated and the paper will present the specificity for each investment type. A comparative study will be also included in this paper in order to reveal the differences between strategies. Based on these results, the profit and the capital exposure will be compared and analyzed in order to qualify the investment methodologies presented and to compare them with any other investment system. As conclusion, some major investment strategies will be revealed and compared in order to be considered for inclusion in any automated investment system.

Keywords: Algorithmic trading, automated investment systems, limit conditions, trading principles, trading strategies

Procedia PDF Downloads 194

Authors: Naheem Olakunle Adesina

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The memristor is considered as one of the promising candidates for mamoelectronic engineering and applications. Owing to its high compatibility with CMOS, nanoscale size, and low power consumption, memristor has been employed in the design of commonly used circuits such as phase-locked loop (PLL). In this paper, we designed a memristor-based loop filter (LF) together with other components of PLL. Following this, we evaluated the noise-rejection feature of loop filter by comparing the noise levels of input and output signals of the filter. Our SPICE simulation results showed that memristor behaves like a linear resistor at high frequencies. The result also showed that loop filter blocks the high-frequency components from phase frequency detector so as to provide a stable control voltage to the voltage controlled oscillator (VCO). In addition, we examined the effects of temperature on the performance of the designed phase locked loop circuit. A critical temperature, where there is frequency drift of VCO as a result of variations in control voltage, is identified. In conclusion, the memristor is a suitable choice for nanoelectronic systems owing to a small area, low power consumption, dense nature, high switching speed, and endurance. The proposed memristor-based loop filter, together with other components of the phase locked loop, can be designed using memristive emulator and EDA tools in current CMOS technology and simulated.

Keywords: Fast Fourier Transform, hysteresis curve, loop filter, memristor, noise, phase locked loop, voltage controlled oscillator

Procedia PDF Downloads 186

Authors: Akinyemi Tomiwa

Abstract:

The microstructure of rainfall is important for predicting and modeling various environmental processes, such as rainfall interception by vegetation, soil erosion, and radar signals in rainfall. This rain microstructure was studied with a vertically pointing Micro Rain Radar (MRR) located at a tropical location in Akure South West Nigeria (7o 15’ N, 5o 15’ E). This research utilizes two years of data (2018 and 2019), and the data obtained comprises rainfall parameters such as Rain rates, radar reflectivity, liquid water content, fall velocity and Drop Size Distribution (DSD) based on vertical profiles. The measurement and variations of rain microstructure of these parameters with heights for different rain types were presented from ground level up to the height of 4800 m at 160 m range gates. It has been found that the convective, stratiform and mixed, which are the three major rain types, have different rain microstructures at different heights and were evaluated in this research. The correlation coefficient and the regression line equation were computed for each rain event. The highest rain rate and liquid water content were observed within the height range of 160-4800. It was found that a good correlation exists between the measured parameters. Hence it shows that specific liquid water content increases with increasing rain rate for both stratiform and convective rain types in this part of the world. The results can be very useful for a better understanding of rain structure over tropical regions.

Keywords: rain microstructure, drop size distribution, rain rates, stratiform, convective.

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Authors: Saad M. Darwish, Magda M. Madbouly, Murad B. Khorsheed

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Sign Languages (SL) are the most accomplished forms of gestural communication. Therefore, their automatic analysis is a real challenge, which is interestingly implied to their lexical and syntactic organization levels. Hidden Markov models (HMM’s) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. In this paper, several results concerning static hand gesture recognition using an algorithm based on Type-2 Fuzzy HMM (T2FHMM) are presented. The features used as observables in the training as well as in the recognition phases are based on Singular Value Decomposition (SVD). SVD is an extension of Eigen decomposition to suit non-square matrices to reduce multi attribute hand gesture data to feature vectors. SVD optimally exposes the geometric structure of a matrix. In our approach, we replace the basic HMM arithmetic operators by some adequate Type-2 fuzzy operators that permits us to relax the additive constraint of probability measures. Therefore, T2FHMMs are able to handle both random and fuzzy uncertainties existing universally in the sequential data. Experimental results show that T2FHMMs can effectively handle noise and dialect uncertainties in hand signals besides a better classification performance than the classical HMMs. The recognition rate of the proposed system is 100% for uniform hand images and 86.21% for cluttered hand images.

Keywords: hand gesture recognition, hand detection, type-2 fuzzy logic, hidden Markov Model

Procedia PDF Downloads 462

Authors: Sk Shamim Hasan Abir, Karen Lozano, Mohammed Jasim Uddin

Abstract:

Over the last few years, nanofiber-based triboelectric nanogenerator (TENG) has caught great attention among researchers all over the world due to its inherent capability of converting mechanical energy to usable electrical energy. In this study, poly(vinylidene fluoride) (PVDF) and thermoplastic polyurethane (TPU) nanofiber prepared by Forcespinning® (FS) technique were used to fabricate TENG for self-charging energy storage device and biomechanical body motion sensor. The surface of the TPU nanofiber was modified by uniform deposition of thin gold film to enhance the frictional properties; yielded 254 V open-circuit voltage (Voc) and 86 µA short circuit current (Isc), which were 2.12 and 1.87 times greater in contrast to bare PVDF-TPU TENG. Moreover, the as-fabricated PVDF-TPU/Au TENG was tested against variable capacitors and resistive load, and the results showed that with a 3.2 x 2.5 cm2 active contact area, it can quick charge up to 7.64 V within 30 seconds using a 1.0 µF capacitor and generate significant 2.54 mW power, enough to light 75 commercial LEDs (1.5 V each) by the hand tapping motion at 4 Hz (240 beats per minutes (bpm)) load frequency. Furthermore, the TENG was attached to different body parts to capture distinctive electrical signals for various body movements, elucidated the prospective usability of our prepared nanofiber-based TENG in wearable body motion sensor application.

Keywords: biomotion sensor, forcespinning, nanofibers, triboelectric nanogenerator

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Authors: D. Venu, N. V. Koteswara Rao

Abstract:

Space – time adaptive processing (STAP) is an effective tool for detecting a moving target in spaceborne or airborne radar systems. Since airborne passive radar systems utilize broadcast, navigation and excellent communication signals to perform various surveillance tasks and also has attracted significant interest from the distinct past, therefore the need of the hour is to have cost effective systems as compared to conventional active radar systems. Moreover, requirements of small number of secondary samples for effective clutter suppression in bi-static passive radar offer abundant illuminator resources for passive surveillance radar systems. This paper presents a framework for incorporating knowledge sources directly in the space-time beam former of airborne adaptive radars. STAP algorithm for clutter mitigation for passive bi-static radar has better quantitation of the reduction in sample size thereby amalgamating the earlier data bank with existing radar data sets. Also, we proposed a novel method to estimate the clutter matrix and perform STAP for efficient clutter suppression based on small sample size. Furthermore, the effectiveness of the proposed algorithm is verified using MATLAB simulations in order to validate STAP algorithm for passive bi-static radar. In conclusion, this study highlights the importance for various applications which augments traditional active radars using cost-effective measures.

Keywords: bistatic radar, clutter, covariance matrix passive radar, STAP

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Authors: Randa Alharbi, Vladislav Vyshemirsky

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Systems biology is an important field in science which focuses on studying behaviour of biological systems. Modelling is required to produce detailed description of the elements of a biological system, their function, and their interactions. A well-designed model requires selecting a suitable mechanism which can capture the main features of the system, define the essential components of the system and represent an appropriate law that can define the interactions between its components. Complex biological systems exhibit stochastic behaviour. Thus, using probabilistic models are suitable to describe and analyse biological systems. Continuous-Time Markov Chain (CTMC) is one of the probabilistic models that describe the system as a set of discrete states with continuous time transitions between them. The system is then characterised by a set of probability distributions that describe the transition from one state to another at a given time. The evolution of these probabilities through time can be obtained by chemical master equation which is analytically intractable but it can be simulated. Uncertain parameters of such a model can be inferred using methods of Bayesian inference. Yet, inference in such a complex system is challenging as it requires the evaluation of the likelihood which is intractable in most cases. There are different statistical methods that allow simulating from the model despite intractability of the likelihood. Approximate Bayesian computation is a common approach for tackling inference which relies on simulation of the model to approximate the intractable likelihood. Particle Markov chain Monte Carlo (PMCMC) is another approach which is based on using sequential Monte Carlo to estimate intractable likelihood. However, both methods are computationally expensive. In this paper we discuss the efficiency and possible practical issues for each method, taking into account the computational time for these methods. We demonstrate likelihood-free inference by performing analysing a model of the Repressilator using both methods. Detailed investigation is performed to quantify the difference between these methods in terms of efficiency and computational cost.

Keywords: Approximate Bayesian computation(ABC), Continuous-Time Markov Chains, Sequential Monte Carlo, Particle Markov chain Monte Carlo (PMCMC)

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Authors: Reagan Rodriguez

Abstract:

The following research is a case study of two white patients with white extremist values and their Latina Clinician. Researchers suggest that white supremacy as an ideology has been documented in the United States since the early 1800s. Ethnicity and race were growing key factors linked to central motives behind hate crimes in U.S., which may suggest that we are living in another wave of white supremacist and domestic terrorism that seek to eradicate a threatening and dangerous “other”. This research seeks to address and contribute a qualitative perspective to white supremacist ideology within a bio-psycho-social framework. The current research seeks to contribute to address the gap in literature on ethnic minority clinicians and white patients with racist ideology. The research also seeks to examine the themes not commonly found in racially matched and gendered matched therapeutic dyads where patients hold white extremist values. This case study examines white supremacist ideology from a psychodynamic perspective, examining themes such as “feeling forgotten”, reduced empathy related to “broken promises”, sexualization of the passing minority counselor, and utilizing minimal autonomy in verbal and non-verbal signals. A thematic analysis of case notes and quotes are used to further contextualize emerging therapeutic themes and the psychodynamic analysis of the manifestation of white supremacist actions ranging from active to passive forms of violence.

Keywords: case study, extremism, race and gender, white supremacist ideology

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Authors: Ugochukwu Ejike Akpudo, Jank-Wook Hur

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The increasing demand for improved productivity, maintainability, and reliability has prompted rapidly increasing research studies on the emerging condition-based maintenance concept- Prognostics and health management (PHM). Varieties of fuel pumps serve critical functions in several hydraulic systems; hence, their failure can have daunting effects on productivity, safety, etc. The need for condition monitoring and assessment of these pumps cannot be overemphasized, and this has led to the uproar in research studies on standard feature extraction techniques for optimized condition assessment of fuel pumps. By extracting time-based, frequency-based and the more robust time-frequency based features from these vibrational signals, a more comprehensive feature assessment (and selection) can be achieved for a more accurate and reliable condition assessment of these pumps. With the aid of emerging deep classification and regression algorithms like the locally linear embedding (LLE), we propose a method for comprehensive condition assessment of electromagnetic fuel pumps (EMFPs). Results show that the LLE as a comprehensive feature extraction technique yields better feature fusion/dimensionality reduction results for condition assessment of EMFPs against the use of single features. Also, unlike other feature fusion techniques, its capabilities as a fault classification technique were explored, and the results show an acceptable accuracy level using standard performance metrics for evaluation.

Keywords: electromagnetic fuel pumps, comprehensive feature extraction, condition assessment, locally linear embedding, feature fusion

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Authors: Kuplevatsky V., Kuplevatskay, Cherkashin M., Berezina N.

Abstract:

After 6 months, a violation of the differentiation of the structure of the gland due to edema in 100%. 20% retained signs of a tumor according to DWI/ADC data. By 12 months, the reduction in the size of the gland is 100%. In all cases, no diffusion restriction was observed. The study after 18 months showed no significant changes in all (100%) patients. In the study, 24 months after treatment, the size of the gland was stable in all cases (+/- up to 5%). Diffuse decrease in T2VI signals from peripheral zones, without signs of diffusion restriction in 100%. After 30 months, signs of recovery of adenomatous changes in the transient zone were revealed in 85%. After 36 and 42 months, the restoration of organ differentiation was observed in 93% of patients. In 4 patients, by the 48th month, signs of biochemical relapse were clinically noted. According to the MRI data, signs of a local relapse were revealed. After 48 months, there were signs of restoration of organ differentiation, which allowed the use of PI-RADS criteria. The study after 54 months showed no changes compared to the control. 60 months after treatment, 97% of patients showed a restoration of differentiation of the gland structure, which allows evaluating the organ according to PI-RADS criteria Conclusions: The beginning of restoration of the structure of the prostate gland began 24 months after proton radiation therapy, the PI-RADS criteria can be fully applied after 48 months of treatment. Control studies every 6 months without clinical signs of relapse are not advisable. Local control of the prostate tumor after proton radiation therapy was achieved in 95% of patients during the entire follow-up period ( 60 months).

Keywords: proton therapy, prostate cancer, MRI imaging, PI-RADS

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Authors: Ahmad Samim Ranjbar, Shoshi Mizokami

Abstract:

Kabul is the heart of political, commercial, cultural, educational and social life in Afghanistan and the Kabul fifth fastest growing city in the world, since 2001 with the establishment of new government Lack of adequate employment opportunities and basic utility services in remote provinces have prompted people to move to Kabul and other urban areas. From 2001 to the present, a rapid increase in population, and also less income of the people most of residence tend to use public transport, especially buses, however there is no proper bus system exist in Kabul city, because of wars, from 1992 to 2001 Kabul suffered damage and destruction of its transportation facilities including pavements, sidewalks, traffic circles, drainage systems, traffic signs and signals, trolleybuses and almost all of the public transit buses (e.g. Millie bus). This research is a primary and very important phase into Kabul city transportation and especially an initial and important step toward using large bus in Kabul city, which the main purpose of this research is to find the demand of Kabul city residence for public transport (Large Bus) and compare it with the actual supply from government. Finding of this research shows that the demand of Kabul city residence for the public transport (Large Bus) exceed the supply from the government, means that current public transportation (Large Bus) is not sufficient to serve people of Kabul city, it is mentionable that according to this research there is no need to build a new road or exclusive way for bus, this research propose to government for investment on the public transportation and exceed the number of large buses to can handle the current demand for public transport.

Keywords: transportation, planning, public transport, large bus, Kabul, Afghanistan

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Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

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Authors: Tianqi Yin

Abstract:

The sharia had been the legislative basis in the Ottoman Empire since its emergence. The authority of sharia was superlative in the Islamic society compared to the power of the sulta, the nominal ruler of the nation, regulating essentially every aspect of people’s lives according to an ethical code. In modernity, however, as European sovereignty employed forces to re-engineer the Islamic world to make it more like their own, a society ruled by a state, the Ottoman legislation system encountered a great challenge of adopting codified laws to replace sharia with the formulation of the Mecelle being a prominent case. Interpretations of this transformation have been contentious, with the key debate revolving around whether these codified laws are authentic representations of sharia or alien legal formulations authorized by the modern nation-state under heavy European colonial influence. Because of the difference in methodology of the diverse theories, challenges toward having a universal conclusion on this issue remain. This paper argues that the formulation of the Mecelle and other codified laws is a discontinuity of sharia due to European modernity’s influence and that the emphasis on elements of Islamic laws is a tactic employed to promote this process. These codified laws signals a complete social transformation from the Islamic society ruled by the sharia to a replication of the European society that is ruled by a comprehensive ruling system of the modern state. In addition to advancing the discussion on the characterization of the codification movement in the Ottoman Empire in modernity, the research also promotes the determination of the nature of the modern codification movement globally.

Keywords: codification, mecelle, modernity, sharia, ottoman empire

Procedia PDF Downloads 90