Search results for: RLS identification algorithm

Authors: R. Rama Kishore, Sunesh

Abstract:

Recent development in the usage of internet for different purposes creates a great threat for the copyright protection of the digital images. Digital watermarking can be used to address the problem. This paper presents detailed review of the different watermarking techniques, latest trends in the field of secured, robust and imperceptible watermarking. It also discusses the different optimization techniques used in the field of watermarking in order to improve the robustness and imperceptibility of the method. Different measures are discussed to evaluate the performance of the watermarking algorithm. At the end, this paper proposes a watermarking algorithm using (2, 2) share visual cryptography and Bezier curve based algorithm to improve the security of the watermark. The proposed method uses fractional transformation to improve the robustness of the copyright protection of the method. The algorithm is optimized using fuzzy entropy for better results.

Keywords: digital watermarking, fractional transform, visual cryptography, Bezier curve, fuzzy entropy

Procedia PDF Downloads 365

Authors: Subodh Chandra Shakya, Badal Shrestha, Suni Thapa, Ashutosh Chauhan, Saugat Adhikari

Abstract:

To tackle our problem of clothes identification, we used different architectures of Convolutional Neural Networks. Among different architectures, the outcome from Inception ResNet V2 and MobileNet V2 seemed promising. On comparison of the metrices, we observed that the Inception ResNet V2 slightly outperforms MobileNet V2 for this purpose. So this paper of ours proposes the cloth identifier using Inception ResNet V2 and also contains the comparison between the outcome of ResNet V2 and MobileNet V2. The document here contains the results and findings of the research that we performed on the DeepFashion Dataset. To improve the dataset, we used different image preprocessing techniques like image shearing, image rotation, and denoising. The whole experiment was conducted with the intention of testing the efficiency of convolutional neural networks on cloth identification so that we could develop a reliable system that is good enough in identifying the clothes worn by the users. The whole system can be integrated with some kind of recommendation system.

Keywords: inception ResNet, convolutional neural net, deep learning, confusion matrix, data augmentation, data preprocessing

Procedia PDF Downloads 187

Authors: Sourabh Joshi, Geetinder Kaur, Sarabjit Kaur, Gulwatanpreet Singh, Geetika Mannan

Abstract:

In this paper, we have use an algorithm that able to obtain an optimal solution to travelling salesman problem from a huge search space, quickly. This algorithm is based upon the ant colony optimization technique and employees roulette wheel selection mechanism. To illustrate it more clearly, a program has been implemented which is based upon this algorithm, that presents the changing process of route iteration in a more intuitive way. In the event, we had find the optimal path between hundred cities and also calculate the distance between two cities.

Keywords: ant colony, optimization, travelling salesman problem, roulette wheel selection

Procedia PDF Downloads 441

Authors: Ali Allahverdi, Harun Aydilek, Asiye Aydilek

Abstract:

The two stage assembly flowshop scheduling problem has lots of application in real life. To the best of our knowledge, the two stage assembly flowshop scheduling problem with total tardiness performance measure and separate setup times has not been addressed so far, and hence, it is addressed in this paper. Different dominance relations are developed and several algorithms are proposed. Extensive computational experiments are conducted to evaluate the proposed algorithms. The computational experiments have shown that one of the algorithms performs much better than the others. Moreover, the experiments have shown that the best performing algorithm performs much better than the best existing algorithm for the case of zero setup times in the literature. Therefore, the proposed best performing algorithm not only can be used for problems with separate setup times but also for the case of zero setup times.

Keywords: scheduling, assembly flowshop, total tardiness, algorithm

Procedia PDF Downloads 344

Authors: Hossein Abbasi

Abstract:

The stability is an important topic to plan and manage the energy in the microgrids as the same as the conventional power systems. The voltage and frequency stability is one of the most important issues recently studied in microgrids. The objectives of this paper are the modelling and designing of the components and optimal controllers for the voltage and frequency control of the AC/DC hybrid microgrid under the different disturbances. Since the PI controllers have the advantages of simple structure and easy implementation, so they are designed and modeled in this paper. The harmony search (HS) algorithm is used to optimize the controllers’ parameters. According to the achieved results, the PI controllers have a good performance in voltage and frequency control of the microgrid.

Keywords: frequency control, HS algorithm, microgrid, PI controller, voltage control

Procedia PDF Downloads 391

Authors: S. Bouhouche, R. Drai, J. Bast

Abstract:

This paper is concerned with a method for uncertainty evaluation of steel sample content using X-Ray Fluorescence method. The considered method of analysis is a comparative technique based on the X-Ray Fluorescence; the calibration step assumes the adequate chemical composition of metallic analyzed sample. It is proposed in this work a new combined approach using the Kalman Filter and Markov Chain Monte Carlo (MCMC) for uncertainty estimation of steel content analysis. The Kalman filter algorithm is extended to the model identification of the chemical analysis process using the main factors affecting the analysis results; in this case, the estimated states are reduced to the model parameters. The MCMC is a stochastic method that computes the statistical properties of the considered states such as the probability distribution function (PDF) according to the initial state and the target distribution using Monte Carlo simulation algorithm. Conventional approach is based on the linear correlation, the uncertainty budget is established for steel Mn(wt%), Cr(wt%), Ni(wt%) and Mo(wt%) content respectively. A comparative study between the conventional procedure and the proposed method is given. This kind of approaches is applied for constructing an accurate computing procedure of uncertainty measurement.

Keywords: Kalman filter, Markov chain Monte Carlo, x-ray fluorescence calibration and testing, steel content measurement, uncertainty measurement

Procedia PDF Downloads 283

Authors: Alena Skalon, Jennifer L. Beaudry

Abstract:

Eyewitness misidentifications can sometimes lead to wrongful convictions of innocent people. This occurs in part because jurors tend to believe confident eyewitnesses even when the identification took place under suggestive conditions. Empirical research demonstrated that jurors are often unaware of the factors that can influence the reliability of eyewitness identification. Most common legal safeguards that are designed to educate jurors about eyewitness evidence are judicial instructions and expert testimony. To date, very few studies assessed the effectiveness of judicial instructions and most of them found that judicial instructions make jurors more skeptical of eyewitness evidence or do not have any effect on jurors’ judgments. Similar results were obtained for expert testimony. However, none of the previous studies focused on the ability of legal safeguards to improve jurors’ assessment of evidence obtained from suggestive identification procedures—this is one of the gaps addressed by this paper. Furthermore, only three studies investigated whether legal safeguards improve the ultimate accuracy of jurors’ judgments—that is, whether after listening to judicial instructions or expert testimony jurors can differentiate between accurate and inaccurate eyewitnesses. This presentation includes two studies. Both studies used genuine eyewitnesses (i.e., eyewitnesses who watched the crime) and manipulated the suggestiveness of identification procedures. The first study manipulated the presence of judicial instructions; the second study manipulated the presence of one of two types of expert testimony: a traditional, verbal expert testimony or expert testimony accompanied by visual aids. All participant watched a video-recording of an identification procedure and of an eyewitness testimony. The results indicated that neither judicial instructions nor expert testimony affected jurors’ judgments. However, consistent with the previous findings, when the identification procedure was non-suggestive, jurors believed accurate eyewitnesses more often than inaccurate eyewitnesses. When the procedure was suggestive, jurors believed accurate and inaccurate eyewitnesses at the same rate. The paper will discuss the implications of these studies and directions for future research.

Keywords: expert testimony, eyewitness evidence, judicial instructions, jurors’ decision making, legal safeguards

Procedia PDF Downloads 177

Authors: Safayat Ali Shaikh

Abstract:

Software has been developed for optimal cropping pattern in an irrigation project considering land constraint, water availability constraint and pick up flow constraint using modified Simplex Algorithm. Artificial Neural Network Models (ANN) have been developed to predict rainfall. AR (1) model used to generate 1000 years rainfall data to train the ANN. Simulation has been done with expected rainfall data. Eight number crops and three types of soil class have been considered for optimization model. Area under each crop and each soil class have been quantified using Modified Simplex Algorithm to get optimum net return. Efficacy of the software has been tested using data of large irrigation project in India.

Keywords: artificial neural network, large irrigation project, modified simplex algorithm, optimal cropping pattern

Procedia PDF Downloads 203

Authors: Jing Zhang, Daniel Nikovski

Abstract:

We propose an approximation algorithm called LINKUMP to compute the Pan Matrix Profile (PMP) under the unnormalized l∞ distance (useful for value-based similarity search) using double-ended queue and linear interpolation. The algorithm has comparable time/space complexities as the state-of-the-art algorithm for typical PMP computation under the normalized l₂ distance (useful for shape-based similarity search). We validate its efficiency and effectiveness through extensive numerical experiments and a real-world anomaly detection application.

Keywords: pan matrix profile, unnormalized euclidean distance, double-ended queue, discord discovery, anomaly detection

Procedia PDF Downloads 247

Authors: Zhoucheng Bao, Haiyan Zhu, Tingting Pang, Zuling Wang

Abstract:

This paper proposes a multi-population DE with adaptive mutation and local search for global optimization, named AMMADE. In order to better coordinate the cooperation between the populations and the rational use of resources. In AMMADE, the population is divided based on the Euclidean distance sorting method at each generation to appropriately coordinate the cooperation between subpopulations and the usage of resources, such that the best-performed subpopulation will get more computing resources in the next generation. Further, an adaptive local search strategy is employed on the best-performed subpopulation to achieve a balanced search. The proposed algorithm has been tested by solving optimization problems taken from CEC2014 benchmark problems. Experimental results show that our algorithm can achieve a competitive or better than related methods. The results also confirm the significance of devised strategies in the proposed algorithm.

Keywords: differential evolution, multi-mutation strategies, memetic algorithm, adaptive local search

Procedia PDF Downloads 157

Authors: Francis O. Nwawuru

Abstract:

The convergence analysis of split monotone inclusion problems and fixed point problems of certain nonlinear mappings are investigated in the setting of real Hilbert spaces. Inertial extrapolation term in the spirit of Polyak is incorporated to speed up the rate of convergence. Under standard assumptions, a strong convergence of the proposed algorithm is established without computing the resolvent operator or involving Yosida approximation method. The stepsize involved in the algorithm does not depend on the spectral radius of the linear operator. Furthermore, applications of the proposed algorithm in solving some related optimization problems are also considered. Our result complements and extends numerous results in the literature.

Keywords: fixedpoint, hilbertspace, monotonemapping, resolventoperators

Procedia PDF Downloads 52

Authors: Ho Jeong Jin, Chang Won Seo, Choon Sik Cho, Bong Yong Choi, Kwang Kyun Na, Sang Rok Lee

Abstract:

In this paper, a radar signal processing technique using the LFM-FSK (Linear Frequency Modulation-Frequency Shift Keying) is proposed for reducing the false alarm rate based on the compressive sensing. The LFM-FSK method combines FMCW (Frequency Modulation Continuous Wave) signal with FSK (Frequency Shift Keying). This shows an advantage which can suppress the ghost phenomenon without the complicated CFAR (Constant False Alarm Rate) algorithm. Moreover, the parametric sparse algorithm applying the compressive sensing that restores signals efficiently with respect to the incomplete data samples is also integrated, leading to reducing the burden of ADC in the receiver of radars. 24 GHz FMCW signal is applied and tested in the real environment with FSK modulated data for verifying the proposed algorithm along with the compressive sensing.

Keywords: compressive sensing, LFM-FSK radar, radar signal processing, sparse algorithm

Procedia PDF Downloads 481

Authors: Sahar Mobeen, Anam Rafique, Irum Baig

Abstract:

Acoustic echo cancellation in multichannel is a system identification application. In real time environment, signal changes very rapidly which required adaptive algorithms such as Least Mean Square (LMS), Leaky Least Mean Square (LLMS), Normalized Least Mean square (NLMS) and average (AFA) having high convergence rate and stable. LMS and NLMS are widely used adaptive algorithm due to less computational complexity and AFA used of its high convergence rate. This research is based on comparison of acoustic echo (generated in a room) cancellation thorough LMS, LLMS, NLMS, AFA and newly proposed average normalized leaky least mean square (ANLLMS) adaptive filters.

Keywords: LMS, LLMS, NLMS, AFA, ANLLMS

Procedia PDF Downloads 566

Authors: O. O. Fola-Matthews, O. O. Soyinka, D. N. Bitalo

Abstract:

Nigeria is one of the major shark fishing nations in Africa, but its fisheries managers still record catch data in aggregates ‘sharks’ with no species-specific details. This is because most of the shark specimens look identical in morphology, and field identification of some closely related species is tricky. This study uses DNA barcoding as a method to identify shark species from five different landing areas off the Nigerian Coast. 100 dorsal fins were sampled in order to provide a Chondrichthyan sequence that would be matched to reference specimens in a DNA barcode database

Keywords: BOLD, DNA barcoding, nigeria, sharks

Procedia PDF Downloads 166

Authors: Rana I. Abdulghani, Amera I. Melhum

Abstract:

With the abnormal growth of the usage of computers over networks and under the consideration or agreement of most of the computer security experts who said that the goal of building a secure system is never achieved effectively, all these points led to the design of the intrusion detection systems(IDS). This research adopts a comparison between two techniques for network intrusion detection, The first one used the (Particles Swarm Optimization) that fall within the field (Swarm Intelligence). In this Act, the algorithm Enhanced for the purpose of obtaining the minimum error rate by amending the cluster centers when better fitness function is found through the training stages. Results show that this modification gives more efficient exploration of the original algorithm. The second algorithm used a (Back propagation NN) algorithm. Finally a comparison between the results of two methods used were based on (NSL_KDD) data sets for the construction and evaluation of intrusion detection systems. This research is only interested in clustering the two categories (Normal and Abnormal) for the given connection records. Practices experiments result in intrude detection rate (99.183818%) for EPSO and intrude detection rate (69.446416%) for BP neural network.

Keywords: IDS, SI, BP, NSL_KDD, PSO

Procedia PDF Downloads 382

Authors: Mallikarjun Warad, C. M. Kalleshwaraswamy, P. R. Shashank

Abstract:

Studies were conducted to record the occurrence of different species of hairy caterpillar on different host plants in and around Shivamogga, Karnataka, India. Twelve genera of hairy caterpillars belonging to Arctiinae and Lymantriinae were recorded on different host plants and reared to adults in laboratory on their respective hosts. The Porthesia sp. feed on castor, Creatonotus gangis on cocoa, Perina nuda on fig, Pericalia ricini on pigeon pea, Utetheisa pulchella on sunhemp and Euproctis sp. on paddy and banana. Illustrations of immature and adults were made to associate them. Along with this, light traps were also set during the rainy season, to capture adults of hairy caterpillars. An illustrated identification key was provided for easy and accurate identification of adult of hairy caterpillars based on their morphological (male genitalial) characters. The study through a light on the existence of sexual dimorphism, polyphagous nature and diapause are the major hindrance in taxonomic identification. Hence, attempts were made to address these issues in the study.

Keywords: Erebidae, hairy caterpillars, male genitalia, taxonomy

Procedia PDF Downloads 206

Authors: V. Nagammai

Abstract:

Very-large-scale integration (VLSI) is the process of creating an integrated circuit (IC) by combining thousands of transistors into a single chip. Development of technology increases the complexity in IC manufacturing which may vary the power consumption, increase the size and latency period. Topology defines a number of connections between network. In this project, NoC topology is generated using atlas tool which will increase performance in turn determination of constraints are effective. The routing is performed by XY routing algorithm and wormhole flow control. In NoC topology generation, the value of power, area and latency are predetermined. In previous work, placement, routing and shortest path evaluation is performed using an algorithm called floor planning with cluster reconstruction and path allocation algorithm (FCRPA) with the account of 4 3x3 switch, 6 4x4 switch, and 2 5x5 switches. The usage of the 4x4 and 5x5 switch will increase the power consumption and area of the block. In order to avoid the problem, this paper has used one 8x8 switch and 4 3x3 switches. This paper uses IPRCA which of 3 steps they are placement, clustering, and shortest path evaluation. The placement is performed using min – cut placement and clustering are performed using an algorithm called cluster generation. The shortest path is evaluated using an algorithm called Dijkstra's algorithm. The power consumption of each block is determined. The experimental result shows that the area, power, and wire length improved simultaneously.

Keywords: application specific noc, b* tree representation, floor planning, t tree representation

Procedia PDF Downloads 393

Authors: Amirhossein Mohajerzadeh, Abolghasem Mohajerzadeh

Abstract:

In this paper, target parameter is estimated with desirable precision in hierarchical wireless sensor networks (WSN) while the proposed algorithm also tries to prolong network lifetime as much as possible, using efficient data collecting algorithm. Target parameter distribution function is considered unknown. Sensor nodes sense the environment and send the data to the base station called fusion center (FC) using hierarchical data collecting algorithm. FC builds underlying phenomena based on collected data. Considering the aggregation level, x, the goal is providing the essential infrastructure to find the best value for aggregation level in order to prolong network lifetime as much as possible, while desirable accuracy is guaranteed (required sample size is fully depended on desirable precision). First, the sample size calculation algorithm is discussed, second, the average queue length based on M/M[x]/1/K queue model is determined and it is used for energy consumption calculation. Nodes can decrease transmission cost by aggregating incoming data. Furthermore, the performance of the new algorithm is evaluated in terms of lifetime and estimation accuracy.

Keywords: aggregation, estimation, queuing, wireless sensor network

Procedia PDF Downloads 186

Authors: Zuoji Huang, Haiming Qian, Chunlin Wang, Jinyan Sun, Nan Xu

Abstract:

In this paper, the strategy to extract double road edge lines from acquired road stripe image was explored. The workflow is as follows: the road stripes are acquired by probabilistic boosting tree algorithm and morphological algorithm immediately, and road centerlines are detected by thinning algorithm, so the initial road edge lines can be acquired along the road centerlines. Then we refine the results with big variation of local curvature of centerlines. Specifically, the energy function of edge line is constructed by gradient feature and spectral information, and Dijkstra algorithm is used to optimize the initial road edge lines. The Snake model is constructed to solve the fracture problem of intersection, and the discrete dynamic programming algorithm is used to solve the model. After that, we could get the final road network. Experiment results show that the strategy proposed in this paper can be used to extract the continuous and smooth road edge lines from high-resolution remote sensing images with an accuracy of 88% in our study area.

Keywords: road edge lines extraction, energy function, intersection fracture, Snake model

Procedia PDF Downloads 338

Authors: Pavan K. Rallabandi, Kailash C. Patidar

Abstract:

In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

Procedia PDF Downloads 388

Authors: Aarthy Anandan, Anitha Narasimhan, Madhan Karky

Abstract:

Word-based games are popular in helping people to improve their vocabulary skills. Games like ‘word search’ and crosswords provide a smart way of increasing vocabulary skills. Word search games are fun to play, but also educational which actually helps to learn a language. Finding the words from word search puzzle helps the player to remember words in an easier way, and it also helps to learn the spellings of words. In this paper, we present a tile distribution algorithm for a Maze-Based Treasure Hunt Game 'Pudhaiyal’ for Tamil words, which describes how words can be distributed horizontally, vertically or diagonally in a 10 x 10 grid. Along with the tile distribution algorithm, we also present an algorithm for the scoring model of the game. The proposed game has been tested with 20,000 Tamil words.

Keywords: Pudhaiyal, Tamil word game, word search, scoring, maze, algorithm

Procedia PDF Downloads 440

Authors: Arvind Kumar, Manoj Kumar, Dattatraya H. Nagaraj, Amanpreet Singh, Jayanthi Prattapati

Abstract:

The purpose of this paper is to study and compare two maximum power point tracking (MPPT) algorithms in a photovoltaic simulation system and also show a simulation study of maximum power point tracking (MPPT) for photovoltaic systems using perturb and observe algorithm and Incremental conductance algorithm. Maximum power point tracking (MPPT) plays an important role in photovoltaic systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize the array efficiency and minimize the overall system cost. Since the maximum power point (MPP) varies, based on the irradiation and cell temperature, appropriate algorithms must be utilized to track the (MPP) and maintain the operation of the system in it. MATLAB/Simulink is used to establish a model of photovoltaic system with (MPPT) function. This system is developed by combining the models established of solar PV module and DC-DC Boost converter. The system is simulated under different climate conditions. Simulation results show that the photovoltaic simulation system can track the maximum power point accurately.

Keywords: incremental conductance algorithm, perturb and observe algorithm, photovoltaic system, simulation results

Procedia PDF Downloads 556

Authors: Jorge Rodriguez-Guerra, Carlos Calleja, Aron Pujana, Iker Elorza, Ana Maria Macarulla

Abstract:

Society demands more reliable manufacturing processes capable of producing high quality products in shorter production cycles. New control algorithms have been studied to satisfy this paradigm, in which Fault-Tolerant Control (FTC) plays a significant role. It is suitable to detect, isolate and adapt a system when a harmful or faulty situation appears. In this paper, a general overview about FTC characteristics are exposed; highlighting the properties a system must ensure to be considered faultless. In addition, a research to identify which are the main FTC techniques and a classification based on their characteristics is presented in two main groups: Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant Controllers (PFTCs). AFTC encompasses the techniques capable of re-configuring the process control algorithm after the fault has been detected, while PFTC comprehends the algorithms robust enough to bypass the fault without further modifications. The mentioned re-configuration requires two stages, one focused on detection, isolation and identification of the fault source and the other one in charge of re-designing the control algorithm by two approaches: fault accommodation and control re-design. From the algorithms studied, one has been selected and applied to a case study based on an industrial hydraulic-press. The developed model has been embedded under a real-time validation platform, which allows testing the FTC algorithms and analyse how the system will respond when a fault arises in similar conditions as a machine will have on factory. One AFTC approach has been picked up as the methodology the system will follow in the fault recovery process. In a first instance, the fault will be detected, isolated and identified by means of a neural network. In a second instance, the control algorithm will be re-configured to overcome the fault and continue working without human interaction.

Keywords: fault-tolerant control, electro-hydraulic actuator, fault detection and isolation, control re-design, real-time

Procedia PDF Downloads 177

Authors: Camilla Elphick, Graham Hole, Samuel Hutton, Graham Pike

Abstract:

Pupil size has been found to change irrespective of luminosity, suggesting that it can be used to make inferences about cognitive processes, such as cognitive load. To see whether identifying a target requires a different cognitive load to rejecting distractors, the effect of viewing a target (compared with viewing distractors) on pupil size was investigated using a sequential video lineup procedure with two lineup sessions. Forty one participants were chosen randomly via the university. Pupil sizes were recorded when viewing pre target distractors and post target distractors and compared to pupil size when viewing the target. Overall, pupil size was significantly larger when viewing the target compared with viewing distractors. In the first session, pupil size changes were significantly different between participants who identified the target (Hits) and those who did not. Specifically, the pupil size of Hits reduced significantly after viewing the target (by 26%), suggesting that cognitive load reduced following identification. The pupil sizes of Misses (who made no identification) and False Alarms (who misidentified a distractor) did not reduce, suggesting that the cognitive load remained high in participants who failed to make the correct identification. In the second session, pupil sizes were smaller overall, suggesting that cognitive load was smaller in this session, and there was no significant difference between Hits, Misses and False Alarms. Furthermore, while the frequency of Hits increased, so did False Alarms. These two findings suggest that the benefits of including a second session remain uncertain, as the second session neither provided greater accuracy nor a reliable way to measure it. It is concluded that pupil size is a measure of face recognition strength in the first session of a target present lineup procedure. However, it is still not known whether cognitive load is an adequate explanation for this, or whether cognitive engagement might describe the effect more appropriately. If cognitive load and cognitive engagement can be teased apart with further investigation, this would have positive implications for understanding eyewitness identification. Nevertheless, this research has the potential to provide a tool for improving the reliability of lineup procedures.

Keywords: cognitive load, eyewitness identification, face recognition, pupillometry

Procedia PDF Downloads 404

Authors: Fatima Mokeddem

Abstract:

The phonocardiogram signal (PCG) is a physiological signal that reflects heart mechanical activity, is a promising tool for curious researchers in this field because it is full of indications and useful information for medical diagnosis. PCG segmentation is a basic step to benefit from this signal. Therefore, this paper presents an algorithm that serves the separation of heart sounds and heart murmurs in case they exist in order to use them in several applications and heart sounds analysis. The separation process presents here is founded on three essential steps filtering, envelope detection, and heart sounds segmentation. The algorithm separates the PCG signal into S1 and S2 and extract cardiac murmurs.

Keywords: phonocardiogram signal, filtering, Envelope, Detection, murmurs, heart sounds

Procedia PDF Downloads 140

Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub

Abstract:

Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.

Keywords: Apriori, Association rules mining, Big Data, Data Mining, Hadoop, MapReduce, MongoDB, NoSQL

Procedia PDF Downloads 160

Authors: Rohit Gupta, Ravinder Agarwal

Abstract:

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

Keywords: classifiers, feature selection, locomotion, sEMG

Procedia PDF Downloads 293

Authors: Pratiksha Saxena, Dipti Singh, Neha Khanna

Abstract:

A self-organizing migrating genetic algorithm(C-SOMGA) is developed for animal diet formulation. This paper presents animal diet formulation using stochastic and genetic algorithm. Tri-objective models for cost minimization and shelf life maximization are developed. These objectives are achieved by combination of stochastic programming and C-SOMGA. Stochastic programming is used to introduce nutrient variability for animal diet. Self-organizing migrating genetic algorithm provides exact and quick solution and presents an innovative approach towards successful application of soft computing technique in the area of animal diet formulation.

Keywords: animal feed ration, feed formulation, linear programming, stochastic programming, self-migrating genetic algorithm, C-SOMGA technique, shelf life maximization, cost minimization, nutrient maximization

Procedia PDF Downloads 442

Authors: Maitree Thamma, Witchupong Wiboonjaroen, Thanat Suknuan, Karan Homchat

Abstract:

First order plus dead time (FOPDT) is a high dynamic system. Therefore, the controller must be intelligent. This paper presents the development and implementation of self-tuning Fuzzy-PID controller for controlling the FOPDT system. The water level process used represented FOPDT system and the mathematical model of the system was approximated by using System Identification toolbox in Matlab. The control programming and Fuzzy-PID algorithm used Matlab/Simulink and run on Microcontroller STM32.

Keywords: real-time control, self-tuning fuzzy-PID, FOPDT system, the water lever process

Procedia PDF Downloads 292

Authors: Isaac Quiros-Fernandez, Angel Cid-Arregui

Abstract:

Tumor-reactive T cell receptors (TCRs) are being subject of intense investigation since they offer great potential in adoptive cell therapies against cancer. However, the identification of tumor-specific TCRs has proven challenging, for instance, due to the limited expansion capacity of tumor-infiltrating T cells (TILs) and the extremely low frequencies of tumor-reactive T cells in the repertoire of patients and healthy donors. We have developed an approach for rapid identification and characterization of neoepitope-reactive TCRs from the T cell repertoire of healthy donors. CD8 T cells isolated from multiple donors are subjected to a first sorting step after staining with HLA multimers carrying the peptide of interest. The isolated cells are expanded for two weeks, after which a second sorting is performed using the same peptide-HLA multimers. The cells isolated in this way are then processed for single-cell sequencing of their TCR alpha and beta chains. Newly identified TCRs are cloned in appropriate expression vectors for functional analysis on Jurkat, NK92, and primary CD8 T cells and tumor cells expressing the appropriate antigen. We have identified TCRs specifically binding HLA-A2 presenting epitopes of tumor antigens, which are capable of inducing TCR-mediated cell activation and cytotoxicity in target cancer cell lines. This method allows the identification of tumor-reactive TCRs in about two to three weeks, starting from peripheral blood samples of readily available healthy donors.

Keywords: cancer, TCR, tumor antigens, immunotherapy

Procedia PDF Downloads 69