Search results for: frequency features

Authors: Cíntia Martins Sanches

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In the first part of Phoenissae of Seneca, Antigone is a guide to Oedipus, and they leave Thebes: he is blind searching for death (inflicting the punishment himself wished on the killer of Laius, ie exile and death); she is trying to convince him to give up such punishment and bring him back to Thebes. Concerning Oedipus lines, we observed a high frequency of Latin neuter in the treatment the protagonist gave to his daughter Antigone. We considered in this study that such frequency may be related to the sanctification of the daughter, who is seen by him as an enlightened being and without defects, free of the human condition (which takes on the existence of failures by essence). This study, thus, puts forward an analysis of the passages the said feature is present, relating them to the effect of meaning found in each occurrence. As part of a doctorate, this study investigates the stylistic idiom of Seneca in the Oedipus and Phoenissae tragedies, aiming at translating both tragedies expressively. The concept of stylistic idiom concerns the stylistic affinity required for a translation to be equivalent to the source text. In this wise, this study inquires into how the Latin text is organized poetically, pointing out the expressive features frequently appearing in both dramas. The method we used is based on the Semiotics theory — observing how connotation, ie a language use in which prevails the poetic function, naturally polysemous, acts to achieve each expressive effect.

Keywords: antigone, neuter, Oedipus, Phoenissae, Seneca

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Authors: Ferry Kurniawan

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In this paper, we nowcast quarterly output growth in Indonesia by exploiting higher frequency data (monthly indicators) using a mixed-frequency factor model and exploiting both quarterly and monthly data. Nowcasting quarterly GDP in Indonesia is particularly relevant for the central bank of Indonesia which set the policy rate in the monthly Board of Governors Meeting; whereby one of the important step is the assessment of the current state of the economy. Thus, having an accurate and up-to-date quarterly GDP nowcast every time new monthly information becomes available would clearly be of interest for central bank of Indonesia, for example, as the initial assessment of the current state of the economy -including nowcast- will be used as input for longer term forecast. We consider a small scale mixed-frequency factor model to produce nowcasts. In particular, we specify variables as year-on-year growth rates thus the relation between quarterly and monthly data is expressed in year-on-year growth rates. To assess the performance of the model, we compare the nowcasts with two other approaches: autoregressive model –which is often difficult when forecasting output growth- and Mixed Data Sampling (MIDAS) regression. In particular, both mixed frequency factor model and MIDAS nowcasts are produced by exploiting the same set of monthly indicators. Hence, we compare the nowcasts performance of the two approaches directly. To preview the results, we find that by exploiting monthly indicators using mixed-frequency factor model and MIDAS regression we improve the nowcast accuracy over a benchmark simple autoregressive model that uses only quarterly frequency data. However, it is not clear whether the MIDAS or mixed-frequency factor model is better. Neither set of nowcasts encompasses the other; suggesting that both nowcasts are valuable in nowcasting GDP but neither is sufficient. By combining the two individual nowcasts, we find that the nowcast combination not only increases the accuracy - relative to individual nowcasts- but also lowers the risk of the worst performance of the individual nowcasts.

Keywords: nowcasting, mixed-frequency data, factor model, nowcasts combination

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Authors: Can Mungan, Gokhan Kilic

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Structural engineers and tunnel owners have good reason to attach importance to the assessment and inspection of tunnels. Regular inspection is necessary to maintain and monitor the health of the structure not only at the present time but throughout its life cycle. Detection of flaws within the structure, such as corrosion and the formation of cracks within the internal elements of the structure, can go a long way to ensuring that the structure maintains its integrity over the course of its life. Other issues that may be detected earlier through regular assessment include tunnel surface delamination and the corrosion of the rebar. One advantage of new technology such as the ground penetrating radar (GPR) is the early detection of imperfections. This study will aim to discuss and present the effectiveness of GPR as a tool for assessing the structural integrity of the heavily used tunnel. GPR is used with various antennae in frequency and application method (2 GHz and 500 MHz GPR antennae). The paper will attempt to produce a greater understanding of structural defects and identify the correct tool for such purposes. Conquest View with 3D scanning capabilities was involved throughout the analysis, reporting, and interpretation of the results. This study will illustrate GPR mapping and its effectiveness in providing information of value when it comes to rebar position (lower and upper reinforcement). It will also show how such techniques can detect structural features that would otherwise remain unseen, as well as moisture ingress.

Keywords: tunnel, GPR, health monitoring, moisture ingress, rebar position

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Authors: Bharatendra Rai

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Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

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Authors: Shuo Liu, Dan Liu

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In the field of named entity recognition, most of the research has been conducted around simple entities. However, for nested named entities, which still contain entities within entities, it has been difficult to identify them accurately due to their boundary ambiguity. In this paper, a hierarchical recognition model is constructed based on the grammatical structure and semantic features of Chinese text for boundary calculation based on lexical features. The analysis is carried out at different levels in terms of granularity, semantics, and lexicality, respectively, avoiding repetitive work to reduce computational effort and using the semantic features of words to calculate the boundaries of entities to improve the accuracy of the recognition work. The results of the experiments carried out on web-based microblogging data show that the model achieves an accuracy of 86.33% and an F1 value of 89.27% in recognizing nested named entities, making up for the shortcomings of some previous recognition models and improving the efficiency of recognition of nested named entities.

Keywords: coarse-grained, nested named entity, Chinese natural language processing, word embedding, T-SNE dimensionality reduction algorithm

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Authors: José A. Menezes, Giordano Cabral, Bruno T. Gomes

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Choosing good features is an essential part of machine learning. Recent techniques aim to automate this process. For instance, feature learning intends to learn the transformation of raw data into a useful representation to machine learning tasks. In automatic audio classification tasks, this is interesting since the audio, usually complex information, needs to be transformed into a computationally convenient input to process. Another technique tries to generate features by searching a feature space. Genetic algorithms, for instance, have being used to generate audio features by combining or modifying them. We find this approach particularly interesting and, despite the undeniable advances of feature learning approaches, we wanted to take a step forward in the use of genetic algorithms to find audio features, combining them with more conventional methods, like PCA, and inserting search control mechanisms, such as constraints over a confusion matrix. This work presents the results obtained on particular audio classification problems.

Keywords: feature generation, feature learning, genetic algorithm, music information retrieval

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Authors: Jyh Sheen, Yang-Hung Cheng

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This paper presents a design of a microstrip bandpass filter with a compact size and wide stopband by using 0.15-μm GaAs pHEMT process. The wide stop band is achieved by suppressing the first and second harmonic resonance frequencies. The slow-wave coupling stepped impedance resonator with cross coupled structure is adopted to design the bandpass filter. A two-resonator filter was fabricated with 13.5GHz center frequency and 11% bandwidth was achieved. The devices are simulated using the ADS design software. This device has shown a compact size and very low insertion loss of 2.6 dB. Microstrip planar bandpass filters have been widely adopted in various communication applications due to the attractive features of compact size and ease of fabricating. Various planar resonator structures have been suggested. In order to reach a wide stopband to reduce the interference outside the passing band, various designs of planar resonators have also been submitted to suppress the higher order harmonic frequencies of the designed center frequency. Various modifications to the traditional hairpin structure have been introduced to reduce large design area of hairpin designs. The stepped-impedance, slow-wave open-loop, and cross-coupled resonator structures have been studied to miniaturize the hairpin resonators. In this study, to suppress the spurious harmonic bands and further reduce the filter size, a modified hairpin-line bandpass filter with cross coupled structure is suggested by introducing the stepped impedance resonator design as well as the slow-wave open-loop resonator structure. In this way, very compact circuit size as well as very wide upper stopband can be achieved and realized in a Roger 4003C substrate. On the other hand, filters constructed with integrated circuit technology become more attractive for enabling the integration of the microwave system on a single chip (SOC). To examine the performance of this design structure at the integrated circuit, the filter is fabricated by the 0.15 μm pHEMT GaAs integrated circuit process. This pHEMT process can also provide a much better circuit performance for high frequency designs than those made on a PCB board. The design example was implemented in GaAs with center frequency at 13.5 GHz to examine the performance in higher frequency in detail. The occupied area is only about 1.09×0.97 mm2. The ADS software is used to design those modified filters to suppress the first and second harmonics.

Keywords: microstrip resonator, bandpass filter, harmonic suppression, GaAs

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Authors: Ágnes Bogárdi-Mészöly, Takeshi Hashimoto, Shohei Yokoyama, Hiroshi Ishikawa

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The spread of Web 2.0 has caused user-generated content explosion. Users can tag resources to describe and organize them. Tag clouds provide rough impression of relative importance of each tag within overall cloud in order to facilitate browsing among numerous tags and resources. The goal of our paper is to enrich visualization of tag clouds. A font distribution algorithm has been proposed to calculate a novel metric based on frequency and content, and to classify among classes from this metric based on power law distribution and percentages. The suggested algorithm has been validated and verified on the tag cloud of a real-world thesis portal.

Keywords: tag cloud, font distribution algorithm, frequency-based, content-based, power law

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Authors: Naser Safdarian, Nader Jafarnia Dabanloo

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In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization.

Keywords: ECG signal processing, myocardial infarction, features extraction, pattern recognition

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Authors: Wittawat Wasusathien, Samran Santalunai, Thanaset Thosdeekoraphat, Chanchai Thongsopa

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This paper presents breast cancer detection by observing the specific absorption rate (SAR) intensity for identification tumor location, the tumor is identified in coordinates (x,y,z) system. We examined the frequency between 4-8 GHz to look for the most appropriate frequency. Results are simulated in frequency 4-8 GHz, the model overview include normal breast with 50 mm radian, 5 mm diameter of tumor, and ultra wideband (UWB) bowtie antenna. The models are created and simulated in CST Microwave Studio. For this simulation, we changed antenna to 5 location around the breast, the tumor can be detected when an antenna is close to the tumor location, which the coordinate of maximum SAR is approximated the tumor location. For reliable, we experiment by random tumor location to 3 position in the same size of tumor and simulation the result again by varying the antenna position in 5 position again, and it also detectable the tumor position from the antenna that nearby tumor position by maximum value of SAR, which it can be detected the tumor with precision in all frequency between 4-8 GHz.

Keywords: specific absorption rate (SAR), ultra wideband (UWB), coordinates, cancer detection

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Authors: Chieh-Fu Chang, Wan-Hsin Hsieh

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We have proposed a novel scheme— iterative word-extension (IWE) to enhance the cliff effect of Reed-Solomon codes regarding the error performance at a specific Eb/N0. The scheme can be readily extended to block codes and the important properties of IWE are further investigated here. In order to select proper block codes specifying the desired cliff Eb/N0, the associated features of IWE are explored. These properties and features grant IWE ability to enhance security regarding the received Eb/N0 in physical layer so that IWE scheme can cooperate with the traditional presentation layer approach — cryptography, to meet the secure requirements in diverse applications. The features and feasibility of IWE scheme in satellite communication are finally discussed.

Keywords: security, IWE, cliff effect, space communications

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Authors: Hwan Su Jung, Ahn Jun Gil, Jong Tae Kim

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Power management techniques are necessary to save power in the microprocessor. By changing the frequency and/or operating voltage of processor, DVFS can control power consumption. In this paper, we perform a case study to find optimal power state transition for DVFS. We propose the equation to find the optimal ratio between executions of states while taking into account the deadline of processing time and the power state transition delay overhead. The experiment is performed on the Cortex-M4 processor, and average 6.5% power saving is observed when DVFS is applied under the deadline condition.

Keywords: deadline, dynamic voltage frequency scaling, power state transition

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Authors: Rana Mahmoud

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This study represents a systematic study of the Operational Transconductance Amplifiers capacitance (OTA-C) filters or as it is often called Gm-C filters. OTA-C filters have been paid a great attention for the last decades. As Gm-C filters operate in an open loop topology, this makes them flexible to perform in low and high frequencies. As such, Gm-C filters can be used in various wireless communication applications. Another property of Gm-C filters is its electronic tunability, thus different filter frequency characteristics can be obtained without changing the inductance and resistance values. This can be achieved by an OTA (Operational Transconductance Amplifier) and a capacitor. By tuning the OTA transconductance, the cut-off frequency will be tuned and different frequency responses are achieved. Different high-order analog filters can be design using Gm-C filters including low pass, high pass and band pass filters. 1st and 2nd order low pass, high pass and band pass filters are presented in this paper.

Keywords: Gm-C, filters, low-pass, high-pass, band-pass

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Authors: Win-Jin Chang, Haw-Long Lee, Yu-Ching Yang

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Atomic finite element simulation is applied to investigate the vibration frequency of a single-layer gamma graphyne with an attached mass for the CCCC, SSSS, CFCF, SFSF boundary conditions using the commercial code ANSYS. The fundamental frequencies of the graphyne sheet are compared with the results of the previous study. The results of the comparison are very good in all considered cases. The attached mass causes a shift in the resonant frequency of the graphyne. The frequencies of the single-layer gamma graphyne with an attached mass for different boundary conditions are obtained, and the order based on the boundary condition is CCCC >SSSS > CFCF> SFSF. The highest frequency shift is obtained when the attached mass is located at the center of the graphyne sheet. This is useful for the design of a highly sensitive graphyne-based mass sensor.

Keywords: graphyne, finite element analysis, vibration analysis, frequency shift

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Authors: Wipassorn Vinicchayakul, Pichaya Supanakoon, Sathaporn Promwong

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A human’s hand localization is revised by using radar cross section (RCS) measurements with a minimum root mean square (RMS) error matching algorithm on a touchless keypad mock-up model. RCS and frequency transfer function measurements are carried out in an indoor environment on the frequency ranged from 3.0 to 11.0 GHz to cover federal communications commission (FCC) standards. The touchless keypad model is tested in two different distances between the hand and the keypad. The initial distance of 19.50 cm is identical to the heights of transmitting (Tx) and receiving (Rx) antennas, while the second distance is 29.50 cm from the keypad. Moreover, the effects of Rx angles relative to the hand of human factor are considered. The RCS input parameters are compared with power loss parameters at each frequency. From the results, the performance of the RCS input parameters with the second distance, 29.50 cm at 3 GHz is better than the others.

Keywords: radar cross section, fingerprint-based localization, minimum root mean square (RMS) error matching algorithm, touchless keypad model

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Authors: M. Helaimi, R. Taleb, D. Benyoucef, B. Belmadani

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This paper presents the design of a polynomial controller with coefficient diagram method (CDM). This controller is used to control the output power of high frequency resonant inverter with LLC tank. One of the most important problems associated with the proposed inverter is achieving ZVS operating during the induction heating process. To overcome this problem, asymmetrical voltage cancellation (AVC) control technique is proposed. The phased look loop (PLL) is used to track the natural frequency of the system. The small signal model of the system with the proposed control is obtained using extending describing function method (EDM). The validity of the proposed control is verified by simulation results.

Keywords: induction heating, AVC control, CDM, PLL, resonant inverter

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

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Authors: Celal Kocatepe, Eyup Taslak, Celal Fadil Kumru, Oktay Arikan

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While choosing insulating oil, characteristic features such as thermal cooling, endurance, efficiency and being environment-friendly should be considered. Mineral oils are referred as petroleum-based oil. In this study, vegetable oils investigated as an alternative insulating liquid to mineral oil. Dissipation factor, breakdown voltage, relative dielectric constant and resistivity changes with the frequency and voltage of mineral, rapeseed and nut oils were measured. Experimental studies were performed according to ASTM D924 and IEC 60156 standards.

Keywords: breakdown voltage, dielectric dissipation factor, mineral oil, vegetable oils

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Narmina Mammadova

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In modern linguistics, the comparative study of languages is becoming increasingly popular, the typology and comparison of languages that have different structures is expanding and deepening. Of particular interest is the study of phraseological units, which makes it possible to identify the specific features of the compared languages in all their national identity. This paper gives a brief analysis of the comparative study of somatic phraseological units (SFU) of the Spanish and Turkish languages with the component "hand" in the semantic aspect; identification of equivalents, analogs and non-equivalent units, as well as a description of methods of translation of non-equivalent somatic phraseological units. Comparative study of the phraseology of unrelated languages is of particular relevance since it allows us to identify both general, universal features and differential and specific features characteristic of a particular language. Based on the results of the generalization of the study, it can be assumed that phraseological units containing a somatic component have a high interlingual phraseological activity, which contributes to an increase in the degree of interlingual equivalence.

Keywords: Linguoculturology, Turkish, Spanish, language picture of the world, phraseological units, semantic microfield

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Authors: Jessemyn Modiini, Timothy Lynar, Elena Sitnikova

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We present a novel enhanced compartmental model for malware spread analysis in cyber security. This paper applies cyber security data features to epidemiological compartmental models to model the infectious potential of malware. Compartmental models are most efficient for calculating the infectious potential of a disease. In this paper, we discuss and profile epidemiologically relevant data features from a Domain Name System (DNS) dataset. We then apply these features to epidemiological compartmental models to network traffic features. This paper demonstrates how epidemiological principles can be applied to the novel analysis of key cybersecurity behaviours and trends and provides insight into threat modelling above that of kill-chain analysis. In applying deterministic compartmental models to a cyber security use case, the authors analyse the deficiencies and provide an enhanced stochastic model for cyber epidemiology. This enhanced compartmental model (SUEICRN model) is contrasted with the traditional SEIR model to demonstrate its efficacy.

Keywords: cybersecurity, epidemiology, cyber epidemiology, malware

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Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

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

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

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Authors: Alexander I. Dovgyallo, Evgeniy A. Zinoviev, Svetlana O. Nekrasova

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The most of works applied for the pulse tube converters contain the workflow description implemented through the use of mathematical models on stationary modes. However, the study of the thermoacoustic systems unsteady behavior in the start, stop, and acoustic load changes modes is in the particular interest. The aim of the present study was to develop a mathematical thermal excitation model of acoustic oscillations in pulse tube engine (PTE) as a small-scale scheme of pulse tube engine operating at atmospheric air. Unlike some previous works this standing wave configuration is a fully closed system. The improvements over previous mathematical models are the following: the model allows specifying any values of porosity for regenerator, takes into account the piston weight and the friction in the cylinder and piston unit, and determines the operating frequency. The numerical method is based on the relation equations between the pressure and volume velocity variables at the ends of each element of PTE which is recorded through the appropriate transformation matrix. A solution demonstrates that the PTE operation frequency is the complex value, and it depends on the piston mass and the dynamic friction due to its movement in the cylinder. On the basis of the determined frequency thermoacoustically induced heat transport and generation of acoustic power equations were solved for channel with temperature gradient on its ends. The results of numerical simulation demonstrate the features of the initialization process of oscillation and show that that generated acoustic power more than power on the steady mode in a factor of 3…4. But doesn`t mean the possibility of its further continuous utilizing due to its existence only in transient mode which lasts only for a 30-40 sec. The experiments were carried out on small-scale PTE. The results shows that the value of acoustic power is in the range of 0.7..1.05 W for the defined frequency range f = 13..18 Hz and pressure amplitudes 11..12 kPa. These experimental data are satisfactorily correlated with the numerical modeling results. The mathematical model can be straightforwardly applied for the thermoacoustic devices with variable temperatures of thermal reservoirs and variable transduction loads which are expected to occur in practical implementations of portable thermoacoustic engines.

Keywords: nonlinear processes, pulse tube engine, thermal excitation, standing wave

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Authors: Vitor M. Vilas Boas, Cleison D. Silva, Gustavo S. Mafra, Alexandre Trofino Neto

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Motor imagery (MI) based brain-computer interfaces (BCI) uses event-related (de)synchronization (ERS/ ERD), typically recorded using electroencephalography (EEG), to translate brain electrical activity into control commands. To mitigate undesirable artifacts and noise measurements on EEG signals, methods based on band-pass filters defined by a specific frequency band (i.e., 8 – 30Hz), such as the Infinity Impulse Response (IIR) filters, are typically used. Spatial techniques, such as Common Spatial Patterns (CSP), are also used to estimate the variations of the filtered signal and extract features that define the imagined motion. The CSP effectiveness depends on the subject's discriminative frequency, and approaches based on the decomposition of the band of interest into sub-bands with smaller frequency ranges (SBCSP) have been suggested to EEG signals classification. However, despite providing good results, the SBCSP approach generally increases the computational cost of the filtering step in IM-based BCI systems. This paper proposes the use of the Fast Fourier Transform (FFT) algorithm in the IM-based BCI filtering stage that implements SBCSP. The goal is to apply the FFT algorithm to reduce the computational cost of the processing step of these systems and to make them more efficient without compromising classification accuracy. The proposal is based on the representation of EEG signals in a matrix of coefficients resulting from the frequency decomposition performed by the FFT, which is then submitted to the SBCSP process. The structure of the SBCSP contemplates dividing the band of interest, initially defined between 0 and 40Hz, into a set of 33 sub-bands spanning specific frequency bands which are processed in parallel each by a CSP filter and an LDA classifier. A Bayesian meta-classifier is then used to represent the LDA outputs of each sub-band as scores and organize them into a single vector, and then used as a training vector of an SVM global classifier. Initially, the public EEG data set IIa of the BCI Competition IV is used to validate the approach. The first contribution of the proposed method is that, in addition to being more compact, because it has a 68% smaller dimension than the original signal, the resulting FFT matrix maintains the signal information relevant to class discrimination. In addition, the results showed an average reduction of 31.6% in the computational cost in relation to the application of filtering methods based on IIR filters, suggesting FFT efficiency when applied in the filtering step. Finally, the frequency decomposition approach improves the overall system classification rate significantly compared to the commonly used filtering, going from 73.7% using IIR to 84.2% using FFT. The accuracy improvement above 10% and the computational cost reduction denote the potential of FFT in EEG signal filtering applied to the context of IM-based BCI implementing SBCSP. Tests with other data sets are currently being performed to reinforce such conclusions.

Keywords: brain-computer interfaces, fast Fourier transform algorithm, motor imagery, sub-band common spatial patterns

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Authors: R. Vetrimurugan, Terry Lim, M. J. Goodson, R. Nagarajan

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This study investigates the cleaning performance of high intensity 360 kHz frequency on the removal of nano-dimensional and sub-micron particles from various surfaces, uniformity of the cleaning tank and run to run variation of cleaning process. The uniformity of the cleaning tank was measured by two different methods i.e 1. ppbTM meter and 2. Liquid Particle Counting (LPC) technique. In the second method, aluminium metal spacer components was placed at various locations of the cleaning tank (such as centre, top left corner, bottom left corner, top right corner, bottom right corner) and the resultant particles removed by 360 kHz frequency was measured. The result indicates that the energy was distributed more uniformly throughout the entire cleaning vessel even at the corners and edges of the tank when megasonic sweeping technology is applied. The result also shows that rinsing the parts with 360 kHz frequency at final rinse gives lower particle counts, hence higher cleaning efficiency as compared to other frequencies. When megasonic sweeping technology is applied each piezoelectric transducers will operate at their optimum resonant frequency and generates stronger acoustic cavitational force and higher acoustic streaming velocity. These combined forces are helping to enhance the particle removal and at the same time improve the overall cleaning performance. The multiple extractions study was also carried out for various frequencies to measure the cleaning potential and asymptote value.

Keywords: power distribution, megasonic sweeping, cavitation intensity, particle removal, laser particle counting, nano, submicron

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Authors: L. Matlawe, E. S. Idemudia

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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 modeling 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 were 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: emotional abuse, employees, mental health, self-concept

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Authors: Akeem Akinboro, Bala Alhaj Kegun

Abstract:

Haemoglobinopathy is a genetic disorder that has the potentiality to cause death of individuals in whom both the alpha (α) and beta (β) globin chains of the haemoglobin molecule are defective due to mutations in their genes. The haemoglobin genotype variants among some residents of Niger state, Nigeria, were determined using the secondary data available at Bida, Minna and Kotangora general hospitals of the state. A total of 1,639 data, representing 434, 655 and 550, collected from the outside patients who visited the medical laboratory units of the three general hospitals, respectively, over five years period (2015-2020) were analyzed into gene frequency, sex and age to determine their haemoglobin genotypes status. More males (51.6 – 58.7%) than females (41.3 – 48.4%) visited the three hospitals during the period covered and most of the patients were between 11 - 20 years old. The frequency of HbA allele in the human population was 0.72, 0.65, 0.68 for Bida, Minna and Kotangora, respectively, while it was 0.25, 0.29 and 0.28 for HbS allele. The HbC allele was prevalent at 0.03, 0.06 and 0.05 among the human population in Bida, Minna and Kotangora cities of Niger state. In overall, the prevalence of HbA, HbS and HbC alleles in Niger state of Nigeria was 0.68, 0.28 and 0.05. Minna being the capital city of Niger state and the most populous among the three cities in the state seems to have influx of more people who are carriers of abnormal haemoglobin genotypes which has resulted to higher frequency of HbS and HbC than those of the other two cities in this study. These results show that the pattern of haemoglobin genotypes frequency of Kontagora could be a prediction for the whole of Niger state. It is therefore necessary and important to take screening of blood for haemoglobin genotype serious among intending couples to prevent and reduce the possibility of having increase in the number of people with abnormal haemoglobin genotypes in the state.

Keywords: haemoglobin, genotype, niger state, gene frequency, general hospitals

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Authors: Hasan Çiçekli, Ahmet Gökçen, Uğur Çam

Abstract:

In this paper, a comparative performance analysis of mostly used four nonlinearity cancellation techniques used to realize the passive resistor by MOS transistors is presented. The comparison is done by using an integrator circuit which is employing sequentially Op-amp, OTRA and ICCII as active element. All of the circuits are implemented by MOS-C realization and simulated by PSPICE program using 0.35 µm process TSMC MOSIS model parameters. With MOS-C realization, the circuits became electronically tunable and fully integrable which is very important in IC design. The output waveforms, frequency responses, THD analysis results and features of the nonlinearity cancellation techniques are also given.

Keywords: integrator circuits, MOS-C realization, nonlinearity cancellation, tuneable resistors

Procedia PDF Downloads 508

Authors: Subal Kar, Madhuja Ghosh, Amitesh Kumar, Arijit Majumder

Abstract:

Complementary split-ring resonator (CSRR) loaded microstrip square patch antenna has been optimally designed with the help of high frequency structure simulator (HFSS). The antenna has been fabricated on the basis of the simulation design data and experimentally tested in anechoic chamber to evaluate its gain, bandwidth, efficiency and polarization characteristics. The CSRR loaded microstrip patch antenna has been found to realize significant size miniaturization (to the extent of 24%) compared to the conventional-type microstrip patch antenna both operating at the same frequency (5.2 GHz). The fabricated antenna could realize a maximum gain of 4.17 dB, 10 dB impedance bandwidth of 34 MHz, efficiency 50.73% and with maximum cross-pol of 10.56 dB down at the operating frequency. This practically designed antenna with its miniaturized size is expected to be useful for airborne and space borne applications at microwave frequency.

Keywords: split ring resonator, metamaterial, CSRR loaded patch antenna, microstrip patch antenna, LC resonator

Procedia PDF Downloads 336

Authors: M. D. Fontana, I. Bejaoui Ouni, D. Chapron, H. Aroui

Abstract:

BaTiO3 (BT) is a well known ferroelectric material which has been thoroughly studied during several decades since it undergoes successive cubic-tetragonal-orthorhombic-rhombohedral phase transitions on cooling. It has several ferroelectric properties that allow it to be a good material for electronic applications such as the design of ferroelectric memories and pyroelectric elements. In the present work, we report the analysis of temperature dependence of Raman frequency and damping of the A1 modes polarized along the FE c axis as well as the optical phonons E corresponding to the ionic motions in the plane normal to c. Measurements were carried out at different temperatures ranging from 298 to 408 K (tetragonal phase) within different scattering configurations. Spectroscopic parameters of BT have determined using a high resolution Raman spectrometer and a fitting program. All the first order frequency modes exhibit a quasi linear decrease as function of the temperature, except for the A1[TO1], E[TO2] and E[TO4] lines which reveal a parabolic dependence illustrating an anharmonic process. The phonon frequency downshifts and damping evolutions are interpreted in terms of normal volume expansion and third- and fourth-order anharmonic potentials.

Keywords: BaTiO3, Raman spectroscopy, frequency, damping, anharmonic potential

Procedia PDF Downloads 282