Search results for: frequency features

Authors: Moustafa Ahmed, Fumio Koyama

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Directly-modulated semiconductor lasers, including edge-emitting and vertical-cavity surface-emitting lasers, have received considerable interest recently for use in data transmitters in cost-effective high-speed data centers, metro, and access networks. Optical feedback has been proved as an efficient technique to boost the modulation bandwidth and enhance the speed of the semiconductor laser. However, both the laser linewidth and frequency chirping in directly-modulated lasers are sensitive to both intensity modulation and optical feedback. These effects along width fiber dispersion affect the transmission bit rate and distance in single-mode fiber links. In this work, we continue our recent research on directly-modulated semiconductor lasers with modulation bandwidth in the millimeter-wave band by introducing simultaneous modeling and simulations on both the frequency chirping and lineshape broadening. The lasers are operating under strong optical feedback. The model takes into account the multiple reflections of laser reflections of laser radiation in the external cavity. The analyses are given in terms of the chirp-to-modulated power ratio, and the results are shown for the possible dynamic states of continuous wave, period-1 oscillation, and chaos.

Keywords: chirp, linewidth, optical feedback, semiconductor laser

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Authors: Claire Xu, Welton Wang, Manasvi Pinnaka, Anqi Pan, Michael Han

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Hospitalizations account for one-third of the total health care spending in the US. Early risk detection and intervention can reduce this high cost and increase the satisfaction of both patients and physicians. Due to the lack of awareness of the potential arising risks in home environment, the opportunities for patients to seek early actions of clinical visits are dramatically reduced. This research aims to offer a highly personalized remote patients monitoring and risk assessment AI framework to identify the potentially preventable hospitalization for both acute as well as chronic diseases. A hybrid-AI framework is trained with data from clinical setting, patients surveys, as well as online databases. 20+ risk factors are analyzed ranging from activities, biometric info, demographic info, socio-economic info, hospitalization history, medication info, lifestyle info, etc. The AI model yields high performance of 87% accuracy and 88 sensitivity with 20+ features. This hybrid-AI framework is proven to be effective in identifying the potentially preventable hospitalization. Further, the high indicative features are identified by the models which guide us to a healthy lifestyle and early intervention suggestions.

Keywords: hospitalization prevention, machine learning, remote patient monitoring, risk prediction

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

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

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

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Authors: Ashraf Aly

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The aim of this study is to examine the impact of 5G communication technology radiation on human health and airport safety. The term 5G refers to the fifth generation of wireless mobile technology. The 5G wireless technology will increase the number of high-frequency-powered base stations and other devices and browsing and download speeds, as well as improve the network connectivity and play a big part in improving the performance of integrated applications, such as self-driving cars, medical devices, and robotics. 4G was the latest embedded version of mobile networking technology called 4G, and 5G is the new version of wireless technology. 5G networks have more features than 4G networks, such as lower latency, higher capacity, and increased bandwidth compared to 4G. 5G network improvements over 4G will have big impacts on how people live, business, and work all over the world. But neither 4G nor 5G have been tested for safety and show harmful effects from this wireless radiation. This paper presents biological factors on the effects of 5G radiation on human health. 5G services use C-band radio frequencies; these frequencies are close to those used by radio altimeters, which represent important equipment for airport and aircraft safety. The aviation industry, telecommunications companies, and their regulators have been discussing and weighing these interference concerns for years.

Keywords: wireless communication, radiofrequency, Electromagnetic field, environmental issues

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Authors: A. J. S. P. Nileema, S. Kulatunga , S. H. Palihawadana

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Computer aided detection (CAD) system was developed for the diagnosis of pulmonary tuberculosis using digital chest X-rays with MATLAB image processing techniques using a statistical approach. The study comprised of 200 digital chest radiographs collected from the National Hospital for Respiratory Diseases - Welisara, Sri Lanka. Pre-processing was done to remove identification details. Lung fields were segmented and then divided into four quadrants; right upper quadrant, left upper quadrant, right lower quadrant, and left lower quadrant using the image processing techniques in MATLAB. Contrast, correlation, homogeneity, energy, entropy, and maximum probability texture features were extracted using the gray level co-occurrence matrix method. Descriptive statistics and normal distribution analysis were performed using SPSS. Depending on the radiologists’ interpretation, chest radiographs were classified manually into PTB - positive (PTBP) and PTB - negative (PTBN) classes. Features with standard normal distribution were analyzed using an independent sample T-test for PTBP and PTBN chest radiographs. Among the six features tested, contrast, correlation, energy, entropy, and maximum probability features showed a statistically significant difference between the two classes at 95% confidence interval; therefore, could be used in the classification of chest radiograph for PTB diagnosis. With the resulting value ranges of the five texture features with normal distribution, a classification algorithm was then defined to recognize and classify the quadrant images; if the texture feature values of the quadrant image being tested falls within the defined region, it will be identified as a PTBP – abnormal quadrant and will be labeled as ‘Abnormal’ in red color with its border being highlighted in red color whereas if the texture feature values of the quadrant image being tested falls outside of the defined value range, it will be identified as PTBN–normal and labeled as ‘Normal’ in blue color but there will be no changes to the image outline. The developed classification algorithm has shown a high sensitivity of 92% which makes it an efficient CAD system and with a modest specificity of 70%.

Keywords: chest radiographs, computer aided detection, image processing, pulmonary tuberculosis

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Authors: Qiong Li

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This study investigated in which condition computer-mediated communication (CMC) could promote pragmatic development. The focal feature included four Chinese sentence final particles (SFPs), a, ya, ba, and ne. They occur frequently in Chinese, and function as mitigators to soften the tone of speech. However, L2 acquisition of SFPs is difficult, suggesting the necessity of additional exposure to or explicit instruction on Chinese SFPs. This study follows this line and aims to explore two research questions: (1) Is CMC combined with data-driven instruction more effective than CMC alone in promoting L2 Chinese learners’ SFP use? (2) How does L2 Chinese learners’ SFP use change over time, as compared to the production of native Chinese speakers? The study involved 19 intermediate-level learners of Chinese enrolled at a private American university. They were randomly assigned to two groups: (1) the control group (N = 10), which was exposed to SFPs through CMC alone, (2) the treatment group (N = 9), which was exposed to SFPs via CMC and data-driven instruction. Learners interacted with native speakers on given topics through text-based CMC over Skype. Both groups went through six 30-minute CMC sessions on a weekly basis, with a one-week interval after the first two CMC sessions and a two-week interval after the second two CMC sessions (nine weeks in total). The treatment group additionally received a data-driven instruction after the first two sessions. Data analysis focused on three indices: token frequency, type frequency, and acceptability of SFP use. Token frequency was operationalized as the raw occurrence of SFPs per clause. Type frequency was the range of SFPs. Acceptability was rated by two native speakers using a rating rubric. The results showed that the treatment group made noticeable progress over time on the three indices. The production of SFPs approximated the native-like level. In contrast, the control group only slightly improved on token frequency. Only certain SFPs (a and ya) reached the native-like use. Potential explanations for the group differences were discussed in two aspects: the property of Chinese SFPs and the role of CMC and data-driven instruction. Though CMC provided the learners with opportunities to notice and observe SFP use, as a feature with low saliency, SFPs were not easily noticed in input. Data-driven instruction in the treatment group directed the learners’ attention to these particles, which facilitated the development.

Keywords: computer-mediated communication, data-driven instruction, pragmatic development, second language Chinese, sentence final particles

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Authors: A. Suresh, Sreehari Rao Patri, K. S. R. Krishnaprasad

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An ultra low power capacitor less low-dropout voltage regulator with improved transient response using gain enhanced feed forward path compensation is presented in this paper. It is based on a cascade of a voltage amplifier and a transconductor stage in the feed forward path with regular error amplifier to form a composite gain-enhanced feed forward stage. It broadens the gain bandwidth and thus improves the transient response without substantial increase in power consumption. The proposed LDO, designed for a maximum output current of 100 mA in UMC 180 nm, requires a quiescent current of 69 µA. An undershoot of 153.79mV for a load current changes from 0mA to 100mA and an overshoot of 196.24mV for current change of 100mA to 0mA. The settling time is approximately 1.1 µs for the output voltage undershoot case. The load regulation is of 2.77 µV/mA at load current of 100mA. Reference voltage is generated by using an accurate band gap reference circuit of 0.8V.The costly features of SOC such as total chip area and power consumption is drastically reduced by the use of only a total compensation capacitance of 6pF while consuming power consumption of 0.096 mW.

Keywords: capacitor-less LDO, frequency compensation, transient response, latch, self-biased differential amplifier

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: K. Yu Vlasova, M. A. Abakumov, H. Wishwarsao, M. Sokolsky, N. V. Nukolova, A. G. Majouga, Y. I. Golovin, N. L. Klyachko, A. V. Kabanov

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Introduction:Nowadays pharmaceutical medicine is aimed to create systems for combined therapy, diagnostic, drug delivery and controlled release of active molecules to target cells. Magnetic nanoparticles (MNPs) are used to achieve this aim. MNPs can be applied in molecular diagnostics, magnetic resonance imaging (T1/T2 contrast agents), drug delivery, hyperthermia and could improve therapeutic effect of drugs. The most common drug containers, containing MNPs, are liposomes, micelles and polymeric molecules bonded to the MNPs surface. Usually superparamagnetic nanoparticles are used (the general diameter is about 5-6 nm) and all effects of high frequency magnetic field (MF) application are based on Neel relaxation resulting in heating of surrounded media. In this work we try to develop a new method to improve drug release from MNPs under super low frequency MF. We suppose that under low frequency MF exposures the Brown’s relaxation dominates and MNPs rotation could occur leading to conformation changes and release of bioactive molecules immobilized on MNPs surface.The aim of this work was to synthesize different systems with active drug (biopolymers coated MNPs nanoclusters with immobilized enzymes and doxorubicin (Dox) loaded magnetic liposomes/micelles) and investigate the effect of super low frequency MF on these drug containers. Methods: We have synthesized MNPs of magnetite with magnetic core diameter 7-12 nm . The MNPs were coated with block-copolymer of polylysine and polyethylene glycol. Superoxide dismutase 1 (SOD1) was electrostatically adsorbed on the surface of the clusters. Liposomes were prepared as follow: MNPs, phosphatidylcholine and cholesterol were dispersed in chloroform, dried to get film and then dispersed in distillated water, sonicated. Dox was added to the solution, pH was adjusted to 7.4 and excess of drug was removed by centrifugation through 3 kDa filters. Results: Polylysine coated MNPs formed nanosized clusters (as observed by TEM) with intensity average diameter of 112±5 nm and zeta potential 12±3 mV. After low frequency AC MF exposure we observed change of immobilized enzyme activity and hydrodynamic size of clusters. We suppose that the biomolecules (enzymes) are released from the MNPs surface followed with additional aggregation of complexes at the MF in medium. Centrifugation of the nanosuspension after AC MF exposures resulted in increase of positive charge of clusters and change in enzyme concentration in comparison with control sample without MF, thus confirming desorption of negatively charged enzyme from the positively charged surface of MNPs. Dox loaded magnetic liposomes had average diameter of 160±8 nm and polydispersity index (PDI) 0.25±0.07. Liposomes were stable in DW and PBS at pH=7.4 at 370C during a week. After MF application (10 min of exposure, 50 Hz, 230 mT) diameter of liposomes raised to 190±10 nm and PDI was 0.38±0.05. We explain this by destroying and/or reorganization of lipid bilayer, that leads to changes in release of drug in comparison with control without MF exposure. Conclusion: A new application of low frequency AC MF for drug delivery and controlled drug release was shown. Investigation was supported by RSF-14-13-00731 grant, K1-2014-022 grant.

Keywords: magnetic nanoparticles, low frequency magnetic field, drug delivery, controlled drug release

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Authors: Yan Yue

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Huang Di Nei Jing (HDNJ) is one of the significant traditional Chinese medicine (TCM) classics which lays the foundation of TCM theory and practice. It is an important work for the world to study the ancient civilizations and medical history of China. Language in HDNJ is highly concise and vague, and notably challenging to translate. This paper investigates the translatability of one particular vagueness in HDNJ: the conceptual vagueness which carries the Chinese philosophical and cultural connotations. The corpora tool Sketch Engine is used to provide potential online contexts and word behaviors. Selected two English translations of HDNJ by TCM practitioner and non-practitioner are used to examine frequency and distribution of linguistic features of the translation. It was found the hypothesis about the universals of translated language (explicitation, normalisation) is true in one translation, but it is on the sacrifice of some original contextual connotations. Transliteration is purposefully used in the second translation to retain the original flavor, which is argued as a violation of the principle of relevance in communication because it yields little contextual effects and demands more processing effort of the reader. The translatability of conceptual vagueness in HDNJ is constrained by source language context and the reader’s cognitive environment.

Keywords: corpus-based translation, translatability, TCM classics, vague language

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Authors: Angelica S. Valeriani, Lorenzo Biasiolo

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In recent years, the scale of the impact of climate change and its related side effects has become ever more massive and devastating. Sustainable Development Goals (SDGs), promoted by United Nations, aim to front issues related to climate change, among others. In particular, the project CROWD4SDG focuses on a bunch of SDGs since it promotes environmental activities and climate-related issues. In this context, we developed a prototype of an application, under advanced development considering web design, that focuses on SDG 13 (SDG on climate action) by providing users with useful instruments to face environmental crises and climate-related disasters. Our prototype is thought and structured for both web and mobile development. The main goal of the application, POLI4SDG, is to help users to get through emergency services. To this extent, an organized overview and classification prove to be very effective and helpful to people in need. A careful analysis of data related to environmental crises prompted us to integrate the user contribution, i.e., exploiting a core principle of Citizen Science, into the realization of a public catalog, available for consulting and organized according to typology and specific features. In addition, gender equality and opportunity features are considered in the prototype in order to allow women, often the most vulnerable category, to have direct support. The overall description of the application functionalities is detailed. Moreover, the implementation features and properties of the prototype are discussed.

Keywords: crowdsourcing, social media, SDG, climate change, natural disasters, gender equality

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Authors: Yu Zhao, Rainer Gruenheid, Gerhard Bauch

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In the microwave and millimeter-wave transmission region, substrate-integrated waveguide (SIW) is a very promising candidate for the development of circuits and components. It facilitates the transmission at the data rates in excess of 200 Gbit/s. An SIW mimics a rectangular waveguide by approximating the closed sidewalls with a via fence. This structure suppresses the low frequency components and makes the channel of the SIW a bandpass or high pass filter. This channel characteristic impedes the conventional baseband transmission using non-return-to-zero (NRZ) pulse shaping scheme. Therefore, mixers are commonly proposed to be used as carrier modulator and demodulator in order to facilitate a passband transmission. However, carrier modulation is not an energy efficient solution, because modulation and demodulation at high frequencies consume a lot of energy. For the first time to our knowledge, this paper proposes a spectrum shaping scheme of low complexity for the channel of SIW, namely spread reshaping code. It aims at matching the spectrum of the transmit signal to the channel frequency response. It facilitates the transmission through the SIW channel while it avoids using carrier modulation. In some cases, it even does not need equalization. Simulations reveal a good performance of this scheme, such that, as a result, eye opening is achieved without any equalization or modulation for the respective transmission channels.

Keywords: bandpass channel, eye-opening, switching frequency, substrate-integrated waveguide, spectrum shaping scheme, spread reshaping code

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Authors: Rik van Leeuwen, Ger Koole

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Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

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Authors: Syed Atif Hassan, Luv Mehta, Syed Asif Hassan

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Hit song science is a field of investigation wherein machine learning techniques are applied to music tracks in order to extract such features from audio signals which can capture information that could explain the popularity of respective tracks. Record companies invest huge amounts of money into recruiting fresh talents and churning out new music each year. Gaining insight into the basis of why a song becomes popular will result in tremendous benefits for the music industry. This paper aims to extract basic musical and more advanced, acoustic features from songs while also taking into account external factors that play a role in making a particular song popular. We use a dataset derived from popular Spotify playlists divided by genre. We use ten genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock), chosen on the basis of clear to ambiguous delineation in the typical sound of their genres. We feed these features into three different classifiers, namely, SVM with RBF kernel, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model at the end. Predicting song popularity is particularly important for the music industry as it would allow record companies to produce better content for the masses resulting in a more competitive market.

Keywords: classifier, machine learning, music tracks, popularity, prediction

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Authors: Ramnath Narhete, Saket Pandey, Puran Gour

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This paper presents of dual-band planner antenna with a compact radiator for 2.4/5.2/5.8 proposed by optimizing its resonant frequency, Bandwidth of operation and radiation frequency using the genetic algorithm. The antenna consists L-shaped and E-shaped radiating element to generate two resonant modes for dual band operation. The above techniques have been successfully used in many applications. Dual band antenna with the compact radiator for 2.4/5.2/5.8 GHz WLAN application design and radiator size only width 8mm and a length is 11.3 mm. The antenna can we used for various application in the field of communication. Genetic algorithm will be used to design the antenna and impedance matching network.

Keywords: genetic algorithm, dual-band E, dual-band L, WLAN, compact radiator

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Authors: Retno A. Vinarti, Lucy M. Hederman

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This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree.

Keywords: epidemiology, knowledge modelling, infectious disease, prediction, risk

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Authors: W. T. A. Mohammed, M. Eltaeb, R. Kashani

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The first bending modes of tall asymmetric structures in the two lateral X and Y-directions have two different natural frequencies. To add tuned damping to these bending modes, one needs to either a) use two pendulum-tuned mass dampers (PTMDs) with one tuning frequency, each PTMD targeting one of the bending modes, or b) use one PTMD with two tuning frequencies (one in each lateral directions). Option (a), being more massive, requiring more space, and being more expensive, is less attractive than option (b). Considering that the tuning frequency of a pendulum depends mainly on the pendulum length, one way of realizing option (b) is by constraining the swinging length of the pendulum in one direction but not in the other; such PTMD is dubbed passive Bi-PTMD. Alternatively, option (b) can be realized by actively setting the tuning frequencies of the PTMD in the two directions. In this work, accurate physical models of passive Bi-PTMD and active PTMD are developed and incorporated into the numerical model of a tall asymmetric structure. The model of PTMDs plus structure is used for a)synthesizing such PTMDs for particular applications and b)evaluating their damping effectiveness in mitigating the dynamic lateral responses of their target asymmetric structures, perturbed by wind load in X and Y-directions. Depending on how elaborate the control scheme is, the active PTMD can either be made to yield the same damping effectiveness as the passive Bi-PTMD of the same size or the passive Bi-TMD twice as massive as the active PTMD.

Keywords: active tuned mass damper, high-rise building, multi-frequency tuning, vibration control

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Authors: Rakesh Dubey, Vaishali Bahl, Dalveer Kaur

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The spiralling urge for high rate data transmission over wireless mediums needs intelligent use of electromagnetic resources considering restrictions like power ingestion, spectrum competence, robustness against multipath propagation and implementation intricacy. Orthogonal frequency division multiplexing (OFDM) is a capable technique for next generation wireless communication systems. For such high rate data transfers there is requirement of proper allocation of resources like power and capacity amongst the sub channels. This paper illustrates various available methods of allocating power and the capacity requirement with the constraint of Shannon limit.

Keywords: Additive White Gaussian Noise, Multi-Carrier Modulation, Orthogonal Frequency Division Multiplexing (OFDM), Signal to Noise Ratio (SNR), Water Filling

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Authors: Rabi Mouhcine, Amrouch Mustapha, Mahani Zouhir, Mammass Driss

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In this paper, we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.

Keywords: recognition, handwriting, Arabic text, HMMs, embedded training

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Authors: Bin Wang, Jian Kang

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After years of development, the study of soundscape has been refined to the types of urban space and building. Traffic complex takes traffic function as the core, with obvious design features of architectural space combination and traffic streamline. The acoustic environment is strongly characterized by function, space, material, user and other factors. Traffic complex integrates various functions of business, accommodation, entertainment and so on. It has various forms, complex and varied experiences, and its acoustic environment is turned rich and interesting with distribution and coordination of various functions, division and unification of the mass, separation and organization of different space and the cross and the integration of multiple traffic flow. In this study, it made field recordings of each space of various traffic complex, and extracted and analyzed different acoustic elements, including changes in sound pressure, frequency distribution, steady sound source, sound source information and other aspects, to make cluster analysis of each independent traffic complex buildings. It divided complicated traffic complex building space into several typical sound space from acoustic environment perspective, mainly including stable sound space, high-pressure sound space, rhythm sound space and upheaval sound space. This classification can further deepen the study of subjective evaluation and control of the acoustic environment of traffic complex.

Keywords: soundscape, traffic complex, cluster analysis, classification

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Authors: Wei Li, Tuo Yang

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In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes.

Keywords: deep learning, computer vision, cigarette smoke detection, YOLOV3, color feature extraction

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Authors: Muneeba Butt

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One of the most important physical evidence which leaves suspects at the crime scene is footprints. Analysis of footprints, which can provide useful information for personal identification, is helpful in crime scene investigation. For the current study, 200 samples collected (144 male and 56 female) from Pakistani population with a consent form. The footprints were collected by using black ink with an ink pad. The entire samples were photographed, and then the magnifying glass was used for visualization of individual characteristics including detail of toes, humps, phalange mark, and flat foot cracks in footprint patterns. The descriptive results of individualizing characteristics features were presented in tabular form with respective frequency and percentage. In the result in the male population, the prevalence of tibialis type (T-type) is highest. In the female population, the prevalence of midularis type (M-type) is highest. Humps on the first toe are more found in the male population rather than other humps. In the female population, humps on the third toe are more found rather than other humps. In the male population, the prevalence of phalange mark by toe 1 is highest followed by toe 3, toe 5, toe 2, toe 4 and in female population the prevalence of phalange mark by toe 1 is highest followed by toe 5, 4, 3 and 2. Creases marks are found highest in male population as compared to the female population.

Keywords: foot prints, toes, humps, cracks

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Authors: Ming Liu, Chong Wu, Bingquan Liu, Lei Chen

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Internet users have to face the massive amount of textual data every day. Organizing texts into categories can help users dig the useful information from large-scale text collection. Clustering, in fact, is one of the most promising tools for categorizing texts due to its unsupervised characteristic. Unfortunately, most of traditional clustering algorithms lose their high qualities on large-scale text collection. This situation mainly attributes to the high- dimensional vectors generated from texts. To effectively and efficiently cluster large-scale text collection, this paper proposes a vector reconstruction based clustering algorithm. Only the features that can represent the cluster are preserved in cluster’s representative vector. This algorithm alternately repeats two sub-processes until it converges. One process is partial tuning sub-process, where feature’s weight is fine-tuned by iterative process. To accelerate clustering velocity, an intersection based similarity measurement and its corresponding neuron adjustment function are proposed and implemented in this sub-process. The other process is overall tuning sub-process, where the features are reallocated among different clusters. In this sub-process, the features useless to represent the cluster are removed from cluster’s representative vector. Experimental results on the three text collections (including two small-scale and one large-scale text collections) demonstrate that our algorithm obtains high quality on both small-scale and large-scale text collections.

Keywords: vector reconstruction, large-scale text clustering, partial tuning sub-process, overall tuning sub-process

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Authors: Bing Gu

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Abstract—Most of Conventional facial recognition systems are based on image features, such as LBP, SIFT. Recently some DBN-based 2D facial recognition systems have been proposed. However, we find there are few DBN-based 3D facial recognition system and relative researches. 3D facial images include all the individual biometric information. We can use these information to build more accurate features, So we present our DBN-based face recognition system using Light Field. We can see Light Field as another presentation of 3D image, and Light Field Camera show us a way to receive a Light Field. We use the commercially available Light Field Camera to act as the collector of our face recognition system, and the system receive a state-of-art performance as convenient as conventional 2D face recognition system.

Keywords: DBN, face recognition, light field, Lytro

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Authors: Shagufta Tabassum, V. P. Pawar

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The study of static dielectric properties in a binary mixture of 1,2 dichloroethane (DE) and n,n dimethylformamide (DMF) polar liquids has been carried out in the frequency range of 10 MHz to 30 GHz for 11 different concentration using time domain reflectometry technique at 10ºC temperature. The dielectric relaxation study of solute-solvent mixture at microwave frequencies gives information regarding the creation of monomers and multimers as well as interaction between the molecules of the binary mixture. The least squares fit method is used to determine the values of dielectric parameters such as static dielectric constant (ε₀), dielectric constant at high frequency (ε_∞) and relaxation time (τ).

Keywords: shagufta shaikhexcess parameters, relaxation time, static dielectric constant, time domain reflectometry

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Authors: Tawfiq Chekifi, Brahim Dennai, Rachid Khelfaoui

Abstract:

Over the last few decades, modeling immiscible fluids such as oil and water have been a classical research topic. Droplet-based microfluidics presents a unique platform for mixing, reaction, separation, dispersion of drops, and numerous other functions. For this purpose, several devices were studied, as well as microfluidic oscillator. The latter was obtained from wall attachment microfluidic amplifiers using a feedback loop from the outputs to the control inputs, nevertheless this device have not well used for microdrops applications. In this paper, we suggest a numerical CFD study of a microfluidic oscillator with two different lengths of feedback loop. In order to produce simultaneous microdrops of gasoil on water, a typical geometry that includes double T-junction is connected to the fluidic oscillator. The generation of microdrops is computed by volume-of-fluid method (VOF). Flow oscillations of microdrops were triggered by the Coanda effect of jet flow. The aim of work is to obtain a high oscillation frequency in output of this passive device, the influence of hydrodynamics and physics parameters on the microdrops frequency in the output of our microsystem is also analyzed, The computational results show that, the length of feedback loop, applied pressure on T-junction and interfacial tension have a significant effect on the dispersion of microdrops and its oscillation frequency. Across the range of low Reynold number, the microdrops generation and its dynamics have been accurately controlled by adjusting applying pressure ratio of two phases.

Keywords: fluidic oscillator, microdrops manipulation, VOF (volume of fluid method), microfluidic oscillator

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Authors: Fatih Kantas

Abstract:

Exhaust systems are key contributors to ground vehicles as a heat source. Understanding heat transfer in exhaust systems is related to defining effective parameter on heat transfer in exhaust system. In this journal, over 20 Nusselt numbers are investigated. This study shows advantages and disadvantages of various Nusselt numbers in different range Re, Pr and pulsating flow amplitude and frequency. Also (CAF) Convective Augmentation Factors are defined to correct standard Nusselt number for geometry and location of exhaust system. Finally, optimum Nusselt number and Convective Augmentation Factors are recommended according to Re, Pr and pulsating flow amplitude and frequency, geometry and location effect of exhaust system.

Keywords: exhaust gas flow, heat transfer correlation, Nusselt, Prandtl, pulsating flow

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Authors: Yuyun Wabula, B. J. Dewancker

Abstract:

In the past decade, the social networking app has been growing very rapidly. Geolocation data is one of the important features of social media that can attach the user's location coordinate in the real world. This paper proposes the use of geolocation data from the Twitter social media application to gain knowledge about urban dynamics, especially on human mobility behavior. This paper aims to explore the relation between geolocation Twitter with the existence of people in the urban area. Firstly, the study will analyze the spread of people in the particular area, within the city using Twitter social media data. Secondly, we then match and categorize the existing place based on the same individuals visiting. Then, we combine the Twitter data from the tracking result and the questionnaire data to catch the Twitter user profile. To do that, we used the distribution frequency analysis to learn the visitors’ percentage. To validate the hypothesis, we compare it with the local population statistic data and land use mapping released by the city planning department of Makassar local government. The results show that there is the correlation between Twitter geolocation and questionnaire data. Thus, integration the Twitter data and survey data can reveal the profile of the social media users.

Keywords: geolocation, Twitter, distribution analysis, human mobility

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Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

Abstract:

In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.

Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction

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Authors: M. Şimşek

Abstract:

Rapidly changing factors that affect daily life also affect operational environment and the way military leaders fulfill their missions. With the help of technological developments, traditional linearity of conflict and war has started to fade away. Furthermore, mission domain has broadened to include traditional threats, hybrid threats and new challenges of cyber and space. Considering the future operational environment, future military leaders need to adapt themselves to the new challenges of the future battlefield. But how to decide what kind of features of leadership are required to operate and accomplish mission in the new complex battlefield? In this article, the main aim is to provide answers to this question. To be able to find right answers, first leadership and leadership components are defined, and then characteristics of future operational environment are analyzed. Finally, leadership features that are required to be successful in redefined battlefield are explained.

Keywords: future operational environment, leadership, leadership components

Procedia PDF Downloads 430