Search results for: weak signal detection.

Authors: Faisal Amin, Abdul Mueed, Xu Jiadong

Abstract:

This paper describes the development of a fully automated measurement software for antenna radiation pattern measurements in a Compact Antenna Test Range (CATR). The CATR has a frequency range from 2-40 GHz and the measurement hardware includes a Network Analyzer for transmitting and Receiving the microwave signal and a Positioner controller to control the motion of the Styrofoam column. The measurement process includes Calibration of CATR with a Standard Gain Horn (SGH) antenna followed by Gain versus angle measurement of the Antenna under test (AUT). The software is designed to control a variety of microwave transmitter / receiver and two axis Positioner controllers through the standard General Purpose interface bus (GPIB) interface. Addition of new Network Analyzers is supported through a slight modification of hardware control module. Time-domain gating is implemented to remove the unwanted signals and get the isolated response of AUT. The gated response of the AUT is compared with the calibration data in the frequency domain to obtain the desired results. The data acquisition and processing is implemented in Agilent VEE and Matlab. A variety of experimental measurements with SGH antennas were performed to validate the accuracy of software. A comparison of results with existing commercial softwares is presented and the measured results are found to be within .2 dBm.

Keywords: Antenna measurement, calibration, time-domain gating, VNA, Positioner controller

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Authors: R.S.Sabeenian, V.Palanisamy

Abstract:

Texture classification is a trendy and a catchy technology in the field of texture analysis. Textures, the repeated patterns, have different frequency components along different orientations. Our work is based on Texture Classification and its applications. It finds its applications in various fields like Medical Image Classification, Computer Vision, Remote Sensing, Agricultural Field, and Textile Industry. Weed control has a major effect on agriculture. A large amount of herbicide has been used for controlling weeds in agriculture fields, lawns, golf courses, sport fields, etc. Random spraying of herbicides does not meet the exact requirement of the field. Certain areas in field have more weed patches than estimated. So, we need a visual system that can discriminate weeds from the field image which will reduce or even eliminate the amount of herbicide used. This would allow farmers to not use any herbicides or only apply them where they are needed. A machine vision precision automated weed control system could reduce the usage of chemicals in crop fields. In this paper, an intelligent system for automatic weeding strategy Multi Resolution Combined Statistical & spatial Frequency is used to discriminate the weeds from the crops and to classify them as narrow, little and broad weeds.

Keywords: crop weed discrimination, MRCSF, MRFM, Weeddetection, Spatial Frequency.

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Authors: Paria Soleimani, Arezoo Neshati

Abstract:

Myocardial infarction is one of the leading causes of death in the world. Some of these deaths occur even before the patient reaches the hospital. Myocardial infarction occurs as a result of impaired blood supply. Because the most of these deaths are due to coronary artery disease, hence the awareness of the warning signs of a heart attack is essential. Some heart attacks are sudden and intense, but most of them start slowly, with mild pain or discomfort, then early detection and successful treatment of these symptoms is vital to save them. Therefore, importance and usefulness of a system designing to assist physicians in early diagnosis of the acute heart attacks is obvious. The main purpose of this study would be to enable patients to become better informed about their condition and to encourage them to seek professional care at an earlier stage in the appropriate situations. For this purpose, the data were collected on 711 heart patients in Iran hospitals. 28 attributes of clinical factors can be reported by patients; were studied. Three logistic regression models were made on the basis of the 28 features to predict the risk of heart attacks. The best logistic regression model in terms of performance had a C-index of 0.955 and with an accuracy of 94.9%. The variables, severe chest pain, back pain, cold sweats, shortness of breath, nausea and vomiting, were selected as the main features.

Keywords: Coronary heart disease, acute heart attacks, prediction, logistic regression.

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Authors: X. Mercilin Raajini, R. Raja Kumar, P. Indumathi, V. Praveen

Abstract:

Many cluster based routing protocols have been proposed in the field of wireless sensor networks, in which a group of nodes are formed as clusters. A cluster head is selected from one among those nodes based on residual energy, coverage area, number of hops and that cluster-head will perform data gathering from various sensor nodes and forwards aggregated data to the base station or to a relay node (another cluster-head), which will forward the packet along with its own data packet to the base station. Here a Game Theory based Diligent Energy Utilization Algorithm (GTDEA) for routing is proposed. In GTDEA, the cluster head selection is done with the help of game theory, a decision making process, that selects a cluster-head based on three parameters such as residual energy (RE), Received Signal Strength Index (RSSI) and Packet Reception Rate (PRR). Finding a feasible path to the destination with minimum utilization of available energy improves the network lifetime and is achieved by the proposed approach. In GTDEA, the packets are forwarded to the base station using inter-cluster routing technique, which will further forward it to the base station. Simulation results reveal that GTDEA improves the network performance in terms of throughput, lifetime, and power consumption.

Keywords: Cluster head, Energy utilization, Game Theory, LEACH, Sensor network.

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Authors: Aspalella A. Rahman

Abstract:

Money laundering has been described by many as the lifeblood of crime and is a major threat to the economic and social well-being of societies. It has been recognized that the banking system has long been the central element of money laundering. This is in part due to the complexity and confidentiality of the banking system itself. It is generally accepted that effective anti-money laundering (AML) measures adopted by banks will make it tougher for criminals to get their "dirty money" into the financial system. In fact, for law enforcement agencies, banks are considered to be an important source of valuable information for the detection of money laundering. However, from the banks- perspective, the main reason for their existence is to make as much profits as possible. Hence their cultural and commercial interests are totally distinct from that of the law enforcement authorities. Undoubtedly, AML laws create a major dilemma for banks as they produce a significant shift in the way banks interact with their customers. Furthermore, the implementation of the laws not only creates significant compliance problems for banks, but also has the potential to adversely affect the operations of banks. As such, it is legitimate to ask whether these laws are effective in preventing money launderers from using banks, or whether they simply put an unreasonable burden on banks and their customers. This paper attempts to address these issues and analyze them against the background of the Malaysian AML laws. It must be said that effective coordination between AML regulator and the banking industry is vital to minimize problems faced by the banks and thereby to ensure effective implementation of the laws in combating money laundering.

Keywords: Banking Industry, Bank Negara Money, Laundering, Malaysia.

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Authors: Madhuri Bhavsar, Anupam K Singh, Shrikant Pradhan

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Long term rainfall analysis and prediction is a challenging task especially in the modern world where the impact of global warming is creating complications in environmental issues. These factors which are data intensive require high performance computational modeling for accurate prediction. This research paper describes a prototype which is designed and developed on grid environment using a number of coupled software infrastructural building blocks. This grid enabled system provides the demanding computational power, efficiency, resources, user-friendly interface, secured job submission and high throughput. The results obtained using sequential execution and grid enabled execution shows that computational performance has enhanced among 36% to 75%, for decade of climate parameters. Large variation in performance can be attributed to varying degree of computational resources available for job execution. Grid Computing enables the dynamic runtime selection, sharing and aggregation of distributed and autonomous resources which plays an important role not only in business, but also in scientific implications and social surroundings. This research paper attempts to explore the grid enabled computing capabilities on weather indices from HOAPS data for climate impact modeling and change detection.

Keywords: Climate model, Computational Grid, GridApplication, Heterogeneous Grid

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Authors: Rahil Garnavi, Mohammad Aldeen, M. Emre Celebi, Alauddin Bhuiyan, Constantinos Dolianitis, George Varigos

Abstract:

Automatic segmentation of skin lesions is the first step towards development of a computer-aided diagnosis of melanoma. Although numerous segmentation methods have been developed, few studies have focused on determining the most discriminative and effective color space for melanoma application. This paper proposes a novel automatic segmentation algorithm using color space analysis and clustering-based histogram thresholding, which is able to determine the optimal color channel for segmentation of skin lesions. To demonstrate the validity of the algorithm, it is tested on a set of 30 high resolution dermoscopy images and a comprehensive evaluation of the results is provided, where borders manually drawn by four dermatologists, are compared to automated borders detected by the proposed algorithm. The evaluation is carried out by applying three previously used metrics of accuracy, sensitivity, and specificity and a new metric of similarity. Through ROC analysis and ranking the metrics, it is shown that the best results are obtained with the X and XoYoR color channels which results in an accuracy of approximately 97%. The proposed method is also compared with two state-ofthe- art skin lesion segmentation methods, which demonstrates the effectiveness and superiority of the proposed segmentation method.

Keywords: Border detection, Color space analysis, Dermoscopy, Histogram thresholding, Melanoma, Segmentation.

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Authors: Guo Xiuhua, Sun Tao, Wu Haifeng, He Wen, Liang Zhigang, Zhang Mengxia, Guo Aimin, Wang Wei

Abstract:

Purpose: To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent.

Keywords: CT image, Curvelet transform, Small pulmonary nodules, Support vector machines, Texture extraction.

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Authors: Yong Zhao, Jian He, Cheng Zhang

Abstract:

Cardiovascular disease resulting from hypertension poses a significant threat to human health, and early detection of hypertension can potentially save numerous lives. Traditional methods for detecting hypertension require specialized equipment and are often incapable of capturing continuous blood pressure fluctuations. To address this issue, this study starts by analyzing the principle of heart rate variability (HRV) and introduces the utilization of sliding window and power spectral density (PSD) techniques to analyze both temporal and frequency domain features of HRV. Subsequently, a hypertension prediction network that relies on HRV is proposed, combining Resnet, attention mechanisms, and a multi-layer perceptron. The network leverages a modified ResNet18 to extract frequency domain features, while employing an attention mechanism to integrate temporal domain features, thus enabling auxiliary hypertension prediction through the multi-layer perceptron. The proposed network is trained and tested using the publicly available SHAREE dataset from PhysioNet. The results demonstrate that the network achieves a high prediction accuracy of 92.06% for hypertension, surpassing traditional models such as K Near Neighbor (KNN), Bayes, Logistic regression, and traditional Convolutional Neural Network (CNN).

Keywords: Feature extraction, heart rate variability, hypertension, residual networks.

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Authors: Sidney A. Lima, Hermann J. H. Kux, Elcio H. Shiguemori

Abstract:

The Unmanned Aircraft Systems (UAS) usually navigate through the Global Navigation Satellite System (GNSS) associated with an Inertial Navigation System (INS). However, GNSS can have its accuracy degraded at any time or even turn off the signal of GNSS. In addition, there is the possibility of malicious interferences, known as jamming. Therefore, the image navigation system can solve the autonomy problem, because if the GNSS is disabled or degraded, the image navigation system would continue to provide coordinate information for the INS, allowing the autonomy of the system. This work aims to evaluate the accuracy of the positioning though photogrammetry concepts. The methodology uses orthophotos and Digital Surface Models (DSM) as a reference to represent the object space and photograph obtained during the flight to represent the image space. For the calculation of the coordinates of the perspective center and camera attitudes, it is necessary to know the coordinates of homologous points in the object space (orthophoto coordinates and DSM altitude) and image space (column and line of the photograph). So if it is possible to automatically identify in real time the homologous points the coordinates and attitudes can be calculated whit their respective accuracies. With the methodology applied in this work, it is possible to verify maximum errors in the order of 0.5 m in the positioning and 0.6º in the attitude of the camera, so the navigation through the image can reach values equal to or higher than the GNSS receivers without differential correction. Therefore, navigating through the image is a good alternative to enable autonomous navigation.

Keywords: Autonomy, navigation, security, photogrammetry, remote sensing, spatial resection, UAS.

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Authors: Amri Yusoff, Shahrizuan Shafiril, Ashardi Abas, Norma Che Yusoff

Abstract:

Nowadays, illegal logging has been causing many effects including flash flood, avalanche, global warming, and etc. The purpose of this study was to maintain the earth ecosystem by keeping and regulate Malaysia’s treasurable rainforest by utilizing a new technology that will assist in real-time alert and give faster response to the authority to act on these illegal activities. The methodology of this research consisted of design stages that have been conducted as well as the system model and system architecture of the prototype in addition to the proposed hardware and software that have been mainly used such as microcontroller, sensor with the implementation of GSM, and GPS integrated system. This prototype was deployed at Royal Belum forest in December 2014 for phase 1 and April 2015 for phase 2 at 21 pinpoint locations. The findings of this research were the capture of data in real-time such as temperature, humidity, gaseous, fire, and rain detection which indicate the current natural state and habitat in the forest. Besides, this device location can be detected via GPS of its current location and then transmitted by SMS via GSM system. All of its readings were sent in real-time for further analysis. The data that were compared to meteorological department showed that the precision of this device was about 95% and these findings proved that the system is acceptable and suitable to be used in the field.

Keywords: Remote monitoring system, forest data, GSM, GPS, wireless sensor.

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Authors: F. Kashanian, M. M. Masoudi, A. Akbari, A. Shamloo, M. R. Zand, S. S. Salehi

Abstract:

Nano-sized materials present new opportunities in biology and medicine and they are used as biomedical tools for investigation, separation of molecules and cells. To achieve more effective cancer therapy, it is essential to select cancer cells exactly. This research suggests that using the antibody-functionalized nontoxic Arginine-doped magnetic nanoparticles (A-MNPs), has been prosperous in detection, capture, and magnetic separation of circulating tumor cells (CTCs) in tumor tissue. In this study, A-MNPs were synthesized via a simple precipitation reaction and directly immobilized Ep-CAM EBA-1 antibodies over superparamagnetic A-MNPs for Mucin BCA-225 in breast cancer cell. The samples were characterized by vibrating sample magnetometer (VSM), FT-IR spectroscopy, Tunneling Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM). These antibody-functionalized nontoxic A-MNPs were used to capture breast cancer cell. Through employing a strong permanent magnet, the magnetic separation was achieved within a few seconds. Antibody-Conjugated nontoxic Arginine-doped Fe₃O₄ nanoparticles have the potential for the future study to capture CTCs which are released from tumor tissue and for drug delivery, and these results demonstrate that the antibody-conjugated A-MNPs can be used in magnetic hyperthermia techniques for cancer treatment.

Keywords: Tumor tissue, antibody, magnetic nanoparticle, CTCs capturing.

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Authors: González-Izal M., Rodríguez-Carreño I, Mallor-Giménez F, Malanda A, Izquierdo M

Abstract:

The purpose of this study was to evaluate and compare new indices based on the discrete wavelet transform with another spectral parameters proposed in the literature as mean average voltage, median frequency and ratios between spectral moments applied to estimate acute exercise-induced changes in power output, i.e., to assess peripheral muscle fatigue during a dynamic fatiguing protocol. 15 trained subjects performed 5 sets consisting of 10 leg press, with 2 minutes rest between sets. Surface electromyography was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were compared to detect peripheral muscle fatigue. These were: mean average voltage (MAV), median spectral frequency (Fmed), Dimitrov spectral index of muscle fatigue (FInsm5), as well as other five parameters obtained from the discrete wavelet transform (DWT) as ratios between different scales. The new wavelet indices achieved the best results in Pearson correlation coefficients with power output changes during acute dynamic contractions. Their regressions were significantly different from MAV and Fmed. On the other hand, they showed the highest robustness in presence of additive white gaussian noise for different signal to noise ratios (SNRs). Therefore, peripheral impairments assessed by sEMG wavelet indices may be a relevant factor involved in the loss of power output after dynamic high-loading fatiguing task.

Keywords: Median Frequency, EMG, wavelet transform, muscle fatigue

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Authors: Consalva J. Msigwa, Beda J. Kundy, Bakari M. M. Mwinyiwiwa

Abstract:

In this paper the reference current for Voltage Source Converter (VSC) of the Shunt Active Power Filter (SAPF) is generated using Synchronous Reference Frame method, incorporating the PI controller with anti-windup scheme. The proposed method improves the harmonic filtering by compensating the winding up phenomenon caused by the integral term of the PI controller. Using Reference Frame Transformation, the current is transformed from om a - b - c stationery frame to rotating 0 - d - q frame. Using the PI controller, the current in the 0 - d - q frame is controlled to get the desired reference signal. A controller with integral action combined with an actuator that becomes saturated can give some undesirable effects. If the control error is so large that the integrator saturates the actuator, the feedback path becomes ineffective because the actuator will remain saturated even if the process output changes. The integrator being an unstable system may then integrate to a very large value, the phenomenon known as integrator windup. Implementing the integrator anti-windup circuit turns off the integrator action when the actuator saturates, hence improving the performance of the SAPF and dynamically compensating harmonics in the power network. In this paper the system performance is examined with Shunt Active Power Filter simulation model.

Keywords: Phase Locked Loop (PLL), Voltage SourceConverter (VSC), Shunt Active Power Filter (SAPF), PI, Pulse WidthModulation (PWM).

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Authors: Ayman A. Aly, Abdallah A. Alshnnaway

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The general idea behind the filter is to average a pixel using other pixel values from its neighborhood, but simultaneously to take care of important image structures such as edges. The main concern of the proposed filter is to distinguish between any variations of the captured digital image due to noise and due to image structure. The edges give the image the appearance depth and sharpness. A loss of edges makes the image appear blurred or unfocused. However, noise smoothing and edge enhancement are traditionally conflicting tasks. Since most noise filtering behaves like a low pass filter, the blurring of edges and loss of detail seems a natural consequence. Techniques to remedy this inherent conflict often encompass generation of new noise due to enhancement. In this work a new fuzzy filter is presented for the noise reduction of images corrupted with additive noise. The filter consists of three stages. (1) Define fuzzy sets in the input space to computes a fuzzy derivative for eight different directions (2) construct a set of IFTHEN rules by to perform fuzzy smoothing according to contributions of neighboring pixel values and (3) define fuzzy sets in the output space to get the filtered and edged image. Experimental results are obtained to show the feasibility of the proposed approach with two dimensional objects.

Keywords: Additive noise, edge preserving filtering, fuzzy image filtering, noise reduction, two dimensional mechanical images.

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Authors: Ersin Gose, Kenan Buyukatak, Onur Osman, Osman N. Ucan

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To compress, improve bit error performance and also enhance 2D images, a new scheme, called Iterative Cellular-Turbo System (IC-TS) is introduced. In IC-TS, the original image is partitioned into 2N quantization levels, where N is denoted as bit planes. Then each of the N-bit-plane is coded by Turbo encoder and transmitted over Additive White Gaussian Noise (AWGN) channel. At the receiver side, bit-planes are re-assembled taking into consideration of neighborhood relationship of pixels in 2-D images. Each of the noisy bit-plane values of the image is evaluated iteratively using IC-TS structure, which is composed of equalization block; Iterative Cellular Image Processing Algorithm (ICIPA) and Turbo decoder. In IC-TS, there is an iterative feedback link between ICIPA and Turbo decoder. ICIPA uses mean and standard deviation of estimated values of each pixel neighborhood. It has extra-ordinary satisfactory results of both Bit Error Rate (BER) and image enhancement performance for less than -1 dB Signal-to-Noise Ratio (SNR) values, compared to traditional turbo coding scheme and 2-D filtering, applied separately. Also, compression can be achieved by using IC-TS systems. In compression, less memory storage is used and data rate is increased up to N-1 times by simply choosing any number of bit slices, sacrificing resolution. Hence, it is concluded that IC-TS system will be a compromising approach in 2-D image transmission, recovery of noisy signals and image compression.

Keywords: Iterative Cellular Image Processing Algorithm (ICIPA), Turbo Coding, Iterative Cellular Turbo System (IC-TS), Image Compression.

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Authors: Hazem M. El-Bakry

Abstract:

In this paper a new fast simplification method is presented. Such method realizes Karnough map with large number of variables. In order to accelerate the operation of the proposed method, a new approach for fast detection of group of ones is presented. Such approach implemented in the frequency domain. The search operation relies on performing cross correlation in the frequency domain rather than time one. It is proved mathematically and practically that the number of computation steps required for the presented method is less than that needed by conventional cross correlation. Simulation results using MATLAB confirm the theoretical computations. Furthermore, a powerful solution for realization of complex functions is given. The simplified functions are implemented by using a new desigen for neural networks. Neural networks are used because they are fault tolerance and as a result they can recognize signals even with noise or distortion. This is very useful for logic functions used in data and computer communications. Moreover, the implemented functions are realized with minimum amount of components. This is done by using modular neural nets (MNNs) that divide the input space into several homogenous regions. Such approach is applied to implement XOR function, 16 logic functions on one bit level, and 2-bit digital multiplier. Compared to previous non- modular designs, a clear reduction in the order of computations and hardware requirements is achieved.

Keywords: Boolean Functions, Simplification, KarnoughMap, Implementation of Logic Functions, Modular NeuralNetworks.

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Authors: Hazem M. El-Bakry

Abstract:

In this paper a new fast simplification method is presented. Such method realizes Karnough map with large number of variables. In order to accelerate the operation of the proposed method, a new approach for fast detection of group of ones is presented. Such approach implemented in the frequency domain. The search operation relies on performing cross correlation in the frequency domain rather than time one. It is proved mathematically and practically that the number of computation steps required for the presented method is less than that needed by conventional cross correlation. Simulation results using MATLAB confirm the theoretical computations. Furthermore, a powerful solution for realization of complex functions is given. The simplified functions are implemented by using a new desigen for neural networks. Neural networks are used because they are fault tolerance and as a result they can recognize signals even with noise or distortion. This is very useful for logic functions used in data and computer communications. Moreover, the implemented functions are realized with minimum amount of components. This is done by using modular neural nets (MNNs) that divide the input space into several homogenous regions. Such approach is applied to implement XOR function, 16 logic functions on one bit level, and 2-bit digital multiplier. Compared to previous non- modular designs, a clear reduction in the order of computations and hardware requirements is achieved.

Keywords: Boolean functions, simplification, Karnough map, implementation of logic functions, modular neural networks.

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Authors: Carolin Lutz, Jörg Matthes, Patrick Waibel, Ulrich Precht, Krassimir Garbev, Günter Beuchle, Uwe Schweike, Peter Stemmermann, Hubert B. Keller

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Online measurement of the product quality is a challenging task in cement production, especially in the production of Celitement, a novel environmentally friendly hydraulic binder. The mineralogy and chemical composition of clinker in ordinary Portland cement production is measured by X-ray diffraction (XRD) and X-ray fluorescence (XRF), where only crystalline constituents can be detected. But only a small part of the Celitement components can be measured via XRD, because most constituents have an amorphous structure. This paper describes the development of algorithms suitable for an on-line monitoring of the final processing step of Celitement based on NIR-data. For calibration intermediate products were dried at different temperatures and ground for variable durations. The products were analyzed using XRD and thermogravimetric analyses together with NIR-spectroscopy to investigate the dependency between the drying and the milling processes on one and the NIR-signal on the other side. As a result, different characteristic parameters have been defined. A short overview of the Celitement process and the challenging tasks of the online measurement and evaluation of the product quality will be presented. Subsequently, methods for systematic development of near-infrared calibration models and the determination of the final calibration model will be introduced. The application of the model on experimental data illustrates that NIR-spectroscopy allows for a quick and sufficiently exact determination of crucial process parameters.

Keywords: Calibration model, celitement, cementitious material, NIR spectroscopy.

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Authors: Chia-Min Lai, Wan-Ching Lin, Hahn-Ming Lee, Ching-Hao Mao

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As world wild internet has non-stop developments, making profit by lending registered domain names emerges as a new business in recent years. Unfortunately, the larger the market scale of domain lending service becomes, the riskier that there exist malicious behaviors or malwares hiding behind parked domains will be. Also, previous work for differentiating parked domain suffers two main defects: 1) too much data-collecting effort and CPU latency needed for features engineering and 2) ineffectiveness when detecting parked domains containing external links that are usually abused by hackers, e.g., drive-by download attack. Aiming for alleviating above defects without sacrificing practical usability, this paper proposes ParkedGuard as an efficient and accurate parked domain detector. Several scripting behavioral features were analyzed, while those with special statistical significance are adopted in ParkedGuard to make feature engineering much more cost-efficient. On the other hand, finding memberships between external links and parked domains was modeled as a graph mining problem, and a coarse-to-fine strategy was elaborately designed by leverage the graphical locality such that ParkedGuard outperforms the state-of-the-art in terms of both recall and precision rates.

Keywords: Coarse-to-fine strategy, domain parking service, graphical locality analysis, parked domain.

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Authors: Kayijuka Idrissa

Abstract:

This work investigates a mathematical study for traffic flow and traffic density in Kigali city roads and the data collected from the national police of Rwanda in 2012. While working on this topic, some mathematical models were used in order to analyze and compare traffic variables. This work has been carried out on Kigali roads specifically at roundabouts from Kigali Business Center (KBC) to Prince House as our study sites. In this project, we used some mathematical tools to analyze the data collected and to understand the relationship between traffic variables. We applied the Poisson distribution method to analyze and to know the number of accidents occurred in this section of the road which is from KBC to Prince House. The results show that the accidents that occurred in 2012 were at very high rates due to the fact that this section has a very narrow single lane on each side which leads to high congestion of vehicles, and consequently, accidents occur very frequently. Using the data of speeds and densities collected from this section of road, we found that the increment of the density results in a decrement of the speed of the vehicle. At the point where the density is equal to the jam density the speed becomes zero. The approach is promising in capturing sudden changes on flow patterns and is open to be utilized in a series of intelligent management strategies and especially in noncurrent congestion effect detection and control.

Keywords: Statistical methods, Poisson distribution, car moving techniques, traffic flow.

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Authors: Hatim Laalej, Salvador Sumohano-Verdeja, Thomas McLeay

Abstract:

Monitoring of tool wear in milling operations is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Although there are numerous statistical models and artificial intelligence techniques available for monitoring the wear of cutting tools, these techniques cannot pin point which cutting edge of the tool, or which insert in the case of indexable tooling, is worn or broken. Currently, the task of monitoring the wear on the tool cutting edges is carried out by the operator who performs a manual inspection, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from lost productivity. The present study is concerned with the development of a flute tracking system to segment signals related to each physical flute of a cutter with three flutes used in an end milling operation. The purpose of the system is to monitor the cutting condition for individual flutes separately in order to determine their progressive wear rates and to predict imminent tool failure. The results of this study clearly show that signals associated with each flute can be effectively segmented using the proposed flute tracking system. Furthermore, the results illustrate that by segmenting the sensor signal by flutes it is possible to investigate the wear in each physical cutting edge of the cutting tool. These findings are significant in that they facilitate the online condition monitoring of a cutting tool for each specific flute without the need for operators/engineers to perform manual inspections of the tool.

Keywords: Tool condition monitoring, tool wear prediction, milling operation, flute tracking.

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Authors: Satyasen Panda, Urmila Bhanja

Abstract:

This paper presents a SAC-OCDMA code with zero cross correlation property to minimize the Multiple Access Interface (MAI) as New Zero Cross Correlation code (NZCC), which is found to be more scalable compared to the other existing SAC-OCDMA codes. This NZCC code is constructed using address segment and data segment. In this work, the proposed NZCC code is implemented in an optical system using the Opti-System software for the spectral amplitude coded optical code-division multiple-access (SAC-OCDMA) scheme. The main contribution of the proposed NZCC code is the zero cross correlation, which reduces both the MAI and PIIN noises. The proposed NZCC code reveals properties of minimum cross-correlation, flexibility in selecting the code parameters and supports a large number of users, combined with high data rate and longer fiber length. Simulation results reveal that the optical code division multiple access system based on the proposed NZCC code accommodates maximum number of simultaneous users with higher data rate transmission, lower Bit Error Rates (BER) and longer travelling distance without any signal quality degradation, as compared to the former existing SAC-OCDMA codes.

Keywords: Cross Correlation, Optical Code Division Multiple Access, Spectral Amplitude Coding Optical Code Division Multiple Access, Multiple Access Interference, Phase Induced Intensity Noise, New Zero Cross Correlation code.

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Authors: Todsaporn Fuangrod, Daryl J. O'Connor, Boyd MC McCurdy, Peter B. Greer

Abstract:

An electronic portal image device (EPID) has become a method of patient-specific IMRT dose verification for radiotherapy. Research studies have focused on pre and post-treatment verification, however, there are currently no interventional procedures using EPID dosimetry that measure the dose in real time as a mechanism to ensure that overdoses do not occur and underdoses are detected as soon as is practically possible. As a result, an EPID-based real time dose verification system for dynamic IMRT was developed and was implemented with MATLAB/Simulink. The EPID image acquisition was set to continuous acquisition mode at 1.4 images per second. The system defined the time constraint gap, or execution gap at the image acquisition time, so that every calculation must be completed before the next image capture is completed. In addition, the <=-evaluation method was used for dose comparison, with two types of comparison processes; individual image and cumulative dose comparison monitored. The outputs of the system are the <=-map, the percent of <=<1, and mean-<= versus time, all in real time. Two strategies were used to test the system, including an error detection test and a clinical data test. The system can monitor the actual dose delivery compared with the treatment plan data or previous treatment dose delivery that means a radiation therapist is able to switch off the machine when the error is detected.

Keywords: real-time dose verification, EPID dosimetry, simulation, dynamic IMRT

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Authors: Hazem M. El-Bakry

Abstract:

In this paper, a new technique for fast painting with different colors is presented. The idea of painting relies on applying masks with different colors to the background. Fast painting is achieved by applying these masks in the frequency domain instead of spatial (time) domain. New colors can be generated automatically as a result from the cross correlation operation. This idea was applied successfully for faster specific data (face, object, pattern, and code) detection using neural algorithms. Here, instead of performing cross correlation between the input input data (e.g., image, or a stream of sequential data) and the weights of neural networks, the cross correlation is performed between the colored masks and the background. Furthermore, this approach is developed to reduce the computation steps required by the painting operation. The principle of divide and conquer strategy is applied through background decomposition. Each background is divided into small in size subbackgrounds and then each sub-background is processed separately by using a single faster painting algorithm. Moreover, the fastest painting is achieved by using parallel processing techniques to paint the resulting sub-backgrounds using the same number of faster painting algorithms. In contrast to using only faster painting algorithm, the speed up ratio is increased with the size of the background when using faster painting algorithm and background decomposition. Simulation results show that painting in the frequency domain is faster than that in the spatial domain.

Keywords: Fast Painting, Cross Correlation, Frequency Domain, Parallel Processing

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Authors: Muritala Babatunde Hassan

Abstract:

In the contemporary global political economy, foreign direct investment (FDI) is gaining currency on daily basis. Notably, the end of the Cold War has brought about the dominance of neoliberal ideology with its mantra of private-sector-led economy. As such, nation-states now see FDI attraction as an important element in their approach to national development. Governments and policy makers are preoccupying themselves with unraveling the best strategies to not only attract more FDI but also to attain the desired socio-economic development status. In Nigeria, the perceived development potentials of FDI have brought about aggressive hunt for foreign investors, most especially since transition to civilian rule in May 1999. Series of liberal and market oriented strategies are being adopted not only to attract foreign investors but largely to stimulate private sector participation in the economy. It is on this premise that this study interrogates the politics of FDI attraction for domestic development in Nigeria between 1999 and 2014, with the ultimate aim of examining the nexus between regime type and the ability of a state to attract and benefit from FDI. Building its analysis within the framework of institutional utilitarianism, the study posits that the essential FDI strategies for achieving the greatest happiness for the greatest number of Nigerians are political not economic. Both content analysis and descriptive survey methodology were employed in carrying out the study. Content analysis involves desk review of literatures that culminated in the development of the study’s conceptual and theoretical framework of analysis. The study finds no significant relationship between transition to democracy and FDI inflows in Nigeria, as most of the attracted investments during the period of the study were market and resource seeking as was the case during the military regime, thereby contributing minimally to the socio-economic development of the country. It is also found that the country placed much emphasis on liberalization and incentives for FDI attraction at the neglect of improving the domestic investment environment. Consequently, poor state of infrastructure, weak institutional capability and insecurity were identified as the major factors seriously hindering the success of Nigeria in exploiting FDI for domestic development. Given the reality of the currency of FDI as a vector of economic globalization and that Nigeria is trailing the line of private-sector-led approach to development, it is recommended that emphasis should be placed on those measures aimed at improving the infrastructural facilities, building solid institutional framework, enhancing skill and technological transfer and coordinating FDI promotion activities by different agencies and at different levels of government.

Keywords: Foreign capital, politics, socio-economic development, FDI attraction strategies, Redemocratization.

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Authors: Lakshmi Pujitha Dachuri, Nalini Uppala

Abstract:

In many applications retransmissions of lost packets are not permitted. OFDM is a multi-carrier modulation scheme having excellent performance which allows overlapping in frequency domain. With OFDM there is a simple way of dealing with multipath relatively simple DSP algorithms.

 In this paper, an image frame is compressed using DWT, and the compressed data is arranged in data vectors, each with equal number of coefficients. These vectors are quantized and binary coded to get the bit steams, which are then packetized and intelligently mapped to the OFDM system. Based on one-bit channel state information at the transmitter, the descriptions in order of descending priority are assigned to the currently good channels such that poorer sub-channels can only affect the lesser important data vectors. We consider only one-bit channel state information available at the transmitter, informing only about the sub-channels to be good or bad. For a good sub-channel, instantaneous received power should be greater than a threshold P_th. Otherwise, the sub-channel is in fading state and considered bad for that batch of coefficients. In order to reduce the system power consumption, the mapped descriptions onto the bad sub channels are dropped at the transmitter. The binary channel state information gives an opportunity to map the bit streams intelligently and to save a reasonable amount of power. By using MAT LAB simulation we can analysis the performance of our proposed scheme, in terms of system energy saving without compromising the received quality in terms of peak signal-noise ratio.

Keywords: Binary channel state, Channel state feedback, DWT-OFDM system, Energy saving, Fading broadcast channel.

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Authors: Kifah Tout, Nisrine Sinno, Mohamad Mikati

Abstract:

Many studies have focused on the nonlinear analysis of electroencephalography (EEG) mainly for the characterization of epileptic brain states. It is assumed that at least two states of the epileptic brain are possible: the interictal state characterized by a normal apparently random, steady-state EEG ongoing activity; and the ictal state that is characterized by paroxysmal occurrence of synchronous oscillations and is generally called in neurology, a seizure. The spatial and temporal dynamics of the epileptogenic process is still not clear completely especially the most challenging aspects of epileptology which is the anticipation of the seizure. Despite all the efforts we still don-t know how and when and why the seizure occurs. However actual studies bring strong evidence that the interictal-ictal state transition is not an abrupt phenomena. Findings also indicate that it is possible to detect a preseizure phase. Our approach is to use the neural network tool to detect interictal states and to predict from those states the upcoming seizure ( ictal state). Analysis of the EEG signal based on neural networks is used for the classification of EEG as either seizure or non-seizure. By applying prediction methods it will be possible to predict the upcoming seizure from non-seizure EEG. We will study the patients admitted to the epilepsy monitoring unit for the purpose of recording their seizures. Preictal, ictal, and post ictal EEG recordings are available on such patients for analysis The system will be induced by taking a body of samples then validate it using another. Distinct from the two first ones a third body of samples is taken to test the network for the achievement of optimum prediction. Several methods will be tried 'Backpropagation ANN' and 'RBF'.

Keywords: Artificial neural network (ANN), automatic prediction, epileptic seizures analysis, genetic algorithm.

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Authors: Nan-Ying Yu, Shao-Hsia Chang

Abstract:

This study explores the clinical features of neurodegenerative disease patients with tremor. We study the motor impairments in patients with Parkinson’s disease (PD) and essential tremor (ET). Since uncertainty exists on whether Parkinson's disease (PD) and essential tremor (ET) patients have similar degree of impairment during motor tasks, this study based on the self-developed computerized handwriting movement analysis to characterize motor functions of these two impairments. The recruited subjects were diagnosed and confirmed one of neurodegenerative diseases. They were undergone general clinical evaluations by physicians in the first year. We recruited 8 participants with PD and 10 with ET. Additional 12 participants without any neuromuscular dysfunction were recruited as control group. This study used fine motor control of penmanship on digital tablet for sensorimotor function tests. The movement speed in PD/ET group is found significant slower than subjects in normal control group. In movement intensity and speed, the result found subject with ET has similar clinical feature with PD subjects. The ET group shows smaller and slower movements than control group but not to the same extent as PD group. The results of this study contribute to the early screening and detection of diseases and the evaluation of disease progression.

Keywords: Parkinson’s disease, essential tremor, motor function, fine motor movement, computerized handwriting evaluation.

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Authors: Mohammad Shoyaib, M. Abdullah-Al-Wadud, Oksam Chae

Abstract:

Proteomics is one of the largest areas of research for bioinformatics and medical science. An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. Predicting Protein-Protein Interaction (PPI) is one of the crucial and decisive problems in current research. Genomic data offer a great opportunity and at the same time a lot of challenges for the identification of these interactions. Many methods have already been proposed in this regard. In case of in-silico identification, most of the methods require both positive and negative examples of protein interaction and the perfection of these examples are very much crucial for the final prediction accuracy. Positive examples are relatively easy to obtain from well known databases. But the generation of negative examples is not a trivial task. Current PPI identification methods generate negative examples based on some assumptions, which are likely to affect their prediction accuracy. Hence, if more reliable negative examples are used, the PPI prediction methods may achieve even more accuracy. Focusing on this issue, a graph based negative example generation method is proposed, which is simple and more accurate than the existing approaches. An interaction graph of the protein sequences is created. The basic assumption is that the longer the shortest path between two protein-sequences in the interaction graph, the less is the possibility of their interaction. A well established PPI detection algorithm is employed with our negative examples and in most cases it increases the accuracy more than 10% in comparison with the negative pair selection method in that paper.

Keywords: Interaction graph, Negative training data, Protein-Protein interaction, Support vector machine.

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