Search results for: Probability of detection

Authors: Piyali Chaudhuri, Peter T. Martin, Aleksandar Z. Stevanovic, Chongkai Zhu

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Loop detectors report traffic characteristics in real time. They are at the core of traffic control process. Intuitively, one would expect that as density of detection increases, so would the quality of estimates derived from detector data. However, as detector deployment increases, the associated operating and maintenance cost increases. Thus, traffic agencies often need to decide where to add new detectors and which detectors should continue receiving maintenance, given their resource constraints. This paper evaluates the effect of detector spacing on freeway travel time estimation. A freeway section (Interstate-15) in Salt Lake City metropolitan region is examined. The research reveals that travel time accuracy does not necessarily deteriorate with increased detector spacing. Rather, the actual location of detectors has far greater influence on the quality of travel time estimates. The study presents an innovative computational approach that delivers optimal detector locations through a process that relies on Genetic Algorithm formulation.

Keywords: Detector, Freeway, Genetic algorithm, Travel timeestimate.

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Authors: Xiaochun Huang, Xuejun Zhao, Yun Zou, Feiyu Yang, Wenbin Liu, Nan Deng, Ming Zhang, Nengbin Cai

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A simple device termed infrared radiation (IR) was developed for rapid visualization of sweat fingerprints deposit on paper with blue light (450 nm, 11 W). In this approach, IR serves as the pretreatment device before the sweat fingerprints was illuminated by blue light. An annular blue light source was adopted for visualizing latent sweat fingerprints. Sample fingerprints were examined under various conditions after deposition, and experimental results indicate that the recovery rate of the latent sweat fingerprints is in the range of 50%-100% without chemical treatments. A mechanism for the observed visibility is proposed based on transportation and re-impregnation of fluorescer in paper at the region of water. And further exploratory experimental results gave the full support to the visible mechanism. Therefore, such a method as IR-pretreated in detecting latent fingerprints may be better for examination in the case where biological information of samples is needed for consequent testing.

Keywords: Forensic science, visualization, infrared radiation, blue light, latent sweat fingerprints, detection.

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Authors: Souvik Bhattacharyya, Indradip Banerjee, Anumoy Chakraborty, Gautam Sanyal

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Recent growth in digital multimedia technologies has presented a lot of facilities in information transmission, reproduction and manipulation. Therefore, the concept of information security is one of the superior articles in the present day situation. The biometric information security is one of the information security mechanisms. It has the advantages as well as disadvantages. The biometric system is at risk to a range of attacks. These attacks are anticipated to bypass the security system or to suspend the normal functioning. Various hazards have been discovered while using biometric system. Proper use of steganography greatly reduces the risks in biometric systems from the hackers. Steganography is one of the fashionable information hiding technique. The goal of steganography is to hide information inside a cover medium like text, image, audio, video etc. through which it is not possible to detect the existence of the secret information. Here in this paper a new security concept has been established by making the system more secure with the help of steganography along with biometric security. Here the biometric information has been embedded to a skin tone portion of an image with the help of proposed steganographic technique.

Keywords: Biometrics, Skin tone detection, Series, Polynomial, Cover Image, Stego Image.

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Authors: N. Moghadam, H. Sadeghi

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In this paper a novel scheme for watermarking digital audio during its compression to MPEG-1 Layer III format is proposed. For this purpose we slightly modify some of the selected MDCT coefficients, which are used during MPEG audio compression procedure. Due to the possibility of modifying different MDCT coefficients, there will be different choices for embedding the watermark into audio data, considering robustness and transparency factors. Our proposed method uses a genetic algorithm to select the best coefficients to embed the watermark. This genetic selection is done according to the parameters that are extracted from the perceptual content of the audio to optimize the robustness and transparency of the watermark. On the other hand the watermark security is increased due to the random nature of the genetic selection. The information of the selected MDCT coefficients that carry the watermark bits, are saves in a database for future extraction of the watermark. The proposed method is suitable for online MP3 stores to pursue illegal copies of musical artworks. Experimental results show that the detection ratio of the watermarks at the bitrate of 128kbps remains above 90% while the inaudibility of the watermark is preserved.

Keywords: Content-Based Audio Watermarking, Genetic AudioWatermarking.

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: Electrocardiogram, dictionary learning, sparse coding, classification.

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Authors: Anderson Rocha, Pedro Miguel de Figueiredo Dinis Oliveira Gaspar

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Machine vision has been widely used in recent years in agriculture, as a tool to promote the automation of processes and increase the levels of productivity. The aim of this work is the development of a path recognition algorithm based on image processing to guide a terrestrial robot in-between tree rows. The proposed algorithm was developed using the software MATLAB, and it uses several image processing operations, such as threshold detection, morphological erosion, histogram equalization and the Hough transform, to find edge lines along tree rows on an image and to create a path to be followed by a mobile robot. To develop the algorithm, a set of images of different types of orchards was used, which made possible the construction of a method capable of identifying paths between trees of different heights and aspects. The algorithm was evaluated using several images with different characteristics of quality and the results showed that the proposed method can successfully detect a path in different types of environments.

Keywords: Agricultural mobile robot, image processing, path recognition, Hough transform.

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Authors: Vahid Anari, Mina Bakhshi

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Visual search and identification of immunohistochemically stained tissue of meningioma was performed manually in pathologic laboratories to detect and diagnose the cancers type of meningioma. This task is very tedious and time-consuming. Moreover, because of cell's complex nature, it still remains a challenging task to segment cells from its background and analyze them automatically. In this paper, we develop and test a computerized scheme that can automatically identify cells in microscopic images of meningioma and classify them into positive (proliferative) and negative (normal) cells. Dataset including 150 images are used to test the scheme. The scheme uses Fuzzy C-means algorithm as a color clustering method based on perceptually uniform hue, saturation, value (HSV) color space. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: Positive cell, color segmentation, HSV color space, immunohistochemistry, meningioma, thresholding, fuzzy c-means.

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Authors: Mehdi Seifbarghy, Keyvan Roshan

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Main goal of preventive healthcare problems are at decreasing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. The levels of establishment and staffing costs along with summation of the travel and waiting time that clients spent are considered as objectives functions of the proposed nonlinear integer programming model. In this paper, we have proposed a bi-objective mathematical model for designing a network of preventive healthcare facilities so as to minimize aforementioned objectives, simultaneously. Moreover, each facility acts as M/M/1 queuing system. The number of facilities to be established, the location of each facility, and the level of technology for each facility to be chosen are provided as the main determinants of a healthcare facility network. Finally, to demonstrate performance of the proposed model, four multi-objective decision making techniques are presented to solve the model.

Keywords: Preventive healthcare problems, Non-linear integer programming models, Multi-objective decision making techniques

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Authors: A. Biran, P. Sobhe Bidari, K. Raahemifar

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Diabetic Retinopathy (DR) is an eye disease that leads to blindness. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness; hence, many automated algorithms have been proposed to extract hemorrhages and exudates. In this paper, an automated algorithm is presented to extract hemorrhages and exudates separately from retinal fundus images using different image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Since Optic Disc is the same color as the exudates, it is first localized and detected. The presented method has been tested on fundus images from Structured Analysis of the Retina (STARE) and Digital Retinal Images for Vessel Extraction (DRIVE) databases by using MATLAB codes. The results show that this method is perfectly capable of detecting hard exudates and the highly probable soft exudates. It is also capable of detecting the hemorrhages and distinguishing them from blood vessels.

Keywords: Diabetic retinopathy, fundus, CHT, exudates, hemorrhages.

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Authors: Serkan Ozbay, Ergun Ercelebi

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Automatic Vehicle Identification (AVI) has many applications in traffic systems (highway electronic toll collection, red light violation enforcement, border and customs checkpoints, etc.). License Plate Recognition is an effective form of AVI systems. In this study, a smart and simple algorithm is presented for vehicle-s license plate recognition system. The proposed algorithm consists of three major parts: Extraction of plate region, segmentation of characters and recognition of plate characters. For extracting the plate region, edge detection algorithms and smearing algorithms are used. In segmentation part, smearing algorithms, filtering and some morphological algorithms are used. And finally statistical based template matching is used for recognition of plate characters. The performance of the proposed algorithm has been tested on real images. Based on the experimental results, we noted that our algorithm shows superior performance in car license plate recognition.

Keywords: Character recognizer, license plate recognition, plate region extraction, segmentation, smearing, template matching.

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Authors: M. Evtyugina, C. A. Alves, A. I. Calvo, T. Nunes, L. Tarelho, M. Duarte, S. O. Prozil

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Smoke from domestic wood burning has been identified as a major contributor to air pollution, motivating detailed emission measurements under controlled conditions. A series of experiments was performed to characterise the emissions from wood combustion in a fireplace and in a woodstove of two common species of trees grown in Spain: Pyrenean oak (Quercus pyrenaica) and black poplar (Populus nigra). Volatile organic compounds (VOCs) in the exhaust emissions were collected in Tedlar bags, re-sampled in sorbent tubes and analysed by thermal desorption-gas chromatography-flame ionisation detection. Pyrenean oak presented substantially higher emissions in the woodstove than in the fireplace, for the majority of compounds. The opposite was observed for poplar. Among the 45 identified species, benzene and benzenerelated compounds represent the most abundant group, followed by oxygenated VOCs and aliphatics. Emission factors obtained in this study are generally of the same order than those reported for residential experiments in the USA.

Keywords: Fireplace, VOC emissions, woodstove.

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Authors: Nour Charara, Iman Jarkass, Maria Sokhn, Elena Mugellini, Omar Abou Khaled

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Intelligent Video-Surveillance (IVS) systems are being more and more popular in security applications. The analysis and recognition of abnormal behaviours in a video sequence has gradually drawn the attention in the field of IVS, since it allows filtering out a large number of useless information, which guarantees the high efficiency in the security protection, and save a lot of human and material resources. We present in this paper ADABeV, an intelligent video-surveillance framework for event recognition in crowded scene to detect the abnormal human behaviour. This framework is attended to be able to achieve real-time alarming, reducing the lags in traditional monitoring systems. This architecture proposal addresses four main challenges: behaviour understanding in crowded scenes, hard lighting conditions, multiple input kinds of sensors and contextual-based adaptability to recognize the active context of the scene.

Keywords: Behavior recognition, Crowded scene, Data fusion, Pattern recognition, Video-surveillance

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Authors: Jaharah A. Ghani, Muhammad Rizal, Ahmad Sayuti, Mohd Zaki Nuawi, Mohd Nizam Ab. Rahman, Che Hassan Che Haron

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This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals using the regression model and I-kaz method. The detection of tool wear was done automatically using the in-house developed regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out on a CNC turning machine Colchester Master Tornado T4 in dry cutting condition, and Kistler 9255B dynamometer was used to measure the cutting force signals, which then stored and displayed in the DasyLab software. The progression of the cutting tool flank wear land (VB) was indicated by the amount of the cutting force generated. Later, the I-kaz was used to analyze all the cutting force signals from beginning of the cut until the rejection stage of the cutting tool. Results of the IKaz analysis were represented by various characteristic of I-kaz 3D coefficient and 3D graphic presentation. The I-kaz 3D coefficient number decreases when the tool wear increases. This method can be used for real time tool wear monitoring.

Keywords: mathematical model, I-kaz method, tool wear

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Authors: Amit Chhabra, Gurvinder Singh, Sandeep Singh Waraich, Bhavneet Sidhu, Gaurav Kumar

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Decrease in hardware costs and advances in computer networking technologies have led to increased interest in the use of large-scale parallel and distributed computing systems. One of the biggest issues in such systems is the development of effective techniques/algorithms for the distribution of the processes/load of a parallel program on multiple hosts to achieve goal(s) such as minimizing execution time, minimizing communication delays, maximizing resource utilization and maximizing throughput. Substantive research using queuing analysis and assuming job arrivals following a Poisson pattern, have shown that in a multi-host system the probability of one of the hosts being idle while other host has multiple jobs queued up can be very high. Such imbalances in system load suggest that performance can be improved by either transferring jobs from the currently heavily loaded hosts to the lightly loaded ones or distributing load evenly/fairly among the hosts .The algorithms known as load balancing algorithms, helps to achieve the above said goal(s). These algorithms come into two basic categories - static and dynamic. Whereas static load balancing algorithms (SLB) take decisions regarding assignment of tasks to processors based on the average estimated values of process execution times and communication delays at compile time, Dynamic load balancing algorithms (DLB) are adaptive to changing situations and take decisions at run time. The objective of this paper work is to identify qualitative parameters for the comparison of above said algorithms. In future this work can be extended to develop an experimental environment to study these Load balancing algorithms based on comparative parameters quantitatively.

Keywords: SLB, DLB, Host, Algorithm and Load.

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Authors: Anne-Lena Kampen, Øivind Kure

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Machine learning (ML) can be implemented in Wireless Sensor Networks (WSNs) as a central solution or distributed solution where the ML is embedded in the nodes. Embedding improves privacy and may reduce prediction delay. In addition, the number of transmissions is reduced. However, quality factors such as prediction accuracy, fault detection efficiency and coordinated control of the overall system suffer. Here, we discuss and highlight the trade-offs that should be considered when choosing between embedding and centralized ML, especially for multihop networks. In addition, we present estimations that demonstrate the energy trade-offs between embedded and centralized ML. Although the total network energy consumption is lower with central prediction, it makes the network more prone for partitioning due to the high forwarding load on the one-hop nodes. Moreover, the continuous improvements in the number of operations per joule for embedded devices will move the energy balance toward embedded prediction.

Keywords: Central ML, embedded machine learning, energy consumption, local ML, Wireless Sensor Networks, WSN.

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Authors: Abu Salim Mustafa

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Rv3873 is a relatively large size protein (371 amino acids in length) and its gene is located in the immunodominant genomic region of difference (RD)1 that is present in the genome of Mycobacterium tuberculosis but deleted from the genomes of all the vaccine strains of Bacillus Calmette Guerin (BCG) and most other mycobacteria. However, when tested for cellular immune responses using peripheral blood mononuclear cells from tuberculosis patients and BCG-vaccinated healthy subjects, this protein was found to be a major stimulator of cell mediated immune responses in both groups of subjects. In order to further identify the sequence of immunodominant epitopes and explore their Human Leukocyte Antigen (HLA)-restriction for epitope recognition, 24 peptides (25-mers overlapping with the neighboring peptides by 10 residues) covering the sequence of Rv3873 were synthesized chemically using fluorenylmethyloxycarbonyl chemistry and tested in cell mediated immune responses. The results of these experiments helped in the identification of an immunodominant peptide P9 that was recognized by people expressing varying HLA-DR types. Furthermore, it was also predicted to be a promiscuous binder with multiple epitopes for binding to HLA-DR, HLA-DP and HLA-DQ alleles of HLA-class II molecules that present antigens to T helper cells, and to HLA-class I molecules that present antigens to T cytotoxic cells. In addition, the evaluation of peptide P9 using an immunogenicity predictor server yielded a high score (0.94), which indicated a greater probability of this peptide to elicit a protective cellular immune response. In conclusion, P9, a peptide with multiple epitopes and ability to bind several HLA class I and class II molecules for presentation to cells of the cellular immune response, may be useful as a peptide-based vaccine against tuberculosis.

Keywords: Mycobacterium tuberculosis, Rv3873, peptides, vaccine

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Authors: R. Geetha, T. Padmavathy

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Security audit is an important aspect or feature to be considered in cloud service customer. It is basically a certification process to audit the controls that deliver the security requirements. Security audits are conducted by trained and qualified staffs that belong to an independent auditing organization. Security audits must be carried as a standard of security controls. Proper check to be made that the cloud user has a proper reporting and logging facilities with the customer's system and hence ensuring appropriate business and operational flow of data through cloud service. We propose a cloud-based secure auditing framework, which enables confided in power to safely store their mystery information on the semi-believed cloud specialist co-ops, and specifically share their mystery information with a wide scope of information recipient, to diminish the key administration intricacy for power proprietors and information collectors. Unique in relation to past cloud-based information framework, data proprietors transfer their mystery information into cloud utilizing static and dynamic evaluating plan. Another propelled determination is, if any information beneficiary needs individual record to download, the information collector will send the solicitation to the expert. The specialist proprietor has the Access Control. At the off probability, the businessman must impart the primary record to the knowledge collector, acknowledge statistics beneficiary solicitation. Once the acknowledgement for the records is over, the recipient downloads the first record and this record shifting time with date and downloading time with date are monitored by the inspector. In addition to deduplication concept, diminished cloud memory area using dynamic document distribution has been proposed.

Keywords: Cloud computing, cloud storage auditing, data integrity, key exposure.

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Authors: E. Sobhani-Tehrani, K. Khorasani, N. Meskin

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This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPE) associated with a set of singleparameter fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. Two NPE structures including series-parallel and parallel are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the NPEs to systems with partial-state measurement.

Keywords: Hybrid fault diagnosis, Dynamic neural networks, Nonlinear systems.

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Authors: Nan-Chyuan Tsai, Jiun-Sheng Liou, Chih-Che Lin, Tuan Li

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For micro-gyroscopes, the angular rate detection components have to oscillate forwards and backwards alternatively. An innovative design of micro-electromagnetic drive module is proposed to make a Π-type disc reciprocally and efficiently rotate within a certain of angular interval. Twelve Electromagnetic poles enclosing the thin disc are designed to provide the magnetic drive power. Isotropic etching technique is employed to fabricate the high-aspect-ratio trench, so that the contact angle of wire against trench can be increased and the potential defect of cavities and pores within the wire can be prevented. On the other hand, a Π-type thin disc is designed to conduct the pitch motion as an angular excitation, in addition to spinning, is exerted on the gyroscope. The efficacy of the micro-magnetic drive module is verified by the commercial software, Ansoft Maxewll. In comparison with the conventional planar windings in micro-scale systems, the magnetic drive force is increased by 150%.

Keywords: Micro-gyroscope, micro-electromagnetic, micro actuator.

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Authors: Candice Moy, Damien Guirco, Thomas Boyle

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Intelligent technologies are increasingly facilitating sustainable water management strategies in Australia. While this innovation can present clear cost benefits to utilities through immediate leak detection and deference of capital costs, the impact of this technology on households is less distinct. By offering real-time engagement and detailed end-use consumption breakdowns, there is significant potential for demand reduction as a behavioural response to increased information. Despite this potential, passive implementation without well-planned residential engagement strategies is likely to result in a lost opportunity. This paper begins this research process by exploring the effect of smart water meters through the lens of three behaviour change theories. The Theory of Planned Behaviour (TPB), Belief Revision theory (BR) and Practice Theory emphasise different variables that can potentially influence and predict household water engagements. In acknowledging the strengths of each theory, the nuances and complexity of household water engagement can be recognised which can contribute to effective planning for residential smart meter engagement strategies.

Keywords: Behaviour, information, household, smart meters, water.

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Authors: Lei Yao, Jia Hao Cheong, Rui-Feng Xue, Minkyu Je

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A closed-loop controlled wireless power transmission circuit block for implantable biomedical applications is described in this paper. The circuit consists of one front-end rectifier, power management sub-block including bandgap reference and low drop-out regulators (LDOs) as well as transmission power detection / feedback circuits. Simulation result shows that the front-end rectifier achieves 80% power efficiency with 750-mV single-end peak-to-peak input voltage and 1.28-V output voltage under load current of 4 mA. The power management block can supply 1.8mA average load current under 1V consuming only 12μW power, which is equivalent to 99.3% power efficiency. The wireless power transmission block described in this paper achieves a maximum power efficiency of 80%. The wireless power transmission circuit block is designed and implemented using UMC 65-nm CMOS/RF process. It occupies 1 mm × 1.2 mm silicon area.

Keywords: Implantable biomedical devices, wireless power transfer, LDO, rectifier, closed-loop power control

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Authors: Saad Chakkor, Mostafa Baghouri, Abderrahmane Hajraoui

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In new energy development, wind power has boomed. It is due to the proliferation of wind parks and their operation in supplying the national electric grid with low cost and clean resources. Hence, there is an increased need to establish a proactive maintenance for wind turbine machines based on remote control and monitoring. That is necessary with a real-time wireless connection in offshore or inaccessible locations while the wired method has many flaws. The objective of this strategy is to prolong wind turbine lifetime and to increase productivity. The hardware of a remote control and monitoring system for wind turbine parks is designed. It takes advantage of GPRS or Wi-Max wireless module to collect data measurements from different wind machine sensors through IP based multi-hop communication. Computer simulations with Proteus ISIS and OPNET software tools have been conducted to evaluate the performance of the studied system. Study findings show that the designed device is suitable for application in a wind park.

Keywords: Embedded System, Monitoring, Wind Turbine, Faults Diagnosis, TCP/IP Protocol, Real Time, Web.

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Authors: Amarunnishad T.M., Govindan V.K., Abraham T. Mathew

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An image compression method has been developed using fuzzy edge image utilizing the basic Block Truncation Coding (BTC) algorithm. The fuzzy edge image has been validated with classical edge detectors on the basis of the results of the well-known Canny edge detector prior to applying to the proposed method. The bit plane generated by the conventional BTC method is replaced with the fuzzy bit plane generated by the logical OR operation between the fuzzy edge image and the corresponding conventional BTC bit plane. The input image is encoded with the block mean and standard deviation and the fuzzy bit plane. The proposed method has been tested with test images of 8 bits/pixel and size 512×512 and found to be superior with better Peak Signal to Noise Ratio (PSNR) when compared to the conventional BTC, and adaptive bit plane selection BTC (ABTC) methods. The raggedness and jagged appearance, and the ringing artifacts at sharp edges are greatly reduced in reconstructed images by the proposed method with the fuzzy bit plane.

Keywords: Image compression, Edge detection, Ground truth image, Peak signal to noise ratio

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Authors: Kuo-Cheng Liu

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In this paper, we propose ablock-wise watermarking scheme for color image authentication to resist malicious tampering of digital media. The thresholding technique is incorporated into the scheme such that the tampered region of the color image can be recovered with high quality while the proofing result is obtained. The watermark for each block consists of its dual authentication data and the corresponding feature information. The feature information for recovery iscomputed bythe thresholding technique. In the proofing process, we propose a dual-option parity check method to proof the validity of image blocks. In the recovery process, the feature information of each block embedded into the color image is rebuilt for high quality recovery. The simulation results show that the proposed watermarking scheme can effectively proof the tempered region with high detection rate and can recover the tempered region with high quality.

Keywords: thresholding technique, tamper proofing, tamper recovery

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Authors: F. J. Moral García, P. Valiente González, F. López Rodríguez

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Ground-level tropospheric ozone is one of the air pollutants of most concern. It is mainly produced by photochemical processes involving nitrogen oxides and volatile organic compounds in the lower parts of the atmosphere. Ozone levels become particularly high in regions close to high ozone precursor emissions and during summer, when stagnant meteorological conditions with high insolation and high temperatures are common. In this work, some results of a study about urban ozone distribution patterns in the city of Badajoz, which is the largest and most industrialized city in Extremadura region (southwest Spain) are shown. Fourteen sampling campaigns, at least one per month, were carried out to measure ambient air ozone concentrations, during periods that were selected according to favourable conditions to ozone production, using an automatic portable analyzer. Later, to evaluate the ozone distribution at the city, the measured ozone data were analyzed using geostatistical techniques. Thus, first, during the exploratory analysis of data, it was revealed that they were distributed normally, which is a desirable property for the subsequent stages of the geostatistical study. Secondly, during the structural analysis of data, theoretical spherical models provided the best fit for all monthly experimental variograms. The parameters of these variograms (sill, range and nugget) revealed that the maximum distance of spatial dependence is between 302-790 m and the variable, air ozone concentration, is not evenly distributed in reduced distances. Finally, predictive ozone maps were derived for all points of the experimental study area, by use of geostatistical algorithms (kriging). High prediction accuracy was obtained in all cases as cross-validation showed. Useful information for hazard assessment was also provided when probability maps, based on kriging interpolation and kriging standard deviation, were produced.

Keywords: Kriging, map, tropospheric ozone, variogram.

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Authors: N. S. Pradeep, M. Balasingh Moses, V. Aarthi

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Any signal transmitted over a channel is corrupted by noise and interference. A host of channel coding techniques has been proposed to alleviate the effect of such noise and interference. Among these Turbo codes are recommended, because of increased capacity at higher transmission rates and superior performance over convolutional codes. The multimedia elements which are associated with ample amount of data are best protected by Turbo codes. Turbo decoder employs Maximum A-posteriori Probability (MAP) and Soft Output Viterbi Decoding (SOVA) algorithms. Conventional Turbo coded systems employ Equal Error Protection (EEP) in which the protection of all the data in an information message is uniform. Some applications involve Unequal Error Protection (UEP) in which the level of protection is higher for important information bits than that of other bits. In this work, enhancement to the traditional Log MAP decoding algorithm is being done by using optimized scaling factors for both the decoders. The error correcting performance in presence of UEP in Additive White Gaussian Noise channel (AWGN) and Rayleigh fading are analyzed for the transmission of image with Discrete Cosine Transform (DCT) as source coding technique. This paper compares the performance of log MAP, Modified log MAP (MlogMAP) and Enhanced log MAP (ElogMAP) algorithms used for image transmission. The MlogMAP algorithm is found to be best for lower Eb/N0 values but for higher Eb/N0 ElogMAP performs better with optimized scaling factors. The performance comparison of AWGN with fading channel indicates the robustness of the proposed algorithm. According to the performance of three different message classes, class3 would be more protected than other two classes. From the performance analysis, it is observed that ElogMAP algorithm with UEP is best for transmission of an image compared to Log MAP and MlogMAP decoding algorithms.

Keywords: AWGN, BER, DCT, Fading, MAP, UEP.

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Authors: Eric Filiol, Edouard Franc, Alessandro Gubbioli, Benoit Moquet, Guillaume Roblot

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Worm propagation profiles have significantly changed since 2003-2004: sudden world outbreaks like Blaster or Slammer have progressively disappeared and slower but stealthier worms appeared since, most of them for botnets dissemination. Decreased worm virulence results in more difficult detection. In this paper, we describe a stealth worm propagation model which has been extensively simulated and analysed on a huge virtual network. The main features of this model is its ability to infect any Internet-like network in a few seconds, whatever may be its size while greatly limiting the reinfection attempt overhead of already infected hosts. The main simulation results shows that the combinatorial topology of routing may have a huge impact on the worm propagation and thus some servers play a more essential and significant role than others. The real-time capability to identify them may be essential to greatly hinder worm propagation.

Keywords: Combinatorial worm, worm spreading, worm virulence, stealth worm, spreading simulation, vertex cover, networktopology, WAST simulator, SuWAST simulator.

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Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale

Abstract:

Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram (PCG) signals can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded PCG signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded Electrocardiograms (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show on average a segmentation testing performance average of 76% sensitivity and 94% specificity.

Keywords: Heart sounds, PCG segmentation, event detection, Recurrent Neural Networks, PCG curve length.

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Authors: Jana Doležalová, Josef Holík, Zdeněk Wimmer, Sándor T. Forczek

Abstract:

Chlorine is one of the most abundant elements in nature, which undergoes a complex biogeochemical cycle. Chlorine bound in some substances is partly responsible for atmospheric ozone depletion and contamination of some ecosystems. As due to international regulations anthropogenic burden of volatile organochlorines (VOCls) in atmosphere decreases, natural sources (plants, soil, abiotic formation) are expected to dominate VOCl production in the near future. Examples of plant VOCl production are methyl chloride, and bromide emission from (sub)tropical ferns, chloroform, 1,1,1-trichloroethane and tetrachloromethane emission from temperate forest fern and moss. Temperate forests are found to emit in addition to the previous compounds tetrachloroethene, and brominated volatile compounds. VOCls can be taken up and further metabolized in plants. The aim of this work is to identify and quantitatively analyze the formed VOCls in temperate forest ecosystems by a cryofocusing/GC-ECD detection method, hence filling a gap of knowledge in the biogeochemical cycle of chlorine.

Keywords: chloroform, cryofocusing-GC-ECD, ozonedepletion, volatile organochlorines

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Authors: S. Sendhilkumar, N. Mohanasundaram, M. Senthilkumar, S. N. Sivanandam

Abstract:

The operational life of rotating machines has to be extended using a predictive condition maintenance tool. Among various condition monitoring techniques, vibration analysis is most widely used technique in industry. Signals are extracted for evaluating the condition of machine; further diagnostics is carried out with detected signals to extend the life of machine. With help of detected signals, further interpretations are done to predict the occurrence of defects. To study the problem of defects, a test rig with various possibilities of defects is constructed and experiments are performed considering the unbalanced condition. Further, this paper presents an approach for fault diagnosis of unbalance condition using Elman neural network and frequency-domain vibration analysis. Amplitudes with variation in acceleration are fed to Elman neural network to classify fault or no-fault condition. The Elman network is trained, validated and tested with experimental readings. Results illustrate the effectiveness of Elman network in rotor-bearing system.

Keywords: Elman neural network, fault detection, rotating machines, unbalance, vibration analysis.

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