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Objective The purpose of this study is to examine the effect of the workstation type on the severity of mental stress by means of measuring prefrontal cortex (PFC) activation using functional near-infrared spectroscopy. Background Workstation type is known to influence worker’s health and performance. Despite the practical implications of ergonomic workstations, limited information is available regarding their impact on brain activity and executive functions. Method Ten healthy participants performed a Montreal imaging stress task (MIST) in ergonomic and nonergonomic workstations to investigate their effects on the severity of the induced mental stress. Results Cortical hemodynamic changes in the PFC were observed during the MIST in both the ergonomic and nonergonomic workstations. However, the ergonomic workstation exhibited improved MIST performance, which was positively correlated with the cortical activation on the right ventrolateral and the left dorsolateral PFC, as well as a marked decrease in salivary alpha‐amylase activity compared with that of the nonergonomic workstation. Further analysis using the NASA Task Load Index revealed a higher weighted workload score in the nonergonomic workstation than that in the ergonomic workstation. Conclusion The findings suggest that ergonomic workstations could significantly improve cognitive functioning and human capabilities at work compared to a nonergonomic workstation. Application Such a study could provide critical information on workstation design and development of mental stress that can be overlooked during traditional workstation design and mental stress assessments.

Stroop color-word test (STW) is a reliable method that has been used in laboratory to induce stress to the subject. This paper describes the development of Stroop color-word Test which was implemented based on five sections of test condition: i. Introductory Section (IS), ii. Resting Section (RS), iii. Congruent Section (CS), iv. Incongruent Section 1 (ICS-1), and v. Incongruent Section 2 (ICS-2). This test condition was identified from normal state until the highest stress state. Psychology experiment and MATLAB software were used in implementing the stress test. Electroencephalogram (EEG) signal of six healthy subjects was recorded while conducting this test. Power Spectral Density (PSD) was computed and analyzed. Results found that PSD can differentiate the level of stress from normal until the highest stress state. As such, it can be used as computer-assisted tool for stress detection.

Traditional methods focus on low-level handcrafted features representations and it is difficult to design a comprehensive classification algorithm for remote sensing scene classification problems. Recently, convolutional neural networks (CNNs) have obtained remarkable performance outcomes, setting several remote sensing benchmarks. Furthermore, direct applications of UAV remote sensing images that use deep convolutional networks are extremely challenging given high input data dimensionality with relatively small amounts of available labelled data. We, therefore, propose a CNN approach to scene classification that architecturally incorporates sparse coding (SC) technique for dimension reduction to minimize overfitting. Outcomes were compared with principal component analysis (PCA) and global average pooling (GAP) alternatives that use fully connected layer(s) in pre-trained CNN architecture(s) to minimize overfitting. SC was used to encode deep features extracted from the last convolutional layer of pre-trained CNN models by using different features maps in which deep features had been converted into low-dimensional SC features. These same sparse-coded features were concatenated by means of different pooling techniques to obtain global image features for scene classification. The proposed algorithm outperformed current state-of-the-art algorithms based on handcrafted features. When using our own UAV-based dataset and existing datasets, it was also exceptionally efficient computationally when learning data representations, producing a 93.64% accuracy rate..

In this paper, we investigated the relationships between EEG and semantic long-term memory retrieval using spectral analysis and functional connectivity. We employed an EEG data set from a previous study, which was recorded from 34 healthy participants during a memory retrieval process. The data set consists of EEG collected during two tasks, a learning task, in which participants studied animated learning material, and a memory recall task, in which they recalled the learned information. We analyzed both EEG signals and behavioral responses. We conducted spectral analyses for the absolute power and relative power of EEG frequency bands, EEG coherence, absolute phase delay, and EEG amplitude asymmetry features. The delta-band EEG and theta-band absolute power were negatively correlated with task performance at frontal regions, while the alpha-band activity was positively correlated in frontal regions and negatively correlated in parietal and temporal sites. High-beta in the frontal lobe was positively correlated with memory recall score. EEG coherence was negatively correlated in the delta band at the fronto-parietal and fronto-central sites, and positively correlated in the gamma band at widespread regions, including all lobes. EEG phase delay showed the highest degree of connectivity in the delta and alpha bands at frontal sites, and was negatively correlated with memory recall score. The negative correlation in phase delay demonstrated the synchronization and positive correlation reflected the de-synchronization of electrode pairs during memory recollection in the delta and alpha frequencies. We concluded that neuronal networks producing low EEG frequencies at frontal regions and those producing high frequencies in widespread regions are active during the memory recollection process. Thus, semantic memory storage involves widespread neuronal networks across the brain.

This work offers an approach to aerial image classification for use in remote sensing object recognition, image processing and computer vision. Sparse coding (SC) is used to classify unmanned-aerial-vehicle (UAV) and satellite images because SC representation can generalize a large dataset and improve the detection of distinctive features by reducing calculation time for feature matching and classification. Features from images are extracted based on the following descriptors: (a) Scale Invariant Feature Transform; (b) Histogram of Oriented Gradients; and (c) Local Binary Patterns. SC representation and local image features are combined to represent global features for classification. Features are deployed in a sparse model to store descriptor features using extant dictionaries such as (a) the Discrete Cosine Transform and (b) the Discrete Wavelet Transform. An additional two dictionaries are proposed as developed for the present work: (c) the Discrete Ridgelet Transform (DRT) and (d) the Discrete Tchebichef Transform. The DRT dictionary is constructed by using the Ricker wavelet function to generate finite Ridgelet transforms as basis elements for a hybrid dictionary. Different pooling methods have also been employed to convert sparse-coded features into a feature matrix. Various machine learning algorithms are then applied to the feature matrix to classify objects contained in UAV and satellite imagery data. Experimental results show that the SC model secured better accuracy rates for extracted discriminative features contained in remote sensing images. The authors concluded that the proposed SC technique and proposed dictionaries provided feasible solutions for image classification and object recognition.

Aerial scene classification purposes to automatically label aerial images with specific semantic categories. However, cataloguing presents a fundamental problem for high-resolution remote-sensing imagery HRRS. Recent developments include several approaches and numerous algorithms address the task. This article proposes a convolutional neural network CNN approach that utilizes sparse coding for scene classification applicable for HRRS unmanned aerial vehicle UAV and satellite imagery. The article has two major sections: the first describes the extraction of dense multiscale features multiple scales from the last convolutional layer of a pre-trained CNN models; the second describes the encoding of extracted features into global image features via sparse coding to achieve scene classification. The authors compared experimental outcomes with existing techniques such as Scale-Invariant Feature Transform and demonstrated that features from pre-trained CNNs generalized well with HRRS datasets and were more expressive than low-and mid-level features, exhibiting an overall 90.3% accuracy rate for scene classification compared to 85.4% achieved by SIFT with sparse coding. Thus, the proposed CNN-based sparse coding approach obtained a robust performance that holds promising potential for future applications in satellite and UAV imaging.

Due to the increasing numbers of vehicles in the world which results in a rise in traffic density, smart city is seeking to minimize transportation problems such as accidents. To solve this issue, VANET has been developed to improve vehicles’ mobility and provide a safe city. VANET is a type of wireless technology which provides vehicle-to-vehicle (V2V) and vehicle-to-infrastructures (V2I) communication system. Despite the advantages of VANET, it faces a lot of challenges; security and privacy, for instance, are the most critical ones. In this paper, we implement an approach used in Optical CDMA system given its success in optical domain. This technique is based on the 2D ZCC/MD code which is characterized by zero cross-correlation property. The transmission system is based on the VLC system, in which the information will circulate in optic domain using light in free space. The proposed system can provide the security and the privacy due to the 2D ZCC/MD code which assigns each user by a specific unique code.

International audience; Driving distraction is a topic of great interest in the transport safety-research community, because it is now a primary cause of road accidents. A recent report has revealed that distraction is more alarming than previously thought, and a suitable measurement to effectively detect distraction is required. Most agree that driving distraction actually comprises the simultaneous interaction of two or more types of distraction. The purpose of this paper is, therefore, to determine the promising method for measuring visual cognitive distraction. We discuss the five common measurement methods for visual and cognitive driving distraction, which include driving performance, driver physical measures, driver biological measures, subjective reports, and hybrid measures. Hybrid measurement of driver's physical measures (e.g., eye movement) and driver's biological measures (e.g., electroencephalogram) is better than other methods at detecting types of visual cognitive distraction. This new perspective on measurement methods will help the field of transport safety to determine the best means of detecting and measuring the effect of visual cognitive distraction.

Blind techniques and algorithms for Multiple-Input Multiple-Output (MIMO) signals interception have recently attracted a great deal of research efforts. This is due to their important applications in the military and civil telecommunications domains. One essential step in the signal interception process is to blindly recognize the modulation scheme of the MIMO signals. This process is formally called Modulation Classification (MC). This paper discusses the modulation classification for MIMO systems and presents a comprehensive and critical literature review of the existing MC algorithms for MIMO systems; where possible, gaps in the knowledge base are identified and future directions for the research work are suggested.

In wireless communication channels, the performance of the wireless communication systems deteriorate due to the severe influence of interference, multipath fading, path loss, shadowing, and noise, respectively. In this paper, we investigate the performance of the system transmitting over correlated ź---µ frequency selective fading channels in terms of average error rates. Based on moment generating function approach, closed form expressions for average error probabilities for the system are derived and represented in terms of Appell's hypergeometric functions and Lauricella's multivariate hypergeometric functions. In addition, probability density function based approach is employed to determine the formulae for average error probabilities of the system operating over correlated ź---µ multipath fading channels. The numerical results reveal that, the effects of correlation, decaying power factor and signal constellation size can be reduced using frequency, spatial antennas and path diversities, respectively. Furthermore, we confirm the correctness of the analytical approaches through Monte Carlo simulation technique.

A number of Claimer-Reporter-Witness based schemes (also called witness node based schemes) have been proposed to solve the problem of clone node attack or node replication attack which brings severe threats to WSNs. Node replication attack is an application independent attack in which an adversary first physically captures and then compromises a sensor node in the network and after creating clones or replicas of the compromised nodes; he/she surreptitiously deploys them at strategic positions of the network. The approaches used by the existing Claimer Reporter Witness-based detection schemes tradeoff between the higher detection probability for detecting clones and various overheads. The communication costs along with the memory and processing costs need to be measured to find out the gain from tradeoff. This is the first such work in which we have developed analytical framework to measure various costs for our proposed distributed clone detection protocol RAND and the other existing distributed schemes for clone detection. Moreover, we have also presented numerical results to demonstrate the impact of network size and the number of selected witnesses on these costs. Our results show that a significant amount of resources are required by the chosen CRW based schemes for detecting clones. Our comprehensive cost model can be used as a framework for estimating the costs of different CRW based protocols and can aid the researchers to choose the most efficient protocol for detecting clones in the network as well as estimate the resource requirements of the network to develop effective schemes in future.

Driver drowsiness is one of the major causes of road accidents. An estimated number of 1.2 million crashes, 8,000 lost lives, 500,000 injuries annually relates to drowsy-driving. From the information provided by a number of statistical reports, it can be proven that a large number of fatalities is caused by driver drowsiness. In earlier researches, to estimate the level of drowsiness, the measures to be focused on are single measures either vehicle-based, physiological measure, behavioural measure or subjective measure. This paper analyses two single measures which includes physiological and behavioural measures such as EEG signals and video sequences together. Features extracted from EEG signals are the Delta, Theta and Alpha frequency bands which includes Delta power (P_(δ-abs)), Theta power (P_(ϑ-abs)) and Alpha power (P_(α-abs)). Features extracted from the video sequences are eyes state specifically the eye closure. With the obtained features extracted, the significant features are combined with the physiological measure chosen to be measured to be the Theta band since it has previously been related to the drowsy state for numerous researches. The behavioural measure chosen are the eye states which are the eye closures. The results obtained through this research for the single physiological measure has an accuracy of 91% for all frequency bands. The behavioural measure obtained shows the eye opening and closing of the eyes. After fusing both the measures, one subject shows the most accurate detection of drowsiness. Three subjects give a contradictory result in detecting drowsiness. Overall, this study shows that EEG frequency band powers and eye closure can be used to infer drowsiness. Thus the study shows a promising prospect of detecting drowsiness and alerting the driver with an alarm.

This study develops a feature-based Automatic Modulation Classification (AMC) algorithm for spatially multiplexed Multiple-Input Multiple-Output (MIMO) systems employing two Higher Order Cumulants (HOCs) of the estimated transmit signal streams as discriminating features and a multiclass Support Vector Machine (SVM) as a classification system. The algorithm under study has the capability to recognize a wide range of modulation schemes without any prior information about the channel state. The classification performance of the proposed algorithm was evaluated via extensive simulations under different operating conditions and was also compared with the one obtained with the optimal Hybrid Likelihood Ratio Test (HLRT) approach. The results show that the proposed algorithm is capable of classifying the considered modulation schemes with good classification accuracy and can achieve performance comparable to that of the HLRT approach while having a significantly lower computational complexity.

International audience; Photoplethysmography and Ballistocardiography are two concepts that are used to measure heart rate from human, by using facial videos. Heart rate estimation is essential to determine the physiological and pathological state of a person. This paper presents a critical review of digital camera based heart rate estimating method on facial skin. This review extends the investigation on to the principles and theory behind photoplethysmography and ballistocardiography. The article contains reviews on the significance of the methods and contributions to overcome challenges such as; poor signal strength, illumination variance, and motion variance. The experiments were conducted to validate the state of the art methods on a challenging database that is available publicly. The implemented methods were validated using the database, on 27 subjects for a range of skin tones from pearl white, fair, olive to black. The results were computed using statistical methods such as: mean error, standard deviation, the root mean square error, Pearson correlation coefficient, and Bland-Altman analysis. The results derived from the experiments showed the reliability of the state of the art methods and provided direction to improve for situations involving illumination variance and motion variance. (C) 2017 Elsevier Ltd. All rights reserved.

International audience; This paper aims to optimize the coverage of a given area from a set of views to allow a complete mosaicing. Among the investigated methods to find the best camera positions, two of them are studied, namely the Particle Swarm Optimization (PSO) and the Genetic Algorithms (GA). After having performed experiments to compare the algorithms, the hybridization of GA and PSO is investigated. To validate the proposed method, it is simulated area of irregular shapes with the cameras mounted on a Unmanned Aerial Vehicles (UAVs). V-REP is used to simulate the UAVs in an indoor environment and satellite images are used for a large outdoor area. The simulation validates the efficiency of the proposed method to find the optimal position of cameras. Then by using the images acquired it is possible to monitor the area and to compute a full mosaic of it.

In many Asian countries, motorcyclists have a higher fatality rate as compared to other vehicles. Among many other factors, rear end collisions are also contributing for these fatalities. Collision detection systems can be useful to minimize these accidents. However, the designing of efficient and cost effective collision detection system for motorcyclist is still a major challenge. In this paper, an acoustic information based, cost effective and efficient collision detection system is proposed for motorcycle applications. The proposed technique uses the Short time Fourier Transform (STFT) to extract the features from the audio signal and Principal component analysis (PCA) has been used to reduce the feature vector length. The reduction of feature length, further increases the performance of this technique. The proposed technique has been tested on self recorded dataset and gives accuracy of 97.87%. We believe that this method can help to reduce a significant number of motorcycle accidents.

To provide anywhere at any time services in an integrated wireless networking environment, seamless mobility is one of the most important and challenging issues. Recent literature shows that the seamless mobility can be achieved by performing significant modifications in existing network designs and protocol architectures. However, such intensive modification increases the network implementation complexity and deployment cost. In this study , we have proposed a Simple and Seamless Vertical Handoff Protocol (SSVHOP) to attain the seamless mobility without implementing any major modification in the existing network topology or protocol architecture. The foundations of the underlying principle of the SSVHOP are based on a proactive RSS handoff triggering mechanism and multi-homing capabilities of mobile terminal. In addition, SSVHOP reduces the signaling and processing cost of the network to achieve low handoff delay and packet loss during upward and downward vertical handoff scenarios. Moreover, it has been observed that the proposed design also improves the data session performance by routing the data packets on the optimal route and by introducing an overhead free mechanism for data transportation in an integrated UMTS/WLAN network. We evaluated and compared the SSVHOP with the tight coupling mechanism. By analyzing mathematical modeling and simulation results, it is evident that the SSVHOP performs better than the tight coupling mechanism for all tested applications and measurement parameters.

The beating of two or more lasers that have the same or a finite difference in the central frequencies, is the main source of noise in spectral amplitude coding optical code division multiple access (SAC OCDMA) systems. In this paper we adopt a spatial multiplexing (SM) scheme for SAC OCDMA systems to mitigate this beat noise. The results show that for different code weights and different data rates SM SAC can support a larger number of users than the conventional SAC for all different laser source configurations. However, SM SAC requires a more complex system than the conventional SAC, and almost twice as much optical component.

In this paper we propose a simple divided spectrum balanced detection (DSBD) for spectral amplitude coding (SAC) optical code division multiple access (OCDMA) systems. SAC OCDMA systems are limited by phase induced intensity noise (PIIN), which is a signal dependent source of noise. Our proposed technique reduces the PIIN by dividing the spectrum of the signal into two or more, and detecting each spectrum by a different photodiode. The DSBD scheme reduces the detected optical power at photodetection, thus resulting in a higher mitigation of the PIIN. Theoretical results show that DSBD demonstrate noticeable improvement over traditional balanced detection technique, for example an up to 33% increase in the number of active users can be achieved, and at least 1 × 10 −3 b/s Hz increase in the spectral efficiency is obtained. However, the SDBD is more complex and append more constrains on system components.

A hybrid spectrally amplitude coded (SAC) optical code division multiple access (CDMA) with subcarrier multiplexing (SCM) scheme is a potential candidate for future broadband access solutions in the communication network. In this work, the performance of hybrid technique is investigated. Theoretical derivations are developed based on the zero cross correlation (ZCC) code. The performances are evaluated taking into account the relevant noises such as thermal noise, shot noise and inter modulation noise. Design parameters such as number of optical channels, number of subcarriers, and input power are varied to see their effect on the system performances. Simulation analysis using Optisys simulator is also done and compared to the numerical theoretical results. We measured the performance based on the merits of received power, input power and bit error rates of the received signals. Results revealed that power penalty due to increased number of channels in the hybrid system is lower compared to the conventional OCDMA system, and hence improving the system performance in term of power efficiency. This exhibits the ability of the hybrid system to be one powerful technique as a candidate for future access network. Key words: Spectral amplitude coding-optical code division multiple access (SAC-OCDMA), subcarrier multiplexing (SCM), zero cross correlation (ZCC), broadband access network.

In this paper, we show details of zero cross correlation (ZCC) code development and investigate performance by measuring bit-error-rates compared to other optical code division multiple access (OCDMA) codes using Avalanche photodiode (APD) and PIN photodetector. We present a configuration to determine how optical OCDMA systems can be applied with PIN and APD photodetector. Analysis of Avalanche photodiode gain optimization is discussed in ZCC code system. We also present analytical and numerical theoretical results for data transmission of spectrally encoded incoherent OCDMA signal and how signal-noise is being affected by using these two photodetectors.

In this paper we present a novel health monitoring method by estimating the heart rate and respiratory rate using an RGB camera. The heart rate and the respiratory rate are estimated from the photoplethysmography (PPG) and the respiratory motion. The method mainly operates by using the green spectrum of the RGB camera to generate a multivariate PPG signal to perform multivariate de-noising on the video signal to extract the resultant PPG signal. A periodicity based voting scheme (PVS) was used to measure the heart rate and respiratory rate from the estimated PPG signal. We evaluated our proposed method with a state of the art heart rate measuring method for two scenarios using the MAHNOB-HCI database and a self collected naturalistic environment database. The methods were furthermore evaluated for various scenarios at naturalistic environments such as a motion variance session and a skin tone variance session. Our proposed method operated robustly during the experiments and outperformed the state of the art heart rate measuring methods by compensating the effects of the naturalistic environment.

Automated detection and identification of abnormal cells in the human body is a critical application for medical image computing. Enhancement and de-noising of images remain challenging tasks and imperative steps for image analysis algorithms. Indeed, due to its early role in the process, the results of advanced operators for feature extraction will highly depend on the quality of enhanced image produced. Depending on the presence of different noise types, particular algorithms will respond better. This paper presents a comprehensive comparison between several linear and non-linear filters applied on fluorescence microscope images for the localization and counting of specific cancer phenotypes from mouth cell samples. The objective analysis proposed is evaluating the PSNR and Delta-SNR (the SNR to SNR measure between original images and filtered ones) for blood sample sequences taken from Cancer Research Malaysia. Thirty Fluorescence microscope images with low contrast and non-uniform illumination have been tested and analysed. Non-linear algorithms seem to show improved contrast and background removal abilities compared to linear blurring and approximating filters.

International audience; Create a complete system of video surveillance using camera mounted on a robot like UAV to maintain optimized vast area coverage and reconstruct an image by using mosaicing techniques. This paper demonstrated the efficiency of using one UAV to cover vast area using optimized positions.

The immune system is a complex network of highly specialized cells (such as lymphocytes) and organs. All of these cells work together to reduce infection (antigens) such as bacteria, virus or tumor cell from the body. Biologic Response Modifiers (BRM) also known as immunotherapy, is a type of treatment which mobilizes and triggers the immune system of the body. This research work provides a description of noise nature of immunofluorescence microscope images. Thus, the study leads to determine the type of noise and choose the filter which best fit the noise presented in fluorescence microscope images. Image histogram and Fast Fourier Transform (FFT) have been applied on blood samples. However, the results show that the Gaussian noise is found in this kind of images. CLAHE filter has been applied to these images. The results obtained from CLAHE demonstrate the ability for this filter to deal with this kind of images.

This paper presents a method to measure the forearm's subcutaneous veins depth by using the theory of light propagation model. In this study, we proposed a practical method to measure the forearm's vein deep by designed an optical phantom and experiment setup to measure the diffuse reflectance (Rd). The Monte Carlo Multilayer (MCML) tool had been used to simulate the light migration into skin layer. This study is designed for those who wish to find the depth information of forearm subcutaneous vein and, in particular, for those investigating for a perfect peripheral intravenous (IVs) in clinical application. Besides covering those aspects of the measurement theory of this model, this study also includes the execution definition of the experiment setup and the fundamental design of tissue-like phantom. In this way, instructional, revision and remedial materials provide the conscientious researcher with a comprehensive text to plan and regulate for the research in vein's depth measurement.

This paper proposes an evaluation metric which derive the effectiveness of background modeling algorithms. Background modeling is a key process on developing visual surveillance systems. The requirement of adapting to dynamic environments has motivated researchers to modify existing background modeling algorithms and develop new algorithms with better adaptability. Having the algorithms developed, credentials of each of the algorithms have to be assessed to exploit their effectiveness. Various evaluation metrics have been used for evaluating the rate of foreground extraction, foreground detection, and overall accuracy. However, the rate of background detection has not been exploited by these metrics. Therefore, this paper would provide an insight to the existing evaluation metrics and introduce our proposed metric for estimating the rate of background detection.

Wireless Sensor Networks (WSNs) are vulnerable to Node Replication attacks or Clone attacks. Among all the existing clone detection protocols in WSNs, RAWL shows the most promising results by employing Simple Random Walk (SRW). More recently, RAND outperforms RAWL by incorporating Network Division with SRW. Both RAND and RAWL have used SRW for random selection of witness nodes which is problematic because of frequently revisiting the previously passed nodes that leads to longer delays, high expenditures of energy with lower probability that witness nodes intersect. To circumvent this problem, we propose to employ a new kind of constrained random walk, namely Single Stage Memory Random Walk and present a distributed technique called SSRWND (Single Stage Memory Random Walk with Network Division). In SSRWND, single stage memory random walk is combined with network division aiming to decrease the communication and memory costs while keeping the detection probability higher. Through intensive simulations it is verified that SSRWND guarantees higher witness node security with moderate communication and memory overheads. SSRWND is expedient for security oriented application fields of WSNs like military and medical.

The monitoring of vegetation near high-voltage transmission power lines and poles is tedious. Blackouts present a huge challenge to power distribution companies and often occur due to tree growth in hilly and rural areas. There are numerous methods of monitoring hazardous overgrowth that are expensive and time-consuming. Accurate estimation of tree and vegetation heights near power poles can prevent the disruption of power transmission in vulnerable zones. This paper presents a cost-effective approach based on a convolutional neural network (CNN) algorithm to compute the height (depth maps) of objects proximal to power poles and transmission lines. The proposed CNN extracts and classifies features by employing convolutional pooling inputs to fully connected data layers that capture prominent features from stereo image patches. Unmanned aerial vehicle or satellite stereo image datasets can thus provide a feasible and cost-effective approach that identifies threat levels based on height and distance estimations of hazardous vegetation and other objects. Results were compared with extant disparity map estimation techniques, such as graph cut, dynamic programming, belief propagation, and area-based methods. The proposed method achieved an accuracy rate of 90%.

A zero cross-correlation (ZCC) code is proposed to reduce the impact of system impairment and multiple access interference (MAI) in spectral amplitude coding optical code division multiple access (SAC–OCDMA) system. Bit-error-rate (BER) performance is derived taking into account the effect of some noises. The key to an effective OCDMA system is the choice of efficient address codes with good or almost zero correlation properties for encoding the source. The use of ZCC code can eradicate phase induced intensity noise (PIIN) which will contribute to better BER. Thus, we demonstrate, theoretically, the performance of optical ZCC code. It is shown that optical ZCC code can accommodate more users simultaneously for the typical error rate of optical communication system of 10 −9 . The result indicates that the established system not only preserves the capability of suppressing MAI, but also improves bit-error-rate performance as compared to the conventional coders.

In this paper, we study the performance of optical code-division multiple access (OCDMA) systems using vector combinatorial (VC) code under various link parameters. The impact of the fiber dispersion effects on the multi-user interference (MUI) is reported using a commercial optical systems simulator, Virtual Instrument Photonic (VPITM). The VC code is compared mathematically with other codes which use similar techniques. We analyzed and optimized the data rate, fiber length, and channel spacing in order to reduce the BER without the need to deploy dispersion compensating devices. The performance and optimization of VC code in OCDMA system is reported. We have demonstrated that, for a high data rate (higher than 2.5 Gb/s), even if dispersion compensated devices are not deployed, the BER can be significantly improved when the VC code desired parameters are selected. We have shown that when compensation dispersion devices are not deployed in the system, there is a tradeoff between the limited dispersion effects and the MUI.

Venipuncture as a mode of gaining intravenous access is a crucial practice in pre-surgical and conventional drug administering procedures. Biomedical engineering has focused on the automation and aid in these processes to promote and encourage non-invasive catheterization of patients. Indeed, medical personnel are highly challenged by the various patient's skin tones and topologies. This paper proposes an in depth analysis of illuminant configuration to provide enhanced visual aids to medical personnel while performing vein catheterisation. The proposed study introduces an optimised illuminant selection to ensure an enhanced vein visualisation independent of patient skin tones. The further selection of combined LED configuration in terms of wavelength and power will improve the contrast of vessels, leading to automated and optimised illumination for all patients and better image quality.

Wireless Sensor Networks (WSNs) are vulnerable to clone attacks as they are deployed in hostile and unattended environtments. Also due to the lack of physical tamper-resistance, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones into network. As a result the adversary is able to mount a wide variety of internal attacks. Several solutions have been proposed in the literature for the detection of these clones from which witness node based distributed solutions have shown satisfactory results. Random Walk (RAWL) is one of the witness node based distributed techniques in which witness nodes are randomly selected by initiating several random walks throughout the network. Although RAWL has achieved high security of witness nodes but in accomplishing high detection probability RAWL suffers from very high communication and memory overhead. In this paper we propose a novel enhancement in RAWL protocol aiming to decrease the communication and memory costs while keeping the detection probability high. Our simulation results show that this improvement in RAWL not only reduces the communication and memory costs but also ensures high security of witness node.

This paper presents a method to improve the real time Near-Infrared (NIR) subcutaneous veins localization by using the image processing technique based segmentation and fusion. The goal of this paper is to enhance the extraction of veins from NIR image through a series of image processing stages. In these image processing stages, input image is first partitioned to a new set of intensity band via the segmentation technique and they are re-combined through the fusion procedure in order to obtain the accurate information for next analysis procedure. After that, the procedure of segmentation and fusion is followed by the procedure of filtering and aggregation to produce an optimum image in the result. Lastly, the output from this veins extraction enhancement model is able to provide an optimum image for a better clinical diagnose of veins.

International audience; Subcutaneous veins localization is basic and important step for any intravenous medication administration. Due to different physiological characteristics, mainly darker skin tone, scars or dehydrated condition of patients, medical staff face difficulty in veins localization. Through near infrared imaging technology the veins can be visualized due to high contrast between veins and skin tissue in this modality. Information on the depth of veins is equally important for proper catheterization or venipuncture procedures. Patients have different veins depth due to the different amount of fat present in the subcutaneous layer. The depth of veins from the skin surface cannot be estimated by simple imaging technique. In this paper a mathematical model to estimate the depth of veins based on measured diffused reflectance is presented. A layered model of Monte Carlo simulations for light transport in turbid medium was used to validate the results.

Blood vessels detection is a common task performed in numerous medical procedures. During regular medical treatments venipuncture procedures are performed for invasive medication and blood sampling. Near infrared imaging technology can be used to visualize the subcutaneous veins in cases of difficult venous access. In this paper the methods for veins centerline detection and back projection is presented. In order to highlight the suitable veins for venipuncture, the centerline of larger veins are detected and back projected to the original image. The method is applied on the near infrared images of subjects selected from four different classes of skin tone. This can be helpful to medical staff to select suitable vein for venipuncture procedures. The method is based on Frangi vesselness filter for 2D images. The artifacts like hair and small vessel-like structures are removed with morphological operations. The presented method shows consistent performance in terms of vascular structure detection for all skin tone classes.

Advance biomedical engineering technologies are continuously changing the medical practices to improve medical care of patients nowadays. In this paper, we describe the concept used to prototype a device for needle insertion navigation during intravenous catheterization process via Near Infrared (NIR) imaging technique. A vein locator prototype using the NIR imaging technique and augmented reality (AR) technology have been developed in this work which is meant to be used in the process of intravenous catheterization. The challenges faced during the development of this prototype included the calibration of composite images in the see-through display. In this prototype, the Vuzix STAR 1200XL eyewear system has been used as the head mounted display and the imaging video is input by IR CCD camera. Additional, We select the optimum illumination by using NIR LED with wavelength of 830nm and 850nm in prototying to obtain the best contrast NIR venous image for different types of skin tone.

Cloud computing is a methodology and not a technology. Adaptation of cloud computing services for robotic applications is relatively straightforward while adaptation of underlying ideas will require a new design attitude. Cloud computing is a cost-effective and dynamic business model. Currently cloud robotics is understood as a client server methodology which enables robots utilize resources and services placed at centralized servers. These cloud servers treat robots as any other client computer offering them platform, infrastructure, process or algorithm as a service. HTM5 is an OMG MDA based multi-view meta-model for agent oriented development of cloud robotic systems. HTM5 encourages design of peer-to-peer service ecosystems based on an open registry and matchmaking mechanism. In peer-to-peer cloud robotics, a robot can trade its hardware, software and functional resources as a service to other robots in the ecosystem. The peer-to-peer trade in such systems may be driven by contracts and relationships between its member agents. This article discusses trade-view model of HTM5 methodology and its use in developing a cloud robotic ecosystem that implements peer-to-peer, contract based economy. The article also presents a case study with experiments that implement distributed artificial intelligence and peer-to-peer service oriented trade on simulated and real robot colonies.

Provisioning network survivability is especially crucial in wireless sensor and actor network (WSAN) because nodes deployed in hostile environments are prone to frequent failures. Failure of an actor significantly impact actor connected coverage which is essential for effective network operation. Existing mobility-based recovery schemes are either geared towards restoring inter-actor connectivity or area coverage. None of them consider sustaining actor coverage (i.e., having sensors reachable to actors) while restoring inter-actor connectivity. This paper presents RACE, a novel mechanism to Restore Actor Connected Coverage with reduced recovery overhead. RACE distinguishes critical/non-critical actors based on 2-hop information to better assess the scope of the failure and optimize the recovery procedure. Neighbors of a failed actor employ a cooperative failure detection scheme and only perform a limited-scale network reconfiguration to adopt any bereaved sensors left unreachable (uncovered by an actor) due to failure of a non-critical actor. In case a critical actor fails, RACE substitutes it with a non-critical neighbor that has the least impact on coverage (i.e., number of sensors). If it is necessary to engage critical actors in the recovery, RACE is recursively applied by relocating actors until a non-critical node is picked. Simulation results confirm the performance advantage of RACE compared to the best contemporary schemes. 2015 IEEE. Scopus 2-s2.0-84953727973

In this letter, we investigate the use of the multiphotodiode balanced detection (MPBD) scheme proposed by Williams in IEEE Photonics Technology Letters, (volume 8, pages 148-150, January 1996), to reduce phase-induced intensity noise (PIIN) in spectral amplitude coding optical code-division multiple access (SAC OCDMA). The MPBD is based on balancing the power reaching the balanced detector by utilizing multiple numbers of photodiodes. Since PIIN is signal dependent, by reducing the power reaching each photodiode, the overall PIIN can be reduced. Results show that MPBD outperforms the conventional balanced detector at a cost of added complexity.

In the matter of detecting clone attacks, we bring in a novel solution called Single Stage Memory Random Walk with Network Division (SSRAND) to circumvent the problems of simple random walk as exploited by both RAND and RAWL protocols. SSRAND combines the single stage memory random walk with network division for clone detection and simulation results verify that it not only reduces the communication and memory costs but also ensures high security of witness nodes with increased detection probability of clones.

International audience; The dot-com bubble bursted in the year 2000 followed by a swift movement towards resource virtualization and cloud computing business model. Cloud computing emerged not as new form of computing or network technology but a mere remoulding of existing technologies to suit a new business model. Cloud robotics is understood as adaptation of cloud computing ideas for robotic applications. Current efforts in cloud robotics stress upon developing robots that utilize computing and service infrastructure of the cloud, without debating on the underlying business model. HTM5 is an OMG's MDA based Meta-model for agent oriented development of cloud robotic systems. The trade-view of HTM5 promotes peer-to-peer trade amongst software agents. HTM5 agents represent various cloud entities and implement their business logic on cloud interactions. Trade in a peer-to-peer cloud robotic system is based on relationships and contracts amongst several agent subsets. Electronic Institutions are associations of heterogeneous intelligent agents which interact with each other following predefined norms. In Dynamic Electronic Institutions, the process of formation, reformation and dissolution of institutions is automated leading to run time adaptations in groups of agents. DEIs in agent oriented cloud robotic ecosystems bring order and group intellect. This article presents DEI implementations through HTM5 methodology.

This paper presented a compressed version of viewpoint feature histogram descriptor (VFH) for object classification based on shape recognition. VFH is known for representing geometrical features (3D rotation angles) of 3D points with concatenated histogram. However this histogram is large and sparse. The proposed descriptor employ eigenvalue decomposition to extract dominant orientation features from the point cloud and using it as a descriptor instead of sparse histogram. Using dominant features maintains the main properties of the object with minimal descriptor length which applicable for recognizing the geometrical class of 3D objects (sphere, rectangle, ... etc). This descriptor was tested for two class and multi-class 3D object classification using support vector machines (SVM). The new descriptor showed promising matching results of 88% for two class classification and 83% for multiple class classification of 3D objects.

Nowadays, the wireless devices are equipped with multiple network interfaces to support different wireless technologies, for example, Universal Mobile Telecommunications System (UMTS), Wireless Local Area Network (WLAN), and Bluetooth etc. However, while increasing the options to connect internet via different access networks, seamless mobility cannot be assured when the wireless device is roaming across heterogeneous wireless access domains. This limitation appears because multiple wireless interfaces cannot be utilized simultaneously for the same data session. Due to the enormous number of Internet Service Providers (ISPs) and the fragmented nature of wireless technologies, the wireless devices must support multiple wireless access networks concurrently. If more than one wireless interface is utilized for the internet connectivity then seamless mobility without any service degradation can be attained. The literature illustrates that most of the existing internetworking techniques are lacking to provide the seamless mobility. In this paper, we have proposed a Multi-homing Seamless Vertical Handoff Protocol (MSVHOP) for the seamless switching of active data session between UMTS and WLAN networks. In order to decrease the vertical handoff delay and transient lost information, the MSVHOP implements multi-homing feature in the proposed internetworking mechanism that leads to attain the less signaling and node processing cost during handoff.

Preserving inter-actor connectivity is essential in most wireless sensor and actor network (WSAN) applications as nodes have to collaborate and coordinate their actions against the events reported by the sensors. However, failure of a critical (i.e., cut-vertex) node partitions inter-actor network into disjoint segments and thus hinder network operation. The prime objective of this paper is to analyze the performance of reactive connectivity restoration algorithms for delay-tolerant WSAN applications. First, we provide insights to the state-of-the-art reactive connectivity restoration algorithms. Then, we present a Nearest Non-critical Neighbor (NNN) algorithm; a localized and distributed reactive approach for reconnecting network partitions. In NNN, each actor periodically determine its criticality (i.e., cut-vertex or not) based on 2-hop information and exchange with its neighbors. In case of a critical actor failure, the neighbors detect and trigger a connectivity restoration procedure that involves controlled and coordinated node relocation. NNN prefer to displace non-critical nodes during relocation in order to minimize recovery overhead in terms of distance movement and message coordination. We analyze the performance of reactive schemes through theoretical analysis and simulations.

In this paper we validate through software simulation that a Gamma probability density function (pdf) can give a very good approximation of the detected intensity of the incoherent light source. We performed two experiments, in the first experiment the pdf of 0.25 nm optical signal is measured for different photo detector's electrical bandwidth. In the second experiment the eye diagram reading is performed for the optical signal at 2.5 Gbps. Results show that the simulated pdf and computed pdf agree to very high extent. Results also show that the variance in the received signal for logic "0" is negligible, meanwhile for logic "1" the variance is very large and increases with electrical bandwidth.

This paper evaluates the performance of spatial multiplexing (SM) in beat noise mitigation in coherent spectral amplitude coding (SAC) OCDMA via the Optisystem software kit. We take into consideration two different laser source configurations; a centralized shared multi laser source and a precisely controlled multi laser source. For different performance merits, the results showed superiority of the SM SAC over the conventional SAC OCDMA. For example, an up to nine order of magnitude improvement in the bit error rate (BER) was reported for six users at 1.25 Gb/s.

International audience; Intravenous (IV) catheterization is the most important phase in medical practices of daily life. It is hard to localize veins in patients who have deep veins, minor age or dark skin; hence multiple attempts become indispensable for proper catheterization in such cases. Near Infrared (NIR) Imaging allow to visualize the veins underneath the skin of persons having non-visibility of veins problem. This paper reports the pre-selection of illuminants that ensure best veins/tissues contrast for patients having different skin tone. The sample subjects have been divided in four different classes based on the Luminance value of their skin tone in order to extract the best illuminant wavelengths range for each class. A multispectral approach has been used which provides the flexibility of wavelength range from visible to NIR (380 to 1040nm). The veins/tissue reflectance contrast obtained helps in determining the best wavelengths range where the contrast is maximum for each of the four classes. Using these results, we are planning to build a prototype system which can automatically select the illuminants based on different physiological characteristics of a subject.

We propose the use of a divided spectrum balanced detection (DSBD) to reduce the intensity noise in spectral amplitude coding (SAC) OCDMA systems. Simulation results show several order of magnitude improvements in bit error rate (BER) compared to the conventional balanced detection.

In this paper we investigate the use of wavelength multiplexed spectral amplitude coding (WM SAC) codes in beat noise mitigation in coherent source SAC OCDMA systems. A WM SAC code is a low weight SAC code, where the whole code structure is repeated diagonally (once or more) in the wavelength domain to achieve the same cardinality as a higher weight SAC code. Results show that for highly populated networks, the WM SAC codes provide better performance than SAC codes. However, for small number of active users the situation is reversed. Apart from their promising improvement in performance, these codes are more flexible and impose less complexity on the system design than their SAC counterparts.