Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network

Water distribution networks are large complex systems affected by leaks, which often entail high costs and may severely jeopardise the overall water distribution performance. Successful leak location is paramount in order to minimize the impact of these leaks when occurring. Sensor placement is a key issue in the leak location process, since the overall performance and success of this process highly depends on the choice of the sensors gathering data from the network. Common problems when isolating leaks in large scale highly gridded real water distribution networks include leak mislabelling and the obtention of large number of possible leak locations. This is due to similarity of leak effect in the measurements, which may be caused by topological issues and led to incomplete coverage of the whole network. The sensor placement strategy may minimize these undesired effects by setting the sensor placement optimisation problem with the appropriate assumptions (e.g. geographically cluster alike leak behaviors) and by taking into account real aspects of the practical application, such as the acceptable leak location distance. In this paper, a sensor placement methodology considering these aspects and a general sensor distribution assessment method for leak diagnosis in water distribution systems is presented and exemplified with a small illustrative case study. Finally, the proposed method is applied to two real District Metered Areas (DMAs) located within the Barcelona water distribution network.     

Design of embedded TCAM based longest prefix match search engine

The content addressable memory (CAM) based solutions are very useful in network applications due to its high speed parallel search mechanism. This paper presents a novel Ternary CAM (TCAM) based NAND Pseudo CMOS–Longest Prefix Match (NPC–LPM) search engine. The proposed system provides a simple hardware based solution using novel 11T TCAM cell structures and NPC word line technique, for network routers. The experiments were performed on 256 × 128 NPC–LPM system under 0.13 μm technology. The simulation result shows that the proposed design provides low power dissipation of 5.78 mW and high search speed of 315 MSearches/s under 1.3 V supply voltage. The presented NPC–LPM system meets the speed requirement of Optical Carrier (OC) 3072 with line-rate of 160 Gb/s in Ethernet networking and IPv6 protocol. The experimental results also show that the proposed system improves power-performance by 65%.     

A two-dimensional nano-positioner: Design,modelling and experiments

In this paper, an X-Y nano-positioner driven by the shear piezoelectric actuators (SPAs) is proposed based on the friction-inertial theory. The nano-positioner can output a two-dimensional motion at a maximum speed of 0.187 mm/s within a 5 mm×5 mm workspace. In order to solve the contradiction between the motion velocity and the motion resolution, the coarse and the fine motion modes are first proposed on the SPAs application. The coarse motion and the fine motion are realized based on the power-function-shaped signal and the combined piecewise-curve signal respectively. The performance of the nano-positioner, e.g. the resolution, the velocity and the fluctuation, are analyzed and discussed based on different driving signals. Compared with other nano-positioners, the proposed system in this paper is superior in respect of compact size, high resolution, high velocity and two switchable motion modes. Furthermore, a prototype system of the proposed nano-positioner has been developed, which is evaluated by using a capacitive sensor based measurement system. Experiment results validate the effectiveness of the proposed methodology that can be employed and extended to a variety of SPA involved systems.     

Electromagnetic characterization of chiral auxetic metamaterials for EMC applications

A new class of auxetic materials, a hexachiral honeycomb structure with good mechanical properties, is investigated through computer simulation and measurement. The electromagnetic properties for shielding applications are taken into account. This new material shows some interesting EMC properties (e.g. − 40 dB transmittance at 2.4 GHz) and promises better performance using different insertion techniques.     

Continuous blood pressure estimation based on multiple parameters from eletrocardiogram and photoplethysmogram by Back-propagation neural network

The cuff-less continuous blood pressure monitoring provides reliable and invaluable information about the individuals’ health condition. Conventional sphygmomanometer with a cuff measures only the value of the blood pressure intermittently and the measurement process is sometimes inconvenient. In this work, a systematic approach with multi-parameter fusion has been proposed to estimate the non-invasive beat-to-beat systolic and diastolic blood pressure with high accuracy. The methods involve real-time monitoring of the electrocardiogram (ECG) and photoplethysmogram (PPG), and extracting the R peak from the ECG and relevant feature parameters from the synchronous PPG. Also, it covers the creation of the topological model of back-propagation neural network that has fifteen neurons in the input layer, ten neurons in the single interlayer, and two neurons in the output layer, where all the neurons are fully connected. As for the results, the proposed method was validated on the volunteers. The reference blood pressure (BP) is from Finometer (MIDI, Finapres Medical System, Netherlands). The results showed that the mean ± S.D. for the estimated systolic BP (SBP) and diastolic BP (DBP) with the proposed method against reference were −0.41 ± 2.02 mmHg and 0.46 ± 2.21 mmHg, respectively. Thus, the continuous blood pressure algorithm based on Back-Propagation neural network provides a continuous BP with a high accuracy.     

Early-stage atherosclerosis detection using deep learning over carotid ultrasound images

This paper proposes a computer-aided diagnosis tool for the early detection of atherosclerosis. This pathology is responsible for major cardiovascular diseases, which are the main cause of death worldwide. Among preventive measures, the intima-media thickness (IMT) of the common carotid artery stands out as early indicator of atherosclerosis and cardiovascular risk. In particular, IMT is evaluated by means of ultrasound scans. Usually, during the radiological examination, the specialist detects the optimal measurement area, identifies the layers of the arterial wall and manually marks pairs of points on the image to estimate the thickness of the artery. Therefore, this manual procedure entails subjectivity and variability in the IMT evaluation. Instead, this article suggests a fully automatic segmentation technique for ultrasound images of the common carotid artery. The proposed methodology is based on machine learning and artificial neural networks for the recognition of IMT intensity patterns in the images. For this purpose, a deep learning strategy has been developed to obtain abstract and efficient data representations by means of auto-encoders with multiple hidden layers. In particular, the considered deep architecture has been designed under the concept of extreme learning machine (ELM). The correct identification of the arterial layers is achieved in a totally user-independent and repeatable manner, which not only improves the IMT measurement in daily clinical practice but also facilitates the clinical research. A database consisting of 67 ultrasound images has been used in the validation of the suggested system, in which the resulting automatic contours for each image have been compared with the average of four manual segmentations performed by two different observers (ground-truth). Specifically, the IMT measured by the proposed algorithm is 0.625 ± 0.167 mm (mean ± standard deviation), whereas the corresponding ground-truth value is 0.619 ± 0.176 mm. Thus, our method shows a difference between automatic and manual measures of only 5.79 ± 34.42 μm. Furthermore, different quantitative evaluations reported in this paper indicate that this procedure outperforms other methods presented in the literature.     

Micromachined tunable filter using fractal electromagnetic bandgap (EBG) structures

A tunable bandstop filter using fractal electromagnetic bandgap (EBG) structure is designed, simulated and fabricated. The uniform fractal EBG (U-FEBG) structure is realized by replacing the etched rectangular holes with a Minkowski loop generator. A new technique of doubly tapered fractal EBG (DT-FEBG) structure is designed by non-uniform Kaiser distribution on the fractal structures. The Kaiser distribution improves the pass band performance and generates two distant bandgaps. The tunable bandstop filter is tuned by micromachined capacitive bridges. The propagation characteristic of the periodic microelectromechanical system (MEMS) bridges is determined by the dispersion behavior. Different types of parametric analysis are applied to investigate the performance of the MEMS bridges. Surface micromachining fabrication process is employed on the high resistivity silicon substrate to fabricate the filter. The measurement results for the DT-FEBG structure show insertion loss of 1.2 dB and the stop-band rejection of 44 dB. The tuning range of the U-FEBG structure is 1.1 GHz with insertion loss of 1.7–2.5 dB.     

Sensitivity analysis for biometric systems: A methodology based on orthogonal experiment designs

The purpose of this paper is to introduce an effective and structured methodology for carrying out a biometric system sensitivity analysis. The goal of sensitivity analysis is to provide the researcher/developer with insight and understanding of the key factors—algorithmic, subject-based, procedural, image quality, environmental, among others—that affect the matching performance of the biometric system under study. This proposed methodology consists of two steps: (1) the design and execution of orthogonal fractional factorial experiment designs which allow the scientist to efficiently investigate the effect of a large number of factors—and interactions—simultaneously, and (2) the use of a select set of statistical data analysis graphical procedures which are fine-tuned to unambiguously highlight important factors, important interactions, and locally-optimal settings. We illustrate this methodology by application to a study of VASIR (Video-based Automated System for Iris Recognition)—NIST iris-based biometric system. In particular, we investigated k = 8 algorithmic factors from the VASIR system by constructing a (2^6?1 × 3¹  × 4¹) orthogonal fractional factorial design, generating the corresponding performance data, and applying an appropriate set of analysis graphics to determine the relative importance of the eight factors, the relative importance of the 28 two-term interactions, and the local best settings of the eight algorithms. The results showed that VASIR’s performance was primarily driven by six factors out of the eight, along with four two-term interactions. A virtue of our two-step methodology is that it is systematic and general, and hence may be applied with equal rigor and effectiveness to other biometric systems, such as fingerprints, face, voice, and DNA.     

On design and performance evaluation of an integrated SIP and HCoP-B architecture for nested network mobility

This paper proposes a fully-integrated SIP + HCoP-B architecture to provide efficient mobility management of the nested mobile network. It achieves the following merits, which are rare in the literature. First, it reduces network deployment costs by only equipping an integrated SIP mobile server. Second, it supports both SIP-based and non-SIP-based applications. Third, by adopting the analytical model proposed in Mohanty and Akyildiz (2007) [19], mathematical analyses are provided to investigate six performance metrics of SIP + HCoP-B and the other four well-known SIP's over NEMO schemes over the error-prone wireless link. Finally, it is shown that SIP + HCoP-B outperforms these four traditional schemes through intensive simulations.     

An automated signalized junction controller that learns strategies by temporal difference reinforcement learning

This paper shows how temporal difference learning can be used to build a signalized junction controller that will learn its own strategies through experience. Simulation tests detailed here show that the learned strategies can have high performance. This work builds upon previous work where a neural network based junction controller that can learn strategies from a human expert was developed (Box and Waterson, 2012). In the simulations presented, vehicles are assumed to be broadcasting their position over WiFi giving the junction controller rich information. The vehicle's position data are pre-processed to describe a simplified state. The state-space is classified into regions associated with junction control decisions using a neural network. This classification is the strategy and is parametrized by the weights of the neural network. The weights can be learned either through supervised learning with a human trainer or reinforcement learning by temporal difference (TD). Tests on a model of an isolated T junction show an average delay of 14.12 s and 14.36 s respectively for the human trained and TD trained networks. Tests on a model of a pair of closely spaced junctions show 17.44 s and 20.82 s respectively. Both methods of training produced strategies that were approximately equivalent in their equitable treatment of vehicles, defined here as the variance over the journey time distributions.     

Measurement and performance issues of transport protocols over 10 Gbps high-speed optical networks

With the integration of IP and optical technology, a high-speed optical network (of the order of 10Gbps) has emerged to support international research cooperation such as massive scientific data transfer and next generation Internet related research. Therefore, it is critical to explore the issues of measurement and evaluation on the performance of transport protocols over 10 Gbps high-speed optical networks. To the best of our knowledge, this is the first work that presents a measurement study on a variety of networking environments. The objectives of this paper are as follows: (i) determine the suitability of TCP parameters such as Jumbo Frame size, buffer sizes of a TCP sender and a receiver; (ii) evaluate TCP performance measurement tools and emulation tools over 10 Gbps high-speed optical networks; and (iii) compare performance of TCP variants with different metrics, such as throughput and fairness, by varying delays and randomized losses controlled with software emulators. The result shows that selection of emulation and performance measurement tools matters for the accurate measurement of TCP performance. The performance of TCP variants is highly impacted by the Linux TCP/IP stack tuning. Finally we present that overall and detailed performance, such as throughput and fairness, of each TCP variant is dependent on different network environments, such as packet loss rate and propagation delay.     

Analyzing MPI performance over 10-Gigabit ethernet

Recent work with 10-Gigabit (10 GbE) network adapters has demonstrated good performance in TCP/IP-based local- and wide-area networks (LANs and WANs). In the present work we present an evaluation of host-based 10 GbE adapters in a system-area network (SAN) in support of a cluster. This evaluation focuses on the performance of the message-passing interface (MPI) when running over a 10 GbE interconnect. We find that MPI over 10 GbE provides communications performance comparable to that of TCP alone and fairly competitive with more exotic technologies such as MPI over Quadrics. The optimization of MPI and MPI-based applications to make use of this performance, however, is a non-trivial task. Consequently, it is difficult for MPI-based applications to realize this performance when running current-generation 10 GbE hardware.     

Estimation and forecasting with logarithmic autoregressive conditional duration models: A comparative study with an application

This paper presents a semi-parametric method of parameter estimation for the class of logarithmic ACD (Log-ACD) models using the theory of estimating functions (EF). A number of theoretical results related to the corresponding EF estimators are derived. A simulation study is conducted to compare the performance of the proposed EF estimates with corresponding ML (maximum likelihood) and QML (quasi maximum likelihood) estimates. It is argued that the EF estimates are relatively easier to evaluate and have sampling properties comparable with those of ML and QML methods. Furthermore, the suggested EF estimates can be obtained without any knowledge of the distribution of errors is known. We apply all these suggested methodology for a real financial duration dataset. Our results show that Log-ACD (1, 1) fits the data well giving relatively smaller variation in forecast errors than in Linear ACD (1, 1) regardless of the method of estimation. In addition, the Diebold–Mariano (DM) and superior predictive ability (SPA) tests have been applied to confirm the performance of the suggested methodology. It is shown that the new method is slightly better than traditional methods in practice in terms of computation; however, there is no significant difference in forecasting ability for all models and methods.     

Identification of higher-dimensional ill-conditioned systems using extensions of virtual transfer function between inputs

A new formulation is proposed to directly extend the virtual transfer function between inputs (VTFBI) approach to ill-conditioned systems with dimensions higher than 2 × 2. The method requires only a single correlated component and is applicable to moderately large systems of up to around six outputs. To cater for systems with even higher dimensions, an indirect approach is further introduced based on subsystem decomposition in which the design for each subsystem achieves D-optimality in the presence of active output variance constraints. New measures of sensitivity to measurement inaccuracy and parameter changes are also introduced. A detailed case study shows that both direct and indirect extensions of the VTFBI technique outperform competing ones in terms of accuracy in the estimation of singular values, robustness towards the effect of noise as well as effectiveness for application in model based control. An additional advantage of the proposed approaches is that their performance does not depend significantly on the specific design choices made within these methods.     

An optimum feature extraction method based on Wavelet–Radon Transform and Dynamic Neural Network for pavement distress classification

Quantification of pavement crack data is one of the most important criteria in determining optimum pavement maintenance strategies. Recently, multi-resolution analysis such as wavelet decompositions provides very good multi-resolution analytical tools for different scales of pavement analysis and distresses classification. This paper present an automatic diagnosis system for detecting and classification pavement crack distress based on Wavelet–Radon Transform (WR) and Dynamic Neural Network (DNN) threshold selection. The algorithm of the proposed system consists of a combination of feature extraction using WR and classification using the neural network technique. The proposed WR + DNN system performance is compared with static neural network (SNN). In test stage; proposed method was applied to the pavement images database to evaluate the system performance. The correct classification rate (CCR) of proposed system is over 99%. This research demonstrated that the WR + DNN method can be used efficiently for fast automatic pavement distress detection and classification. The details of the image processing technique and the characteristic of system are also described in this paper.     

A novel two-stage evolutionary optimization method for multiyear expansion planning of distribution systems in presence of distributed generation

In this paper, a new approach for multiyear expansion planning of distribution systems (MEPDS) is presented. The proposed MEPDS model optimally specifies the expansion schedule of distribution systems including reinforcement scheme of distribution feeders as well as sizing and location of distributed generations (DGs) during a certain planning horizon. Moreover, it can determine the optimal timing (i.e. year) of each investment/reinforcement. The objective function of the proposed MEPDS model minimizes the total investment, operation and emission costs while satisfying various technical and operational constraints. In order to solve the presented MEPDS model as a complicated multi-dimensional optimization problem, a new two-stage solution approach composed of binary modified imperialist competitive algorithm (BMICA) and Improved Shark Smell Optimization (ISSO), i.e. BMICA + ISSO, is presented. The performance of the suggested MEPDS model and also two-stage solution approach of BMICA + ISSO is verified by applying them on two distribution systems including a classic 34-bus and a real-world 94-bus distribution system as well as a well-known benchmark function. Additionally, the achieved results of BMICA + ISSO are compared with the obtained results of other two-stage solution methods.     

Performance comparison of reduced models for leak detection in water distribution networks

This paper presents a methodology for comparing the performance of model-reduction strategies to be used with a diagnostic methodology for leak detection in water distribution networks. The goal is to find reduction strategies that are suitable for error-domain model falsification, a model based data interpretation methodology. Twelve reduction strategies are derived from five strategy categories. Categories differ according to the manner in which nodes are selected for deletion. A node is selected for deletion according to: (1) the diameter of the pipes; (2) the number of pipes linked to a node; (3) the angle of the pipes in the case of two-pipe nodes; (4) the distribution of the water demand; and, (5) a pair-wise combination of some categories.The methodology is illustrated using part of a real network. Performance is evaluated first by judging the equivalency of the reduced network with the initial network (before the application of any reduction procedure) and secondly, by assessing the compatibility with the diagnostic methodology. The results show that for each reduction strategy the equivalency of networks is verified. Computational time can be reduced to less than 20% of the non-reduced network in the best case. Results of diagnostic performance show that the performance decreases when using reduced networks. The reduction strategy with the best diagnostic performance is that based on the angle of two-pipe nodes, with an angle threshold of 165°. In addition, the sensitivity of the performance of the reduced networks to variation in leak intensity is evaluated. Results show that the reduction strategies where the number of nodes is significantly reduced are the most sensitive.Finally this paper describes a Pareto analysis that is used to select the reduction strategy that is a good compromise between reduction of computational time and performance of the diagnosis. In this context, the extension strategy is the most attractive.     

An expert system for detection of breast cancer based on association rules and neural network

This paper presents an automatic diagnosis system for detecting breast cancer based on association rules (AR) and neural network (NN). In this study, AR is used for reducing the dimension of breast cancer database and NN is used for intelligent classification. The proposed AR + NN system performance is compared with NN model. The dimension of input feature space is reduced from nine to four by using AR. In test stage, 3-fold cross validation method was applied to the Wisconsin breast cancer database to evaluate the proposed system performances. The correct classification rate of proposed system is 95.6%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR + NN model can be used to obtain fast automatic diagnostic systems for other diseases.     

Experimental evaluation of decentralized cooperative cruise control for heavy-duty vehicle platooning

In this paper, we consider the problem of finding decentralized controllers for heavy-duty vehicle (HDV) platooning by establishing empiric results for a qualitative verification of a control design methodology. We present a linear quadratic control framework for the design of a high-level cooperative platooning controller suitable for modern HDVs. A nonlinear low-level dynamical model is utilized, where realistic response delays in certain modes of operation are considered. The controller performance is evaluated through numerical and experimental studies. It is concluded that the proposed controller behaves well in the sense that experiments show that it allows for short time headways to achieve fuel efficiency, without compromising safety. Simulation results indicate that the model mimics real life behavior. Experiment results show that the dynamic behavior of the platooning vehicles depends strongly on the gear switching logic, which is confirmed by the simulation model. Both simulation and experiment results show that the third vehicle never displays a bigger undershoot than its preceding vehicle. The spacing errors stay bounded within 6.8 m in the simulation results and 7.2 m in the experiment results for varying transient responses. Furthermore, a minimum spacing of −0.6 m and −1.9 m during braking is observed in simulations and experiments, respectively. The results indicate that HDV platooning can be conducted at close spacings with standardized sensors and control units that are already present on commercial HDVs today.     

Investigation of Ni-based thermal bimaterial structure for sensor and actuator application

Thermal bimaterial structures made of Ni and Ni-diamond nanocomposite for sensor and actuator application are proposed, fabricated, and tested. Two deflection types of thermal bimaterial structures, including upward and downward bending types, can be easily fabricated by controlling electroplating sequence of Ni and Ni-diamond nanocomposite. According to thermal performance measurement, the tip deflection of upward and downward types can reach about 82.5 μm and ?22.5 μm for a temperature change of 200 °C, respectively. In the condition, the thermomechanical sensitivity and output force are 412.5 nm/K and 97.0 μN for upward type thermal bimaterial structure; and ?112.5 nm/K and ?26.5 μN for downward type one. Due to the low electroplating process temperature (～50 °C) for both Ni-based layers, diminutive pre-deformation of as-fabricated structure and strong interlaminar bonding strength are verified by SEM and vibrational test. The resonant frequency of the structure remains unchanged after 10⁹ cycles.