Power efficient packet classification using cascaded bloom filter and off-the-shelf ternary CAM for WDM networks

In wavelength division multiplexing (WDM) networks, tens or hundreds of wavelengths can be transmitted over a single fiber. As transmission line speed goes to 10 Gb/s and beyond, ternary CAM (TCAM) is usually employed for wire speed packet classification. To the best of authors’ knowledge, this is the first paper that addresses the impact of WDM transmission on the power consumption of packet classification. We show that as the number of wavelengths increases in the WDM networks, the power consumption of TCAMs can become the limiting factor for WDM network expansion. For example, the power consumption of IPv4 and IPv6 packet classification with merely 32 channels at 40 Gb/s can be as high as 700 and 1400 W, respectively, while technology wise it is feasible to transmit over 500 channels over a single fiber. Existing power efficient TCAM designs require special modification to TCAM cell structures, which makes the adoption of the technology difficult. This paper proposes a novel approach which cascades bloom filter with off-the-shelf TCAM to greatly reduce the power consumption of packet classification. In particular, the proposed solution takes advantage of the fact that bloom filters may give false positive alarms but never give false negative alarms. By eliminating majority of non-matching packets before passing the packets to the TCAM, the TCAM is only activated to exam packets with a high potential of matching in the filter set. The proposed scheme greatly reduces the activation frequency of the TCAM, thus achieving great power savings.     

A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition

This paper presents a novel adaptive cuckoo search (ACS) algorithm for optimization. The step size is made adaptive from the knowledge of its fitness function value and its current position in the search space. The other important feature of the ACS algorithm is its speed, which is faster than the CS algorithm. Here, an attempt is made to make the cuckoo search (CS) algorithm parameter free, without a Levy step. The proposed algorithm is validated using twenty three standard benchmark test functions. The second part of the paper proposes an efficient face recognition algorithm using ACS, principal component analysis (PCA) and intrinsic discriminant analysis (IDA). The proposed algorithms are named as PCA + IDA and ACS–IDA. Interestingly, PCA + IDA offers us a perturbation free algorithm for dimension reduction while ACS + IDA is used to find the optimal feature vectors for classification of the face images based on the IDA. For the performance analysis, we use three standard face databases—YALE, ORL, and FERET. A comparison of the proposed method with the state-of-the-art methods reveals the effectiveness of our algorithm.     

A high-throughput and high-capacity IPv6 routing lookup system

With the growing number of routing entries, IP routing lookup has become the major performance bottleneck in backbone routers. In this paper, a complete hardware-based routing lookup system is proposed to achieve high-throughput and high-capacity for IPv6. The proposed system is a cache-centric, hash-based architecture that contains a routing lookup application specific integrated circuit (ASIC) and a memory set. A hash function is used to reduce lookup time for the routing table and ternary content addressable memory (TCAM) effectively resolves the collision problem. The gate count of the ASIC, excluding the binary content addressable memory (BCAM), is about 5306 gates, using an in-house 0.18 μm CMOS single-poly six-metal standard cell library. The results of post-layout simulations show that the ASIC operates in 3.6 ns so that the routing lookup system approaches 260 Mega lookups per second (Mlps), which is sufficient for 100 Gbps networks. The memory density is good, with each routing entry requiring only 64 bits. Moreover, the routing table only needs 10.24 KB on-chip BCAM, 20.04 KB off-chip TCAM and 29.29 MB DRAM for 3.6 M routing entries in the proposed system.     

Guided local search as a network planning algorithm that incorporates uncertain traffic demands

A search based heuristic for the optimisation of communication networks where traffic forecasts are uncertain and the problem is NP-complete is presented. While algorithms such as genetic algorithms (GA) and simulated annealing (SA) are often used for this class of problem, this work applies a combination of newer optimisation techniques specifically: fast local search (FLS) as an improved hill climbing method and guided local search (GLS) to allow escape from local minima. The GLS + FLS combination is compared with an optimised GA and SA approaches. It is found that in terms of implementation, the parameterisation of the GLS + FLS technique is significantly simpler than that for a GA and SA. Also, the self-regularisation feature of the GLS + FLS approach provides a distinctive advantage over the other techniques which require manual parameterisation. To compare numerical performance, the three techniques were tested over a number of network sets varying in size, number of switch circuit demands (network bandwidth demands) and levels of uncertainties on the switch circuit demands. The results show that the GLS + FLS outperforms the GA and SA techniques in terms of both solution quality and optimisation speed but even more importantly GLS + FLS has significantly reduced parameterisation time.     

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.     

An ergonomic study on the optimal gear ratio for a multi-speed bicycle

With respect to human performance and power efficiency, the gear system in typical multi-speed bicycles is often biased and redundant. A preliminary user survey in this study reveals that the average utilization of each shift for a multi-speed gear system is less than 40%. This study attempts to measure the optimal pedaling rates for given power output levels as well as design the optimal number of gears and the corresponding gear ratios.Heart rate, ratings of perceived exertion and electromyogram of quadriceps femoris for five male subjects are measured at three different power output levels (40, 80 and 120 W) and four different pedaling rate levels (40, 60, 80 and 100 rpm). Various riding conditions including slope gradient and cruising velocity are also converted to the equivalent power output level.The optimal pedaling rates for the given power output are 40 rpm for 40 W power output level, 40 – 60 rpm for 80 and 120 W power output levels. By using a heuristic rule which finds the least number of gears and the most efficient gear ratio under the given physiological condition, a four speed gear system with the ratio of 0.26–0.38, 0.38–0.53, 0.53–0.7 and 0.7–1.0 is recommended as the most efficient gear system. Based on the optimal gear ratio suggested in this study, an ergonomic gear system using a novel/unique type of planet gear sets (US patent No. 5 378 201) is developed.Relevance to industryA bicycle's gear system is frequently designed without ergonomic expertise in terms of performance and efficiency. This study provides guidelines, design specifications, and performance measures to design an efficient bicycle gear system. This study also contributes valuable finding regarding the optimal performance during bicycle riding, thereby facilitating the efficiency and effectiveness of human exercise using a bicycle.     

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.     

Piezoelectric-actuated drop-on-demand droplet generator control using adaptive wavelet neural network controller

This paper presents the design, fabrication and control of a piezoelectric-type droplet generator which is applicable for on-line dispensing. Adaptive wavelet neural network (AWNN) control is applied to overcome nonlinear hysteresis inherited in the LPM. The adaptive learning rates are derived based on the Lyapunov stability theorem so that the stability of the closed-loop system can be assured. Unlike open-loop dispensing system, the system proposed can potentially generate droplets with high accuracy. Experimental verifications focusing on regulating control are performed firstly to assure the reliability of the proposed control schemes. Real dispensing is then conducted to validate the feasibility of the piezoelectric-actuated drop-on-demand droplet generator. In order to illustrate the effectiveness of the proposed method, experimental results obtained using the AWNN scheme are compared with their counterparts using traditional PID control. The results indicate that the proposed AWNN scheme not only outperforms PID control but also works well in developing the piezoelectric-actuated drop-on-demand dispensing system. The proposed dispensing system provides droplet chains with an averaged mass as small as 31.5 mg while the associated standard deviation is as low as 0.72%.     

Lighting,font style,and polarity on visual performance and visual fatigue with electronic paper displays

Visual performance and visual fatigue of electronic paper displays (electrophoretic display and cholesteric liquid crystal display) under ambient illuminances and light sources were studied and compared with paper. Forty-eight participants participated in a character-search task in the experiment. The results showed that search speed depends on the illuminance but not light source. Search speed increased as illumination increased from 300 lx (45.6 sec), 700 lx (44.18 sec) to 1500 lx (43.24 sec). The effect of medium display and polarity on accuracy was also significant. Accuracy was greater for electrophoretic display and positive polarity. However, the effect of illuminance and light source on visual fatigue was not statistically significant. Based on the results of this study, it seems that E-paper displays may need greater illumination (700 lx or higher).     

ANN based simulation and experimental verification of analytical four- and five-parameters models of PV modules

In this article, artificial neural network (ANN) is adopted to predict photovoltaic (PV) panel behaviors under realistic weather conditions. ANN results are compared with analytical four and five parameter models of PV module. The inputs of the models are the daily total irradiation, air temperature and module voltage, while the outputs are the current and power generated by the panel. Analytical models of PV modules, based on the manufacturer datasheet values, are simulated through Matlab/Simulink environment. Multilayer perceptron is used to predict the operating current and power of the PV module. The best network configuration to predict panel current had a 3–7–4–1 topology. So, this two hidden layer topology was selected as the best model for predicting panel current with similar conditions. Results obtained from the PV module simulation and the optimal ANN model has been validated experimentally. Results showed that ANN model provide a better prediction of the current and power of the PV module than the analytical models. The coefficient of determination (R²), mean square error (MSE) and the mean absolute percentage error (MAPE) values for the optimal ANN model were 0.971, 0.002 and 0.107, respectively. A comparative study among ANN and analytical models was also carried out. Among the analytical models, the five-parameter model, with MAPE = 0.112, MSE = 0.0026 and R² = 0.919, gave better prediction than the four-parameter model (with MAPE = 0.152, MSE = 0.0052 and R² = 0.905). Overall, the 3–7–4–1 ANN model outperformed four-parameter model, and was marginally better than the five-parameter model.     

Hybrid wireless sensor network for building energy management systems based on the 2.4 GHz and 400 MHz bands

In this paper, a hybrid wireless sensor network (WSN) system is considered and implemented for the building energy management systems. Characteristics of the radios, which are based on the 2.4 GHz and 400 MHz bands, respectively, are analyzed for the building environments. For battery-operated portable sensors, narrow-bandwidth radios of the 400 MHz band are employed in a star connection between their parent nodes. Between the parent nodes, a mesh network is constructed for an efficient and fast data transmission based on the wide-bandwidth radios of the 2.4 GHz band. The hybrid WSN system is implemented and tested for a building environment and provides a reliable wireless communication link for gathering sensing data.     

Characteristics of a Pd–oxide–In0.49Ga0.51P high electron mobility transistor (HEMT)-based hydrogen sensor

An interesting hydrogen sensor based on a high electron mobility transistor (HEMT) device with a Pd–oxide–In_0.49Ga_0.51P gate structure is fabricated and demonstrated. The hydrogen sensing characteristics including hydrogen detection sensitivity and transient responses of the studied device under different hydrogen concentrations and temperature are measured and studied. The hydrogen detection sensitivity is related to a change in the contact potential at the Pd/insulator interface. The kinetic and thermodynamic properties of hydrogen adsorption are also studied. Experimentally, good hydrogen detection sensitivities, large magnitude of current variations (3.96 mA in 9970 ppm H₂/air gas at room temperature) and shorter absorption response time (22 s in 9970 ppm H₂/air gas at room temperature) are obtained for a 1.4 μm × 100 μm gate dimension device. Therefore, the studied device provides a promise for high-performance solid-state hydrogen sensor, integrated circuit (IC) and micro electro-mechanical system (MEMS) applications.     

Prediction of flow pattern of gas–liquid flow through circular microchannel using probabilistic neural network

The present study attempts to develop a flow pattern indicator for gas–liquid flow in microchannel with the help of artificial neural network (ANN). Out of many neural networks present in literature, probabilistic neural network (PNN) has been chosen for the present study due to its speed in operation and accuracy in pattern recognition. The inbuilt code in MATLAB R2008a has been used to develop the PNN. During training, superficial velocity of gas and liquid phase, channel diameter, angle of inclination and fluid properties such as density, viscosity and surface tension have been considered as the governing parameters of the flow pattern. Data has been collected from the literature for air–water and nitrogen–water flow through different circular microchannel diameters (0.53, 0.25, 0.100 and 0.050 mm for nitrogen–water and 0.53, 0.22 mm for air–water). For the convenience of the study, the flow patterns available in literature have been classified into six categories namely; bubbly, slug, annular, churn, liquid ring and liquid lump flow. Single PNN model is unable to predict the flow pattern for the whole range (0.53 mm–0.050 mm) of microchannel diameter. That is why two separate PNN models has been developed to predict the flow patterns of gas–liquid flow through different channel diameter, one for diameter ranging from 0.53 mm to 0.22 mm and another for 0.100 mm–0.05 mm. The predicted map and their transition boundaries have been compared with the corresponding experimental data and have been found to be in good agreement. Whereas accuracy in prediction of transition boundary obtained from available analytical models used for conventional channel is less for all diameter of channel as compared to the present work. The percentage accuracy of PNN (～94% for 0.53 mm ID and ～73% for 0.100 mm ID channel) has also been found to be higher than the model based on Weber number (～86% for 0.53 mm ID and ～36% for 0.05 mm ID channel).     

Bivariate quality control using two-stage intelligent monitoring scheme

In manufacturing industries, it is well known that process variation is a major source of poor quality products. As such, monitoring and diagnosis of variation is essential towards continuous quality improvement. This becomes more challenging when involving two correlated variables (bivariate), whereby selection of statistical process control (SPC) scheme becomes more critical. Nevertheless, the existing traditional SPC schemes for bivariate quality control (BQC) were mainly designed for rapid detection of unnatural variation with limited capability in avoiding false alarm, that is, imbalanced monitoring performance. Another issue is the difficulty in identifying the source of unnatural variation, that is, lack of diagnosis, especially when dealing with small shifts. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Design consideration involved extensive simulation experiments to select input representation based on raw data and statistical features, artificial neural network recognizer design based on synergistic model, and monitoring–diagnosis approach based on two-stage technique. The study focused on bivariate process for cross correlation function, ρ = 0.1–0.9 and mean shifts, μ = ±0.75–3.00 standard deviations. The proposed two-stage intelligent monitoring scheme (2S-IMS) gave superior performance, namely, average run length, ARL₁ = 3.18–16.75 (for out-of-control process), ARL₀ = 335.01–543.93 (for in-control process) and recognition accuracy, RA = 89.5–98.5%. This scheme was validated in manufacturing of audio video device component. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC.     

A fault detection scheme based on minimum identified uncertainty bounds violation for broken rotor bars in induction motors

In this paper, a novel method for broken bars fault detection in the case of three-phase induction motors and under different payloads will be presented and experimentally evaluated. In the presented approach, the cases of a partially or full broken rotor bars are being also considered, caused by: (a) drilling 4 mm and 8 mm out of the 17 mm thickness of the same rotor bar and (b) fully drilled (17 mm) one, two and three broken bars. The proposed fault detection method is based on the Set Membership Identification (SMI) technique and a novel proposed minimum boundary violation fault detection scheme, applied on the identified motor's parameters. The system identification procedure is being carried out on the simplified equivalent model of the induction motor, during the steady-state operation (non-fault case), while at the same time the proposed scheme is able to calculate on-line the corresponding safety bounds for the identified variables, based on a priori knowledge of the measuring corrupting noise (worst case encountered). The efficiency, the robustness and the overall performance of the established fault detection scheme is being extensively evaluated in multiple experimental studies and under various time instances of faults and load conditions.     

A traffic cellular automata model based on road network grids and its spatial and temporal resolution’s influences on simulation

Microscopic Traffic Simulation Model based on Cellular Automata (CA) has become more and more popular since it was firstly introduced by Creamer and Ludwig in 1986. Cellular automata are simpler to implement on computers, provide a simple physical picture of the system and can be easily modified to deal with different aspects of traffic. However, in a traditional traffic CA model, the spatial resolution of CA and temporal resolution of simulation are low. Take TRANSIMS for example. The size of cellular automata is 7.5 m and the time step equals 1 s. In such a case, if a vehicle drives at a speed of 4 cells per s, the speed difference between 95 km/h (3.5 1 7.5 m/s) and 121 km/h (4.4999 1 7.5 m/s) will not be distinguished by simulation models. And the temporal resolution of 1 s makes the system hard to model different drivers’ reaction time, which plays a very important role in vehicular movement models. In this paper, a microscopic traffic cellular automata model based on road network grids is proposed to overcome the low spatial and temporal resolutions of traditional traffic CA models. In our model, spatial resolution can be changed by setting different grid size for lanes and intersections before or during simulation and temporal resolution can be defined according to simulation needs to model different drivers’ reaction time, whereas the vehicular movement models are still traditional CA models. By doing so, the low spatial and temporal resolution of CA model can be overcome and the advantages of using CA to simulate traffic are preserved. The paper also presents analyses of the influences on simulation of different 1D lane grid size, 2D intersection grid size and different combinations of temporal resolution and mean drivers’ reaction time. The analysis results prove the existence of spatial and temporal resolution thresholds in traffic CA models. They also reveal that the size of grids, the combinations of different temporal resolutions and mean drivers’ reaction time do pose influences on the speed of vehicles and lane/intersection occupancy, but do not affect the volume of traffic greatly.     

GPU-based parallel vertex substitution algorithm for the p-median problem

We introduce a GPU-based parallel vertex substitution (pVS) algorithm for the p-median problem using the CUDA architecture by NVIDIA. pVS is developed based on the best profit search algorithm, an implementation of vertex substitution (VS), that is shown to produce reliable solutions for p-median problems. In our approach, each candidate solution in the entire search space is allocated to a separate thread, rather than dividing the search space into parallel subsets. This strategy maximizes the usage of GPU parallel architecture and results in a significant speedup and robust solution quality. Computationally, pVS reduces the worst case complexity from sequential VS’s O(p · n²) to O(p · (n ? p)) on each thread by parallelizing computational tasks on GPU implementation. We tested the performance of pVS on two sets of numerous test cases (including 40 network instances from OR-lib) and compared the results against a CPU-based sequential VS implementation. Our results show that pVS achieved a speed gain ranging from 10 to 57 times over the traditional VS in all test network instances.     

GC-ASM: Synergistic integration of graph-cut and active shape model strategies for medical image segmentation

Image segmentation methods may be classified into two categories: purely image based and model based. Each of these two classes has its own advantages and disadvantages. In this paper, we propose a novel synergistic combination of the image based graph-cut (GC) method with the model based ASM method to arrive at the GC-ASM method for medical image segmentation. A multi-object GC cost function is proposed which effectively integrates the ASM shape information into the GC framework. The proposed method consists of two phases: model building and segmentation. In the model building phase, the ASM model is built and the parameters of the GC are estimated. The segmentation phase consists of two main steps: initialization (recognition) and delineation. For initialization, an automatic method is proposed which estimates the pose (translation, orientation, and scale) of the model, and obtains a rough segmentation result which also provides the shape information for the GC method. For delineation, an iterative GC-ASM algorithm is proposed which performs finer delineation based on the initialization results. The proposed methods are implemented to operate on 2D images and evaluated on clinical chest CT, abdominal CT, and foot MRI data sets. The results show the following: (a) An overall delineation accuracy of TPVF > 96%, FPVF < 0.6% can be achieved via GC-ASM for different objects, modalities, and body regions. (b) GC-ASM improves over ASM in its accuracy and precision to search region. (c) GC-ASM requires far fewer landmarks (about 1/3 of ASM) than ASM. (d) GC-ASM achieves full automation in the segmentation step compared to GC which requires seed specification and improves on the accuracy of GC. (e) One disadvantage of GC-ASM is its increased computational expense owing to the iterative nature of the algorithm.     

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.     

A comparative study of different local search application strategies in hybrid metaheuristics

This paper presents results of a comparative study with the objective to identify the most effective and efficient way of applying a local search method embedded in a hybrid algorithm. The hybrid metaheuristic employed in this study is called “DE–HS–HJ” because it is comprised of two cooperative metaheusitic algorithms, i.e., differential evolution (DE) and harmony search (HS), and one local search (LS) method, i.e., Hooke and Jeeves (HJ) direct search. Eighteen different ways of using HJ local search were implemented and all of them were evaluated with 19 problems, in terms of six performance indices, covering both accuracy and efficiency. Statistic analyses were conducted accordingly to determine the significance in performance differences. The test results show that overall the best three LS application strategies are applying local search to every generated solution with a specified probability and also to each newly updated solution (NUS + ESP), applying local search to every generated solution with a specified probability (ESP), and applying local search to every generated solution with probability and also to the updated current global best solution (EUGbest + ESP). ESP is found to be the best local search application strategy in terms of success rate. Integrating it with NUS further improve the overall performance. EUGbest + ESP is the most efficient and it is also able to achieve high level of accuracy (the fourth place in terms of success rate with an average above 0.9).