Medical sensor network consist of heterogeneous nodes, wireless, mobile and wired with varied functionality. The
resources at each sensor require to be exploited minimally while sensitive information is sensed and communicated to
its access points using secure data mules. In this paper, we analyze the flat architecture, where different functionality
and priority information require varied resources forms a non-deterministic polynomial-time hard problem. Hence, a
bio-inspired data mule that helps to obtain dynamic multi-objective solution with minimal resource and secure path is
applied. The performance of the proposed approach is based on reduced latency, data delivery rate and resource cost.
Raman spectroscopy is a powerful technique for determining the chemical composition of a substance. Our objective
is to determine the chemical composition of an unknown substance given a reference library of Raman spectra. The
unknown spectrum is expressed as a linear combination of the reference library spectra and the non-zero mixing
coefficients represent the presence of individual substances, which are not known. This approach is known as the
supervised learning method. The mixing coefficients are usually estimated using the nonnegative least squares (NNLS)
or nonnegative weighted least squares (NNWLS). This problem is a constrained estimation problem due to the presence
of the nonnegativity constraint. In this paper, we present a swarm based algorithm, the particle swarm optimization
(PSO), to estimate the mixing coefficients and Raman spectra. The PSO is used to determine the mixing coefficients.
PSO efficiently finds an optimum solution. Results are presented for simulated data obtained from the Jennifer Kelly
Raman spectra library. The reference library consists of Raman spectra for nine minerals and the measured spectrum is
simulated by using spectrum/spectra of single/multiple minerals. We compare the root mean square error (RMSE) for
parameter estimation and measurement residual and computational time of the NNWLS and nonnegative weighted PSO
Wind is an important renewable energy
source. The energy and economic return from building
wind farms justify the expensive investments in doing so.
However, without an effective monitoring system, underperforming
or faulty turbines will cause a huge loss in
revenue. Early detection of such failures help prevent
these undesired working conditions. We develop three
tests on power curve, rotor speed curve, pitch angle curve
of individual turbine. In each test, multiple states are
defined to distinguish different working conditions,
including complete shut-downs, under-performing states,
abnormally frequent default states, as well as normal
working states. These three tests are combined to reach a
final conclusion, which is more effective than any single
Through extensive data mining of historical data and
verification from farm operators, some state combinations
are discovered to be strong indicators of spindle failures,
lightning strikes, anemometer faults, etc, for fault detection.
In each individual test, and in the score fusion of
these tests, we apply multidimensional scaling (MDS) to
reduce the high dimensional feature space into a 3-dimensional
visualization, from which it is easier to discover
turbine working information. This approach gains a qualitative
understanding of turbine performance status to
detect faults, and also provides explanations on what has
happened for detailed diagnostics.
The state-of-the-art SCADA (Supervisory Control And
Data Acquisition) system in industry can only answer the
question whether there are abnormal working states, and
our evaluation of multiple states in multiple tests is also
promising for diagnostics. In the future, these tests can be
readily incorporated in a Bayesian network for intelligent
analysis and decision support.
This paper proposes a new discrete particle swarm optimization (DPSO) algorithm with a multiplicative likeliness
enhancement rule for unordered feature selection. In this paper, the pool of features for face recognition are
derived from direct fractional-step linear discriminant analysis (DFLDA). Each particle is associated with a
subset of features, and their recognition performance on the validation set influences the particle's fitness with
randomness. Features are selected by their assigned likeliness, which is enhanced by the agreement between a
particle and its attractors (its previous location, pbest and gbest). The new DPSO double-asserts or triple-asserts
the selection if the attractors share common features. The feature selection technique proposed in this paper is
a modular procedure and thus can be applied to other features if a separate validation set is available for fitness
evaluation. This DPSO algorithm is successfully applied on the FERET database. The recognition performance
is improved for both L1 and L2 norm distance metrics. The cumulative matching score (CMS) is improved for
higher ranks, which indicates that this performance improvement is beneficial for identification task. In overall
comparison, the multiplicative updating rule achieves higher fitness and smaller standard deviation than the
additive likeliness enhancement rule.
In this paper, a swarm based ultra-wideband waveform and routing protocol is used for communicating messages in
the form of short pulses in sensor based health care application. Due to the time sensitivity of the application, a cognitive
protocol is applied to make decisions based on resource availability and quality-of-service. The combination of
swarm based physical and routing layer protocol helps in achieving an energy, bandwidth and time efficient application.
This paper compares the performance of cross layer protocol when exhaustive search and swarm based waveform
design is used.
Security in wireless sensor networks is typically sacrificed or kept minimal due to limited resources such as memory
and battery power. Hence, the sensor nodes are prone to Denial-of-service attacks and detecting the threats is crucial in
any application. In this paper, the Sybil attack is analyzed and a novel prediction method, combining Bayesian algorithm
and Swarm Intelligence (SI) is proposed. Bayesian Networks (BN) is used in representing and reasoning problems,
by modeling the elements of uncertainty. The decision from the BN is applied to SI forming an Hybrid
Intelligence Scheme (HIS) to re-route the information and disconnecting the malicious nodes in future routes. A performance
comparison based on the prediction using HIS vs. Ant System (AS) helps in prioritizing applications where decisions
This paper is a survey on biometrics and forensics, especially on the techniques and applications of face recognition in forensics. This paper describes the differences and connections between biometrics and forensics, and bridges each other by formulating the conditions when biometrics can be applied in forensics. Under these conditions, face recognition, as a non-intrusive and non-contact biometrics, is discussed in detail as an illustration of applying biometrics in forensics. The discussion on face recognition covers different approaches, feature extractions, and decision procedures. The advantages and limitations of biometrics in forensic applications are also addressed.
Learning curve phenomenon indicates that not all available images need to be used in training. This paper
proposes a three-step intelligent sampling to construct a representative and efficient training database, where
both the number of training images and which images to be included are determined. Firstly, clustering on a
subset of huge face database is implemented as preparation. Secondly, systematic sampling on clusters is utilized
to improve the efficiency. Thirdly, performance is evaluated to check whether the learning curve has reached
a point of diminishing returns, and a new metric of difficulty is defined to determine which images from the
complementary subset of initial training set should be added into training. The proposed intelligent three step
sampling design enhances recognition rate and generalizability while improving efficiency, which exerts the full
potential of any given face recognition algorithm without system overhaul.
A face recognition system gains flexibility and cost efficiency while being integrated into a wireless network. Meanwhile, face recognition enhances the functionality and security of the wireless network. This paper proposes a distributed wireless network prototype, consisting of feature net and database net, to accomplish face identification task by optimally allocating network resources. The face recognition technique used in this paper is subspace-based modular processing with score and decision level fusion. The subspace features are selected by a step-wise statistical procedure, Modified Indifference-Zone Method, which improves efficiency and accuracy. Fusion further improves the performance from using either the whole face or modules alone. The face recognition techniques are re-engineered to be implemented on the distributed wireless network, and the simulation result shows promising improvement over centralized recognition.
KEYWORDS: Facial recognition systems, Feature selection, Databases, Principal component analysis, Monte Carlo methods, Detection and tracking algorithms, Feature extraction, Interference (communication), Statistical analysis, Signal to noise ratio
We propose a multistep statistical procedure to determine
the confidence interval of the number of features that should
be retained in appearance-based face recognition, which is based
on the eigen decomposition of covariance matrices. In practice, due
to sampling variation, the empirical eigenpairs differ from their underlying
population counterparts. The empirical distribution is difficult
to derive, and it deviates from the asymptotic approximation
when the sample size is limited, which hinders effective feature selection.
Hence, we propose a new technique, MIZM (modified indifference
zone method), to estimate the confidence interval of the
number of features. MIZM overcomes the singularity problem in face
recognition and extends the indifference zone selection from PCA to
LDA. The simulation results on the ORL, UMIST, and FERET databases
show that the overall recognition performance based on
MIZM is improved from that using all available features or heuristically
selected features. The relatively small number of features also
indicates the efficiency of the proposed feature selection method.
MIZM is motivated by feature selection for face recognition, but it
extends the indifference zone method from PCA to LDA and can be
applied in general LDA tasks.
Sensors have varied constraints, which makes the network challenging for communicating with its peers. In this
paper, an extension to the security of physical layer of a predictive sensor network model using the ant system is proposed.
The Denial of Service (DoS) attack on sensor networks not only diminishes the network performance but also
affects the reliability of the information making detection of a DoS threat is more crucial than recovering from the
attack. Hence, in this paper, a novel approach in detecting the DoS attack is introduced and analyzed for a variety of
scenarios. The DoS attack is dependent on the vulnerabilities in each layer, with the physical layer being the lowest
layer and the first to be attacked by jammers. In this paper, the physical layer DoS attack is analyzed and a defense
mechanism is proposed. Classification of the jammer under various attack scenarios is formulated to predict the
genunity of the DoS attacks on the sensor nodes using receiver operating characteristics (ROC). This novel approach
helps in achieving maximum reliability on DoS claims improving the Quality of Service (QoS) of WSN.
A face recognition system consists of two integrated parts: One is the face recognition algorithm, the other is the selected classifier and derived features by the algorithm from a data set. The face recognition algorithm definitely plays a central role, but this paper does not aim at evaluating the algorithm, but deriving the best features for this algorithm from a specific database through sampling design of the training set, which directs how the sample should be collected and dictates the sample space. Sampling design can help exert the full potential of the face recognition algorithm without overhaul. Conventional statistical analysis usually assume some distribution to draw the inference, but the design-based inference does not assume any distribution of the data and it does not assume the independency between the sample observations. The simulations illustrates that the systematic sampling scheme performs better than the simple random sampling scheme, and the systematic sampling is comparable to using all available training images in recognition performance. Meanwhile the sampling schemes can save the system resources and alleviate the overfitting problem. However, the post stratification by sex is not shown to be significant in improving the recognition performance.
Sensors have varied constraints, which make the network challenging for communicating with peers. In this paper, an extension, to the physical layer of the previous predictive sensor network model using the ant system is proposed. The tiny and low-cost sensor nodes are made of RF wireless links, where the states of the nodes vary with respect to time and environment. The ant system is a learning algorithm, that can be used to solve any NP hard communication problem and possesses characteristics such as robustness and versatility. The ant system possesses unique features that keep the network functional by detecting weak links and re-routing the agents. The swarm agents are distributed along the network, where the agent communicates with its neighbors (agents) by means of pheromone deposition and tabu list. The transition probability in the ant system includes an objective function, which is influenced by the poset weights. The poset weights on each of the orthogonal communication parameters greatly affects the decisions made by ant system. The agents carry updated information of its previous nodes, which helps in monitoring the strength of the communication links.
Through simulation, comparison between DSSS-BPSK and Bluetooth-GFSK signals are shown. This paper demonstrates the robustness of the model under slow/fast fading, and energy loss at node during transmission. Implementation of this algorithm should be able to handle hostile environmental conditions and human tampering of data. The performance of the network is evaluated based on accuracy and response time of the agents within the network.
Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network’s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.
This paper utilizes the intra-difference in still images to segment a face from its background and then combines the intra-difference detection result with the eigenface/eigenfeature methods to identify the face. This novel diverse scheme can finally solve the problem of accuracy in practical applications, thus broadening the application of face recognition into more versatile situations such as security building entrance, customs and mug spotting. The organic combination of intra-difference detection method and eigenface/eigenfeature methods into one system is shown to be more robust and have a better identification rate than either method alone. This paper first addresses the problems of the real-time accuracy issue and the need of pre-processing (mainly normalization). And then it proposes to use intra-difference to effectively segment a human face. The segmented face is further processed by both intra-difference detection method and eigenface/eigenfeature methods to determine its identity. Correspondingly, the proposed algorithm consists of three parts: segmentation, pre-processing, and multi-phase face identification by fusing the results from both the intra-difference detection method and the eigenface/eigenfeature methods.
The need for a robust predictive sensor communication network inspired this research. There are many critical issues in a communication network with different data rate requirements, limited power and bandwidth. Energy consumption is one of the key issues in a sensor network as energy dissipation occurs during routing, communication and monitoring of the environment. This paper covers the routing of a sensor communication network by applying an evolutionary algorithm -- the ant system. The issues considered include optimal energy, data fusion from different sensor types and predicting changes in environment with respect to time.
Autonomous sensor manager agents are presented as an algorithm to perform sensor management within a multisensor fusion network. The design of the hybrid ant system/particle swarm agents is described in detail with some insight into their performance. Although the algorithm is designed for the general sensor management problem, a simulation example involving 2 radar systems is presented. Algorithmic parameters are determined by the size of the region covered by the sensor network, the number of sensors, and the number of parameters to be selected. With straight forward modifications, this algorithm can be adapted for most sensor management problems.
This paper introduces the sensor management problem and uses Bayesian networks as a scalable approach to handling
the operational decisions concerning the sensor network. In general, single sensor systems only provide partial
information on the state of the event or environment while multisensor systems provide a synergistic effect, which
improves the quality and availability of information. Data fusion techniques can effectively combine this environmental
information from similar and/or dissimilar sensors. Until recently, the operator could manage the systems easily, but
current systems are more complex and produce data more quickly than earlier versions. A sensor manager becomes necessary
when this occurs to assist the operators. Researchers have developed many single point sensor management solutions.
General sensor management algorithms that can handle a variety of sensor network applications have yet to
This paper presents a Swarm Intelligence based approach for sensor management of a multi sensor network. Alternate sensor configurations and fusion strategies are evaluated by swarm agents, and an optimum configuration and fusion strategy evolves. An evolutionary algorithm, particle swarm optimization, is modified to optimize two objectives: accuracy and time. The output of the algorithm is the choice of sensors, individual sensor’s thresholds and the optimal decision fusion rule. The results achieved show the capability of the algorithm in selecting optimal configuration for a given requirement consisting of multiple objectives.
This paper introduces a new algorithm called “Adaptive Multimodal Biometric Fusion Algorithm”(AMBF), which is a combination
of Bayesian decision fusion and particle swarm optimization. A Bayesian framework is implemented to fuse decisions received
from multiple biometric sensors. The system’s accuracy improves for a subset of decision fusion rules. The optimal rule is a function of the
error cost and a priori probability of an intruder. This Bayesian framework formalizes the design of a system that can adaptively increase
or reduce the security level. Particle swarm optimization searches the decision and sensor operating points (i.e. thresholds) space to
achieve the desired security level. The optimization function aims to minimize the error in a Bayesian decision fusion. The particle swarm
optimization algorithm results in the fusion rule and the operating points of sensors at which the system can work. This algorithm is important
to systems designed with varying security needs and user access requirements. The adaptive algorithm is found to achieve desired
security level and switch between different rules and sensor operating points for varying needs.