Distributed and Load-Adaptive Self Configuration in Sensor Networks

Proactive self-configuration is crucial for MANETs such as sensor networks, as these are often deployed in hostile environments and are ad hoc in nature. The dynamic architecture of the network is monitored by exchanging so-called Network State Beacons (NSBs) between key network nodes. The Beacon Exchange rate and the network state define both the time and nature of a proactive action to combat network performance degradation at a time of crisis. It is thus essential to optimize these parameters for the dynamic load profile of the network. This paper presents a novel distributed adaptive optimization Beacon Exchange selection model which considers distributed network load for energy efficient monitoring and proactive reconfiguration of the network. The results show an improvement of 70% in throughput, while maintaining a guaranteed quality-of- service for a small control-traffic overhead

Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images

Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.Comment: Accepted in Proc. IEEE International Conference on Image Processing (ICIP), 201

A collateral missing value estimation algorithm for DNA microarrays

Genetic microarray expression data often contains multiple missing values that can significantly affect the performance of statistical and machine learning algorithms. This paper presents an innovative missing value estimation technique, called collateral missing value estimation (CMVE) which has demonstrated superior estimation performance compared with the K-nearest neighbour (KNN) imputation algorithm, the least square impute (LSImpute) and Bayesian principal component analysis (BPCA) techniques. Experimental results confirm that CMVE provides an improvement of 89%, 12% and 10% for the BRCA1, BRCA2 and sporadic ovarian cancer mutations, respectively, compared to the average error rate of KNN, LSImpute and BPCA imputation methods, over a range of randomly selected missing values. The underlying theory behind CMVE also means that it is not restricted to bioinformatics data, but can be successfully applied to any correlated data set

Dynamic symmetrical topology models for pervasive sensor networks

The success of pervasive computing environments using ubiquitous loco-dynamic sensing devices is very dependent upon the sensor deployment topology (DT) employed. This paper presents a systematic mathematical model for efficient sensor deployment and provides a comparison with other popular topologies. The model focuses upon blanket coverage of a surveillance area using a minimum number of sensing devices, with minimal infra-sensor overlapping to reduce collisions and co-existence problems. Simulation results are presented for the hexagonal, triangular and square grid topologies for various dimensions of surveillance area. The results confirm that the hexagonal model gives optimal performance in terms of requiring the minimal number of sensors. The paper also highlights the improved performance of ubiquitous wireless sensor networks when a hexagonal topology (HT) is used

A hybrid neural network based speech recognition system for pervasive environments

One of the major drawbacks to using speech as the input to any pervasive environment is the requirement to balance accuracy with the high processing overheads involved. This paper presents an Arabic speech recognition system (called UbiqRec), which address this issue by providing a natural and intuitive way of communicating within ubiquitous environments, while balancing processing time, memory and recognition accuracy. A hybrid approach has been used which incorporates spectrographic information, singular value decomposition, concurrent self-organizing maps (CSOM) and pitch contours for Arabic phoneme recognition. The approach employs separate self-organizing maps (SOM) for each Arabic phoneme joined in parallel to form a CSOM. The performance results confirm that with suitable preprocessing of data, including extraction of distinct power spectral densities (PSD) and singular value decomposition, the training time for CSOM was reduced by 89%. The empirical results also proved that overall recognition accuracy did not fall below 91%

How to Improve Postgenomic Knowledge Discovery Using Imputation

While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputation, which exploit the biological transactional behaviour of functionally correlated genes to afford accurate missing value estimation. This paper examines the influence of missing value imputation techniques upon postgenomic knowledge inference methods with results for various algorithms consistently corroborating that instead of ignoring missing values, recycling microarray data by flexible and robust imputation can provide substantial performance benefits for subsequent downstream procedures

Stacked regression ensemble for cancer class prediction

Design of a machine learning algorithm as a robust class predictor for various DNA microarray datasets is a challenging task, as the number of samples are very small as compared to the thousands of genes (feature set). For such datasets, a class prediction model could be very successful in classifying one type of dataset but may fail to perform in a similar fashion for other datasets. This paper presents a stacked regression ensemble (SRE) model for cancer class prediction. Results indicate that SRE has provided performance stability for various microarray datasets. The performance of SRE has been cross validated using the k-fold cross validation method (leave one out) technique for BRCA1, BRCA2 and sporadic classes for ovarian and breast cancer microarray datasets. The paper also presents comparative results of SRE with most commonly used SVM and GRNN. Empirical results confirmed that SRE has demonstrated better performance stability as compared to SVM and GRNN for the classification of assorted cancer data

Titanium Plasma Spectroscopy Studies under Double Pulse Laser Excitation

Laser-induced breakdown spectroscopy (LIBS) was applied for parametric studies of titanium (Ti) plasma using single and double pulsed laser excitation scheme. Here a pulsed Nd:YAG laser was employed for generation of laser produced plasma from solid Ti target at ambient pressure. Several ionized titanium lines were recorded in the 312-334 nm UV region. The temporal evolution of plasma parameters such as excitation temperature and electron number density was evaluated. The effect of incident laser irradiance, position of the laser beam focal point with respect to the surface of illumination, single and double laser pulse effect on plasma parameters were also investigated. This study contributes to a better understanding of the LIBS plasma dynamics of the double laser pulse effect on the temporal evolution of various Ti emission lines, the detection sensitivity and the optimal dynamics of plasma for ionized states of Ti. The results demonstrate a faster decay of the continuum and spectral lines and a shorter plasma life time for the double pulse excitation scheme as compared with single laser pulse excitation. For double pulse excitation technique, the emissions of Ti lines intensities are enhanced by a factor of five which could help in the improvement of analytical performance of LIBS technique. In addition, this study proved that to avoid inhomogeneous effects in the laser produced plasma under high laser intensities, short delay times between the incident laser pulse and ICCD gate are required

Enhanced Photoactivity on Ag/Ag3PO4 Composites by Plasmonic Effect

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