Routing Protocol Enhancement for handling Node Mobility in Wireless Sensor Networks

In wireless sensor networks, the routing algorithms currently available assume that the sensor nodes are stationary. Therefore when mobility modulation is applied to the wireless sensor networks, most of the current routing algorithms suffer from performance degradation. The path breaks in mobile wireless networks are due to the movement of mobile nodes, node failure, channel fading and shadowing. It is desirable to deal with dynamic topology changes with optimal effort in terms of resource and channel utilization. As the nodes in wireless sensor medium make use of wireless broadcast to communicate, it is possible to make use of neighboring node information to recover from path failure. Cooperation among the neighboring nodes plays an important role in the context of routing among the mobile nodes. This paper proposes an enhancement to an existing protocol for accommodating node mobility through neighboring node information while keeping the utilization of resources to a minimum.

Impact of Node Mobility on Routing Protocols for Wireless Sensor Networks

Wireless sensor networks monitor their surrounding environment for the occurrence of some anticipated phenomenon. Most of the research related to sensor networks considers the static deployment of sensor nodes. Mobility of sensor node can be considered as an extra dimension of complexity, which poses interesting and challenging problems. Node mobility is a very important aspect in the design of effective routing algorithm for mobile wireless networks. In this work we intent to present the impact of different mobility models on the performance of the wireless sensor networks. Routing characteristics of various routing protocols for ad-hoc network were studied considering different mobility models. Performance metrics such as end-to-end delay, throughput and routing load were considered and their variations in the case of mobility models like Freeway, RPGM were studied. This work will be useful to figure out the characteristics of routing protocols depending on the mobility patterns of sensors

Evaluation of the Power Consumption of Routing Protocols for Wireless Sensor Networks

Sensor networks are one of the fastest growing areas in broadwireless ad hoc networking (?Eld. A sensor node, typically'contains signal-processing circuits, micro-controllers and awireless transmitter/receiver antenna. Energy saving is oneof the critical issue for sensor networks since most sensorsare equipped with non-rechargeable batteries that have limited lifetime.In thiswork, four routing protocols for wireless sensor networks vizFlooding, Gossiping, GBR and LEACH have been simulated using Tiny OS and their power consumption is studied usingcaorwreiredTOoSuStIuMs.ingAMirceaal2izMaotitoens.of these protocols has been carried out using mica 2 motes

A Classification of Sandhi Rules for Suffix Separation in Malayalam

Suffix separation plays a vital role in improving the quality of training in the Statistical Machine Translation from English into Malayalam. The morphological richness and the agglutinative nature of Malayalam make it necessary to retrieve the root word from its inflected form in the training process. The suffix separation process accomplishes this task by scrutinizing the Malayalam words and by applying sandhi rules. In this paper, various handcrafted rules designed for the suffix separation process in the English Malayalam SMT are presented. A classification of these rules is done based on the Malayalam syllable preceding the suffix in the inflected form of the word (check_letter). The suffixes beginning with the vowel sounds like ആല, ഉെെ, ഇല etc are mainly considered in this process. By examining the check_letter in a word, the suffix separation rules can be directly applied to extract the root words. The quick look up table provided in this paper can be used as a guideline in implementing suffix separation in Malayalam language

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions

Towards the development of a new wavelet for ECG classification

In this paper an attempt has been made to determine the number of Premature Ventricular Contraction (PVC) cycles accurately from a given Electrocardiogram (ECG) using a wavelet constructed from multiple Gaussian functions. It is difficult to assess the ECGs of patients who are continuously monitored over a long period of time. Hence the proposed method of classification will be helpful to doctors to determine the severity of PVC in a patient. Principal Component Analysis (PCA) and a simple classifier have been used in addition to the specially developed wavelet transform. The proposed wavelet has been designed using multiple Gaussian functions which when summed up looks similar to that of a normal ECG. The number of Gaussians used depends on the number of peaks present in a normal ECG. The developed wavelet satisfied all the properties of a traditional continuous wavelet. The new wavelet was optimized using genetic algorithm (GA). ECG records from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database have been used for validation. Out of the 8694 ECG cycles used for evaluation, the classification algorithm responded with an accuracy of 97.77%. In order to compare the performance of the new wavelet, classification was also performed using the standard wavelets like morlet, meyer, bior3.9, db5, db3, sym3 and haar. The new wavelet outperforms the rest

Classification of Mammograms and DW T based Detection of Microcalcification

Cancer treatment is most effective when it is detected early and the progress in treatment will be closely related to the ability to reduce the proportion of misses in the cancer detection task. The effectiveness of algorithms for detecting cancers can be greatly increased if these algorithms work synergistically with those for characterizing normal mammograms. This research work combines computerized image analysis techniques and neural networks to separate out some fraction of the normal mammograms with extremely high reliability, based on normal tissue identification and removal. The presence of clustered microcalcifications is one of the most important and sometimes the only sign of cancer on a mammogram. 60% to 70% of non-palpable breast carcinoma demonstrates microcalcifications on mammograms [44], [45], [46].WT based techniques are applied on the remaining mammograms, those are obviously abnormal, to detect possible microcalcifications. The goal of this work is to improve the detection performance and throughput of screening-mammography, thus providing a ‘second opinion ‘ to the radiologists. The state-of- the- art DWT computation algorithms are not suitable for practical applications with memory and delay constraints, as it is not a block transfonn. Hence in this work, the development of a Block DWT (BDWT) computational structure having low processing memory requirement has also been taken up.

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing

Electrochemical studies of tamsulosin hydrochloride using multiwalled carbon nanotube-modified glassy carbon sensor

A differential pulse voltammetric sensor for the determination of tamsulosin hydrochloride (TAM) using multiwalled carbon nanotubes (MWNTs)–Nafion-modified glassy carbon electrode (GCE) has been developed. MWNTs were dispersed in water with the help of Nafion and were used to modify the surface of GCE via solvent evaporation. At MWNT-modified electrode, TAM gave a well-defined oxidation peak at a potential of 1084 mV in 0.1 M acetate buffer solution of pH 5. Compared to the bare electrode, the peak current of TAM showed a marked increase and the peak potential showed a negative deviation. The determination conditions, such as the amount of MWNT–Nafion suspension, pH of the supporting electrolyte and scan rate, were optimised. Under optimum conditions, the oxidation peak current was proportional to the concentration of TAM in the range 1 × 1023 M–3 × 1027 M with a detection limit of 9.8 × 1028 M. The developed sensor showed good stability, selectivity and was successfully used for the determination of TAM in pharmaceutical formulations and urine samples

Classification of recycled sands and their applications as fine aggregates for concrete and bituminous mixtures

As a result of the drive towards waste-poor world and reserving the non-renewable materials, recycling the construction and demolition materials become very essential. Now reuse of the recycled concrete aggregate more than 4 mm in producing new concrete is allowed but with natural sand a fine aggregate while. While the sand portion that represent about 30\% to 60\% of the crushed demolition materials is disposed off. To perform this research, recycled concrete sand was produced in the laboratory while nine recycled sands produced from construction and demolitions materials and two sands from natural crushed limestone were delivered from three plants. Ten concrete mix designs representing the concrete exposition classes XC1, XC2, XF3 and XF4 according to European standard EN 206 were produced with partial and full replacement of natural sand by the different recycled sands. Bituminous mixtures achieving the requirements of base courses according to Germany standards and both base and binder courses according to Egyptian standards were produced with the recycled sands as a substitution to the natural sands. The mechanical properties and durability of concrete produced with the different recycled sands were investigated and analyzed. Also the volumetric analysis and Marshall test were performed hot bituminous mixtures produced with the recycled sands. According to the effect of replacement the natural sand by the different recycled sands on the concrete compressive strength and durability, the recycled sands were classified into three groups. The maximum allowable recycled sand that can be used in the different concrete exposition class was determined for each group. For the asphalt concrete mixes all the investigated recycled sands can be used in mixes for base and binder courses up to 21\% of the total aggregate mass.

Patent DE10 2004 037 519 B4 “Sensor Process and Device for Determining a Physical Value“. A Proof of Concept

At present, a fraction of 0.1 - 0.2% of the patients undergoing surgery become aware during the process. The situation is referred to as anesthesia awareness and is obviously very traumatic for the person experiencing it. The reason for its occurrence is mostly an insufficient dosage of the narcotic Propofol combined with the incapability of the technology monitoring the depth of the patient’s anesthetic state to notice the patient becoming aware. A solution can be a highly sensitive and selective real time monitoring device for Propofol based on optical absorption spectroscopy. Its working principle has been postulated by Prof. Dr. habil. H. Hillmer and formulated in DE10 2004 037 519 B4, filed on Aug 30th, 2004. It consists of the exploitation of Intra Cavity Absorption effects in a two mode laser system. In this Dissertation, a two mode external cavity semiconductor laser, which has been developed previously to this work is enhanced and optimized to a functional sensor. Enhancements include the implementation of variable couplers into the system and the implementation of a collimator arrangement into which samples can be introduced. A sample holder and cells are developed and characterized with a focus on compatibility with the measurement approach. Further optimization concerns the overall performance of the system: scattering sources are reduced by re-splicing all fiber-to-fiber connections, parasitic cavities are eliminated by suppressing the Fresnel reflexes of all one fiber ends by means of optical isolators and wavelength stability of the system is improved by the implementation of thermal insulation to the Fiber Bragg Gratings (FBG). The final laser sensor is characterized in detail thermally and optically. Two separate modes are obtained at 1542.0 and 1542.5 nm, tunable in a range of 1nm each. Mode Full Width at Half Maximum (FWHM) is 0.06nm and Signal to Noise Ratio (SNR) is as high as 55 dB. Independent of tuning the two modes of the system can always be equalized in intensity, which is important as the delicacy of the intensity equilibrium is one of the main sensitivity enhancing effects formulated in DE10 2004 037 519 B4. For the proof of concept (POC) measurements the target substance Propofol is diluted in the solvents Acetone and DiChloroMethane (DCM), which have been investigated for compatibility with Propofol beforehand. Eight measurement series (two solvents, two cell lengths and two different mode spacings) are taken, which draw a uniform picture: mode intensity ratio responds linearly to an increase of Propofol in all cases. The slope of the linear response indicates the sensitivity of the system. The eight series are split up into two groups: measurements taken in long cells and measurements taken in short cells.