Review on Localization based Routing Protocols for Underwater Wireless Sensor Network

Underwater Wireless Sensor Network (UWSN) can enable many scientific, military, safety, commercial and environmental applications. Majority of the network models has been introduced for the deployment of sensor nodes through routing schemes and methodologies along with different algorithms but still the design of routing protocol for underwater environment is a challenging issue due to distinctive characteristics of underwater medium. The majority of the issues are also needed to fulfill the appropriate approach for the underwater medium like limited bandwidth, high bit error rates, propagation delay, and 3D deployment. This paper focuses the comparative analysis of the localization based routing protocols for UWSN. This comparative analysis plays a significant attention to construct a reliable routing protocol, which provides the effectual discovery of the route between the source node and the sink node. In addition this comparative analysis also focuses the data packets forwarding mechanism, the deployment of sensor nodes and location based routing for UWSN in different conditions

RMEER: Reliable Multi-path Energy Efficient Routing Protocol for Underwater Wireless Sensor Network

Underwater Wireless Sensor Networks (UWSNs) is interesting area for researchers.To extract the information from seabed to water surface the the majority numbers of routing protocols has been introduced. The design of routing protocols faces many challenges like deployment of sensor nodes, controlling of node mobility, development of efficient route for data forwarding, prolong the battery power of the sensor nodes, and removal of void nodes from active data forwarding paths. This research article focuses the design of the Reliable Multipath Energy Efficient Routing (RMEER) which develops the efficient route between sensor nodes, and prolongs the battery life of the nodes. RMEER is a scalable and robust protocol which utilizes the powerful fixed courier nodes in order to enhance the network throughput, data delivery ratio, network lifetime and reduces the end-to-end delay. RMEER is also an energy efficient routing protocol for saving the energy level of the nodes. We have used the NS2.30 simulator with AquaSim package for performance analysis of RMEER.We observed that the simulation performance of RMEER is better than D-DBR protocol

Fast and Effective Bag-of-Visual-Word Model to Pornographic Images Recognition Using the FREAK Descriptor

Recently, the Bag of Visual Word (BoVW) has gained enormous popularity between researchers to object recognition. Pornographic image recognition with respect to computational complexity, appropriate accuracy, and memory consumption is a major challenge in the applications with time constraints such as the internet pornography filtering. Most of the existing researches based on the Bow, using the very popular SIFT and SURF algorithms to description and match detected keypoints in the image. The main problem of these methods is high computational complexity due to constructing the high dimensional feature vectors. This research proposed a BoVW based model by adopting very fast and simple binary descriptor FREAK to speed-up pornographic recognition process. Meanwhile, the keypoints are detected in the ROI of images which improves the recognition speed due to eliminating many noise keypoints placed in the image background. Finally, in order to find the most representational visual-vocabulary, different vocabularies are generated from size 150 to 500 for BoVW. Compared with the similar works, the experimental results show that the proposed model has gained remarkable improvement in the terms of computational complexity

Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter

The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications

Multi-Scale Skin Sample Approach for Dynamic Skin Color Detection: An Analysis

Skin detection is an important step in many computer vision applications. It has been employed in face detection, hand gesture recognition, illicit image filtering, steganography and content based image retrieval. This is due to the skin colour that attractive feature in detecting the skin in coloured image. In contrast, skin colour detection suffers in low accuracy due to colour properties between the real skin surface and the skin-like objects. Therefore, this paper proposes a dynamic skin colour detection using multi-scales online skin sampling approach. This dynamic skin colour detection involved two procedures for generating the dynamic threshold in colour spaces. Moreover, six colour spaces have been studied to find the best colour models for our proposed method. The first procedure is the online skin sampling that obtained directly from the face candidates to generate the dynamic threshold values of each studied colour spaces. Alongside with the first procedure, we obtained optimal scale for skin sample with 0.25, 0.2 reduction, Meanwhile, the second procedure known as skin pixel classification uses the dynamic threshold obtained from the first procedure to classify the skin in the image. We achieved a satisfactory result in term of precision, recall, accuracy and F1. The experimental result shows that the proposed dynamic skin colour detection achieved good performance vi

Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification

Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification in a multi-class which consist of several ethnic classes may degrade the accuracy of the ethnic identification. Thus, this paper purposely analyses the accuracy of the texture-based ethnicity identification model from facial components under four-class ethnics. The proposed model involved several phases such as face detection, feature selection, and classification. The detected face then exploited by three proposed face block which are 1×1, 1×2 and 2×2. In the feature extraction process, a Grey Level Co-occurrence Matrix (GLCM) under different face blocks were employed. Then, final stage was undergone with several classification algorithms such as Naïve Bayes, BayesNet, kNearest Neighbour (k-NN), Random Forest, and Multilayer Perceptron (MLP). From the experimental result, we achieved a better result 2×2 face block feature compared to 1×1 and 2×2 feature representation under Random Forest algorithm

Solving time complexity issue in copy-move forgery detection thru pre-processing techniques

Copy-move forgery detection (CMFD) has become a popular an important research focus in digital image forensic. Copy-move forgery happens when a region in an image is copied and paste into the same image. Apart from the main problem of detection robustness and accuracy, CMFD is struggle with time complexity issue. One of the options to resolve this problem was by including pre-processing step in CMFD pipeline. This paper reviews on the importance of pre-processing step, and available techniques in reducing time complexity of copy-move forgery detection. An experiment using discrete wavelet transform (DWT) as a pre-processing technique was carried out to evaluate the performance of adopting pre-processing technique in CMFD pipeline. The experimental result has shown a significant reduction in processing time with some trade off to detection accuracy

A multi-color based features from facial images for automatic ethnicity identification model

Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification technique using color-based feature mostly failed to determine the ethnicity classes accurately due to low properties of features in color-based. Thus, this paper purposely analyses the accuracy of the color-based ethnicity identification model from various color spaces. The proposed model involved several phases such as skin color feature extraction, feature selection, and classification. In the feature extraction process, a dynamic skin color detection is adapted to extract the skin color information from the face candidate. The multi-color feature was formed from the descriptive statistical model. Feature selection technique applied to reduce the feature space dimensionality. Finally, the proposed ethnicity identification was tested using several classification algorithms. From the experimental result, we achieved a better result in multi-color feature compared to individual color space model under Random Forest algorithm