LEARNet Dynamic Imaging Network for Micro Expression Recognition

Unlike prevalent facial expressions, micro expressions have subtle, involuntary muscle movements which are short-lived in nature. These minute muscle movements reflect true emotions of a person. Due to the short duration and low intensity, these micro-expressions are very difficult to perceive and interpret correctly. In this paper, we propose the dynamic representation of micro-expressions to preserve facial movement information of a video in a single frame. We also propose a Lateral Accretive Hybrid Network (LEARNet) to capture micro-level features of an expression in the facial region. The LEARNet refines the salient expression features in accretive manner by incorporating accretion layers (AL) in the network. The response of the AL holds the hybrid feature maps generated by prior laterally connected convolution layers. Moreover, LEARNet architecture incorporates the cross decoupled relationship between convolution layers which helps in preserving the tiny but influential facial muscle change information. The visual responses of the proposed LEARNet depict the effectiveness of the system by preserving both high- and micro-level edge features of facial expression. The effectiveness of the proposed LEARNet is evaluated on four benchmark datasets: CASME-I, CASME-II, CAS(ME)^2 and SMIC. The experimental results after investigation show a significant improvement of 4.03%, 1.90%, 1.79% and 2.82% as compared with ResNet on CASME-I, CASME-II, CAS(ME)^2 and SMIC datasets respectively.Comment: Dynamic imaging, accretion, lateral, micro expression recognitio

AN EFFICIENT LOAD BALANCING CLUSTERING SCHEME FOR DATA CENTRIC WIRELESS SENSOR NETWORKS

Clustering is an efficient approach to capitalize the energy of energy constraint sensor nodes in wireless sensor networks. Clustering schemes do not guarantee formation of clusters with equal number of nodes. So data frames transmitted by the nodes vary. TDMA schedule of nodes of smaller cluster is smaller than others that results more number of data frames and hence more energy consumption. The non uniform energy consumption of nodes affects the load balancing of network and these nodes are more prone to die earlier than others. In this paper, an improved scheme for cluster head selection is proposed. Clusters having variable frame slots for nodes are applied to E-LEACH and improved E-LEACH to make the cluster more load balanced. Simulation is carried out in NS-2 to analyze the performance of E-LEACH and improved E-LEACH with variable frame length. Variable frame slot scheme for clusters is also measured with the varying distance of base station from the field. Simulation results show that clustering with variable frame length has an improvement of 7% in node death rate over E-LEACH and an improvement of 9% in node death rate over improved ELEACH. Results suggest that variable frame length scheme improves the performance of clustering schemes for WSNs and have most significant result at base station located at 75m from the field

WATER PURIFICATION: A BRIEF REVIEW ON TOOLS AND TECHNIQUES USED IN ANALYSIS, MONITORING AND ASSESSMENT OF WATER QUALITY

Drinking water sources are regularly polluted by various human activities that cause severe health problem all over the world. In recent years, water quality research has drawn great attention from scientific communities. A lot number of tools and techniques are used for proper water quality analysis, monitoring and assessment. This paper includes brief information about some of the them namely, physio-chemical water analysis (PCWA), adsorption, metal pollution index (MPI), water quality index (WQI), water quality modelling tools (WQMT) and multivariable statistical models that include five multivariate data mining approaches i.e. cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), multiple linear regression analysis (MLRA), discriminant analysis (DA). Present paper also explores the interaction between science and technologies and provides basic knowledge of emerging tools and techniques used in water purification

Energy Efficient Clustering Scheme for Wireless Sensor Networks: A Survey

Abstract Wireless sensor networks are application specific networks co mposed of large number of sensor nodes. Limited energy resource of sensor nodes make efficient energy consumption of nodes as main design issue. Energy efficiency is achieved from hardware level to network protocol levels. Clustering of nodes is an effective approach to reduce energy consumption of nodes. Clustering algorith ms group nodes in independent clusters. Each cluster has atleast one cluster head. Nodes send data to respective cluster heads. Cluster heads send data to base station. Clustering algorith ms prolong network lifetime by avoiding long distance communicat ion of nodes to base station. In literature various clustering approaches are proposed. Work of this paper discusses working o f few of them and distinguishes them according to operational mode and state of clustering. Work of this paper helps to understand classification of clustering schemes

Texture‐based feature extraction of smear images for the detection of cervical cancer

In India, cervical cancer is the second most common type of cancer in females. Pap smear is a simple cytology test for the detection of cancer in its early stages. To obtain the best results from the Pap smear, expert pathologist are required. Availability of pathologist in India is far below the required numbers, especially in rural parts. In this paper, multiple texture‐based features are introduced for the extraction of relevant and informative features from single‐cell images. First‐order histogram, GLCM, LBP, Laws, and DWT are used for texture feature extraction. These methods help to recognise the contour of the nucleus and cytoplasm. ANN and SVM are used to classify the single‐cell images either normal or cancerous based on the trained features. ANN and SVM are used on every single feature as well as on the combination of all features. Best results are obtained with a combination of all features. The system is evaluated on generated dataset MNITJ, containing 330 single cervical cell images and also on publicly available benchmark Herlev data set. Experimental results show that the proposed texture‐based features give significantly better results in cervical cancer detection when compared with state of the art shape‐based features regarding accuracy

SCHS: Smart Cluster Head Selection Scheme for Clustering Algorithms in Wireless Sensor Networks

Wireless sensor networks are energy constraint networks. Energy efficiency, to prolong the network for a longer time is critical issue for wireless sensor network protocols. Clustering protocols are energy efficient approaches to extend the lifetime of network. Intra-cluster communication is the main driving factor for energy efficiency of clustering protocols. Intra-cluster energy consumption depends upon the position of cluster head in the cluster. Wrongly positioned clusters head make cluster more energy consuming. In this paper, a simple and efficient cluster head selection scheme is pro-posed, named Smart Cluster Head Selection (SCHS). It can be implemented with any distributed clustering approach. In SCHS, the area is divided into two parts: border area and inner area. Only inner area nodes are eligible for cluster head role. SCHS reduces the intra-cluster communication distance hence improves the energy efficiency of cluster. The simulation results show that SCHS has significant improvement over LEACH in terms of lifetime of network and data units gathered at base station