Pixel Classification of SAR ice images using ANFIS-PSO Classifier

Synthetic Aperture Radar (SAR) is playing a vital role in taking extremely high resolution radar images. It is greatly used to monitor the ice covered ocean regions. Sea monitoring is important for various purposes which includes global climate systems and ship navigation. Classification on the ice infested area gives important features which will be further useful for various monitoring process around the ice regions. Main objective of this paper is to classify the SAR ice image that helps in identifying the regions around the ice infested areas. In this paper three stages are considered in classification of SAR ice images. It starts with preprocessing in which the speckled SAR ice images are denoised using various speckle removal filters; comparison is made on all these filters to find the best filter in speckle removal. Second stage includes segmentation in which different regions are segmented using K-means and watershed segmentation algorithms; comparison is made between these two algorithms to find the best in segmenting SAR ice images. The last stage includes pixel based classification which identifies and classifies the segmented regions using various supervised learning classifiers. The algorithms includes Back propagation neural networks (BPN), Fuzzy Classifier, Adaptive Neuro Fuzzy Inference Classifier (ANFIS) classifier and proposed ANFIS with Particle Swarm Optimization (PSO) classifier; comparison is made on all these classifiers to propose which classifier is best suitable for classifying the SAR ice image. Various evaluation metrics are performed separately at all these three stages

A study on non-destructive method for detecting Toxin in pepper using Neural networks

Mycotoxin contamination in certain agricultural systems have been a serious concern for human and animal health. Mycotoxins are toxic substances produced mostly as secondary metabolites by fungi that grow on seeds and feed in the field, or in storage. The food-borne Mycotoxins likely to be of greatest significance for human health in tropical developing countries are Aflatoxins and Fumonisins. Chili pepper is also prone to Aflatoxin contamination during harvesting, production and storage periods.Various methods used for detection of Mycotoxins give accurate results, but they are slow, expensive and destructive. Destructive method is testing a material that degrades the sample under investigation. Whereas, non-destructive testing will, after testing, allow the part to be used for its intended purpose. Ultrasonic methods, Multispectral image processing methods, Terahertz methods, X-ray and Thermography have been very popular in nondestructive testing and characterization of materials and health monitoring. Image processing methods are used to improve the visual quality of the pictures and to extract useful information from them. In this proposed work, the chili pepper samples will be collected, and the X-ray, multispectral images of the samples will be processed using image processing methods. The term "Computational Intelligence" referred as simulation of human intelligence on computers. It is also called as "Artificial Intelligence" (AI) approach. The techniques used in AI approach are Neural network, Fuzzy logic and evolutionary computation. Finally, the computational intelligence method will be used in addition to image processing to provide best, high performance and accurate results for detecting the Mycotoxin level in the samples collected.Comment: 11 pages,1 figure; International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.4, July 201

Energy Aware Routing using AODV Protocol for Low Energy Consumption in WSN

Wireless Sensor Network (WSN) is a self-configured and infrastructure-less network that is used to monitor the environmental conditions and transfer sensor data to the desired destination in a particular region. Energy consumption is the most important concern in WSN, which is considered as an active research area. Routing selection is one method that is used to optimize energy in WSN. There are many protocols for discovering a route between two nodes. However the performance of the Ad hoc On-Demand Distance Vector Protocol (AODV) routing protocol is a more suitable one. It is a generic reactive protocol for routing mostly used in MANET (Mobile Ad Hoc Networks) and WSNs (Wireless Sensor Networks). This protocol supports unicasting and multicasting and will also identify the shortest path. The aim of this paper is discuses about energy-aware routing,  is implemented in the AODV protocol which is derived from nodes remaining energy. The remaining energy of node is computed by Max-Min energy algorithm in order to extend the network's life span and facilitates to keep the network lively. The performance of AODV is compared with Modified AODV protocols. The comparison is done by various performance metrics such as PDR (Packet Delivery Ratio), throughput, delay time, loss rate, and energy consumption. Analysis on the experimental results showed that MAODV protocol gives better results than traditional AODV protocol and it is also inferred that MAODV avoids too much energy consumption of nodes in the network

Optimized connected Median filter using Particle Swarm Optimization

In the image processing Median filter were used to remove the impulse noise. It preserves the edges for the next level operations such as segmentation and object recognition. The present paper deals with the preprocessing of chili x-ray images. The researcher has already preprocessed the chili x-ray images by adopting the Average filter, Median filter, Wiener filter, Gamma intensity correction, CLAHE, 4-connected Median filter and weighted 4-connected median filter. The result of the above stated preprocess methods to contain noise in the pixels, hence it is considered as unsuitable for next level operations. To remove such noise from the image, this paper contributes a precise and well-organized algorithm. The proposed noise removal algorithm replaces the noisy pixels by using ‘4-connected median value’ and replaces the remaining pixels by using ‘weighted 4-connected median value’ in the selected window. The replacement of middle pixel value in 4-connected median filter is done through particle swarm optimization algorithm. Peak Signal to Noise Ratio used as the fitness function in the particle swarm optimization algorithm. The performance measures were taken for all the noise removal algorithm. Among the various results obtained, the proposed algorithm works better than others

Improved Canny Edges Using Cellular Based Particle Swarm Optimization Technique for Tamil Sign Digital Images

The development of computer based sign language recognition system, for enabling communication with hearing impaired people, is an important research area that faces different challenges in the pre-processing stage of image processing, particularly in boundary detection stage. In edge detection, the possibility of achieving high quality images significantly depends on the fitting threshold values, which are generally selected using canny method, and these threshold values may vary, based on the type of images and the applications chosen. This research work presents a novel idea of establishing a hybrid particle swarm optimization algorithm, which is a combination of PSO with the behavioural pattern of cellular organism in canny method, that defines an objective to find optimal threshold values for the implementation of double thresholding hysteresis method, which is viewed as a non-linear complex problem. The attempt to incorporate the model has minimized the problem of quick convergence of PSO algorithm which has improved the detection of broken edges. The efficiency of the proposed algorithm is proved through the experimental observation, done in Tamil sign images to indicate the better performance of canny operator by introducing new variant based PSO

CELLULAR ORGANISM BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR COMPLEX NON-LINEAR PROBLEMS

Particle Swarm Optimization (PSO) is the global optimization technique that inspires many researchers to solve large scale of non-linear optimization problems. For certain complex scenarios, the premature convergence problem of PSO algorithm cannot find global optimum in dynamic environments. In this paper, a new variant motility factor based Cellular Particle Swarm Optimization (m-CPSO) algorithm is proposed which is developed by the migration behavior observed from fibroblast cellular organism to overcome this problem. The proposed m-CPSO algorithm is modeled in two different social best and individual best models. The performance of m-CPSO is tested in the benchmark and real-time data instances and compared with classical PSO. The outcome of experimental results has demonstrated that m-CPSO algorithm produces promising results than classical PSO on all evaluated environments

Effect in comparison between intrathecal dexmedetomidine and intrathecal fentanyl added to bupivacaine in lower limb orthopaedic surgeries

Introduction: Various adjuvants have been used with local anaesthetics in spinal anaesthesia to avoid intraoperative visceral and somatic pain and to provide prolonged postoperative analgesia. Dexmedetomidine, the new highly selective α2-agonist drug, is now being used as a neuraxial adjuvant. Objectives: The purpose of this study was to evaluate the onset and duration of sensory and motor block as well as postoperative analgesia and adverse effects of dexmedetomidine or fentanyl given intrathecally with hyperbaric 0.5% bupivacaine for spinal anaesthesia in lower limb orthopaedic surgeries. This study was conducted in Hospital Universiti Sains Malaysia, Kelantan. Methodology: Sixty four patients classified as American Society of Anesthesiologists (ASA) status I and II scheduled for lower limb orthopaedic surgeries were prospectively studied. Patients were randomly allocated to receive intrathecally either 2.5 mls hyperbaric 0.5% bupivacaine plus 5 μg dexmetedomidine (n=32) or 2.5 mls hyperbaric 0.5% bupivacaine plus 25 mg fentanyl (n = 32), the onset time to reach peak sensory and motor level, the regression time for sensory and motor block, haemodynamic changes, postoperative time to rescue analgesia with total dosage of voltaren in first 24 hours and side effects were recorded by an anaesthetist colleague blinded to the groups. Results: Patients in the dexmedetomidine group had a significantly longer sensory and motor block time than patients in fentanyl group. The mean time of sensory regression to S1 was 323.75±20.28 min in dexmedetomidine group and 256.56±33.76 min in fentanyl group (P<0.001). The mean regression time of motor block to reach modified Bromage 0 was 271.56±24.64 min in dexmedetomidine group and 202.81±44.09 min in fentanyl group (P<0.001). Postoperative analgesic requirements were less and time to rescue analgesia were prolonged in dexmedetomidine group compared to fentanyl. Mean time to rescue analgesia was 8.84±0.57 minute in dexmedetomidine group compared to 7.53±0.57 minutes in fentanyl group (p<0.001). There were no significant difference in the adverse effect of both the drugs. Conclusions: Intrathecal dexmedetomidine is associated with prolonged motor and sensory block, haemodynamic stability, and reduced demand for rescue analgesics in 24 h as compared to fentanyl

The neural mechanisms of antennal positioning in flying moths

Summary: In diverse insects, the forward positioning of the antenna is often among the first behavioral indicators of the onset of flight. This behavior may be important for the proper acquisition of the mechanosensory and olfactory inputs by the antennae during flight. Here, we describe the neural mechanisms of antennal positioning in hawk moths from behavioral, neuroanatomical and neurophysiological perspectives. The behavioral experiments indicated that a set of sensory bristles called Bohm's bristles (or hair plates) mediate antennal positioning during flight. When these sensory structures were ablated from the basal segments of their antennae, moths were unable to bring their antennae into flight position, causing frequent collisions with the flapping wing. Fluorescent dye-fills of the underlying sensory and motor neurons revealed that the axonal arbors of the mechanosensory bristle neurons spatially overlapped with the dendritic arbors of the antennal motor neurons. Moreover, the latency between the activation of antennal muscles following stimulation of sensory bristles was also very short (&#60;10 ms), indicating that the sensorimotor connections may be direct. Together, these data show that Bohm's bristles control antennal positioning in moths via a reflex mechanism. Because the sensory structures and motor organization are conserved across most Neoptera, the mechanisms underlying antennal positioning, as described here, are likely to be conserved in these diverse insects