We successfully demonstrate 40 GB/s 8 channels' Dense Wavelength Division Multiplexing (DWDM) over free space optical (FSO) communication system. Each channel is transmitting 5 GB/s data rate in downstream separated by 0.8 nm (100 GHz) channel spacing with 1.8 GHz filter bandwidth. DWDM over FSO communication system is very effective in providing high data rate transmission with very low bit error rate (BER). The maximum reach of designed system is 4000 m without any compensation scheme. The simulation work reports minimum BER for Return-to-Zero (RZ) modulation format at different channels 1, 4, and 8 are found to be 2.32 −17 , 1.70 −16 , and 9.51 −15 at 4000 m distance, respectively. Sharp increase in BER occurs if data rate and distance increase up to 10 GB/s and 5000 m.

Mobile Adhoc Networks (MANETs)

Purpose The purpose of this paper is to investigate the face processing responses of children with autism spectrum disorder (ASD) using skin conductance response (SCR) patterns and to compare it with typically developed (TD) children. Design/methodology/approach Two experiments have been designed to analyze the effect of face processing. In the first experiment, learned non-face (objects) vs unknown face stimuli have been shown and in the second experiment, familiar vs unfamiliar face stimuli have been shown to ten ASD and ten TD children and SCR patterns have been recorded, analyzed and compared for both the groups. Findings It has been observed that children with ASD were able to differentiate faces out of learned non-face stimuli and their SCR patterns were similar as TD children in the first experiment. In the second experiment, children with ASD were unable to recognize familiar faces from unfamiliar faces but TD children could easily discriminate between familiar and unfamiliar faces as their SCR patterns were different from children with ASD. Research limitations/implications The present study advocates that impairment in face identification exists in children with ASD. Hence, it can be concluded that in children with ASD face processing is present but they do not recognize familiar faces or it can be said that face familiarization effect is absent in children with ASD. Originality/value There are very few findings that used SCR signal as main analysis parameter for face processing in children with ASD, in most of the studies; Electroencephalography signal has been used as analysis parameter. Moreover, familiar and unfamiliar face processing with multiple stimuli used in present work adds novelty to the literature.

The main aim of this research work was to develop and validate a novel graphical user interface based hierarchical fuzzy autism detection tool named as "Fast and Accurate Diagnosis of Autism" for the diagnosis of autism disorder quickly and accurately and in addition, this tool also highlights the highly impaired area in each participant. Two groups of children had been participated in this study which includes autism group (N = 40) and normal group (N = 40). The hierarchical fuzzy expert system had been developed using IF-Then rules based on the experiences of the specialists and both the groups were tested on the designed system. It had been validated that the designed system was easily discriminating between the autistic participants and normal participants with an accuracy of 99%. Moreover, the results of the designed system were compared with Childhood Autism Rating Scale; also the tool was clearly highlighting the most impaired area in each participant. It had also been seen that the designed system has a sensitivity of 98.2% and specificity of 99.2%. It can be said that the designed tool can be used by doctors to diagnose autism along with its severity levels and to highlight the highly impaired area accurately and in no time.

No abstract

Epilepsy is a perilous neurological disease covering about 4-5% of total population of the world. Its main characteristics are seizures which occur due to certain disturbance in brain function. During epileptic seizures the patient is unaware of their physical as well as mental condition and hence physical injury may occur. Proper health care must be provided to the patients and this can be achieved only if the seizures are detected correctly in time. In this dissertation work, a system is designed using wavelet decomposition method and different training algorithms to train the neural network for classification of the EEG signals. The system was tested and compared with Support Vector Machine (SVM) classifier. The system accuracy comes out to be 99.97%.

Brain-computer interface (BCI) is combination of hardware and software systems that allows the severely or partially disabled persons to communicate with their surroundings. The study of brain activities precisely is an important step in BCI system. Many invasive and non-invasive neuro-imaging techniques are being conducted. In this paper, a comparative analysis of these different approaches has been reviewed such as electroencephalography (EEG), electro-corticograph (ECoG), magneto-encephalograph (MEG), intra-cortical neuron recording (INR), and magnetic resonance imaging (MRI).

In this paper, performance analysis of high-speed superdense wavelength-division-multiplexing (SDWDM) optical add-drop multiplexer (OADM) optical ring network for 6 nodes, 45 wavelengths having channel spacing of 0.2 nm on 300 km unidirectional nonlinear single-mode fiber ring of 10 Gbit/s has been reported. The performance optimization of the system by comparing different modulation formats has been reported on the basis of eye diagram and bit error rate (BER). It has been reported that CSRZ modulation format can achieve BER as better as e-24, which gives best performance. This paper also presents a study of performance degradation caused by the crosstalk and the effect of channel spacing on SWDM system.