Channel Estimation for MIMO MC-CDMA Systems

The concepts of MIMO MC-CDMA are not new but the new technologies to improve their functioning are an emerging area of research. In general, most mobile communication systems transmit bits of information in the radio space to the receiver. The radio channels in mobile radio systems are usually multipath fading channels, which cause inter-symbol interference (ISI) in the received signal. To remove ISI from the signal, there is a need of strong equalizer. In this thesis we have focused on simulating the MIMO MC-CDMA systems in MATLAB and designed the channel estimation for them

Effect of Relay Nodes on End-To-End Delay in Multi-Hop Wireless Ad-Hoc Networks

Channel access delay in a wireless adhoc network is the major source of delay while considering the total end to-end delay. Channel access delays experienced by different relay nodes are different in multi-hop adhoc network scenario. These delays in multi-hop network are analysed in the literature assuming channel access delays are independent and are of same magnitude at all the nodes in the network. In this work, the end to-end delay in a multi-hop adhoc network is analysed taking into account the silent relay nodes. Along with silent relay node effect, Channel access probability (p), transmission radius (r) analogous to transmit power, network throughput and density of nodes arête other factors considered for the end-to-end delay analysis. Effect of network parameters along with silent relay nodes on end-to-end delay is found to be considerably high compared to the previous literature results. Given a bound on end-to-end delay with percentage of silent relay nodes, throughput, node density requirements for a multi-hop adhoc network, optimal ranges of transmission radius and channel access probability can be obtained from the proposed analysis. End-to-end delay increases with silent relay nodes along with transmission radius(r), channel access probability(p), node density and throughput. It is clear from the analysis, that the effect of silent relay nodes on end to-end delay cannot be ignored to maintain certain Quality of service (QoS) metrics for the multi-hop wireless adhoc networ

Analytical Model of Adaptive CSMA-CA MAC for Reliable and Timely Clustered Wireless Multi-Hop Communication

Reliability and delay of a single cluster wireless network is well analysed in the literature. Multi-hop communication over the number of clusters is essential to scale the network. Analytical model for reliability and end-to-end delay optimization for multi-hop clustered network is presented in this paper. Proposed model is a three dimensional markov chain. Three dimensions of markov model are the adaptable mac parameters of CSMA-CA. Model assumes wakeup rates for each cluster. Results show that reliability and delay are significantly improved than previous analytical models proposed. It has been observed that overall reliability of multi-hop link is improved, with reduction in end-to-end delay is reduced even at lower wakeup rates of a cluste

Neural Network based Short Term Forecasting Engine To Optimize Energy And Big Data Storage Resources Of Wireless Sensor Networks

Energy efficient wireless networks is the primary research goal for evolving billion device applications like IoT, smart grids and CPS. Monitoring of multiple physical events using sensors and data collection at central gateways is the general architecture followed by most commercial, residential and test bed implementations. Most of the events monitored at regular intervals are largely redundant/minor variations leading to large wastage of data storage resources in Big data servers and communication energy at relay and sensor nodes. In this paper a novel architecture of Neural Network (NN) based day ahead steady state forecasting engine is implemented at the gateway using historical database. Gateway generates an optimal transmit schedules based on NN outputs thereby reducing the redundant sensor data when there is minor variations in the respective predicted sensor estimates. It is observed that NN based load forecasting for power monitoring system predicts load with less than 3% Mean Absolute Percentage Error (MAPE). Gateway forward transmit schedules to all power sensing nodes day ahead to reduce sensor and relay nodes communication energy. Matlab based simulation for evaluating the benefits of proposed model for extending the wireless network life time is developed and confirmed with an emulation scenario of our testbed. Network life time is improved by 43% from the observed results using proposed model

Automatic organ validation of b-mode ultrasound images for transmission to cloud

Miniaturization in size of Medical ultrasound scanning machine made it to use in point of care applications. Lack of sonographers and their unwillingness to work in rural areas limit the benefits of ultrasound system in rural healthcare. Diagnosis of patients through ultrasound is done by visualizing the ultrasound scanned images of organs. Diagnosis through telemedicine involves transmitting of ultrasound images from rural locations to cloud, where sonographer can remotely access the ultrasound data from cloud and generate the report, thus reducing the geographical separation between patients and doctors. Due to lack of adequate sonographers, ultrasound scanning in remote areas is operated by semi-skilled clinicians. Most of the images generated by semi-skilled clinicians are not useful for diagnosis. Transmitting all these images increases the data in cloud, drains the battery of portable ultrasound machine and increases latency in medication. This paper provides automatic B-mode ultrasound image validation based on organ information present in the image for diagnosis, thus avoiding transmission of invalid images to cloud. Linear kernel SVM classifier trained with first order statistic features of image with/without organs is used to classify the images into valid and invalid for diagnosis. The algorithm resulted with a recognition efficiency of 94.2% in classifying the ultrasound images

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications

Fast Region of Interest detection for fetal genital organs in B-mode ultrasound images

Genital organ detection of fetus in B-mode ultrasound images has a considerable significance. It is useful to know any malformations present in the genital organs and also to determine the sex of the fetus. In this paper we propose a Feature from Accelerated Segment Test (FAST) technique for approximate detection of fetal genitals in ultrasound images. FAST algorithm is capable of producing the corner points at a higher speed which falls on the fetal genital organs. A window of size 60×60 pixels being corner point as a center is considered as Region of Interest (ROI), where genital organ of fetus is anticipated. The efficiency of the algorithm is calculated as the ratio of number of images where corner points are placed on the fetus genital organ to the total number of images tested. FAST algorithm is robust to speckles present in the image, machine independent, fast and also computationally less intensive to implement in real time with an efficiency of 96.7%

FPGA based implementation of low complex adaptive speckle suppression filter for B-mode medical ultrasound images

Speckles are considered as noise, which masks the fine information present in B-mode ultrasound images. Speckles appears as small snakes and dense granular like structures which has serious impact on visual perception of an image. Adaptive filter based on local statistics of an image is used to enhance the image by suppressing the noise. Adaptive speckle suppression filter enhance the image by reducing the variance between intrapixel intensities in homogeneous regions and preserving variance across interpixel intensities across the nonhomogeneous regions. In this paper, we implemented low complex adaptive speckle suppression filter on FPGA based kintex7 board. The performance of the filter is evaluated by plotting the pixel variations of original image with filtered image of an ultrasound phantom. The results show that proposed algorithm can be implemented on mobile ultrasound platforms due to 50% less computations needed per pixel compared to traditional adaptive speckle suppression algorithms, which aids better diagnosis for healthcare

Reliability and delay analysis of slotted anycast multi-hop wireless networks targeting dense traffic iot applications

Studies on IEEE 802.15.4 MAC in the current literature for anycast multi-hop networks do not capture a node's behaviour accurately. Due to the inaccurate modeling of state-wise behaviour of a node, the optimization of network parameters has not been efficient so far. In this work, we include the state-wise behaviour of a relay node into a 3D Markov model to more accurately investigate the protocol performance. Performance analysis of the proposed analytical model is evaluated for different variants of active state length, packet length and wake up rates considering reliability and delay as key performance metrics. Performance analysis shows that the model captures the behaviour of relay nodes most accurately