Digitization of health records in public health facility and its instant availability in the form of electronic records anywhere any time health service is yet to be implemented in developing nations like India and other countries. In India, patient care is mainly delivered through 3 levels namely Primary/Community Healthcare Centre (PHC/CHC), Secondary healthcare centre (District Hospital), and Tertiary Healthcare Centre (National level). The healthcare facilities face many challenges in collecting, processing, and storing these data and managing it without compromising security and privacy. Presently, some of the secondary and tertiary care facilities have started implementing healthcare IT application in terms of Hospital Information System, Hospital Management Information System, Electronic Medical Records (EMR) etc. to manage the patient data in electronic format. However, these systems are developed by different vendors by using different programming languages and databases. This approach makes the system unique but the patient details remains in the same hospital and cannot be shared with other hospitals when patient moves from one hospital to other for advanced or specialized treatment. This is because the data is not interoperable and semantic. In the proposed work, a standard secure Electronic Health Record(EHR) framework is developed using standard medical terminology and coding standards. Implementation of EHR framework for Indian health system will improve the work-flow of health services to the population. EHR at all levels of healthcare system enable efficient and continuous care to the patient.

Semantic segmentation of aerial videos has been extensively used for decision making in monitoring environmental changes, urban planning, and disaster management. The reliability of these decision support systems is dependent on the accuracy of the video semantic segmentation algorithms. The existing CNN based video semantic segmentation methods have enhanced the image semantic segmentation methods by incorporating an additional module such as LSTM or optical flow for computing temporal dynamics of the video which is a computational overhead. The proposed research work modifies the CNN architecture by incorporating temporal information to improve the efficiency of video semantic segmentation. In this work, an enhanced encoder-decoder based CNN architecture (UVid-Net) is proposed for UAV video semantic segmentation. The encoder of the proposed architecture embeds temporal information for temporally consistent labelling. The decoder is enhanced by introducing the feature-refiner module, which aids in accurate localization of the class labels. The proposed UVid-Net architecture for UAV video semantic segmentation is quantitatively evaluated on extended ManipalUAVid dataset. The performance metric mIoU of 0.79 has been observed which is significantly greater than the other state-of-the-art algorithms. Further, the proposed work produced promising results even for the pre-trained model of UVid-Net on urban street scene with fine tuning the final layer on UAV aerial videos.

The modification of the well known GPSR routing protocol, with the concept of lifetime is proposed. The lifetime is calculated between the node and each of its neighbors. A lifetime timer is set to the lifetime value. This timer helps in determining the quality of link and duration of the neighbor¿s existence. During the next hop selection process, the node selects the neighbor which is closest to the destination with good link quality and non-zero lifetime timer value in contrast to GPSR. This results in appropriate selection of the next hop node in a highly mobile and noisy environment, thus reducing the packet loss. The simulation is conducted for two scenarios where the source and destination are travelling in same and opposite directions. The results showed that GPSR with Lifetime achieved 20% to 40% increase in the packet delivery rate and significant improvement in packet delivery ratio for different ¿HELLO¿ message intervals when compared to GPSR.

The advancement of electronic gadgets makes it possible for a device to be multipurpose, which calls for attributes such as compactness and larger bandwidth, with improved data transfer rate. This paper introduces the compact, closely placed two-port dual band-notched UWB antenna using a neutralization line as a decoupling structure. The projected antenna design comprised a circle and rectangle embedded monopole radiator with the defected ground structure to attain the UWB spectrum. Further dual notches are attained by carving the U-shape and inverted U-shape slots on the feedline and radiator. The dual band-notched UWB antennas are placed with the separation of 3.8 mm (0.04 λ; λ is computed using 3.4 GHz frequency). The coupling effect between the close proximity elements is decoupled using the neutralization line. The presented antenna has overall dimensions of 21.5 × 28 × 1.6 mm³ (0.24 × 0.31 × 0.01 λ³) and exhibits S11 below −10 dB from 3.4–11.9 GHz, with isolation better than 16 dB throughout the impedance bandwidth. The antenna also provides frequency band rejection of 4.5–5.3 GHz and 7.2–9 GHz covering the WLAN and entire X-band satellite communication. The projected antenna is explored through characteristic mode analysis, time-domain characteristics, and MIMO diversity features to analyze the effectiveness and usefulness of the antenna. The group delay is less than 1 ns except for the frequency rejection band and fidelity factor greater than 0.96. The projected antenna exhibits MIMO diversity metrics ECC < 0.3, DG > 9.6 dB, MEG < −3 dB, TARC < −10 dB, CCL < 0.3 bps/Hz, and ME < −2 dB across the operational frequency, except for the notched bands. The designed two-port antenna is validated by printing on an FR4 substrate. The simulated and measured findings are in line with and appropriate for MIMO wireless applications.

Semantic segmentation of videos helps in scene understanding, thereby assisting in other automated video processing techniques like anomaly detection, object detection, event detection, etc. However, there has been limited study on semantic segmentation of videos acquired using Unmanned Aerial Vehicles (UAV), primarily due to the absence of standard dataset. In this paper, a new UAV aerial video dataset (ManipalUAVid) for semantic segmentation is presented. The videos have been acquired in a closed university campus, and fine annotation is provided for four background classes viz. constructions, greeneries, roads, and waterbodies. Also, the performance of four semantic segmentation approaches: Conditional Random Field (CRF), U-Net, Fully Convolutional Network (FCN) and DeepLabV3+ are analysed on ManipalUAVid dataset. It is seen that these algorithms perform competitively on UAV aerial video dataset and achieves an mIoU of 0.86, 0.86, 0.86 and 0.83 respectively.