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Network slicing is one of the core features of the 5G network to meet the requirements of various network services, namely, enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC) by building an isolated virtual network of resources typically in a Network Function Virtualization (NFV) environment. We have built a 3GPP compliant 5G Core (5GC) testbed and enabled network slicing on it to provide eMBB, uRLLC, and mMTC services to end users. In this demonstration, we showcase the capabilities of our zero-touch framework of 5GC network slicing management and orchestration in an NFV environment with the network functions of 5GC and the applications on eMBB and uRLLC slice services. We believe that this work will guide Mobile Network Operator (MNO)s in building zero-touch autonomous network slices in 5GC along with their smart management and orchestration in closed loop automation. © 2021 IFIP.

Network slicing is one of the core features of the 5G network to meet the requirements of various network services, namely, enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC) by building an isolated virtual network of resources typically in a Network Function Virtualization (NFV) environment. We have built a 3GPP compliant 5G Core (5GC) testbed and enabled network slicing on it to provide eMBB, uRLLC, and mMTC services to end users. In this demonstration, we showcase the capabilities of our zero-touch framework of 5GC network slicing management and orchestration in an NFV environment with the network functions of 5GC and the applications on eMBB and uRLLC slice services. We believe that this work will guide Mobile Network Operator (MNO)s in building zero-touch autonomous network slices in 5GC along with their smart management and orchestration in closed loop automation. © 2021 IFIP.

Network slicing is one of the core features of the 5G network to meet the requirements of various network services, namely, enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC) by building an isolated virtual network of resources typically in a Network Function Virtualization (NFV) environment. We have built a 3GPP compliant 5G Core (5GC) testbed and enabled network slicing on it to provide eMBB, uRLLC, and mMTC services to end users. In this demonstration, we showcase the capabilities of our zero-touch framework of 5GC network slicing management and orchestration in an NFV environment with the network functions of 5GC and the applications on eMBB and uRLLC slice services. We believe that this work will guide Mobile Network Operator (MNO)s in building zero-touch autonomous network slices in 5GC along with their smart management and orchestration in closed loop automation. © 2021 IFIP.

Recognizing a license plate clearly while seeing a surveillance camera snapshot is often important in cases where the troublemaker vehicle(s) have to be identified. In many real world situations, these images are blurred due to fast motion of the vehicle and cannot be recognized by the human eye. For this kind of blurring, the kernel involved can be said to be a linear uniform convolution described by its angle and length. We propose a new de-blurring technique in this paper to parametrically estimate the kernel as accurately as possible with emphasis on the length estimation process. We use a technique which employs Hough transform in estimating the kernel angle. To accurately estimate the kernel length, a novel approach using the cepstral transform is introduced. We compare the de-blurred results obtained using our scheme with those of other recently introduced blind de-blurring techniques. The comparisons corroborate that our scheme can remove a large blur from the image captured by the camera to recover vital semantic information about the license plate., Comment: Accepted and Published in 2017 IEEE International Conference on Intelligent Computing and Control (I2C2),23 Jun - 24 Jun 2017, India

Background: Jala neti (nasal irrigation) is an ancient yogic practice believed to cleanse the nasal passages and enhance respiratory health. The objective of this study was to evaluate the immediate effect of jala neti on nasal airflow measured by peak nasal inspiratory flow (PNIF) in healthy volunteers.Methods: This was a pre-post study involving 60 healthy volunteers (16 males, 44 females) aged 18-25 years. Baseline PNIF measurements were taken before the participants performed jala neti under expert supervision. PNIF was re-measured immediately after the jala neti practice. Paired t-tests were used to compare pre and post PNIF values.Results: The mean±SD pre-neti PNIF was 84.56±12.56 L/min which increased significantly to 110.85±12.56 L/min post-neti (p=0.001). The pre and post PNIF values for males were 86.45±12.82 and 124.25±10.96 L/min respectively (p=0.02). For females, the values were 78.32±14.52 pre-neti and 98.65±12.78 L/min post-neti (p=0.04).Conclusion: Jala neti practice resulted in an immediate significant improvement in nasal inspiratory flow in healthy volunteers, suggesting it may be a beneficial practice for enhancing nasal airflow and respiratory health.