Abstract

Buildings damaged in past earthquakes have exemplified the poor performance of reinforced concrete moment resisting frame (RC MRF) buildings due to inadequate design and wrong construction practices. The only way to avoid damage to existing buildings is to retrofit them. There is a large stock of RC MRF buildings constructed as per old Indian standards. With the latest revisions in codal provisions, these buildings are considered to be inadequately designed. Therefore, a large stock of existing buildings needs to be retrofitted. However, retrofitting of houses is not a common practice among the general public in India. To encourage retrofitting among the general public, the efficacy of retrofitting needs to be proven with the well-laid process. In this paper, a case study is done to understand the performance of G+5 storey RC MRF precode building after partial and full column retrofitting. Pushover Analysis (POA) is performed to obtain the capacity of the precode building. The capacity is compared with the seismic demand of that area to decide whether retrofitting is required or not. Initially, ground storey columns are retrofitted and POA is performed to study the damage distribution and obtain the updated capacity curve. It is observed that damage shifted to the first storey. Further, the first storey columns are retrofitted and POA is performed again. From the hinge mechanism, it is observed that the damage propagated to the second storey. Further, the second storey columns are retrofitted and POA is performed again to obtain updated capacity and

Abstract

Estimating the time, location and the size of an earthquake event using neural networks is an emerging technology. Present study aims to predict the near future earthquake events in each of the regions partitioned as clusters of epi-central locations of earthquakes related to Indian subcontinent. K-means clustering algorithm is applied to develop a catalogue of earthquake epicenters. Two approaches are considered for the purpose of forecasting. In the first approach regions obtained as clusters are considered as the area sources. The distribution of time series of (temporal distribution) events are divided into five time periods viz., 15 days, 30 days, 60 days, 90 days and 180 days. The accuracy of forecasting a magnitude for the next time period is studied using application of Recurrent Neural Network In the second approach the seismic data of each cluster is further grouped into three categories of magnitude viz., minor (≤ 4 Mw), moderate (between 4 Mw and 6 Mw) and strong (> 6 Mw). Accuracy of forecasting for each of the group belonging to the clusters is computed considering the same five time periods. The epicentral locations represented as latitude and longitude is the inputs for recurrent neural network. The results show that accuracy of second approach is significantly more than first approach. Finally, an attempt has been made to compare the accuracy level of Recurrent Neural Network method with the accuracy level of Gutenberg Richter law in predicting the earthquakes for future 60 days. It has been observed that Gutenberg-Richter law poorly forecasts near future earthquake.

Abstract

An extensive research of MPC protocols in different adversarial settings over the past few years has led to various improvements in this domain. Goyal et al.[14] in their paper addressed the issue of an efficient MPC protocol in active adversarial setting by removing the dependency on multiplication depth Dm in the arithmetic circuit. This development was followed by Hirt et al.[16] which proposed an efficient MPC protocol tolerating mixed adversary with communication complexity of O ((ci+ cm+ co) nκ+ ciBA (κ)+ Dm (n3κ+ nBA (κ))) bits, where Dm is the multiplicative depth of the circuit and κ is the size of an element in the field. Additionally, Hirt et al.[16], proposed an open problem to construct a protocol for the mixed adversarial setting, independent of the multiplicative depth Dm, with linear communication complexity. In this paper, we resolve this problem in the affirmative by providing an efficient MPC protocol in the mixed adversarial setting independent of the multiplicative depth of the circuit.

Abstract

Locally recoverable codes (LRCs) are known to reduce the repair locality, i.e., the number of nodes accessed during node repair, in distributed storage systems. Regenerating codes, on the other hand, offer a decreased repair bandwidth, which is the repair traffic incurred during node repairs. Locally regenerating codes (LRGCs), which are constructed via intertwining regenerating codes of smaller block lengths, simultaneously offer a small repair locality and repair bandwidth. In this letter, we construct a family of LRGCs where the constituent codes are bandwidth-efficient, minimum bandwidth cooperative regenerating codes. The newly constructed LRGCs are optimal with respect to both minimum distance and rate.

Abstract

In distributed storage systems, cooperative regenerating codes tradeoff storage for repair bandwidth in the case of multiple node failures. In rack-aware distributed storage systems, there is no cost associated with transferring symbols within a rack. Hence, the repair bandwidth will only take into account cross-rack transfer. Rack-aware regenerating codes for the case of single node failures have been studied and their repair bandwidth tradeoff characterized. In this paper, we consider the framework of rack-aware cooperative regenerating codes for the case of multiple node failures where the node failures are uniformly distributed among a certain number of racks. We characterize the storage repair-bandwidth tradeoff as well as derive the minimum storage and minimum repair bandwidth points of the tradeoff. We also provide constructions of minimum bandwidth rack-aware cooperative regenerating codes for all parameters.

Abstract

Rural areas are often neglected while deploying newer mobile technologies. Hence, these place are digitally disconnected from the world. To overcome this challenge, 5G network slicing supporting multi-tenancy, also known as neutral host network, is studied to improve rural connectivity. The infrastructure provider (InP) deploys the rural 5G network and mobile network operators (MNOs) lease the slices from InP to serve their end-users. This aims to study the value network configuration (VNC) for the 5G network slicing architecture to understand the possible business model. As a result, three configurations are defined driven by micro-operator, MNO and community end-users respectively. The business models are constructed using SWOT analysis and business canvas models. The revenue streams for the proposed rural network are analyzed.

Abstract

In this paper, we propose spectrum sensing schemes for a secondary user (SU) with multiple antennas to detect a primary user (PU) transmission in a cognitive mmWave network. The channel model considered at mmWave carrier frequencies is the clustered Rician fading channel, which has few multipaths. For the considered scenario, we propose three beamformed energy detection (BFED) schemes where beamforming is done in the dominant direction of arrival (DoA) at the SU and then energy detection (ED) is applied. The three schemes differ in the amount of information assumed about the DoAs at the SU. The performance of these schemes has been compared with the traditional ED and maximal ratio combining (MRC) schemes for multiantenna systems. It is shown through simulations that the proposed BFED approaches provide significant performance gains over the ED and negligible loss as compared to the MRC, which makes an impractical assumption of the channel between the PU and the SU to be exactly known.

Abstract

The design of monolithic power management circuits is critical in energy harvesting applications. A Pulse width modulation (PWM) boost converter with feedforward and feedback control mitigates the changes in input-output parameters and avoids the use of compensation circuit. However, the conventional design of a clock generator in feedback path employs power hungry comparators and latches. This paper proposes an improved design of clock generator replacing comparators and latches with a schmitt trigger circuit. As a result, a low power and a low ripple PWM was acheived. The design was implemented in UMC 180 nm CMOS technology generating a stable output of 3.3 V for an input range of 1.2 V - 1.6 V. Due to the application of the proposed technique, a reduced power consumption of 60.19 mW and low ripple voltage of 18.29 mV was achieved, for a maximum load current of 100 mA at an efficiency of 98%. Thus making the design suitable for energy harvesting applications.

Abstract

Model checking is now a standard technology for verifying large and complex systems. While there are a range of tools and techniques to verify various properties of a system under consideration, in this work, we restrict our attention to safety checking procedures using explicit state space generation. The necessary hardware resources required in this approach depends on the model complexity and the resulting state transition graph that gets generated. This cannot be estimated apriori. For reasonably realistic models, the available main memory in even high end servers may not be sufficient. Hence, we have to use distributed safety verification approaches on a cluster of nodes. However, the problem of estimating the minimum number of nodes in the cluster for the verification procedure to complete successfully remains unsolved. In this paper, we propose a dynamically scalable model checker using an actor based architecture. Using the proposed approach, an end user can invoke a model checker hosted on a cloud platform in a push button fashion. Our safety verification procedures automatically expands the cluster by requesting more virtual machines from the cloud provider. Finally, the user gets to pay only for the hardware resources he rented for the duration of the verification procedure. We refer to this as Model Checking as Service. We approach this problem by proposing an asynchronous algorithm for safety checking in actor framework. The actor based approach allows for scaling the resources on a need basis and redistributes the work load transparently through state migration. We tested our approach by developing a distributed version of SpinJA model checker using Akka actor framework. We conducted our experiments on Google Cloud Engine (GCE) platform wherein we scale our resources automatically using the GCE API. On large models such as anderson.8 from BEEM benchmark suite, our approach reduced the model checking cost in dollars by 8.6x while reducing the wall clock time to complete the safety checking procedure 5.5x times.

Abstract

In one aspect, a computerized method of automatically generating video content using a one click artificial-intelligence assistant for generating an animation video includes the step of providing a do-it-yourself (DIY) computer animation generation system. The DIY computer animation generation system includes an animation generation dashboard. The method includes the step of providing a one click AI assistant for generating an animation video in the DIY computer animation generation system. The method includes the step of providing a one click virtual button that is displayed in the animation generation dashboard. The one click AI assistant automatically suggests a set of animation choices to a user on a single button press of the one click virtual button.