International Institute of Information Technology, Hyderabad

About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Computer science & Authentication. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).

A new efficient and unbiased approach for clustering quality evaluation

Abstract: Traditional quality indexes (Inertia, DB, …) are known to be method-dependent indexes that do not allow to properly estimate the quality of the clustering in several cases, as in that one of complex data, like textual data. We thus propose an alternative approach for clustering quality evaluation based on unsupervised measures of Recall, Precision and F-measure exploiting the descriptors of the data associated with the obtained clusters. Two categories of index are proposed, that are Macro and Micro indexes. This paper also focuses on the construction of a new cumulative Micro precision index that makes it possible to evaluate the overall quality of a clustering result while clearly distinguishing between homogeneous and heterogeneous, or degenerated results. The experimental comparison of the behavior of the classical indexes with our new approach is performed on a polythematic dataset of bibliographical references issued from the PASCAL database.

Quo Vadis, Skeleton Action Recognition?

Abstract: In this paper, we study current and upcoming frontiers across the landscape of skeleton-based human action recognition. To study skeleton-action recognition in the wild, we introduce Skeletics-152, a curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset. We extend our study to include out-of-context actions by introducing Skeleton-Mimetics, a dataset derived from the recently introduced Mimetics dataset. We also introduce Metaphorics, a dataset with caption-style annotated YouTube videos of the popular social game Dumb Charades and interpretative dance performances. We benchmark state-of-the-art models on the NTU-120 dataset and provide multi-layered assessment of the results. The results from benchmarking the top performers of NTU-120 on the newly introduced datasets reveal the challenges and domain gap induced by actions in the wild. Overall, our work characterizes the strengths and limitations of existing approaches and datasets. Via the introduced datasets, our work enables new frontiers for human action recognition.

A −40 dB EVM, 77 MHz Dual-Band Tunable Gain Sub-Sampling Receiver Front End in 65-nm CMOS

Abstract: In this paper, the first dual-band sub-sampling receiver front end with sampling frequency optimization to meet the ultimate receiver error vector magnitude (EVM) of −40 dB over wide input power range of 19 dB is proposed. A systematic sub-sampling receiver chain EVM optimization with respect to major system-level impairments, such as noise folding, sampling frequency, IQ mismatches, phase noise of the sub-sampling clock, and unit capacitor value realizable at the decimation filter, is presented. The proposed dual-band sub-sampling receiver has a 26–41 dB continuously tunable gain for 2.4 GHz and 26–38.5 dB for the 5 GHz WLAN band. Continuously tunable gain ensures the ultimate receiver EVM performance over wider input power levels. In addition, the 5 GHz band is continuously tunable from 4.5 to 5.7 GHz. An active balun feedback low-noise amplifier followed by a sub-sampling down-conversion mixer is implemented to down-convert both WLAN bands to an intermediate frequency in the range from 445 to 538 MHz. Sub-sampling frequency optimization proposed in this paper down-converts both WLAN bands with the sampling frequency from 1.78 to 2.15 GHz to reach the target EVM. Additionally, a switched capacitor decimation filter running at 90 MHz is implemented to provide dual functionalities of down-conversion to baseband and band selection. A test-chip is implemented in a 1.2 V 65-nm CMOS technology. The proposed dual-band sub-sampling receiver occupies a total active area of 0.72 mm2 and has a total power dissipation of 55.6 mW. The overall receiver chain shows a noise figure of 11.5 dB at the highest gain and an IIP3 of −8 dBm at the lowest gain.

Model Predictive Control for Autonomous Driving Based on Time Scaled Collision Cone

Abstract: In this paper, we present a Model Predictive Control (MPC) framework based on path velocity decomposition paradigm for autonomous driving. The optimization underlying the MPC has a two layer structure where in first, an appropriate path is computed for the vehicle followed by the computation of optimal forward velocity along it. The very nature of the proposed path velocity decomposition allows for seamless compatibility between the two layers of the optimization.A key feature of the proposed work is that it offloads most of the responsibility of collision avoidance to velocity optimization layer for which computationally efficient formulations can be derived. In particular, we extend our previously developed concept of time scaled collision cone (TSCC) constraints and formulate the forward velocity optimization layer as a convex quadratic programming problem.We perform validation on autonomous driving scenarios wherein proposed MPC repeatedly solves both the optimization layers in receding horizon manner to compute lane change, overtaking and merging maneuvers among multiple dynamic obstacles.