Abstract

Currently, around half of the world's population lives in urban areas, which is only expected to increase in the future. The urban water resources took the brunt of this uncontrolled urbanization and expansion resulting in a swift decline in lakes and ponds in the cities. Bengaluru is one of the most prominent cities in India where the urban water body areas have fallen drastically over the years. This study attempts to assess the impact of urban growth in reducing the surface area of the water bodies in Bengaluru. The study used decadal Land Use Land Cover (LULC) maps between 1988 to 2020 generated from Landsat 4–5 and 8 datasets using the Maximum Likelihood (ML) technique to study and understand urban sprawl in Bengaluru and the changes occurring in water spread area. The results indicate that Bengaluru city has undergone a massive growth from 100 Sq. km to 600 Sq. km between 1988 to 2020. This excessive sprawl has blocked the ephemeral streams that replenish the water bodies, thus causing heavy damage not only to water bodies in the region but also to the vegetation present in the periphery of the expanding city.

Abstract

International Conference on Emerging Frontiers in Electrical and Electronic Technologies intends to provide scientists, researchers, academicians, and industry personnel with a forum to deliberate exchange ideas, and produce research collaborations for the technologies those have the potential to significantly impact the domains of Power system, Power Converters, Control systems, Machine Drives, Embedded systems, Measurement and Sensing technologies.

Abstract

We propose a pair of deep learning models, which employ unsupervised pretraining, attention mechanisms and contrastive learning for representation learning from dictionary definitions, and definition modeling from such representations. Our systems, the Transformers for Learning Dictionaries and Representations (TLDR), were submitted to the SemEval 2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE), where they officially ranked first on the definition modeling subtask, and achieved competitive performance on the reverse dictionary subtask. In this paper we describe our methodology and analyse our system design hypotheses.

Abstract

)e temperament (Mizaj) of the body is an essential constituent for health conservancy and diagnosis of several diseases. Hence, general body temperament and uterine dystemperament (Su’-i-Mizaj) with abnormal vaginal discharge (Salayan al-Rahim) need evaluation. In addition, we also applied a computational intelligence technique for enhancing scientific validity to classify the warm-cold and wet-dry temperaments. )is trial included a total of 66 participants with a vaginal discharge of reproductive age. Data included demographic characteristics of the participants, symptoms associated with vaginal discharge, women’s general temperament, and symptoms of uterine dystemperament. Correlation between general body temperament and age, abnormal vaginal discharge, and its associated symptoms was also performed. Additionally, we used the Support Vector Machine-Radial Basis Function (SVM-RBF) model to classify the warm-cold and wet-dry temperaments. Warm general temperament was highly prevalent (77.27%), followed by moderate (19.69%) on the warm-cold temperament scale. In wet-dry temperament, moderate general body temperament (50%) was more prevalent. In warm-cold and wet-dry scores, 78.78% and 74.24% had warm and wet uterine dystemperament, respectively. )e age and symptoms were correlated with general temperament. A strong positive correlation was found between warm general temperament and warm dystemperament of the uterus (r � 0.40, p < 0.009). In addition, our SVM-RBF CV-5 classification model achieved the highest accuracy (99.2%). Our results showed that vaginal discharge is more common in warm general temperament and warm-wet dystemperament of the uterus. )e same has been proven by computational intelligence. Nevertheless, vaginal discharge can also happen in normal and other temperaments.

Abstract

The distributed nature of Internet of Things (IoT) and fog computing mandates that the security provided to the resources of networking and data on these applications is based on a distributed structure. The nodes in a fog computing environment should be working with equal capacities without any necessity of trust among them, in order to allow the layers and infrastructure that constitute the stack of a fog node to be owned and managed by different entities. This idea of security necessitates the need for distributed trust in fog computing that can be realized using the concept of blockchain technology. Blockchain is a distributed and

Abstract

Long documents like contracts, financial documents, etc., are often tedious to read through. Linearly consuming (via scrolling or navigation through default table of content) these documents is time-consuming and challenging. These documents are also authored to be consumed by varied entities (referred to as persona in the paper) interested in only certain parts of the document. In this work, we describe DYNAMICTOC, a dynamic table of contentbased navigator, to aid in the task of non-linear, persona-based document consumption. DYNAMICTOC highlights sections of interest in the document as per the aspects relevant to different personas. DYNAMICTOC is augmented with short questions to assist the users in understanding underlying content. This uses a novel deep-reinforcement learning technique to generate questions on these persona-clustered paragraphs. Human and automatic evaluations suggest the efficacy of both end-to-end pipeline and different components of DYNAMICTOC.

Abstract

The Hopfield network is an example of an artificial neural network used to implement associative memories. A binary digit represents the neuron’s state of a traditional Hopfield neural network. Inspired by the human brain’s ability to cope simultaneously with multiple sensorial inputs, this paper presents three multi-modal Hopfield-type neural networks that treat multi-dimensional data as a single entity. In the first model, called the vector-valued Hopfield neural network, the neuron’s state is a vector of binary digits. Synaptic weights are modeled as finite impulse response (FIR) filters in the second model, yielding the so-called convolutional associative memory. Finally, the synaptic weights are modeled by linear time-varying (LTV) filters in the third model. Besides their potential applications for multi-modal

Abstract

Progress in 3D object understanding has relied on manually canonicalized shape datasets that contain instances with consistent position and orientation (3D pose). This has made it hard to generalize these methods to in-the-wild shapes, eg., from internet model collections or depth sensors. ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds. We build on top of Tensor Field Networks (TFNs), a class of permutation- and rotation-equivariant, and translation-invariant 3D networks. During inference, our method takes an unseen full or partial 3D point cloud at an arbitrary pose and outputs an equivariant canonical pose. During training, this network uses self-supervision losses to learn the canonical pose from an un-canonicalized collection of full and partial 3D point clouds. ConDor can also learn to consistently co-segment object parts without any supervision. Extensive quantitative results on four new metrics show that our approach outperforms existing methods while enabling new applications such as operation on depth images and annotation transfer.

Abstract

This work presents an ultra low phase noise 30 GHz oscillator topology in 14 nm technology for an active mode Micro-electro-mechanical systems (MEMS) based resonator that utilizes resonant-fin transistors (RFT). A novel oscillator architecture has been presented for the active mode RFT, which can be modelled as a voltage controlled current source with 270° phase shift between its output current and input voltage. The proposed oscillator has been designed in 14 nm GF technology and simulation results show that it achieves phase noise less than −144 dBc/Hz at 1 MHz offset for 30 GHz carrier frequency for active mode RFT with quality factor of 10,000, while consuming 5.5 mW power from 0.8 V supply.

Abstract

Fully monolithic carrier synthesis at mmWave frequencies (>10 GHz) seek on-chip high quality factor resonators in micro-electromechanical systems (MEMS) technology. However, at mmWave frequencies, MEMS resonator’s usage is severely limited by its high static capacitance (C 0 ) and low electromechanical coupling. In this work, we propose a technique of partial cancellation of C 0 of mmWave MEMS resonator to utilize it for the development of an extremely low phase noise oscillator. Based on the proposed technique, we also present a methodology for low phase noise fully monolithic mmWave oscillator design. To validate the proposed technique and methodology, design and simulation results of an oscillator with 10.33 GHz on-chip MEMS resonator model in 65 nm CMOS have been also presented in this paper. Simulation results show that the oscillator achieves phase noise and FoM of -137 dBc/Hz and 212.8 dBc/Hz, respectively at 1 MHz offset, while consuming 2.8 mW power from 1 V supply.