Abstract

In this paper, we study the three-node Decode-and-Forward (D&F) relay network subject to random and burst packet erasures. The source wishes to transmit an infinite stream of packets to the destination via the relay. The three-node D&F relay network is constrained by a decoding delay of T packets, i.e., the packet transmitted by the source at time i must be decoded by the destination by time i + T . For the individual channels from source to relay and relay to destination, we assume a delay-constrained sliding-window (DCSW) based packet-erasure model that can be viewed as a tractable approximation to the commonly-accepted Gilbert-Elliot channel model. Under the model, any time-window of width w contains either up to a random erasures or else erasure burst of length at most b (≥ a). Thus the source-relay and relay-destination channels are modelled as (a 1 , b 1 , w 1 , T 1 ) and (a 2 , b 2 , w 2 , T 2 ) DCSW channels. We first derive an upper bound on the capacity of the three-node D&F relay network. We then show that the upper bound is tight for the parameter regime: max{b 1 , b 2 } | (T − b 1 − b 2 − max {a 1 , a 2 } + 1) by constructing streaming codes achieving the bound. The code construction requires field size linear in T , and has decoding complexity equivalent to that of decoding an MDS code.

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

)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

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

—Stack Overflow is a Community Question Answering service that attracts millions of users to seek answers to their questions. Maintaining high-quality content is necessary for relevant question retrieval, question recommendation, and enhancing the user experience. Manually removing low-quality content from the platform is time-consuming and challenging for site moderators. Thus, it is imperative to assess the content quality by automatically detecting and ‘closing’ the low-quality questions. Previous works have explored lexical, communitybased, vote-based, and style-based features to detect low-quality questions. These approaches are limited to writing styles, textual, and handcrafted features. However, these features fall short in understanding semantic features and capturing the implicit relationships between tags and questions. In contrast, we propose LQuaD (Low-Quality Question Detection), a multi-tier hybrid framework that, a) incorporates semantic information of questions associated with each post using transformers, b) includes the question and tag information that enables learning via a graph convolutional network. LQuaD outperforms the state-of-the-art methods by a 21% higher F1-score on the dataset of 2.8 million questions. Furthermore, we apply survival analysis which acts as a proactive intervention to reduce the number of questions closed by informing users to take appropriate action. We find that the timeframe between the stages from the question’s creation till it gets ‘closed’ varies significantly for tags and different ‘closing’ reasons for these questions. Index Terms—Stack Overflow, Community Question Answering, Low-quality questions

Abstract

A fire alarm system is mandatory for all nuclear and industrial establishments to protect the safety of the public, environment, and plant personnel from a plant fire and its potential effects on safe reactor operation. The system shall be provided for its rapid detection of fires and an adequate level of fire protection for structures, systems, and components important to safety. A fire that is not promptly extinguished will prevent essential plant safety functions from being performed. All the NPP(Nuclear power plants) and facilities are equipped with fire detection systems. Fire detection systems consist of thousands of fire sensors and associated electronics. Detectors need to be installed as per the design, selection, and installation criteria mentioned in the standards. This calls for procurement of thousands of various types of smoke detectors and associated electronics from private industry, which are met only from imports, as there is no established certified Indian manufacturer. Hence, there is a great need for the indigenous development of these detectors. Attempts are made, and feasibility prototypes are tested at ISS/EIG to develop smoke sensors, electronics, and communication protocols using simple techniques at throw-away costs