A Perceptive Model of Traffic Flow: Using Arduino Board

In general, traffic is being controlled by time delay of traffic signals. It creates much problem for public to wait for their corresponding signal. In a 4- Road junction if a lane contains more traffic whereas remaining lanes contain less traffic flow in such cases the public who are on lane 1(which has high traffic) has to wait for a long duration which is not favorable. This mean present traffic control system does not follow the dynamic flow of traffic which resembles the traffic flow with respect to number of vehicle moving per lane. Some technologies developed to resolve the traffic flow such as RFID technology [1] but these are limited to some situations. The present RFID tag and field sensors can work to the maximum limit of 80m to 100m in Metropolitan cities the normal traffic is considered to be of 80m to 100m [2] in this case RFID technology can’t resolve the traffic in an extensive manner. So considering all these cases we managed to employ a load cell [3] to rectify those abnormal situations. These work in the normal situations as well as in the abnormal conditions. This work involves the utilization of Arduino to operate the signals according to the condition. At the end this work achieved a solution to clear the traffic situations and peak hours in other aspects. The aim is to smooth control of vehicles at a junction. The present traffic congestion is represented in Fig. 1. Keywords: Arduino, Load cell, LCD (16*2), Traffic congestion, density based vehicle movement, smart cities, RFID technology

NOVEL RESOURCE EFFICIENT CIRCUIT DESIGNS FOR REBOOTING COMPUTING

CMOS based computing is reaching its limits. To take computation beyond Moores law (the number of transistors and hence processing power on a chip doubles every 18 months to 3 years) requires research explorations in (i) new materials, devices, and processes, (ii) new architectures and algorithms, (iii) new paradigm of logic bit representation. The focus is on fundamental new ways to compute under the umbrella of rebooting computing such as spintronics, quantum computing, adiabatic and reversible computing. Therefore, this thesis highlights explicitly Quantum computing and Adiabatic logic, two new computing paradigms that come under the umbrella of rebooting computing. Quantum computing is investigated for its promising application in high-performance computing. The first contribution of this thesis is the design of two resource-efficient designs for quantum integer division. The first design is based on non-restoring division algorithm and the second one is based on restoring division algorithm. Both the designs are compared and shown to be superior to the existing work in terms of T-count and T-depth. The proliferation of IoT devices which work on low-power also has drawn interests to the rebooting computing. Hence, the second contribution of this thesis is proving that Adiabatic Logic is a promising candidate for implementation in IoT devices. The adiabatic logic family called Symmetric Pass Gate Adiabatic Logic (SPGAL) is implemented in PRESENT-80 lightweight algorithm. Adiabatic Logic is extended to emerging transistor devices

Comparing Decision Making Using Expected Utility, Robust Decision Making, and Information-Gap: Application to Capacity Expansion for Airplane Manufacturing

Airplane manufacturing industry is a low-volume high-value industry; however, there is a very high uncertainty associated with it. The industry has long lead times and capacity expansion for such an industry requires huge capital investments. Therefore, capacity planning requires accurate demand forecasting based on the historical data. Various demand forecasting models based on the forecasted demand can serve as an influential tool for the decision making. Based on the profit requirements, cost saving, and the risk attitude of a decision maker, he or she may choose a different strategy. This primary purpose of this research is to model the uncertainty and analyze different decision-making approaches for long-term capacity planning for painting the Boeing 737 airplanes. The first part of the research focusses on identifying the underlying demand trends for the Boeing 737 and Boeing 777 airplane models based on the historical data. Probabilistic models were evaluated for the demand based on model assumptions and statistical analysis. The stochastic processes Brownian motion and a modified geometric Brownian motion were used to predict the demand for the Boeing 737 and Boeing 777 respectively for the next 20 years. The second part of the research focusses on decision making based on the forecasted demand for the Boeing 737 airplanes. The decision is when to construct new hangars to paint new airplanes. Three decision-making approaches were applied to this decision: expected utility, robust decision making, and information gap. Since significant uncertainty exists with the number of airplanes, it is important to compare the decision-making methodologies for different risk tolerances, probabilities, and required profits. The circumstances and assumptions favoring each of the decision-making philosophy under deep uncertainty was discussed and, based on the simulation results, the optimal strategies for the capacity expansion were summarized

Permutation Strikes Back: The Power of Recourse in Online Metric Matching

In the classical Online Metric Matching problem, we are given a metric space with $k$ servers. A collection of clients arrive in an online fashion, and upon arrival, a client should irrevocably be matched to an as-yet-unmatched server. The goal is to find an online matching which minimizes the total cost, i.e., the sum of distances between each client and the server it is matched to. We know deterministic algorithms~\cite{KP93,khuller1994line} that achieve a competitive ratio of $2k-1$, and this bound is tight for deterministic algorithms. The problem has also long been considered in specialized metrics such as the line metric or metrics of bounded doubling dimension, with the current best result on a line metric being a deterministic $O(\log k)$ competitive algorithm~\cite{raghvendra2018optimal}. Obtaining (or refuting) $O(\log k)$-competitive algorithms in general metrics and constant-competitive algorithms on the line metric have been long-standing open questions in this area. In this paper, we investigate the robustness of these lower bounds by considering the Online Metric Matching with Recourse problem where we are allowed to change a small number of previous assignments upon arrival of a new client. Indeed, we show that a small logarithmic amount of recourse can significantly improve the quality of matchings we can maintain. For general metrics, we show a simple \emph{deterministic} $O(\log k)$-competitive algorithm with $O(\log k)$-amortized recourse, an exponential improvement over the $2k-1$ lower bound when no recourse is allowed. We next consider the line metric, and present a deterministic algorithm which is $3$-competitive and has $O(\log k)$-recourse, again a substantial improvement over the best known $O(\log k)$-competitive algorithm when no recourse is allowed

Representation Learning With Hidden Unit Clustering For Low Resource Speech Applications

The representation learning of speech, without textual resources, is an area of significant interest for many low resource speech applications. In this paper, we describe an approach to self-supervised representation learning from raw audio using a hidden unit clustering (HUC) framework. The input to the model consists of audio samples that are windowed and processed with 1-D convolutional layers. The learned "time-frequency" representations from the convolutional neural network (CNN) module are further processed with long short term memory (LSTM) layers which generate a contextual vector representation for every windowed segment. The HUC framework, allowing the categorization of the representations into a small number of phoneme-like units, is used to train the model for learning semantically rich speech representations. The targets consist of phoneme-like pseudo labels for each audio segment and these are generated with an iterative k-means algorithm. We explore techniques that improve the speaker invariance of the learned representations and illustrate the effectiveness of the proposed approach on two settings, i) completely unsupervised speech applications on the sub-tasks described as part of the ZeroSpeech 2021 challenge and ii) semi-supervised automatic speech recognition (ASR) applications on the TIMIT dataset and on the GramVaani challenge Hindi dataset. In these experiments, we achieve state-of-art results for various ZeroSpeech tasks. Further, on the ASR experiments, the HUC representations are shown to improve significantly over other established benchmarks based on Wav2vec, HuBERT and Best-RQ

GR-273 Building a Chatbot

A chatbot is now a part of many online applications like Health Care, Education, E-commerce, etc. It made the conversation between the customers and the service providers much more convenient as the chatbot can answer most of the queries without human intervention from the website side. This saves a lot of time and work

Light‐Regulated Pro‐Angiogenic Engineered Living Materials

Regenerative medicine aims to restore damaged cells, tissues, and organs, for which growth factors are vital to stimulate regenerative cellular transformations. Major advances have been made in growth factor engineering and delivery like the development of robust peptidomimetics and controlled release matrices. However, their clinical applicability remains limited due to their poor stability in the body and need for careful regulation of their local concentration to avoid unwanted side-effects. In this study, a strategy to overcome these limitations is explored using engineered living materials (ELMs), which contain live microorganisms that can be programmed with stimuliresponsive functionalities. Specifically, the development of an ELM that releases a pro-angiogenic protein in a light-regulated manner is described. This is achieved by optogenetically engineering bacteria to synthesize and secrete a vascular endothelial growth factor peptidomimetic (QK) linked to a collagen-binding domain. The bacteria are securely encapsulated in bilayer hydrogel constructs that support bacterial functionality but prevent their escape from the ELM. In situ control over the release profiles of the proangiogenic protein using light is demonstrated. Finally, it is shown that the released protein is able to bind collagen and promote angiogenic network formation among vascular endothelial cells, indicating the regenerative potential of these ELMs

Look Before, Before You Leap: Online Vector Load Balancing with Few Reassignments

In this paper we study two fully-dynamic multi-dimensional vector load balancing problems with recourse. The adversary presents a stream of n job insertions and deletions, where each job j is a vector in ?^d_{? 0}. In the vector scheduling problem, the algorithm must maintain an assignment of the active jobs to m identical machines to minimize the makespan (maximum load on any dimension on any machine). In the vector bin packing problem, the algorithm must maintain an assignment of active jobs into a number of bins of unit capacity in all dimensions, to minimize the number of bins currently used. In both problems, the goal is to maintain solutions that are competitive against the optimal solution for the active set of jobs, at every time instant. The algorithm is allowed to change the assignment from time to time, with the secondary objective of minimizing the amortized recourse, which is the average cardinality of the change of the assignment per update to the instance. For the vector scheduling problem, we present two simple algorithms. The first is a randomized algorithm with an O(1) amortized recourse and an O(log d/log log d) competitive ratio against oblivious adversaries. The second algorithm is a deterministic algorithm that is competitive against adaptive adversaries but with a slightly higher competitive ratio of O(log d) and a per-job recourse guarantee bounded by O?(log n + log d log OPT). We also prove a sharper instance-dependent recourse guarantee for the deterministic algorithm. For the vector bin packing problem, we make the so-called small jobs assumption that the size of all jobs in all the coordinates is O(1/log d) and present a simple O(1)-competitive algorithm with O(log n) recourse against oblivious adversaries. For both problems, the main challenge is to determine when and how to migrate jobs to maintain competitive solutions. Our central idea is that for each job, we make these decisions based only on the active set of jobs that are "earlier" than this job in some ordering ? of the jobs

Discovery of a high-performance phage-derived promoter/repressor system for probiotic lactobacillus engineering

Background: The Lactobacillus family comprises many species of great importance for the food and healthcare industries, with numerous strains identified as beneficial for humans and used as probiotics. Hence, there is a growing interest in engineering these probiotic bacteria as live biotherapeutics for animals and humans. However, the genetic parts needed to regulate gene expression in these bacteria remain limited compared to model bacteria like E. coli or B. subtilis. To address this deficit, in this study, we selected and tested several bacteriophage-derived genetic parts with the potential to regulate transcription in lactobacilli. Results: We screened genetic parts from 6 different lactobacilli-infecting phages and identified one promoter/repressor system with unprecedented functionality in L. plantarum WCFS1. The phage-derived promoter was found to achieve expression levels nearly 9-fold higher than the previously reported strongest promoter in this strain and the repressor was able to almost completely repress this expression by reducing it nearly 500-fold. Conclusions: The new parts and insights gained from their engineering will enhance the genetic programmability of lactobacilli for healthcare and industrial applications. Competing Interest Statement: A patent application has been filed based on the results of this work (Application no. is DE 102022 119024.2)

Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing

Single-image haze removal is a long-standing hurdle for computer vision applications. Several works have been focused on transferring advances from image classification, detection, and segmentation to the niche of image dehazing, primarily focusing on contrastive learning and knowledge distillation. However, these approaches prove computationally expensive, raising concern regarding their applicability to on-the-edge use-cases. This work introduces a simple, lightweight, and efficient framework for single-image haze removal, exploiting rich "dark-knowledge" information from a lightweight pre-trained super-resolution model via the notion of heterogeneous knowledge distillation. We designed a feature affinity module to maximize the flow of rich feature semantics from the super-resolution teacher to the student dehazing network. In order to evaluate the efficacy of our proposed framework, its performance as a plug-and-play setup to a baseline model is examined. Our experiments are carried out on the RESIDE-Standard dataset to demonstrate the robustness of our framework to the synthetic and real-world domains. The extensive qualitative and quantitative results provided establish the effectiveness of the framework, achieving gains of upto 15\% (PSNR) while reducing the model size by $\sim$20 times.Comment: Preprint version. Accepted at Opti