Abstract

In this article, a novel adaptive controller is designed for Euler-Lagrangian systems under predefined time-varying state constraints. The proposed controller could achieve this objective without a priori knowledge of system parameters and, crucially, of state-dependent uncertainties. The closed-loop stability is verified using the Lyapunov method, while the overall efficacy of the proposed scheme is verified using a simulated robotic arm compared to the state of the art.

Abstract

Safe autonomous driving critically depends on how well the ego-vehicle can predict the trajectories of neighboring vehicles. To this end, several trajectory prediction algorithms have been presented in the existing literature. Many of these approaches output a multi-modal distribution of obstacle trajectories instead of a single deterministic prediction to account for the underlying uncertainty. However, existing planners cannot handle the multi-modality based on just sample-level information of the predictions. With this motivation, this paper proposes a trajectory optimizer that can leverage the distributional aspects of the prediction in a computationally tractable and sample-efficient manner. Our optimizer can work with arbitrarily complex distributions and thus can be used with output distribution represented as a deep neural network. The core of our approach is built on embedding distribution in Reproducing Kernel Hilbert Space (RKHS), which we leverage in two ways. First, we propose an RKHS embedding approach to select probable samples from the obstacle trajectory distribution. Second, we rephrase chance-constrained optimization as distribution matching in RKHS and propose a novel sampling-based optimizer for its solution. We validate our approach with hand-crafted and neural network-based predictors trained on real-world datasets and show improvement over the existing stochastic optimization approaches in safety metrics.

Abstract

Music listening is a dynamic process that entails complex interactions between sensory, cognitive, and emotional processes. The naturalistic paradigm provides a means to investigate these processes in an ecologically valid manner by allowing experimental settings that mimic real-life musical experiences. In this paper, we highlight the importance of the naturalistic paradigm in studying dynamic music processing and discuss how it allows for investigating both the segregation and integration of brain processes using model-based and model-free methods. We further suggest that studying individual difference-modulated music processing in this paradigm can provide insights into the mechanisms of brain plasticity, which can have implications for the development of interventions and therapies in a personalized way. Finally, despite the challenges that the naturalistic paradigm poses, we end with a discussion on future prospects of music and neuroscience research, especially with the continued development and refinement of naturalistic paradigms and the adoption of open science practices.

Abstract

This study investigates the impact of loss-framing and individual risk attitude on willingness- to purchase insurance products utilizing a game-like interface as choice architecture. The application presents events as experienced in real life. Both financial and emotional loss-framing events are followed by choices to purchase insurance. The participant cohorts considered were undergraduate students and older participants; the latter group was further subdivided by income and education. The within-subject analysis reveals that the loss framing effect on insurance consumption is higher in the younger population, though contingent on the insurance product type. Health and accident insurance shows a negative correlation with risk attitudes for younger participants and a positive correlation with accident insurance for older participants. Risk attitude and life insurance products showed no dependency. The findings elucidate the role of age, income, family responsibilities, and risk attitude in purchasing insurance products. Importantly, it confirms the heuristics of framing/nudging.

Abstract

Remote Labs refer to an end-to-end system, including hardware and software built to access scientific equipment and resources remotely. The software platform built for such purposes needs to be robust enough to handle the communication of inputs and outputs between the client and the hardware nodes with minimal latency and simultaneously provide a seamless user experience. This paper highlights the importance of scalability in Remote Labs and presents multi-user multiplexing as a solution, to essentially provide users with concurrent access to the hardware node for experiments which can generate outputs instantaneously. The paper discusses the inefficiency of existing web-based Remote Labs with 4-layered architectures and proposes the use of WebSocket with a 3-layer software architecture to enhance user experience, accelerate input-output communication and implement multi-user multiplexing. To showcase the effectiveness of the proposed architecture over existing implementations using Blynk IoT platform as the middleware, a comprehensive communication pipeline was developed from scratch to perform Kirchhoff’s Voltage Law (KVL) experiment remotely.

Abstract

This paper proposes deep architectures for learning instance-specific abstain (reject) option multiclass classifiers. The proposed approach uses novel bounded multiclass abstention loss for multiclass classification as a performance measure. This approach uses rejection cost as the rejection parameter in con- trast to coverage-based approaches. To show the effectiveness of the proposed approach, we experiment with several real-world datasets and compare them with state-of-the-art coverage-based and cost-of-rejection-based techniques. The ex- perimental results show that the proposed method improves performance over the state-of-the-art approaches.

Abstract

The increase of IoT devices and the emergence of smart cities have revolutionized urban development, offering numerous benefits while addressing environmental concerns. This has caused an increase in the usage of IoT frameworks, and the need for efficient architecture and standardized ontology is imminent. In this regard, we propose a distributed, multi-layered data platform architecture comprising the Data Monitoring Layer (DML), Data Storage Layer (DSL), Data Enhancement Layer (DEnL), and Data Exchange Layer (DEL). Our architecture achieves interoperability, facilitates data transfer between nodes, enables telemetry data retrieval, and ensures cross-platform and cross-device compatibility. It addresses the challenges of handling increased sensor data and user demands by providing high throughput and scalability support. We investigated Smart City Living Lab at IIIT Hyderabad an existing large-scale system deployed within a 66-acre campus. This system consists of 291 nodes. By studying this deployed system, we were able to gather valuable real-world data, allowing us to analyze the challenges and potential solutions related to data architecture. Our results show improvements of up to 41.23\% in throughput and a decrease in latency by 29.19\% for data insertion from the sensor nodes. The retrieval by the data client gives an increase of over 800\% in both throughput and number of requests through DENL. These metrics are compared to a centralised data platform architecture. We conclude by discussing the implications of our findings and suggesting future work.

Abstract

This paper talks over an efficient VLSI realization of the simplified arithmetic radix-2 Decimation In Time (DIT) Fast Fourier Transform (FFT) technique. High performance FFT processors are used in several types of signal processing, and in order to fulfil these performance demands, the processor must be parallel and pipelined. This enhanced radix-2 method is used to create an improved ASIC design that implements a fully pipelined and parallel architecture for the hardware realization of a 64 point FFT. This scheme uses lesser multipliers by reducing bit-width of twiddle representation for improved power, area and speed with a considerable Signal to Noise Ratio (SNR). A reduction of 32.8% is obtained in the area occupied, 25.08% reduction in power consumption and 20.8% improvement in speed in comparison to the Cooley–Tukey FFT realization. Compared with serial implementation an improvement of 90

Abstract

Respiratory diseases contribute to a majority of deaths worldwide every year. Diseases such as asthma, bronchitis and pneumonia also adversely impact a person’s social and economic conditions. They can seriously threaten their health if left undiagnosed and untreated. Techniques such as auscultation are used in the diagnosis of most respiratory diseases. However, using such techniques requires an experienced physician and the diagnosis is subjective. To overcome these challenges, in this work, a portable handheld system has been proposed and a proof of concept implemented to detect and classify respiratory diseases automatically through the use of convolutional neural networks (CNN) running on mobile platforms. Mel spectrograms are generated from the audio signals and fed to the CNN for classification. This work makes use of the publicly available HF Lung dataset. The classification accuracy achieved by the current implementation using a Raspberry Pi for processing is 80.55% with a sensitivity of 95.65% and specificity of 98.80% on the HF Lung dataset.

Abstract

One of the widely used peak reduction methods in smart grids is demand response, where one analyzes the shift in customers’ (agents’) usage patterns in response to the signal from the dis- tribution company. Often, these signals are in the form of incentives offered to agents. This work studies the effect of incentives on the prob- abilities of accepting such offers in a real-world smart grid simulator, PowerTAC. We first show that there exists a function that depicts the prob- ability of an agent reducing its load as a func- tion of the discounts offered to them. We call it reduction probability (RP). RP function is fur- ther parametrized by the rate of reduction (RR), which can differ for each agent. We provide an optimal algorithm, MJS–EXPRESPONSE, that outputs the discounts to each agent by maximiz- ing the expected reduction under a budget con- straint. When RRs are unknown, we propose a Multi-Armed Bandit (MAB) based online algo- rithm, namely MJSUCB–EXPRESPONSE, to learn RRs. Experimentally we show that it exhibits sub- linear regret. Finally, we showcase the efficacy of the proposed algorithm in mitigating demand peaks in a real-world smart grid system using the Power- TAC simulator as a test bed.