Abstract

Empirical investigations demonstrate similar cognitive processing patterns for objects and sexualized women. However, sexual objectification (SO) extends beyond sexualized women. To explore SO, we apply eye-tracking technique in conjunction with local/global and body-inversion paradigms. Ninety-four college students participated in the study. The visual gaze on non-sexualized South-Asian wo(men) images and the response time in Navon task post- priming with upright and inverted images is analyzed. Results indicate that participants of both genders gaze objectify females. Interestingly, male images are also processed as objects and body-inversion effect is observed. A comparison of attention allocation to face versus sexual body parts in upright versus inverted female images shows a reduced face- to-body ratio for the latter orientation, indicating a gender- specific attention shift. Combining the two SO theories, the study objectively substantiates the claim that women undergo objectification in even in non-sexual attire.

Abstract

Investors rely on judgmental heuristics and comparative analysis for future stock price prediction based on specific components of information in hand. Information components are used as anchors for price estimation. Through an eye-tracking experiment, we aim to understand the perceived significance of various formats of information, particularly focusing on graphical and numerical components, and to explore the influence of complex time-varying patterns in stock price line plots. Results show that graphical components capture higher visual attention. Participants are not always loss-averse and prominently exhibit disposition effects for investment decisions in profitable scenarios. The 52-week high is allotted the highest fixation duration, signifying its perception as a strong reference point. Investment choices were found to be varying based on levels of prior knowledge and experience. The visual gaze analysis provides behavioural insights into complex decision-making processes.

Abstract

Various studies have shown the advantages of using Machine Learning (ML) techniques for analog and digital IC design automation and optimization. Data scarcity is still an issue for electronic designs, while training highly accurate ML models. This work proposes generating and evaluating artificial data using generative adversarial networks (GANs) for circuit data to aid and improve the accuracy of ML models trained with a small training data set. The training data is obtained by various simulations in the Cadence Virtuoso, HSPICE, and Microcap design environment with TSMC 180 nm and 22 nm CMOS technology nodes. The artificial data is generated and tested for an appropriate set of analog circuits and digital cells. The experimental results show that the proposed artificial data generation significantly improves ML models and reduces the percentage error by more than 50% of the original percentage error, which were previously trained with insufficient data. Furthermore, this research aims to contribute to the extensive application of AI/ML in the field of VLSI design and technology by relieving the training data availability-related challenges.

Abstract

In this article, an incoherent beam combination of higher-order Gaussian beams through atmospheric turbulence is studied. An analytical expression of the combined intensity and spot size of higher-order Gaussian beams such as Hermite Gaussian (HG), Laguerre Gaussian (LG), and Bessel Gaussian (BG) are derived. The performance of these higher-order Gaussian beams is analyzed in various modes including the effect of beam wander, jitter, bore-sight error, Strehl ratio, and Visibility. A series of analytical simulations shows the intensity variation of 19 higher-order combined beams. Spot size, peak, and average intensity comparisons are made between various modes of higher-order Gaussian beam combinations. It is seen that the spot size of the combined beam increases rapidly in a higher mode of HG and LG beam. We evaluate the efficiency of combining beams at different distances, noting that it increases with higher mode orders and reaches its maximum with the 𝐻𝐺22 mode. Additionally, we explore the performance of higher-order Gaussian beam combinations under varying ground turbulence conditions. We observe that higher modes such as 𝐻𝐺22 and 𝐿𝐺22 are more susceptible to strong turbulence compared to lower modes.

Abstract

In this paper, a cost-effective and easy-to- implement auto-trim technique is introduced for PTAT or CTAT resistance based current references. The resistance with a process-insensitive temperature coefficient requires only a single point trim at room temperature, achieving process and temperature-insensitive current. The approach utilizes a data comparison method where on-chip current data is compared with off-chip reference data to trim the resistance of the current reference. The off-chip reference data is generated using a low-cost external resistor that is used only during the trimming operation. The proposed trimming sensor effectively calibrates the current, offering precision close to manual trimming, leading to cost, time, and resource savings. To validate the working of the proposed technique, the auto trim sensor with on-chip current reference is designed in TSMC 180nm technology. The auto trim sensor calibrates the on-chip current from ±25% (due to voltage and resistance process variation) to ±1.5% across the process and 3σ mismatch

Abstract

In this letter, we have modeled and analyzed the impact of alignment errors in fiber lasers and collimating lens arrays on incoherent beam combination systems. The combined intensity expression is calculated considering different errors, and efficiency comparisons are made between 7 and 61 laser beam arrays. The results show that the misalignment error also increases with an increase in the number of beam arrays, leading to a decrease in the combination efficiency. We observed that to achieve more than 80% combination efficiency, maximum error tolerances should be 3 µm, 0.3 mm, and 4 mrad for displacement, focal and tilt errors, respectively. The findings presented in this letter have practical relevance for enhancing the design, operation, and optimization of beam combination systems.

Abstract

Human posture analysis has gained a significant research interest in the recent times. It helps in many applications such as gait analysis for detecting neurological disorders, fall detection of elderly people, and continuous monitoring of severely ill patients. Camera-based vision systems are commonly employed for detecting human postures; however, they cause concerns over the subjects' privacy. To address this challenge, we present a millimeter wave (mmWave) radar-based, truly non-contact, non-intrusive, and privacy-conscious posture detection and classification system in this research. The proposed system utilizes three-dimensional point cloud data of the subject to comprehensively classify body postures, capturing intricate real-time details. In this work, we also present a custom-designed Convolutional Neural Network (CNN) and its comparison with other models, which are conventionally used for posture classification. We also demonstrate the hardware implementation of the proposed system and present the measurement results using Texas Instruments' IWR1843BOOST radar module. The proposed CNN model achieves an accuracy of 97.10% while classifying standing, sitting, lying and bending postures.

Abstract

The number of individuals having identical names on the internet is increasing. Thus making the task of searching for a specific individual tedious. The user must vet through many profiles with identical names to get to the actual individual of interest. The online presence of an individual forms the profile of the individual. We need a solution that helps users by consolidating the profiles of such individuals by retrieving factual information available on the web and providing the same as a single result. We present a novel solution that retrieves web profiles belonging to those bearing identical Full Names through an end-to-end pipeline. Our solution involves information retrieval from the web (extraction), LLM-driven Named Entity Extraction (retrieval), and standardization of facts using Wikipedia, which returns profiles with fourteen multi-valued attributes. After that, profiles that correspond to the same real-world individuals are determined. We accomplish this by identifying similarities among profiles based on the extracted facts using a Prefix Tree inspired data structure (validation) and utilizing ChatGPT's contextual comprehension (revalidation). The system offers varied levels of strictness while consolidating these profiles, namely strict, relaxed, and loose matching. The novelty of our solution lies in the innovative use of GPT -- a highly powerful yet an unpredictable tool, for such a nuanced task. A study involving twenty participants, along with other results, found that one could effectively retrieve information for a specific individual.

Abstract

This paper presents the design and implementation of a hardware system for real-time object detection and range estimation utilizing Frequency-Modulated Continuous Wave (FMCW) millimeter wave radar signals, which are commonly used in Advance Driver Assistance Systems (ADAS) and robotic applications. The proposed system utilizes the Fast Fourier Transform (FFT) algorithm to process the FMCW radar signal for estimating the range of an object. For developing a resource-efficient and low latency range-estimation hardware, this paper also presents a comparative analysis for the implementation of three popular FFT architectures (iterative Radix-2, iterative Radix-4, and pipelined Radix-2) on FPGA. Based on the presented analysis the lowest latency range-estimation architecture has been chosen for FFT implementation and a system prototype is developed as a proof-of-concept by integrating the commercially available Texas Instruments' 77 GHz AWR1642BOOST FMCW radar module with a FPGA to host the chosen FFT architecture. Measurement results of the proposed system hardware are also presented in this paper, which shows >99.21% range-estimation accuracy.

Abstract

Architectural Knowledge Management (AKM) involves the organized handling of information related to architectural decisions and design within a project or organization. An essential artefact of AKM is the Architecture Decision Records (ADR), which documents key design decisions. ADRs are documents that capture decision context, decision made and various aspects related to a design decision, thereby promoting transparency, collaboration, and understanding. Despite their benefits, ADR adoption in software development has been slow due to challenges like time constraints and inconsistent uptake. Recent advancements in Large Language Models (LLMs) may help bridge this adoption gap by facilitating ADR generation. However, the effectiveness of LLM for ADR generation or understanding is something that has not been explored. To this end, in this work, we perform an exploratory study which aims to investigate the feasibility of using LLM for the generation of ADRs given the decision context. In our exploratory study, we utilize GPT and T5-based models with 0-shot, few-shot, and fine-tuning approaches to generate the Decision of an ADR given its Context. Our results indicate that in a 0-shot setting, state-of-the-art models such as GPT-4 generate relevant and accurate Design Decisions, although they fall short of human-level performance. Additionally, we observe that more cost-effective models like GPT-3.5 can achieve similar outcomes in a few-shot setting, and smaller models such as Flan-T5 can yield comparable results after fine-tuning. To conclude, this exploratory study suggests that LLM can generate Design Decisions, but further research is required to attain human-level generation and establish standardized widespread adoption.