Publications

Abstract

Developing efficient electromagnetic interference shielding materials has become significantly important in present times. This paper reports a series of (Ba1-xCax)(Ti0.9Sn0.1)O3 (BCTS) ((x =0, 0.01, 0.05, & 0.1)ceramics synthesized by conventional method which were studied for electromagnetic interference shielding (EMI) applications in X-band (8-12.4 GHz). EMI shielding properties and all S parameters (S11 & S12) of BCTS ceramic pellets were measured in the frequency range (8-12.4 GHz) using a Vector Network Analyser (VNA). The BCTS ceramic pellets for x = 0.05 showed maximum total effective shielding of 46 dB indicating good shielding behaviour for high-frequency applications. However, the development of lead-free ceramics with different concentrations usually requires iterative experiments resulting in, longer development cycles and higher costs. To address this, we used a machine learning (ML) strategy to predict the EMI shielding for different concentrations and experimentally verify the concentration predicted to give the best EMI shielding. The ML model predicted BCTS ceramics with concentration (x = 0.06, 0.07, 0.08, and 0.09) to have higher shielding values. On experimental verification, a shielding value of 58 dB was obtained for x = 0.08, which was significantly higher than what was obtained experimentally before applying the ML approach. Our results show the potential of using ML in accelerating the process of optimal material development, reducing the need for repeated experimental measures significantly.

Abstract

Speakers tend to engage in adaptive behavior, known as entrainment, when they reuse their partner's linguistic representations, including lexical, acoustic prosodic, semantic, or syntactic structures during a conversation. Studies have explored the relationship between entrainment and social factors such as likeability, task success, and rapport. Still, limited research has investigated the relationship between entrainment and gaze. To address this gap, we conducted a within-subjects user study (N = 33) to test if gaze behavior of a robotic head affects entrainment of subjects toward the robot on four linguistic dimensions: lexical, syntactic, semantic, and acoustic-prosodic. Our results show that participants entrain more on lexical and acoustic-prosodic features when the robot exhibits well-timed gaze aversions similar to the ones observed in human gaze behavior, as compared to when the robot keeps staring at participants constantly. Our results support the predictions of the computers as social actors (CASA) model and suggest that implementing well-timed gaze aversion behavior in a robot can lead to speech entrainment in human-robot interactions.

Abstract

This paper investigates the fractional order behavior of ZnO ceramics at different frequencies. ZnO ceramic was prepared by high energy ball milling technique (HEBM) sintered at 1300℃ to study the frequency response properties. The frequency response properties (impedance and phase
angles) were examined by analyzing through impedance analyzer (100 Hz - 1 MHz). Constant phase angles (84°-88°) were obtained at low temperature ranges (25 ℃-125 ℃). The structural and
morphological composition of the ZnO ceramic was investigated using X-ray diffraction techniques and FESEM. Raman spectrum was studied to understand the different modes of ZnO ceramics. Machine learning (polynomial regression) models were trained on a dataset of 1280
experimental values to accurately predict the relationship between frequency and temperature with respect to impedance and phase values of the ZnO ceramic FOC. The predicted impedance values were found to be in good agreement (R2 ~ 0.98, MSE ~ 0.0711) with the experimental results.
Impedance values were also predicted beyond the experimental frequency range (at 50 Hz and 2 MHz) for different temperatures (25℃ - 500℃) and for low temperatures (10°, 15° and 20℃)
within the frequency range (100Hz - 1MHz).

Abstract

Gaze cues serve an important role in facilitating human conversations and are generally considered to be one of the most important non-verbal cues. Gaze cues are used to manage turn-taking, coordinate joint attention, regulate intimacy, and signal cognitive effort. In particular, it is well established that gaze aversion is used in conversations to avoid prolonged periods of mutual gaze. Given the numerous functions of gaze cues, there has been extensive work on modelling these cues in social robots. Researchers have also tried to identify the impact of robot gaze on human participants. However, the influence of robot gaze behavior on human gaze behavior has been less explored. We conducted a within-subjects user study (N = 33) to verify if a robot’s gaze aversion influenced human gaze aversion behavior. Our results show that participants tend to avert their gaze more when the robot keeps staring at them as compared to when the robot exhibits well-timed gaze aversions. We interpret our findings in terms of intimacy regulation: humans try to compensate for the robot’s lack of gaze aversion.

Abstract

Affective behaviors enable social robots to not only establish better connections with humans but also serve as a tool for the robots to express their internal states. It has been well established that emotions are important to signal understanding in Human-Robot Interaction (HRI). This work aims to harness the power of Large Language Models (LLM) and proposes an approach to control the affective behavior of robots. By interpreting emotion appraisal as an Emotion Recognition in Conversation (ERC) tasks, we used GPT-3.5 to predict the emotion of a robot’s turn in real-time, using the dialogue history of the ongoing conversation. The robot signaled the predicted emotion using facial expressions. The model was evaluated in a within-subjects user study (N = 47) where the model-driven emotion generation was compared against conditions where the robot did not display any emotions and where it displayed incongruent emotions. The participants interacted with the robot by playing a card sorting game that was specifically designed to evoke emotions. The results indicated that the emotions were reliably generated by the LLM and the participants were able to perceive the robot’s emotions. It was found that the robot expressing congruent model-driven facial emotion expressions were perceived to be significantly more human-like, emotionally appropriate, and elicit a more positive impression. Participants also scored significantly better in the card sorting game when the robot displayed congruent facial expressions. From a technical perspective, the study shows that LLMs can be used to control the affective behavior of robots reliably in real-time. Additionally, our results could be used in devising novel human-robot interactions, making robots more effective in roles where emotional interaction is important, such as therapy, companionship, or customer service.