Abstract

This paper introduces a novel method for determining the best room to place an object in, for embodied scene rearrangement. While state-of-the-art approaches rely on large language models (LLMs) or reinforcement learned (RL) policies for this task, our approach, CLIPGraphs, efficiently combines commonsense domain knowledge, data-driven methods, and recent advances in multimodal learning. Specifically, it (a) encodes a knowledge graph of prior human preferences about the room location of different objects in home environments, (b) incorporates vision-language features to support multimodal queries based on images or text, and (c) uses a graph network to learn object-room affinities based on embeddings of the prior knowledge and the vision-language features. We demonstrate that our approach provides better estimates of the most appropriate location of objects from a benchmark set of object categories in comparison with state-of-the-art baselines

Abstract

Social media provides a supportive and anonymous environment for discussing mental health issues, including depression. Existing research on identifying the cause of depression focuses primarily on improving classifier models, while neglecting the importance of learning better data representations. To address this gap, we introduce an architecture that enhances the identification of the cause of depression by learning improved data representations. Our work enables a deeper interpretation of the cause of depression in social media contexts, emphasizing the significance of effective rep- resentation learning for this task. Our work can act as a foundation for self-help applications in the field of mental health.

Abstract

With increasing connectivity and sophisticated software, modern vehicles are able to leverage different kinds of services provided by the environment. One such service recommended by the Automotive Edge Computing Consortium (AECC) is downloading high-definition map data by vehicles. This high volume of data can be provided to the vehicles when moving by pre-allocating resources on edge server nodes or roadside units if the routes are known apriori. However, this is not a realistic assumption to make in general. Therefore, in this work, we propose a two-stage optimization framework for efficient data delivery to connected vehicles via edge nodes while considering dynamic route changes. We have evaluated the efficiency of this proposed approach (considering a real-world dataset) with respect to (a) offline optimization strategies considering fixed routes and (b) a greedy approach considering route changes. Our proposed approach works considerably better than the existing approaches in the context of dynamic route changes.

Abstract

Unmanned Aerial Vehicles (UAVs), particularly low-cost UAVs, have become increasingly important due to their wide range of applications and ease of use. However, with the rapid growth of the UAV market, the rising security concerns pose a greater risk. One such primary concern is location spoofing attacks which can compromise UAV's navigation system, making it crucial to analyze various location-based attacks. In this paper, we identify 16 such GPS spoofing scenarios based on environmental conditions, attack type, and spoof signal propagation path. We evaluate these scenarios based on various GPS parameters like Horizontal Dilution Of Precision (HDOP), Vertical Dilution Of Precision (VDOP), GPS satellite count in view, and avg signal-to-noise power density (CN0). We then analyze the variations in GPS parameters for various such attack scenarios. Further, we analyze the impact of distance on average CN0 and the effect of satellite count on effective spoofable distance. We also discuss several critical insights which are empirically observed during our experimental trials. Our experiments revealed that the natural conditions within indoor and outdoor scenarios can vary considerably, and effective spoofable distance can be up to 100 meters when the satellite count is less than 10.

Abstract

The high rate of groundwater usage has resulted in a rapid decline in groundwater levels, which has necessitated its monitoring. This paper focuses on estimating the groundwater level inside borewells remotely and in real-time using the Internet of Things (IoT). For this, a solution is proposed which is based on string tension and does not require any electrical components to be lowered into the borewell. The solution provides a completely automated, real-time, reliable, and IoT-enabled alternative to existing methods for borewell water monitoring. The proposed approach is compared with an ultrasonic sensor-based approach in a controlled environment as well as in a tank. Additionally, four nodes were deployed in a small educational campus in the Indian city of Hyderabad to ascertain its usability and reliability in practical situations. The observations from the data collected over a month show that the proposed low-cost solution is reliable and has a good performance in the field. Index Terms—Borewell, Real-time, IoT, String tension, Water-level.

Abstract

This study presents an Internet of Things (IoT)- based system that utilises machine learning (ML) techniques to estimate water flow through pipes based on pressure. The system incorporates an ESP-32 microcontroller, a Danfoss MBS 3000 pressure sensor, and a flow meter deployed at three locations to collect data for three months. To model the relationship between pressure and flow rate, ML algorithms such as linear regression (LR), support vector regression (SVR), and convolutional neural network (CNN) were trained, analysed, and compared. By establishing a model to estimate the flow rate based on pressure, the need for a flow meter in the setup can be eliminated. The system’s low-cost, easy-to-implement, and non-invasive nature makes it suitable for widespread adoption in residential areas offering a promising solution for optimising water distribution and reducing water wastage.

Abstract

Climate classification plays a significant role in the development of building codes and standards. It guides the design of buildings’ envelope and systems by considering their location’s climate conditions. Various methods, such as ASHRAE Standard 169, Köppen, Trewartha utilize climate parameters such as temperature, humidity, solar radiation, precipitation, etc., to classify climates. When establishing requirements in building codes and standards, it is crucial to validate the classification based on the building’s thermal loads. This paper introduces a novel methodology for classifying cities based on the number of similar days between them. It calculates similarity using daily mean temperature, relative humidity, and solar radiation by applying threshold values. A matrix of similar days is analyzed through agglomerative hierarchical clustering with different thresholds. A scoring system based on building thermal load, where lower scores signify better classification, is employed to select the best method. The method was tested using U.S. weather data, yielding a lower score of 54.5 compared to ASHRAE Standard 169’s score of 63.09. This suggests that the new approach results in less

Abstract

The performance of high-power laser systems based on incoherent beam combining (IBC) depends on laser beam propagation properties in the atmosphere and their orientation on the target. We have designed an IBC system based on three different beam directing mechanisms, viz., collimated, inclined, and focused-inclined. We have mathematically modeled the laser beam propagation in the atmosphere, including turbulence, wandering, jitter, Strehl ratio, and thermal blooming. The beam propagation efficiency is evaluated for the above combinations to evaluate the IBC system performance in the turbulent atmosphere. We report the simulation results of an IBC system of 44 individual laser sources, each of 1 kW combined incoherently at a target. A comparative study of 3 beam-directing mechanisms is performed, and it is observed that the collimated beam IBC system is most affected by the atmospheric parameters due to the maximum wandering and spreading of the beam at the target compared to inclined and focused-inclined IBC. Whereas a focused-inclined IBC system is found to be the most effective for high-power energy applications, with an average intensity of more than 250 W/cm^2 even at the 10 km target location.

Abstract

Residential District Cooling Systems (DCS) are crucial for maintaining thermal comfort in urban areas, making it imperative to understand occupant cooling behavior as it significantly influences DCS operation. While several studies have investigated cooling behavior within small user groups through on-site measurements or surveys, these often fall short in representing the broader population. In this study we considered 387 homes in Hyderabad, which has a DCS connection for chilled water supply. Operational data spanning three months across different seasons were meticulously selected to assess the residents’ behavior for the cooling demand. In order to better facilitate the operation of the residential DCS, user load profile and system performance were analyzed using real world data for different seasons in the year. Our findings revealed notable disparities in DCS electrical consumption, with summer usage being 1.9 times higher than the monsoon period and 2.7 times higher than the winter period. Furthermore, a strong positive correlation emerged between outdoor temperature and thermal energy usage by the residents. On average, the daily thermal consumption per residence during winter, summer and monsoon is 3.3 kWhth, 35.1 kWhth and 10.4 kWhth respectively. Interestingly, the probability of a residence using AC during the day for the winter, summer and monsoon seasons are 0.07, 0.41 and 0.18 respectively.

Abstract

This study used IoT-based smart plugs and energy meters to collect device-level electricity data from homes for understanding the seasonal load patterns. The electrical energy consumption of five air conditioners (ACs) and five geysers was monitored using smart plugs during summer and winter to understand the power demand for cooling and hot water consumption. The study offered insights into the seasonal power usage patterns of these appliances. Further, a detailed analysis of daily electrical energy consumption in two homes was conducted to understand disaggregated electricity consumption. Five common household appliances (refrigerator, washing machine, television, water purifier, and microwave oven) were monitored using smart plugs in these homes. Along with smart plugs, smart energy meters were installed in these homes to monitor the total household electricity consumption. Their daily electricity consumption in summer was twice that of winter.