arXiv subjects

Shubham Deshmukh

Publications and source records attributed to Shubham Deshmukh.

6 recordsLinked to original sources

arXiv2026

Analytical framework for interpreting thermoreflectance measurements of nanoline gratings under finite spot illumination

A frequency-domain thermoreflectance (FDTR)-based technique is presented to measure the effective thermal properties of a material stack whose surface is patterned with metallic nanoline gratings that are heated and probed by lasers. An analytical heat diffusion model is derived for predicting the FDTR phase response that incorporates a finite laser spot radius with a Gaussian intensity profile for the pump and probe lasers, as well as the dimensions of the nanoline grating. The model is validated by comparing with measurements of Au nanoline gratings, with widths ranging from 100 to 1000 nm, fabricated on a-SiO$_2$ and $c$-sapphire substrates. Due to the short phonon mean free paths (MFPs) in these substrates, thermal transport remains in the diffusive regime for the whole range of nanoline widths. The model and the measurement show excellent agreement across different laser spot radii and nanoline widths, with a bulk thermal conductivity value of $c$-sapphire and the thermal boundary conductances at the Au-Cr/a-SiO$_2$ and Au-Cr/$c$-sapphire interfaces comparable to our prior measurements and the literature. The validated model will serve as a robust baseline for extending this method to measure effective thermal conductivity as a function of the heater size. This approach is particularly relevant for materials where non-diffusive thermal transport regimes enable probing phonon MFP dependent contributions to the thermal conductivity.

cond-mat.mtrl-sci↗

arXiv2023

BEAVIS: Balloon Enabled Aerial Vehicle for IoT and Sensing

UAVs are becoming versatile and valuable platforms for various applications. However, the main limitation is their flying time. We present BEAVIS, a novel aerial robotic platform striking an unparalleled trade-off between the manoeuvrability of drones and the long lasting capacity of blimps. BEAVIS scores highly in applications where drones enjoy unconstrained mobility yet suffer from limited lifetime. A nonlinear flight controller exploiting novel, unexplored, aerodynamic phenomena to regulate the ambient pressure and enable all translational and yaw degrees of freedom is proposed without direct actuation in the vertical direction. BEAVIS has built-in rotor fault detection and tolerance. We explain the design and the necessary background in detail. We verify the dynamics of BEAVIS and demonstrate its distinct advantages, such as agility, over existing platforms including the degrees of freedom akin to a drone with 11.36x increased lifetime. We exemplify the potential of BEAVIS to become an invaluable platform for many applications.

cs.RO↗

arXiv2022

Security For System-On-Chip (SoC) Using Neural Networks

With the growth of embedded systems, VLSI design phases complexity and cost factors across the globe and has become outsourced. Modern computing ICs are now using system-on-chip for better on-chip processing and communication. In the era of Internet-of-Things (IoT), security has become one of the most crucial parts of a System-on-Chip (SoC). Malicious activities generate abnormal traffic patterns which affect the operation of the system and its performance which cannot be afforded in a computation hungry world. SoCs have a chance of functionality failure, leakage of information, even a denial of services (DoS), Hardware Trojan Horses and many more factors which are categorized as security threats. In this paper, we aim to compare and describe different types of malicious security threats and how neural networks can be used to prevent those attacks. Spiking Neural Networks (SNN), Runtime Neural Architecture (RTNA) are some of the neural networks which prevent SoCs from attacks. Finally, the development trends in SoC security are also highlighted.

cs.CR↗

arXiv2022

SANIP: Shopping Assistant and Navigation for the visually impaired

The proposed shopping assistant model SANIP is going to help blind persons to detect hand held objects and also to get a video feedback of the information retrieved from the detected and recognized objects. The proposed model consists of three python models i.e. Custom Object Detection, Text Detection and Barcode detection. For object detection of the hand held object, we have created our own custom dataset that comprises daily goods such as Parle-G, Tide, and Lays. Other than that we have also collected images of Cart and Exit signs as it is essential for any person to use a cart and also notice the exit sign in case of emergency. For the other 2 models proposed the text and barcode information retrieved is converted from text to speech and relayed to the Blind person. The model was used to detect objects that were trained on and was successful in detecting and recognizing the desired output with a good accuracy and precision.

cs.CV↗

arXiv2022

Suspicious and Anomaly Detection

In this project we propose a CNN architecture to detect anomaly and suspicious activities; the activities chosen for the project are running, jumping and kicking in public places and carrying gun, bat and knife in public places. With the trained model we compare it with the pre-existing models like Yolo, vgg16, vgg19. The trained Model is then implemented for real time detection and also used the. tflite format of the trained .h5 model to build an android classification.

cs.CV↗

arXiv2022

Sign Language Detection

With the advancements in Computer vision techniques the need to classify images based on its features have become a huge task and necessity. In this project we proposed 2 models i.e. feature extraction and classification using ORB and SVM and the second is using CNN architecture. The end result of the project is to understand the concept behind feature extraction and image classification. The trained CNN model will also be used to convert it to tflite format for Android Development.

cs.CV↗