Environmental & Financial Benefits of 360 kW Photo Voltaic Solar System (On-Grid) in University of Wah

In the 21st century, the utilization and application of renewable energy resources are the need of the hour. Currently, in Pakistan, the cost of electricity per unit is very high and it has a huge effect on financial matters. In this study, we have analyzed the 360 kW Photovoltaic (PV) Solar system (On-Grid) installed at the University of Wah, its effects on the financial aspects, and environment change before and after its installation and operation. There are many types of renewable energy resources but not all of them are environmentally friendly. The University of Wah opted for the PV Solar system because it is environmentally friendly with no carbon emissions and requires very less maintenance. In this paper, we have also discussed, how this system benefits the local community and benefits the environment. All the facts and statistics about the 360 kW PV Solar System (On-Grid) are shared in detail.Keyword: Photovoltaic solar panels, Electricity Demand, Renewable energy, Environmentally friendly, Climate chang

Graphene growth at low temperatures using RF-plasma enhanced chemical vapour deposition

The advantage of plasma enhanced chemical vapour deposition (PECVD) method is the ability to deposit thin films at relatively low temperature. Plasma power supports the growth process by decomposing hydrocarbon to carbon radicals which will be deposited later on metal catalyst. In this work, we have successfully synthesis graphene on Ni and Co films at relatively low temperature and optimize the synthesis conditions by adjusting the plasma power. Low temperature growth of graphene was optimized at 600°C after comparing the quality of as-grown graphene at several temperatures from 400 to 800°C and by varying plasma powers in the range of 20 - 100 W. Raman analysis of the as-grown samples showed that graphene prefers lower plasma power of 40 W. The annihilation of graphene formation at higher plasma powers is attributed to the presence of high concentration of hydrogen radical from methane which recombines with carbon elements on thin film surface. The optimum graphene growth conditions were obtained at growth temperature of 600°C, plasma power of 40 W and growth time of 10 min with methane flow rate of 120 sccm

Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model

It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due to anatomical heterogeneity and the requirement for pixel-level labeling. Unsupervised anomaly detection approaches provide an alternative solution by relying only on sample-level labels of healthy brains to generate a desired representation to identify abnormalities at the pixel level. Although, generative models are crucial for generating such anatomically consistent representations of healthy brains, accurately generating the intricate anatomy of the human brain remains a challenge. In this study, we present a method called masked-DDPM (mDPPM), which introduces masking-based regularization to reframe the generation task of diffusion models. Specifically, we introduce Masked Image Modeling (MIM) and Masked Frequency Modeling (MFM) in our self-supervised approach that enables models to learn visual representations from unlabeled data. To the best of our knowledge, this is the first attempt to apply MFM in DPPM models for medical applications. We evaluate our approach on datasets containing tumors and numerous sclerosis lesions and exhibit the superior performance of our unsupervised method as compared to the existing fully/weakly supervised baselines. Code is available at https://github.com/hasan1292/mDDPM.Comment: Accepted in MICCAI 2023 Workshop

Detect-and-describe: Joint learning framework for detection and description of objects

Traditional object detection answers two questions; “what” (what the object is?) and “where” (where the object is?). “what” part of the object detection can be fine grained further i-e. “what type”, “what shape” and “what material” etc. This results in shifting of object detection task to object description paradigm. Describing object provides additional detail that enables us to understand the characteristics and attributes of the object (“plastic boat” not just boat, “glass bottle” not just bottle). This additional information can implicitly be used to gain insight about unseen objects (e.g. unknown object is “metallic”, “has wheels”), which is not possible in traditional object detection. In this paper, we present a new approach to simultaneously detect objects and infer their attributes, we call it Detectand- Describe (DaD) framework. DaD is a deep learning-based approach that extends object detection to object attribute prediction as well. We train our model on aPascal train set and evaluate our approach on aPascal test set. We achieve 97.0% in Area Under the Receiver Operating Characteristic Curve (AUC) for object attributes prediction on aPascal test set. We also show qualitative results for object attribute prediction on unseen objects, which demonstrate the effectiveness of our approach for describing unknown objects

CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction

Human-robot interaction (HRI) is a rapidly growing field that encompasses social and industrial applications. Machine learning plays a vital role in industrial HRI by enhancing the adaptability and autonomy of robots in complex environments. However, data privacy is a crucial concern in the interaction between humans and robots, as companies need to protect sensitive data while machine learning algorithms require access to large datasets. Federated Learning (FL) offers a solution by enabling the distributed training of models without sharing raw data. Despite extensive research on Federated learning (FL) for tasks such as natural language processing (NLP) and image classification, the question of how to use FL for HRI remains an open research problem. The traditional FL approach involves transmitting large neural network parameter matrices between the server and clients, which can lead to high communication costs and often becomes a bottleneck in FL. This paper proposes a communication-efficient FL framework for human-robot interaction (CEFHRI) to address the challenges of data heterogeneity and communication costs. The framework leverages pre-trained models and introduces a trainable spatiotemporal adapter for video understanding tasks in HRI. Experimental results on three human-robot interaction benchmark datasets: HRI30, InHARD, and COIN demonstrate the superiority of CEFHRI over full fine-tuning in terms of communication costs. The proposed methodology provides a secure and efficient approach to HRI federated learning, particularly in industrial environments with data privacy concerns and limited communication bandwidth. Our code is available at https://github.com/umarkhalidAI/CEFHRI-Efficient-Federated-Learning.Comment: Accepted in IROS 202

SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-guided Video Editing

Text-to-Image (T2I) diffusion models have achieved remarkable success in synthesizing high-quality images conditioned on text prompts. Recent methods have tried to replicate the success by either training text-to-video (T2V) models on a very large number of text-video pairs or adapting T2I models on text-video pairs independently. Although the latter is computationally less expensive, it still takes a significant amount of time for per-video adaption. To address this issue, we propose SAVE, a novel spectral-shift-aware adaptation framework, in which we fine-tune the spectral shift of the parameter space instead of the parameters themselves. Specifically, we take the spectral decomposition of the pre-trained T2I weights and only control the change in the corresponding singular values, i.e. spectral shift, while freezing the corresponding singular vectors. To avoid drastic drift from the original T2I weights, we introduce a spectral shift regularizer that confines the spectral shift to be more restricted for large singular values and more relaxed for small singular values. Since we are only dealing with spectral shifts, the proposed method reduces the adaptation time significantly (approx. 10 times) and has fewer resource constrains for training. Such attributes posit SAVE to be more suitable for real-world applications, e.g. editing undesirable content during video streaming. We validate the effectiveness of SAVE with an extensive experimental evaluation under different settings, e.g. style transfer, object replacement, privacy preservation, etc.Comment: 23 pages, 18 figure