SOccDPT: Semi-Supervised 3D Semantic Occupancy from Dense Prediction Transformers trained under memory constraints

We present SOccDPT, a memory-efficient approach for 3D semantic occupancy prediction from monocular image input using dense prediction transformers. To address the limitations of existing methods trained on structured traffic datasets, we train our model on unstructured datasets including the Indian Driving Dataset and Bengaluru Driving Dataset. Our semi-supervised training pipeline allows SOccDPT to learn from datasets with limited labels by reducing the requirement for manual labelling by substituting it with pseudo-ground truth labels to produce our Bengaluru Semantic Occupancy Dataset. This broader training enhances our model's ability to handle unstructured traffic scenarios effectively. To overcome memory limitations during training, we introduce patch-wise training where we select a subset of parameters to train each epoch, reducing memory usage during auto-grad graph construction. In the context of unstructured traffic and memory-constrained training and inference, SOccDPT outperforms existing disparity estimation approaches as shown by the RMSE score of 9.1473, achieves a semantic segmentation IoU score of 46.02% and operates at a competitive frequency of 69.47 Hz. We make our code and semantic occupancy dataset public.Comment: This work has been submitted to the ICRA 2024 IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Preparation of Silica Nanoparticles using Microemulsion Techniques

Silica nanoparticles have been prepared in this work using water in oil (W/O) emulsion system at room temperature that employs a water-soluble amine as catalyst and tetraethylorthosilicate (TEOS) as the silica source. The pH value of the aqueous phase and the water: surfactant ratio were found to be the key factors contributing to the formation and final size of stable and regular spherical silica particles. When the pH value of the aqueous phase was controlled between 8 and 9, silica particles could be synthesized. The shell thickness of the hollow particles as and when prepared was found to increase with the length of the hydrocarbon tail of the amine catalyst. The viscosity of the external oil phase determined the shape regularity of the spherical silica hollow particles. The kinetics of the formation of silica hollow particles was believed to be based on the difference between the hydrolysis rate and the condensation rate of TEOS, which can be adjusted by the pH value of the aqueous phase. After treating the core-shell particles with concentric nitric acid, the hollow silica spheres were obtained correspondingly. The particles were characterized by Scanning electron Microscope (SEM), Optical Microscope and UV Spectrophotometer. The study shows that through further processing, advanced materials could be prepared; and that the hollow silica spheres could be potentially used as a novel class of catalyst supports

Eastern Ghats tragedy: If the mines don’t get them… the dams will

“We hunt the leopard. I can show you how we track them too,” said the Konda Reddi tribal villager. We were at Villarthi village, in a remote forested stretch between the towns of Upper Sileru and Donkarayi in the northern Eastern Ghats of Andhra Pradesh’s Visakhapatnam district. Across the border lay Odisha. Historically this region supported a rich floral diversity, but in recent decades the Eastern Ghats have largely been ignored in favour of other hill regions across India. The rediscovery of the Indian golden gecko Calodactylodes aureus and the Jeypore ground gecko Geckoella jeyporensis, earlier presumed locally extinct has caused some resurgence of public interest, but not enough to make a significant difference. Several researchers have begun to study the faunal diversity of this incredible region, but the hazards confronting this landscape seem almost insurmountable

Mammals of Papfikonda Hfills, northern Eastern Ghats, Indfia

Papikonda National Park covering an area of 1,012km2 holds high conservation value as the only national park in the geographically vast northern Eastern Ghats. The tropical moist deciduous forests support species assemblages characteristic of the Eastern Ghats. We conducted the first comprehensive assessment of the mammal diversity in Papikonda National Park using camera traps, sign surveys and community interviews between October 2014 and March 2015, combined with a comprehensive literature review of research articles, field guides and IUCN species range reports. A total of 55 species from 46 genera belonging to 24 families were enumerated. There was a high diversity of carnivores (15 species), followed by chiropterans (13 species) and rodents (11 species)

Mobile Cloud Encrypted Searching and Traffic Reduction

Now days, cloud infrastructure have been popular for storing data in the world. User can store his public and private data on cloud. To secure the private data it must be encrypted. This encrypted data should be retrieved and stored efficiently. This era is digital era. Nearly about each person has mobile phone. So smart phone would be the best client for the cloud. But using smart phone use wireless network which face many difficulties like low bandwidth, low latency, low battery, low transmission etc. The traditional search is not developed on focusing on smart phone so using smart phone it require the extra network traffic and long time for search. The application use the light weight trapdoor which reduce trapdoor size and provide feasible method for the network traffic efficiency. Also it use and Ranked Serial Binary Search algorithm 0and Trapdoor Mapping Table (TMT) to minimize the search time. The proposed system reduce the search time and network traffic

Understanding the Effect of the Long Tail on Neural Network Compression

Network compression is now a mature sub-field of neural network research: over the last decade, significant progress has been made towards reducing the size of models and speeding up inference, while maintaining the classification accuracy. However, many works have observed that focusing on just the overall accuracy can be misguided. E.g., it has been shown that mismatches between the full and compressed models can be biased towards under-represented classes. This raises the important research question, \emph{can we achieve network compression while maintaining ``semantic equivalence'' with the original network?} In this work, we study this question in the context of the ``long tail'' phenomenon in computer vision datasets observed by Feldman, et al. They argue that \emph{memorization} of certain inputs (appropriately defined) is essential to achieving good generalization. As compression limits the capacity of a network (and hence also its ability to memorize), we study the question: are mismatches between the full and compressed models correlated with the memorized training data? We present positive evidence in this direction for image classification tasks, by considering different base architectures and compression schemes

OCTraN: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios

Modern approaches for vision-centric environment perception for autonomous navigation make extensive use of self-supervised monocular depth estimation algorithms that output disparity maps. However, when this disparity map is projected onto 3D space, the errors in disparity are magnified, resulting in a depth estimation error that increases quadratically as the distance from the camera increases. Though Light Detection and Ranging (LiDAR) can solve this issue, it is expensive and not feasible for many applications. To address the challenge of accurate ranging with low-cost sensors, we propose, OCTraN, a transformer architecture that uses iterative-attention to convert 2D image features into 3D occupancy features and makes use of convolution and transpose convolution to efficiently operate on spatial information. We also develop a self-supervised training pipeline to generalize the model to any scene by eliminating the need for LiDAR ground truth by substituting it with pseudo-ground truth labels obtained from boosted monocular depth estimation.Comment: This work was accepted as a spotlight presentation at the Transformers for Vision Workshop @CVPR 202

Bottom-Up and Top-Down Approaches for MgO

In this chapter, we present an overview of synthesis of MgO nanoparticles and thin films by using top-down and bottom-up approaches. The bottom-up approaches are generally utilized to grow nanoparticles by the methods that involve chemical reactions. Sometimes, methods based on these reactions are also able to grow thin films. The top-down approaches are preferred for growing thin films where bulk material is used for depositions. The methods, which are frequently used, are radio frequency sputtering, pulsed lased deposition, and molecular beam epitaxy and e-beam evaporation. Sometimes, methods like mechanical milling and high energy ball milling are used to grow nanoparticles

A Review: Efficient Encrypted Searching and Traffic Reduction As Mobile Cloud Services

Documentation of information on the Cloud Computing run as fast as Cloud entirely in the world. Even so it carriage distress to partron. Unless the data are encrypted For hostage. Encrypted data should be energetically searchable and retrievable Without any concealment particularly for the cellphone user. Although modern Interdisciplinary studies has solved many distress , the architectonically can not be applied on cellphone directly under the cellphone cloud environment. This is due to the contradict charged by wireless networks, such as latency sensitivity ,Poor connectivity, and low transmission rates. due to this extend to a chronic search Time and extra network traffic value. When using the conventional search schemes. This paper solve these matter by providing an efficient encrypted data search Method as cellphone cloud service. This method include lightweight trapdoor (encrypted Keyword) differentiate method, which is optimization of data sending process by decreasing the trapdoors size for traffic efficiency. In this publication we also include two Optimization method for data search, known as the trapdoor mapping table module and Ranked serial binary search algorithm to quick the search time. So by using Efficient data search over mobile cloud it Decreases search time by 34% to 47% and also network traffic by 17% to 41%