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SuperTac - tactile data super-resolution via dimensionality reduction
No full textThe advancement of tactile sensing in robotics and prosthetics is constrained by the trade-off between spatial and temporal resolution in artificial tactile sensors. To address this limitation, we propose SuperTac, a novel tactile super-resolution framework that enhances tactile perception beyond the sensor’s inherent resolution. Unlike existing approaches, SuperTac combines dimensionality reduction and advanced upsampling to deliver high-resolution tactile information without compromising the performance. Drawing inspiration from the spatiotemporal processing of mechanoreceptors in human tactile systems, SuperTac bridges the gap between sensor limitations and practical applications. In this study, an in-house-built active robotic finger system equipped with a 4 × 4 tactile sensor array was used to palpate textured surfaces. The system, comprising a tactile sensor array mounted on a spring-loaded robotic finger connected to a 3D printer nozzle for precise spatial control, generated spatiotemporal tactile maps. These maps were processed by SuperTac, which integrates a Variational Autoencoder for dimensionality reduction and Residual-In-Residual Blocks (RIRB) for high-quality upsampling. The framework produces super-resolved tactile images (16 × 16), achieving a fourfold improvement in spatial resolution while maintaining computational efficiency for real-time use. Experimental results demonstrate that texture classification accuracy improves by 17% when using super-resolved tactile data compared to raw sensor data. This significant enhancement in classification accuracy highlights the potential of SuperTac for applications in robotic manipulation, object recognition, and haptic exploration. By enabling robots to perceive and interpret high-resolution tactile data, SuperTac marks a step toward bridging the gap between human and robotic tactile capabilities, advancing robotic perception in real-world scenarios
A Hardware-Software Co-Design Platform to Evaluate SNN Workloads for ReRAM-based IMC
No full textResistive random access memory (ReRAM) based analog in-memory-compute (IMC) coupled with spiking neural networks (SNN) offers a promising solution to implement efficient matrix multiplication. This work presents an ARM Cortex-based ReRAM IMC for rapid SNN workload evaluation. While the software flexibility and the scheduling are provided by the ARM processing system (PS), the programmable logic (PL) provides a scalable interface to the ReRAM array through mixed-signal digital-to-analog converters (DAC). A prototype system is presented using a Zynq 7000 SoC comprising an ARM PS and PL infrastructure. Custom 8x8 ReRAM array along with row and column DACs and leaky-integrate and fire (LIF) neurons are implemented to realize the end-to-end system. A use-case of a stashing-based MNIST classification task is demonstrated using the prototype system
Chemically engineered nanoceria-vancomycin enriched multifaceted hydrogel for combatting deep wounds: Insights from preclinical research
No full textThe elevation of bacterial infection and generation of excessive radicals are critical challenges to address in deep wounds because they slow down the repair process by jeopardizing cellular activity and increasing the risk of complications. Therefore, we synthesized a multifaceted chitosan/polyacrylamide hydrogel enriched with nanoceria and vancomycin to provide an effective solution for combatting deep wounds. Hydrogels with different concentrations of nanoceria were prepared and characterized for functional group determination, rheological behavior, morphology, hydrophilic nature, adhesiveness, and stretchability properties. The release study results indicated a sustained release of vancomycin from all the nanocomposite hydrogels, and the release mechanism followed the Higuchi model. An antioxidant assay demonstrated the effectiveness of nanocomposite hydrogels in inactivating the experimentally generated free radicals. Hemocompatibility assay showed <5 % hemolysis by nanocomposite hydrogels, indicating good compatibility with blood. Among all, hydrogel loaded with 500 μg/mL nanoceria (CPN-5 V) exhibited the highest cell proliferation of human dermal fibroblast-neonatal cells and bacterial inhibition of 17.06 mm and 11.05 mm against S. aureus and E. coli. In a deep wound model, CPN-5 V nanocomposite hydrogel demonstrated 99.63 % wound closure at day 12 with 16.09 μg/mg of hydroxyproline content and epithelization with compact and well-organized fibrous tissue deposition. In conclusion, the synthesized multifaceted CPN-5 V nanocomposite hydrogel is a potential candidate for effectively managing deep wounds
Computing mixed multiplicities, mixed volumes and sectional Milnor numbers
No full textThis is an expository version of our paper [J. Softw. AlgebraGeom., 13 (2023), 1–12]. Our aim is to present recent Macaulay 2 algorithms for computation of mixed multiplicities of ideals in a Noetherian ring which is either local or a standard graded algebra over a field. These algorithms are based on computation of the equations of multi-Rees algebras of ideals that generalises a result of Cox, Lin and Sosa [Proc. Amer. Math. Soc. 147(2019), 4605–4616]. Using these equations we propose efficient algorithms for computation of mixed volumes of convex lattice polytopes and sectional Milnor numbers of hyper surfaces with an isolated singularity
A Computationally Efficient Method for Determining Permeability in Rocks with Dynamically Varying Pore Topology
No full textPermeability is a key rock property important for scientific applications that require simulation of fluid flow. Although permeability is determined using core flooding experiments, recent advancements in micro-CT imaging and pore scale fluid flow simulations have made it possible to constrain permeability honoring pore scale rock structure. However, due to high computational and experimental costs, it might be difficult to determine permeability for systems which might undergo a dynamic variation in the underlying pore topology caused by a range of geological processes. This study presents a graph theory-based approach to determine permeability for such systems. The method involves transforming a given micro-CT rock image to a graph network map followed by the identification of the least resistance path to fluid flow between the inlet and the outlet faces. The method was tested on a variety of micro-CT images. Our analysis suggested a strong correlation of flow resistance determined from graph theory and numerically determined permeability. This indicates that the graph theory method can be used as a proxy for full physics simulations for determining effective permeability for samples with changing pore structure while improving computational efficiency by a factor of 250
Uncovering the limits of lithium cobalt oxide: challenges and innovations for high-voltage lithium-ion batteries
No full textLithium-ion batteries (LIBs) have become a crucial power source for several uses, such as portable devices, the military and space exploration, etc. The performance of LIBs is significantly influenced by the cathode materials. Lithium Cobalt Oxide (LiCoO2) is one of the most researched cathode materials used in LIBs. It was discovered by the Prof. John B. Goodenough in 1980. Due to their high specific capacities, high energy densities, and outstanding cycle life, LiCoO2 has attracted a lot of interest for use as cathode materials in LIBs. As the demand has increased for high energy density batteries due to its application in portable electronics devices, the upper cut-off voltage of LiCoO2-based batteries has been continuously increased. However, charging to high voltages (>4.2 V vs. Li/Li+) can cause several detrimental issues, such as surface degradation, inhomogeneous reactions, and damage induced by destructive phase transitions, leading to the rapid decay of efficiency, capacity, and cycle life. This chapter is focussed to an overview of the recent work done on the LiCoO2, with a significant discussion on the fundamental failure mechanisms of LiCoO2 at high voltages. Furthermore, the chapter discusses synthesis techniques for modifying LiCoO2 and the current state of LiCoO2-based LIBs. Overall, this chapter provides valuable insights into the challenges faced by LiCoO2 in high-voltage applications and highlights the need for continued research to improve its performance
COVID-19 and the Indigenous Migrants’ Question in Urban India
No full textThe COVID-19 pandemic has emerged as one of the most severe global health crises, profoundly impacting economies and societies worldwide. Among the many affected groups, migrant workers in India have borne a disproportionate share of the burden. The sudden imposition of a nationwide lockdown on March 24, 2020, aimed at curbing the spread of the virus, left millions of migrant workers stranded in cities without jobs, income, or means of transportation to return to their native places. This humanitarian crisis has highlighted the deep-seated vulnerabilities of India's migrant workforce, particularly those hailing from tribal communities
Tab-Shapley: Identifying Top-k Tabular Data Quality Insights
No full textWe present an unsupervised method for aggregating anomalies in tabular datasets by identifying the top-k tabular data quality insights. Each insight consists of a set of anomalous attributes and the corresponding subsets of records that serve as evidence to the user. The process of identifying these insight blocks is challenging due to (i) the absence of labeled anomalies, (ii) the exponential size of the subset search space, and (iii) the complex dependencies among attributes, which obscure the true sources of anomalies. Simple frequency-based methods fail to capture these dependencies, leading to inaccurate results. To address this, we introduce Tab-Shapley, a cooperative game theory based framework that uses Shapley values to quantify the contribution of each attribute to the data's anomalous nature. While calculating Shapley values typically requires exponential time, we show that our game admits a closed-form solution, making the computation efficient. We validate the effectiveness of our approach through empirical analysis on real-world tabular datasets with ground-truth anomaly labels
Visualisation Patterns in Visual Reasoning Tasks with Different Complexity Levels: Insights from Human and Machine Approach
No full textTask complexity plays a crucial role in cognitive development, problem-solving skills, and effective learning. Engaging with complex tasks stimulates higher-order thinking, boosts learner motivation, and prepares students for real-world challenges and advanced studies. This study investigates how different levels of task complexity (low, medium, high) affect visual attention sequences and contrasts human scanpaths with those derived from the Compositional Language and Elementary Visual Reasoning (CLEVR) dataset’s computational model. Using eye-tracking technology, we analyzed the visual attention patterns of Indian students (ages 18–35) working on visual reasoning tasks of varying complexity within a controlled laboratory setting. Our findings show that greater task complexity leads to more dispersed and prolonged fixations, reflecting a shift in attention sequence and a more thorough search strategy. Additionally, we compared human scanpaths with the CLEVR model and found notable differences. Human search behavior is more intricate, featuring frequent revisits and considering various attributes such as color, shape, size, and material. In contrast, the CLEVR model operates with a more linear and streamlined approach. These results highlight the significant differences between human and algorithmic visual reasoning processes. The complexity and frequent revisits observed in human behavior demonstrate the adaptable nature of human cognition. This study underscores the importance of integrating task complexity into educational strategies to enhance cognitive development, engagement, and the alignment of educational tools with human cognitive processes, ultimately improving learning outcomes