Other affiliations: Academy of Scientific and Innovative Research, Council of Scientific and Industrial Research
Bio: Sanjay Singh is an academic researcher from Central Electronics Engineering Research Institute. The author has contributed to research in topics: Field-programmable gate array & Video tracking. The author has an hindex of 9, co-authored 69 publications receiving 288 citations. Previous affiliations of Sanjay Singh include Academy of Scientific and Innovative Research & Council of Scientific and Industrial Research.
TL;DR: In this article, a review of the status of research on the application of nanoceria to treat diseases caused by reactive oxygen species (ROS) and reactive nitrogen species is presented.
Abstract: Research on cerium oxide nanoparticles (nanoceria) has captivated the scientific community due to their unique physical and chemical properties, such as redox activity and oxygen buffering capacity, which made them available for many technical applications, including biomedical applications. The redox mimetic antioxidant properties of nanoceria have been effective in the treatment of many diseases caused by reactive oxygen species (ROS) and reactive nitrogen species. The mechanism of ROS scavenging activity of nanoceria is still elusive, and its redox activity is controversial due to mixed reports in the literature showing pro-oxidant and antioxidant activity. In light of its current research interest, it is critical to understand the behavior of nanoceria in the biological environment and provide answers to some of the critical and open issues. This review critically analyzes the status of research on the application of nanoceria to treat diseases caused by ROS. It reviews the proposed mechanism of action and shows the effect of surface coatings on its redox activity. It also discusses some of the crucial issues in deciphering the mechanism and redox activity of nanoceria and suggests areas of future research.
TL;DR: This paper presents an efficient dual integrated convolution neural network (DICNN) model for the recognition of facial expressions in the wild in real-time, running on an embedded platform and optimized the designed DICNN model using TensorRT SDK and deployed it on an Nvidia Xavier embedded platform.
Abstract: Automatic recognition of facial expressions in the wild is a challenging problem and has drawn a lot of attention from the computer vision and pattern recognition community. Since their emergence, the deep learning techniques have proved their efficacy in facial expression recognition (FER) tasks. However, these techniques are parameter intensive, and thus, could not be deployed on resource-constrained embedded platforms for real-world applications. To mitigate these limitations of the deep learning inspired FER systems, in this paper, we present an efficient dual integrated convolution neural network (DICNN) model for the recognition of facial expressions in the wild in real-time, running on an embedded platform. The designed DICNN model with just 1.08M parameters and 5.40 MB memory storage size achieves optimal performance by maintaining a proper balance between recognition accuracy and computational efficiency. We evaluated the DICNN model on four FER benchmark datasets (FER2013, FERPlus, RAF-DB, and CKPlus) using different performance evaluation metrics, namely the recognition accuracy, precision, recall, and F1-score. Finally, to provide a portable solution with high throughput inference, we optimized the designed DICNN model using TensorRT SDK and deployed it on an Nvidia Xavier embedded platform. Comparative analysis results with the other state-of-the-art methods revealed the effectiveness of the designed FER system, which achieved competitive accuracy with multi-fold improvement in the execution speed.
TL;DR: This paper presents an alternative computationally efficient approach for Yoga pose recognition in complex real-world environments using deep learning, and is among the first studies, which utilized the inherent spatial–temporal relationship among Yoga poses for their recognition.
Abstract: Existing techniques for Yoga pose recognition build classifiers based on sophisticated handcrafted features computed from the raw inputs captured in a controlled environment. These techniques often fail in complex real-world situations and thus, pose limitations on the practical applicability of existing Yoga pose recognition systems. This paper presents an alternative computationally efficient approach for Yoga pose recognition in complex real-world environments using deep learning. To this end, a Yoga pose dataset was created with the participation of 27 individual (8 males and 19 females), which consists of ten Yoga poses, namely Malasana, Ananda Balasana, Janu Sirsasana, Anjaneyasana, Tadasana, Kumbhakasana, Hasta Uttanasana, Paschimottanasana, Uttanasana, and Dandasana. To capture the videos, we used smartphone cameras having 4 K resolution and 30 fps frame rate. For the recognition of Yoga poses in real time, a three-dimensional convolutional neural network (3D CNN) architecture is designed and implemented. The designed architecture is a modified version of the C3D architecture initially introduced for the recognition of human actions. In the proposed modified C3D architecture, the computationally intensive fully connected layers are pruned, and supplementary layers such as the batch normalization and average pooling were introduced for computational efficiency. To the best of our knowledge, this is among the first studies, which utilized the inherent spatial–temporal relationship among Yoga poses for their recognition. The designed 3D CNN architecture achieved test recognition accuracy of 91.15% on the in-house prepared Yoga pose dataset consisting of ten Yoga poses. Furthermore, on the publicly available dataset, the designed architecture achieved competitive test recognition accuracy of 99.39%, along with multifold improvement in the execution speed compared to the existing state-of-the-art technique. To promote further study, we will make the in-house created Yoga pose dataset publicly available to the research community.
TL;DR: The proposed architecture uses single processing element for both horizontal and vertical gradient computation for Sobel operator and utilised approximately 38% less FPGA resources as compared to standard Sobel edge detection architecture while maintaining real-time frame rates for high definition videos.
Abstract: A new resource efficient FPGA-based hardware architecture for real-time edge detection using Sobel operator for video surveillance applications has been proposed. The choice of Sobel operator is due to its property to counteract the noise sensitivity of the simple gradient operator. FPGA is chosen for this implementation due to its flexibility to provide the possibility to perform algorithmic changes in later stage of the system development and its capability to provide real-time performance, hard to achieve with general purpose processor or digital signal processor, while limiting the extensive design work, time and cost required for application specific integrated circuit. The proposed architecture uses single processing element for both horizontal and vertical gradient computation for Sobel operator and utilised approximately 38% less FPGA resources as compared to standard Sobel edge detection architecture while maintaining real-time frame rates for high definition videos (1920 × 1080 image sizes). The...
TL;DR: The present study demonstrates the response of Firmicutes due to Iron fertilization which was not observed in previous southern ocean iron fertilization studies and identifies three unique phylogenetic clusters LOHAFEX Cluster 1, 2, and 3 (affiliated to Firmicute) which were not detected in any of the earlier studies onIron fertilization.
Abstract: Ocean iron fertilization is an approach to increase CO2 sequestration. The Indo-German iron fertilization experiment "LOHAFEX" was carried out in the Southern Ocean surrounding Antarctica in 2009 to monitor changes in bacterial community structure following iron fertilization-induced phytoplankton bloom of the seawater from different depths. 16S rRNA gene libraries were constructed using metagenomic DNA from seawater prior to and after iron fertilization and the clones were sequenced for identification of the major bacterial groups present and for phylogenetic analyses. A total of 4439 clones of 16S rRNA genes from ten 16S rRNA gene libraries were sequenced. More than 97.35% of the sequences represented four bacterial lineages i.e. Alphaproteobacteria, Gammaproteobacteria, Bacteroidetes, and Firmicutes and confirmed their role in scavenging of phytoplankton blooms induced following iron fertilization. The present study demonstrates the response of Firmicutes due to Iron fertilization which was not observed in previous southern ocean Iron fertilization studies. In addition, this study identifies three unique phylogenetic clusters LOHAFEX Cluster 1 (affiliated to Bacteroidetes), 2, and 3 (affiliated to Firmicutes) which were not detected in any of the earlier studies on iron fertilization. The relative abundance of these clusters in response to iron fertilization was different. The increase in abundance of LOHAFEX Cluster 2 and Papillibacter sp. another dominant Firmicutes may imply a role in phytoplankton degradation. Disappearance of LOHAFEX Cluster 3 and other bacterial genera after iron fertilization may imply conditions not conducive for their survival. It is hypothesized that heterotrophic bacterial abundance in the Southern Ocean would depend on their ability to utilize algal exudates, decaying algal biomass and other nutrients thus resulting in a dynamic bacterial succession of distinct genera.
TL;DR: A Deadline-Aware memory Scheduler for Heterogeneous systems (DASH), which overcomes problems using three key ideas, with the goal of meeting HWAs’ deadlines while providing high CPU performance.
Abstract: Modern SoCs integrate multiple CPU cores and hardware accelerators (HWAs) that share the same main memory system, causing interference among memory requests from different agents. The result of this interference, if it is not controlled well, is missed deadlines for HWAs and low CPU performance. Few previous works have tackled this problem. State-of-the-art mechanisms designed for CPU-GPU systems strive to meet a target frame rate for GPUs by prioritizing the GPU close to the time when it has to complete a frame. We observe two major problems when such an approach is adapted to a heterogeneous CPU-HWA system. First, HWAs miss deadlines because they are prioritized only when close to their deadlines. Second, such an approach does not consider the diverse memory access characteristics of different applications running on CPUs and HWAs, leading to low performance for latency-sensitive CPU applications and deadline misses for some HWAs, including GPUs.In this article, we propose a Deadline-Aware memory Scheduler for Heterogeneous systems (DASH), which overcomes these problems using three key ideas, with the goal of meeting HWAs’ deadlines while providing high CPU performance. First, DASH prioritizes an HWA when it is not on track to meet its deadline any time during a deadline period, instead of prioritizing it only when close to a deadline. Second, DASH prioritizes HWAs over memory-intensive CPU applications based on the observation that memory-intensive applications’ performance is not sensitive to memory latency. Third, DASH treats short-deadline HWAs differently as they are more likely to miss their deadlines and schedules their requests based on worst-case memory access time estimates.Extensive evaluations across a wide variety of different workloads and systems show that DASH achieves significantly better CPU performance than the best previous scheduler while always meeting the deadlines for all HWAs, including GPUs, thereby largely improving frame rates.
01 Jan 2005
TL;DR: In this article, the structural, electronic and magnetic properties of (Pd, Pt)-Mn-Ni-(Ga, In, Sn, Sb) alloys, which display multifunctional properties like the magnetic shape-memory, magnetocaloric and exchange bias effect, were investigated.
Abstract: First-principles calculations are used to study the structural, electronic and magnetic properties of (Pd, Pt)-Mn-Ni-(Ga, In, Sn, Sb) alloys, which display multifunctional properties like the magnetic shape-memory, magnetocaloric and exchange bias effect. The ab initio calculations give a basic understanding of the underlying physics which is associated with the complex magnetic behavior arising from competing ferro- and antiferromagnetic interactions with increasing number of Mn excess atoms in the unit cell. This information allows to optimize, for example, the magnetocaloric effect by using the strong influence of compositional changes on the magnetic interactions. Thermodynamic properties can be calculated by using the ab initio magnetic exchange parameters in finite-temperature Monte Carlo simulations. We present guidelines of how to improve the functional properties. For Pt-Ni-Mn-Ga alloys, a shape memory effect with 14% strain can be achieved in an external magnetic field.
TL;DR: In this paper , a review of 99 Q1 articles covering explainable artificial intelligence (XAI) techniques is presented, including SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, and others.
Abstract: Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community.Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded.In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others.We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.