GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction

High Dynamic Range (HDR) content (i.e., images and videos) has a broad range of applications. However, capturing HDR content from real-world scenes is expensive and time-consuming. Therefore, the challenging task of reconstructing visually accurate HDR images from their Low Dynamic Range (LDR) counterparts is gaining attention in the vision research community. A major challenge in this research problem is the lack of datasets, which capture diverse scene conditions (e.g., lighting, shadows, weather, locations, landscapes, objects, humans, buildings) and various image features (e.g., color, contrast, saturation, hue, luminance, brightness, radiance). To address this gap, in this paper, we introduce GTA-HDR, a large-scale synthetic dataset of photo-realistic HDR images sampled from the GTA-V video game. We perform thorough evaluation of the proposed dataset, which demonstrates significant qualitative and quantitative improvements of the state-of-the-art HDR image reconstruction methods. Furthermore, we demonstrate the effectiveness of the proposed dataset and its impact on additional computer vision tasks including 3D human pose estimation, human body part segmentation, and holistic scene segmentation. The dataset, data collection pipeline, and evaluation code are available at: https://github.com/HrishavBakulBarua/GTA-HDR.Comment: Submitted to IEE

Investigations on Reactivity Controlled Compression Ignition Combustion with Different Injection Strategies using Alternative Fuels Produced from Waste Resources

Reactivity-controlled compression ignition (RCCI) is a promising low-temperature combustion (LTC) strategy that results in low oxides of nitrogen (NOx) and soot emissions while maintaining high thermal efficiency. At the same time, RCCI leads to increased unburned hydrocarbon (HC) and carbon monoxide (CO) emissions in the exhaust, particularly under low loads. The current work experimented novel port-injected RCCI (PI-RCCI) strategy to overcome the high unburned emission limitations at low load conditions in RCCI. PI-RCCI is a port injection strategy in which low-reactivity fuel (LRF) is injected using a low-pressure injector, and the high-reactivity fuel (HRF) is injected through a high-pressure common rail direct injection (CRDI) injector. The low volatile HRF is injected into a heated fuel vaporizer maintained at 180°C in the intake manifold during the suction stroke. Modifying a singlecylinder, light-duty diesel engine with the necessary intake and fuel injection systems allows engine operation in both RCCI and PI-RCCI modes. Alternative fuels from waste resources such as waste cooking oil biodiesel (WCO) and plastic waste oil (WPO) are used as the HRF and LRF fuel in RCCI and PI-RCCI. To achieve maximum thermal efficiency in RCCI, the premixed energy ratio and the start of injection of the direct-injected fuel are optimized at all load conditions. The engine performance and exhaust emissions characteristics in PI-RCCI are compared with RCCI as a baseline reference. The results show a 70% and 48% reduction in CO and HC emissions, respectively, in PI-RCCI than in RCCI. Further, the brake thermal efficiency (BTE) was enhanced by around 20%, and the brake-specific fuel consumption (BSFC) was reduced by 13% in PI-RCCI. The NOx emissions decreased without any considerable changes in soot emission in PI-RCCI. The current study shows that fuels derived from waste resources can be used in RCCI and PI-RCCI modes with better engine performance and lower emissions

Production of biofuel from AD digestate waste and their combustion characteristics in a low-speed diesel engine

Anaerobic digestion biogas plants generate large amounts of digestate that cannot always be valorised as fertilizer. This study proposes an alternative use through pyrolysis of the digestate for the production of liquid fuels for compression ignition engines. The digestate pyrolysis oil (DPO) and two types of biodiesel were produced and mixed with different alcohols. A total of five blends of DPO, biodiesel and alcohol were prepared and characterized, showing that their acidity and viscosity were higher than for pure diesel, and their heating value was lower. Blends containing 60 % biodiesel, 20 % DPO, and 20 % butanol were then tested in an engine, showing that the maximum in-cylinder pressure and heat release rate were 4.6 % and 3 % lower, respectively, compared to diesel, and the engine thermal efficiency at full load was 6–8% lower. The nitric oxide and smoke emissions were 7 % and 40 % lower, respectively, but the carbon dioxide emissions were 7–10 % higher than with diesel. The blends showed retarded start of combustion by 1.5° crank angle, which delays the ignition by about 6.4 %. This study concludes that blends can be used as a fuel for agriculture and marine diesel engines, although their viscosity should be reduced by improving the pyrolysis conditions

Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy

Feature selection and classification are widely utilized for data analysis. Recently, considerable advancement has been achieved in semi-supervised multi-task feature selection algorithms, where they have exploited the shared information from multiple related tasks. However, these semi-supervised multi-task selection feature algorithms are unable to naturally handle the multi-view data since they are designed to deal with single-view data. Existing studies have demonstrated that mining information enclosed in multiple views can drastically enhance the performance of feature selection. As for classification, researchers have used semi-supervised learning for extreme learning machine (ELM), where they have exploited both the labeled and unlabeled data in order to boost the learning performances. They have incorporated Laplacian regularization to determine the geometry of the underlying manifold. However, Laplacian regularization lacks extrapolating power and biases the solution towards a constant function. These drawbacks affect the performances of Laplacian regularized semi-supervised ELMs when a few labeled data is used. In the first part of the study, a novel mathematical framework is introduced for multi-view Laplacian semi-supervised feature selection by mining the correlations among multiple tasks. The proposed algorithm is capable of exploiting complementary information from different feature views in each task while exploring the shared knowledge between multiple related tasks in a joint framework when the labeled training data is sparse. An efficient iterative algorithm is developed to optimize the objective function of the proposed algorithm since it is non-smooth and difficult to solve. The proposed algorithm is compared with the state-of-the-art feature selection algorithms using three different datasets. These datasets include consumer video dataset, 3D motion recognition dataset and handwritten digits recognition dataset. In these experiments, all the training and testing data are represented as feature vectors. By using the proposed algorithm, the sparse coefficients are learned by exploiting the relationships among different multi-view features and leveraging the knowledge from multiple related tasks. Then, the sparse coefficients are applied to both the feature vectors of the training and testing data to select the most representative features. The selected features are then fed into a linear support vector machine (SVM) for classification. The experimental results show that the proposed feature selection framework performed better when compared to other state-of-the-art feature selection algorithms. In the second part of the study, a novel classification algorithm called Hessian semi-supervised ELM (HSS-ELM) is proposed to enhance the semi-supervised learning of ELM. Unlike the Laplacian regularization, the Hessian regularization favours function whose values vary linearly along the geodesic distance and preserves the local manifold structure well. It leads to good extrapolating power. Furthermore, HSS-ELM maintains almost all the advantages of the traditional ELM such as the significant training efficiency and straightforward implementation for multiclass classification problems. The proposed algorithm is tested on publicly available datasets. These datasets include G50C, COIL20 (B), COIL20, USPST(B) and USPST. The experimental results demonstrate that the proposed algorithm is competitive compared to the state-of-the-art semi-supervised learning algorithms in terms of accuracy. Additionally, HSS-ELM requires remarkably less training time compared to semi-supervised SVMs/regularized least-squares algorithms

Multiview Laplacian semisupervised feature selection by leveraging shared knowledge among multiple tasks

Recently, considerable advancement has been achieved in semisupervised multitask feature selection methods, which they exploit the shared information from multiple related tasks. Besides, these algorithms have adopted manifold learning to leverage both the unlabeled and labeled data since its laborious to obtain adequate labeled training data. However, these semisupervised multitask selection feature algorithms are unable to naturally handle the multiview data since they are designed to deal single-view data. Existing studies have demonstrated that mining information enclosed in multiple views can drastically enhance the performance of feature selection. Multiview learning is capable of exploring the complementary and correlated knowledge from different views. In this paper, we incorporate multiview learning into semisupervised multitask feature selection framework and present a novel semisupervised multiview multitask feature selection framework. Our proposed algorithm is capable of exploiting complementary information from different feature views in each task while exploring the shared knowledge between multiple related tasks in a joint framework when the labeled training data is sparse. We develop an efficient iterative algorithm to optimize it since the objective function of the proposed method is non-smooth and difficult to solve. Experiment results on several multimedia applications have shown that the proposed algorithm is competitive compared with the other single-view feature selection algorithms

A Heuristic Angular Clustering Framework for Secured Statistical Data Aggregation in Sensor Networks

Clustering in wireless sensor networks plays a vital role in solving energy and scalability issues. Although multiple deployment structures and cluster shapes have been implemented, they sometimes fail to produce the expected outcomes owing to different geographical area shapes. This paper proposes a clustering algorithm with a complex deployment structure called radial-shaped clustering (RSC). The deployment structure is divided into multiple virtual concentric rings, and each ring is further divided into sectors called clusters. The node closest to the midpoint of each sector is selected as the cluster head. Each sector’s data are aggregated and forwarded to the sink node through angular inclination routing. We experimented and compared the proposed RSC performance against that of the existing fan-shaped clustering algorithm. Experimental results reveal that RSC outperforms the existing algorithm in scalability and network lifetime for large-scale sensor deployments

Plastic waste to liquid fuel: A review of technologies, applications, and challenges

One of the most promising approaches for converting waste plastics into oil is fast pyrolysis. This study reviews the current state of the art and recent progress made on the thermal conversion of plastic to oil technologies, and their uses as alternatives to fossil fuels. The fuel properties of waste plastic pyrolysis oil (WPPO) are close to the diesel fuel. The WPPO produced from high-density polyethylene, low-density polyethylene, polypropylene, and polystyrene have higher heating values ranging from 40 to 43 MJ/kg. The thermal efficiency of neat WPPO (or blends) was slightly lower than diesel or gasoline. The WPPO has a shorter ignition delay than diesel due to its high cetane number. The WPPO fuels have a lower peak in-cylinder pressure and heat release rate than diesel. Engine-out emissions such as smoke, CO, and CO 2, are lower than diesel. The NO x emissions are higher than diesel, which can be reduced with exhaust gas recirculation or use of additives. Our study reveals that the WPPO is a promising alternative fuel for diesel engine applications