Perceptual Quality Assessment of 360° Images Based on Generative Scanpath Representation.

Despite substantial efforts dedicated to the design of heuristic models for omnidirectional (i.e., 360°) image quality assessment (OIQA), a conspicuous gap remains due to the lack of consideration for the diversity of viewing behaviors that leads to the varying perceptual quality of 360° images.

Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment.

The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widely acknowledged that screen content images (SCIs), which are typically computer generated, do not hold such statistics. Here we make the first attempt to learn the statistics of SCIs, based upon which the quality of SCIs can be effectively determined.

Exceedances of Secondary Aerosol Formation from In-Use Natural Gas Heavy-Duty Vehicles Compared to Diesel Heavy-Duty Vehicles.

This work, for the first time, assessed the secondary aerosol formation from both in-use diesel and natural gas heavy-duty vehicles of different vocations when they were operated on a chassis dynamometer while the vehicles were exercised on different driving cycles. Testing was performed on natural gas vehicles equipped with three-way catalysts (TWCs) and diesel trucks equipped with diesel oxidation catalysts, diesel particulate filters, and selective catalytic reduction systems. Secondary aerosol was measured after introducing dilute exhaust into a 30 m environmental chamber.

No-Reference Image Quality Assessment by Hallucinating Pristine Features.

In this paper, we propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination. The proposed quality assessment framework is rooted in the view that the perceptually meaningful features could be well exploited to characterize the visual quality, and the natural image statistical behaviors are exploited in an effort to deliver the accurate predictions. Herein, the PR features from the distorted images are learned by a mutual learning scheme with the pristine reference as the supervision, and the discriminative characteristics of PR features are further ensured with the triplet constraints.

Effects off hydrogenated vegetable oil (HVO) and HVO/biodiesel blends on the physicochemical and toxicological properties of emissions from an off-road heavy-duty diesel engine.

In this study, the regulated emissions, gaseous toxics, and the physical, chemical, and toxicological properties of particulate matter (PM) emissions from a legacy off-road diesel engine operated on hydrogenated vegetable oil (HVO) and HVO blends with biodiesel were investigated. This is one of the very few studies currently available examining the emissions and potential health effects of HVO and its blends with biodiesel from diesel engines. Extended testing was conducted over the nonroad transient cycle (NRTC) and the 5-mode D2 ISO 8718 cycle.

The impact of hydrogenated vegetable oil (HVO) on the formation of secondary organic aerosol (SOA) from in-use heavy-duty diesel vehicles.

This manuscript contains an assessment of tailpipe emissions and secondary aerosol formation from two in-use heavy-duty diesel vehicles (HDDVs) with different aftertreatment systems when operated with ultra-low sulfur diesel (ULSD) and hydrogenated vegetable oil (HVO) operated on a chassis dynamometer. Secondary aerosol formation was characterized from the HDDVs' diluted exhaust collected and photochemically aged in a 30 m mobile atmospheric chamber. Primary nitrogen oxide (NOx) and particulate matter (PM) emissions were reduced for both vehicles operating on HVO compared to ULSD.

Real-world NOx emissions from heavy-duty diesel, natural gas, and diesel hybrid electric vehicles of different vocations on California roadways.

This study assessed the real-world nitrogen oxide (NOx) emissions from 50 heavy-duty vehicles of different vocations and engine technologies using portable emissions measurement systems (PEMS). This is one of the most comprehensive in-use emissions studies conducted to date, which played a key role in the development of CARB's (California Air Recourses Board) updated EMission FACtor (EMFAC) model, especially for natural gas vehicles. In-use emissions testing was performed on school and transit buses, refuse haulers, goods movement vehicles, and delivery vehicles while were driven over their normal operating routes in the South Coast Air Basin.

Superpixel-Based Quality Assessment of Multi-Exposure Image Fusion for Both Static and Dynamic Scenes.

Multi-exposure image fusion (MEF) algorithms have been used to merge a stack of low dynamic range images with various exposure levels into a well-perceived image. However, little work has been dedicated to predicting the visual quality of fused images. In this work, we propose a novel and efficient objective image quality assessment (IQA) model for MEF images of both static and dynamic scenes based on superpixels and an information theory adaptive pooling strategy.

On-road gaseous and particulate emissions from GDI vehicles with and without gasoline particulate filters (GPFs) using portable emissions measurement systems (PEMS).

This study assessed the on-road gaseous and particulate emissions from three current technology gasoline direct injection (GDI) vehicles using portable emissions measurement systems (PEMS). Two vehicles were also retrofitted with catalyzed gasoline particulate filters (GPFs). All vehicles were exercised over four routes with different topological and environmental characteristics, representing urban, rural, highway, and high-altitude driving conditions.

Deep Guided Learning for Fast Multi-Exposure Image Fusion.

We propose a fast multi-exposure image fusion (MEF) method, namely MEF-Net, for static image sequences of arbitrary spatial resolution and exposure number. We first feed a low-resolution version of the input sequence to a fully convolutional network for weight map prediction. We then jointly upsample the weight maps using a guided filter.

Perceptual Evaluation for Multi-Exposure Image Fusion of Dynamic Scenes.

A common approach to high dynamic range (HDR) imaging is to capture multiple images of different exposures followed by multi-exposure image fusion (MEF) in either radiance or intensity domain. A predominant problem of this approach is the introduction of the ghosting artifacts in dynamic scenes with camera and object motion. While many MEF methods (often referred to as deghosting algorithms) have been proposed for reduced ghosting artifacts and improved visual quality, little work has been dedicated to perceptual evaluation of their deghosting results.

[Analysis of drug-induced acute renal failure in Shanghai].

Objective: To investigate the incidence and prognosis of drug-induced acute renal failure (ARF) in Shanghai.

Methods: The registration forms of ARF patients admitted in 17 hospitals of and over the middle class in Shanghai from January 1, 2004 to December 31, 2006 were screened prospectively. The data, such as epidemiology, survival, mortality, and morbidity were analyzed.