No abstract

No abstract

Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.

Background: Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. Methods: We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Results: Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Conclusion: Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach.Keywords: Patch-based image denoising, Bilateral filter, Non-local means filtering, Probabilistic patch-based filtering, Dictionary learning filtering, K-SVD, Gaussian patch-PCA filtering, BM3D Review IntroductionThe noise level in digital images may vary from being almost imperceptible to being very noticeable. Image denoising techniques attempt to produce a new image that has less noise, i.e., closer to the original noise-free image. Image denoising techniques can be grouped into two main approaches: pixel-based image filtering and patch-based *Correspondence: elsakka@csd.uwo.ca 2 Department of Computer Science, Middlesex College, Western University, 1151 Richmond Street, N6A 5B7, London, Ontario, Canada Full list of author information is available at the end of the article image filtering. A pixel-based image filtering scheme is mainly a proximity operation used for manipulating one pixel at a time (pixel-wise) based on its spatial neighboring pixels located within a kernel. On the other hand, in patch-based image filtering, the noisy image is divided into patches, or "blocks, " which are then manipulated separately in order to provide an estimate of the true pixel values (patch-wise) based on similar patches located within a search window. This approach utilizes the redundancy and the similarity among the various parts of the input image. Figure 1 shows the mechanism of the two approaches.

Textile wastewater contains large quantities of azo dyes mixed with various contaminants especially heavy metal ions. The discharge of effluents containing methyl orange (MO) dye and Cu2+ ions into water is harmful because they have severe toxic effects to humans and the aquatic ecosystem. The dried algal biomass was used as a sustainable, cost-effective and eco-friendly for the treatment of the textile wastewater. Box–Behnken design (BBD) was used to identify the most significant factors for achieving maximum biosorption of Cu2+ and MO from aqueous solutions using marine alga Fucus vesiculosus biomass. The experimental results indicated that 3 g/L of F. vesiculosus biomass was capable of removing 92.76% of copper and 50.27% of MO simultaneously from aqueous solution using MO (60 mg/L), copper (200 mg/L) at pH 7 within 60 min with agitation at 200 rpm. The dry biomass was also investigated using SEM, EDS, and FTIR before and after MO and copper biosorption. FTIR, EDS and SEM analyses revealed obvious changes in the characteristics of the algal biomass as a result of the biosorption process. The dry biomass of F. vesiculosus can eliminate MO and copper ions from aquatic effluents in a feasible and efficient method.