Unintentional lane departure accidents are one of the biggest reasons for the causalities that occur due to human errors. By incorporating lane-keeping features in vehicles, many accidents can be avoided. The lane-keeping system operates by auto-steering the vehicle in order to keep it within the desired lane, despite of changes in road conditions and other interferences. Accurate steering angle prediction is crucial to keep the vehicle within the road boundaries, which is a challenging task. The main difficulty in this regard is to identify the drivable road area on heterogeneous road types varying in color, texture, illumination conditions, and lane marking types. This strenuous problem can be addressed by two approaches, namely, ‘computer-vision-based approach’ and ‘imitation-learning-based approach’. To the best of our knowledge, at present, there is no such detailed review study covering both the approaches and their related optimization techniques. This comprehensive review attempts to provide a clear picture of both approaches of steering angle prediction in the form of step by step procedures. The taxonomy of steering angle prediction has been presented in the paper for a better comprehension of the problem. We have also discussed open research problems at the end of the paper to help the researchers of this area to discover new research horizons.

In this research, a novel T shape antenna is proposed for millimetre-wave (mmwave) 5G systems. Designed on 0.254mm thin Roggers 5880 substrate with a dielectric constant of 2.3 and the loss tangent of 0.0009, the proposed antenna offers a wideband characteristics of nearly 8GHz with gain of 4.25dBi for single element. Based on these characteristics, the single element is further constructed into a four-element linear array with a compact size of 18.5×24mm² The proposed antenna array exhibited dual beam radiation patterns with a high realized gain of 11.5dBi and 94 efficiency. The measured results from the fabricated prototype well agree with the simulated results and thus, therefore, make the proposed antenna system a well-suited candidate for future mmwave devices.

Access control mechanisms define the level of access to the resources among specified users. It distinguishes the users as authorized or unauthorized based on appropriate policies. Several traditional and hybrid access control models have been proposed in previous researches over the last few decades. In this study, we provide a detailed survey of access control models and compare the traditional and hybrid access control models based on their access control criteria. This survey focuses on the growing literature of access control models and summarizes it through comparative analysis, identifying limitations and illustrating the advantages of both traditional and hybrid models. This study will help the researchers to get a deep understanding of the traditional and hybrid access control models.

The next-generation Ethernet passive optical network (NG-EPON) is basically classified into two architectures on the basis of the wavelength sharing by the optical network units (ONUs). The single scheduling domain (SSD) and multi-scheduling domain (MSD) EPON are the two different design architectures for NG-EPON. A vital task in NG-EPON is to design dynamic wavelength bandwidth allocation (DWBA) algorithms that can meet the future demands of the network subscribers. A number of DWBA algorithms have been designed for time and wavelength division multiplex (TWDM) EPON. The existing DWBA algorithms for TWDM-EPON could be used in MSD-EPON by making necessary parametric changes. The design and implementation of new DWBA algorithms for MSD-EPON are still required specifically. In this paper, we have proposed a quality of service (QoS)-based DWBA algorithm for NG-EPON. We have comparatively analyzed our proposed DWBA with the existing algorithms like earlier finished time (EFT), weighted bipartite matching (WBM), and earlier finished time with void filling (EFT-VF). The results show that our proposed DWBA algorithm performs better as compared to EFT, WBM, and EFT-VF on the basis of average packet delay and average completion time for NG-EPON.

In recent decades, agricultural goods demand has grown exponentially due to the growth of the human population. Agricultural production demands new and simple techniques. Moreover, safe, efficient, and cost-effective methods are required for monitoring agriculture crops. This research aims to provide a simple image processing technique that detects and distinguishes the object (agricultural goods) in drone-based agrarian imagery. We used cotton crop images as experimental data in this research due to its different spectral characteristics according to drone camera sensors, minimum weather constraints, and flight schedule timing-related constraints. The proposed method used fuzzy reasoning-based tactics combined with RGB and HSV color spaces by manipulating image color pixel values and setting the upper/lower limits values of the colors for detection and distinguishing the agricultural objects such as white cotton bolls from the rest of the image.