Radar/INS Integration and Map Matching for Land Vehicle Navigation in Urban Environments.

Autonomous navigation requires multi-sensor fusion to achieve a high level of accuracy in different environments. Global navigation satellite system (GNSS) receivers are the main components in most navigation systems. However, GNSS signals are subject to blockage and multipath effects in challenging areas, e.g., tunnels, underground parking, and downtown or urban areas. Therefore, different sensors, such as inertial navigation systems (INSs) and radar, can be used to compensate for GNSS signal deterioration and to meet continuity requirements. In this paper, a novel algorithm was applied to improve land vehicle navigation in GNSS-challenging environments through radar/INS integration and map matching. Four radar units were utilized in this work. Two units were used to estimate the vehicle's forward velocity, and the four units were used together to estimate the vehicle's position. The integrated solution was estimated in two steps. First, the radar solution was fused with an INS through an extended Kalman filter (EKF). Second, map matching was used to correct the radar/INS integrated position using OpenStreetMap (OSM). The developed algorithm was evaluated using real data collected in Calgary's urban area and downtown Toronto. The results show the efficiency of the proposed method, which had a horizontal position RMS error percentage of less than 1% of the distance traveled for three minutes of a simulated GNSS outage.

Metal-Organic Frameworks (MOFs)-Based Mixed Matrix Membranes (MMMs) for Gas Separation: A Review on Advanced Materials in Harsh Environmental Applications.

The booming of global environmental awareness has driven the scientific community to search for alternative sustainable approaches. This is accentuated in the 13th sustainable development goal (SDG13), climate action, where urgent efforts are salient in combating the drastic effects of climate change. Membrane separation is one of the indispensable gas purification technologies that effectively reduces the carbon footprint and is energy-efficient for large-scale integration. Metal-organic frameworks (MOFs) are recognized as promising fillers embedded in mixed matrix membranes (MMMs) to enhance gas separation performance. Tremendous research studies on MOFs-based MMMs have been conducted. Herein, this review offers a critical summary of the MOFs-based MMMs developed in the past 3 years. The basic models to estimate gas transport, preparation methods, and challenges in developing MMMs are discussed. Subsequently, the application and separation performance of a variety of MOFs-based MMMs including those of advanced MOFs materials are summarized. To accommodate industrial needs and resolve commercialization hurdles, the latest exploration of MOF materials for a harsh operating condition is emphasized. Along with the contemplation on the outlook, future perspective, and opportunities of MMMs, it is anticipated that this review will serve as a stepping stone for the coming MMMs research on sustainable and benign environmental application.

Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments.

In recent years, due to the advancements in machine learning, object detection has become a mainstream task in the computer vision domain. The first phase of object detection is to find the regions where objects can exist. With the improvements in deep learning, traditional approaches, such as sliding windows and manual feature selection techniques, have been replaced with deep learning techniques. However, object detection algorithms face a problem when performed in low light, challenging weather, and crowded scenes, similar to any other task. Such an environment is termed a challenging environment. This paper exploits pixel-level information to improve detection under challenging situations. To this end, we exploit the recently proposed hybrid task cascade network. This network works collaboratively with detection and segmentation heads at different cascade levels. We evaluate the proposed methods on three complex datasets of ExDark, CURE-TSD, and RESIDE, and achieve a mAP of 0.71, 0.52, and 0.43, respectively. Our experimental results assert the efficacy of the proposed approach.

Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments.

Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of deep learning-based object detection in challenging environments. However, there is no consolidated reference to cover the state of the art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present a quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.

Magnetic Communication Using High-Sensitivity Magnetic Field Detectors.

In this article, an innovative approach for magnetic data communication is presented. For this purpose, the receiver coil of a conventional magneto-inductive communication system is replaced by a high-sensitivity wideband magnetic field sensor. The results show decisive advantages offered by sensitive magnetic field sensors, including a higher communication range for small receiver units. This approach supports numerous mobile applications where receiver size is limited, possibly in conjunction with multiple detectors. Numerical results are supported by a prototype implementation employing an anisotropic magneto-resistive sensor.

New Approach of High Sensitivity Techniques Using Collective Detection Method with Multiple GNSS Receivers.

The Collective Detection (CD) technique is a promising approach to meet the requirements for signal acquisition in GNSS-harsh environments. The CD approach has been proposed because of its potential to operate as both a direct positioning method and a high-sensitivity acquisition method. This paper is dedicated to the development of a new CD architecture for processing satellite signals in challenging environments. It proposes the best signal acquisition method used according to the reception conditions of the different receivers that can assist the user in difficulty. Knowing that the CD approach is beneficial in the case where the maximum of satellite signals can be combined, the proposed approach consists in choosing the best receiver(s) from several connected receivers to serve as a reference station, as smart cooperative navigation concept. New metrics of the CD with optimal weighting of visible satellites are exploited. Analysis of optimization method in order to use better satellites according to some defined parameters (elevation, C / N 0 , and GDOP) were carried out. Real GPS L1 C/A signals are exploited to analyze the efficiency of the proposed approach. A comparison of the results through the accumulation of some good satellites among all visible satellites have shown the effectiveness of this method.

Unpacking intersecting complexities for WASH in challenging contexts: A review.

Ensuring access to water, sanitation, and hygiene (WASH) for all requires a thorough understanding of the many contextual complexities that influence access to these services. Complexities spanning environmental, economic, political, and social dimensions, amongst others, can intersect and compound to hinder sustainable access to WASH for certain demographics or entire communities. This is of particular importance for challenging contexts where conventional WASH approaches are ineffective. Targeted approaches are required for these contexts to ensure that communities are not left behind in pursuit of the Sustainable Development Goals. Review of WASH literature identified seven broad types of challenging contexts: challenging environments, transient or environmentally-dependant communities, climate vulnerable communities, remote communities, poor urban communities, refugee camps, and emergency contexts. This review explores the intersecting complexities affecting access to WASH in these challenging contexts and how failure to understand the interconnectedness of these complexities has resulted in WASH solutions that are unaffordable, not inclusive, or unsustainable. To our knowledge, this review is the first of its kind. We emphasise the need to unpack intersecting complexities affecting WASH in challenging contexts, and we believe that incorporating such an approach early in WASH programs can ensure that intersecting complexities are accounted for in the design of WASH solutions. Ultimately, this novel lens may provide critical guidance for WASH programs in challenging contexts, ensuring that WASH solutions are contextually appropriate.