Visual Simultaneous Localization and Mapping in an Active Dynamic Environment

In recent years, the work on simultaneous localization and mapping has matured significantly. Robust techniques have been developed to explore and map a static environment in real-time. However, the problem of localizing and mapping a dynamic environment is still to be solved. The dynamic part of the environment not only makes the localization difficult but it introduces a diverse set of challenges to the existing problems such as detecting, tracking and segmenting the moving objects, and 3D reconstruction of the moving objects and/or static environment. This thesis focuses on studying the problem of simultaneously localizing and mapping an actively dynamic environment. A comprehensive review and analysis of the state-of-the-art methods are provided for both static and dynamic cases. A stereo camera is used to explore the dynamic environment and obtain semi-dense point clouds for the image sequence. The proposed approach is a variant of the standard ICP where the outliers of the registration process are not discarded. All 3D points are assigned a confidence measure based on their association in their respective neighborhood. The confidence measure decides if a 3D point is classified static or dynamic in the global map. Hence, the approach does not require any prior information about the environment or the moving objects. In the latter part of this study, the moving objects are segmented in 3D space and 2D images for any potential future analysis. The framework is tested with highly dynamic scenes from both indoor and outdoor environments. The results demonstrate the effectiveness of the proposed approach

Methods, Models, and Datasets for Visual Servoing and Vehicle Localisation

Machine autonomy has become a vibrant part of industrial and commercial aspirations. A growing demand exists for dexterous and intelligent machines that can work in unstructured environments without any human assistance. An autonomously operating machine should sense its surroundings, classify different kinds of observed objects, and interpret sensory information to perform necessary operations. This thesis summarizes original methods aimed at enhancing machine’s autonomous operation capability. These methods and the corresponding results are grouped into two main categories. The first category consists of research works that focus on improving visual servoing systems for robotic manipulators to accurately position workpieces. We start our investigation with the hand-eye calibration problem that focuses on calibrating visual sensors with a robotic manipulator. We thoroughly investigate the problem from various perspectives and provide alternative formulations of the problem and error objectives. The experimental results demonstrate that the proposed methods are robust and yield accurate solutions when tested on real and simulated data. The work package is bundled as a toolkit and available online for public use. In an extension, we proposed a constrained multiview pose estimation approach for robotic manipulators. The approach exploits the available geometric constraints on the robotic system and infuses them directly into the pose estimation method. The empirical results demonstrate higher accuracy and significantly higher precision compared to other studies. In the second part of this research, we tackle problems pertaining to the field of autonomous vehicles and its related applications. First, we introduce a pose estimation and mapping scheme to extend the application of visual Simultaneous Localization and Mapping to unstructured dynamic environments. We identify, extract, and discard dynamic entities from the pose estimation step. Moreover, we track the dynamic entities and actively update the map based on changes in the environment. Upon observing the limitations of the existing datasets during our earlier work, we introduce FinnForest, a novel dataset for testing and validating the performance of visual odometry and Simultaneous Localization and Mapping methods in an un-structured environment. We explored an environment with a forest landscape and recorded data with multiple stereo cameras, an IMU, and a GNSS receiver. The dataset offers unique challenges owing to the nature of the environment, variety of trajectories, and changes in season, weather, and daylight conditions. Building upon the future works proposed in FinnForest Dataset, we introduce a novel scheme that can localize an observer with extreme perspective changes. More specifically, we tailor the problem for autonomous vehicles such that they can recognize a previously visited place irrespective of the direction it previously traveled the route. To the best of our knowledge, this is the first study that accomplishes bi-directional loop closure on monocular images with a nominal field of view. To solve the localisation problem, we segregate the place identification from the pose regression by using deep learning in two steps. We demonstrate that bi-directional loop closure on monocular images is indeed possible when the problem is posed correctly, and the training data is adequately leveraged. All methodological contributions of this thesis are accompanied by extensive empirical analysis and discussions demonstrating the need, novelty, and improvement in performance over existing methods for pose estimation, odometry, mapping, and place recognition

Genetic Diversity and Traits Association in Tetraploid and Hexaploid Wheat Genotypes in Khyber Pakhtunkhwa Province of Pakistan

Information regarding the magnitude of variability as well as the correlation among agronomicallyimportant traits renders the basis for development of a successful crop improvement program. An experimentcontaining 16 wheat genotypes (8 durum and 8 spring wheat) was conducted in crop season of year 2015-2016, at The University of Agriculture, Peshawar. The experimental design used was a Randomized CompleteBlock Design (RCBD) with three replications. The parameters under study were days to heading, days tomaturity, flag leaf area, plant height, tillers m-2, spikes m-2, spikelets spike-1, 1000-grain weight, grain yieldand harvest index. Among the genotypes for various study traits, statistically significant differences wereobserved. Except for the flag leaf area and days to maturity, durum vs. spring wheat contrast was significantfor all other studied parameters. The flag leaf area of durum wheat was more than that of spring wheat andit took fewer days to initiate heading as well. In contrast, spring wheat genotypes had more average plantheight, tillers m-2, spikes m-2, 1000-grain weight, grain yield and harvest index than durum wheat genotypes.Correlation analysis revealed that tillers m-2 and spikes m-2 had significantly positive association with grainyield while grain yield had significantly strong positive association with harvest index in tested germplasm.Durum genotypes DWE3 and DWE7 performed best for yield contributing traits while spring wheat verities,Janbaz, Barsat and Shahkar outperformed others in terms of 1000-grain weight, grain yield, and harvest index,respectively. These genotypes are recommended to be further tested at multi-locations to check for wideradaptability and a possible use in future wheat breeding programs in the areaPeer reviewe

Targeting of protein expression in renal disease using siRNA – A review

The kidneys have rarely been used as a target in the systemic delivery of siRNA when compared to other tissues or organs in the body. This review article deals with various modalities adopted to deliver siRNA to the renal system under different normal and pathophysiological states. In this article, the authors have reviewed extensive clinical data that describe the use of siRNA for the treatment of renal diseases. Conventional and 3D modeling utilizes the existing genome-based RNA libraries, which facilitated the identification of molecular pathways involved in renal diseases. Keywords: siRNA, kidney disease, targeting proteins, signal pathway

PROTACs: The Future of Leukemia Therapeutics

The fight to find effective, long-lasting treatments for cancer has led many researchers to consider protein degrading entities. Recent developments in PROteolysis TArgeting Chimeras (PROTACs) have signified their potential as possible cancer therapies. PROTACs are small molecule, protein degraders that function by hijacking the built-in Ubiquitin-Proteasome pathway. This review mainly focuses on the general design and functioning of PROTACs as well as current advancements in the development of PROTACs as anticancer therapies. Particular emphasis is given to PROTACs designed against various types of Leukemia/Blood malignancies

Primary plant nutrients modulate the reactive oxygen species metabolism and mitigate the impact of cold stress in overseeded perennial ryegrass

Overseeded perennial ryegrass (Lolium perenne L.) turf on dormant bermudagrass (Cynodon dactylon Pers. L) in transitional climatic zones (TCZ) experience a severe reduction in its growth due to cold stress. Primary plant nutrients play an important role in the cold stress tolerance of plants. To better understand the cold stress tolerance of overseeded perennial ryegrass under TCZ, a three-factor and five-level central composite rotatable design (CCRD) with a regression model was used to study the interactive effects of nitrogen (N), phosphorus (P), and potassium (K) fertilization on lipid peroxidation, electrolyte leakage, reactive oxygen species (ROS) production, and their detoxification by the photosynthetic pigments, enzymatic and non-enzymatic antioxidants. The study demonstrated substantial effects of N, P, and K fertilization on ROS production and their detoxification through enzymatic and non-enzymatic pathways in overseeded perennial ryegrass under cold stress. Our results demonstrated that the cold stress significantly enhanced malondialdehyde, electrolyte leakage, and hydrogen peroxide contents, while simultaneously decreasing ROS-scavenging enzymes, antioxidants, and photosynthetic pigments in overseeded perennial ryegrass. However, N, P, and K application mitigated cold stress-provoked adversities by enhancing soluble protein, superoxide dismutase, peroxide dismutase, catalase, and proline contents as compared to the control conditions. Moreover, N, P, and, K application enhanced chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids in overseeded perennial ryegrass under cold stress as compared to the control treatments. Collectively, this 2−years study indicated that N, P, and K fertilization mitigated cold stress by activating enzymatic and non-enzymatic antioxidants defense systems, thereby concluding that efficient nutrient management is the key to enhanced cold stress tolerance of overseeded perennial ryegrass in a transitional climate. These findings revealed that turfgrass management will not only rely on breeding new varieties but also on the development of nutrient management strategies for coping cold stress

Integration of differential interferometric synthetic aperture radar and persistent scatterer interferometric approaches to assess deformation in enshi city, hubei, China

Land surface deformation can severely damage socioeconomic living conditions around the world. This study aimed to identify the Shaziba landslide and further assess deformation activities in Enshi city. For this purpose, the sentinel-1 C-bands data acquired in ascending directions were processed with Sentinel Application Platform and Stanford Method for Persistent Scatterers (StaMPS) software packages. Our results revealed the location of a landslide that occurred on 21 July 2020 in the Shaziba area, Enshi Prefecture. More interesting deformation results were found in Enshi city for the first time with a deformation range from −51.6 to 54.2 mm/year. We conducted a thorough observation of different urban infrastructures such as commercial and residential buildings, roads, bridges, and airports in Enshi city and along the Qingjiang River to evaluate land surface deformation. Observations revealed that there are a number of influencing factors contributing to disturbing the natural environment and resources in Enshi Prefecture. Of these influencing factors, intensive rainfall is a major cause as are the infiltration of rainfall into the subsurface Silurian strata together with the load of infrastructure in the study area. If this issue is not addressed it could lead to devastating geo-hazard disasters in the future. Scientific approaches to determine various causes of frequent geo-hazards in this region are of great significance for developing early warning systems for disasters and ensuring the safety of residents’ lives and property

Turn on the Lights: Macroeconomic Factors Affecting Renewable in Pakistan

The objective of the study is to examine the relationship between macroeconomic factors (i.e., population growth; urbanization, industrialization, exchange rate, price level, food production index and live stock production index) and renewable energy in Pakistan over a period of 1975-2012. In addition, this study uses oil rent as an intervening variable to overcome the biasness of the single equation model. The results indicate that macroeconomic factors positively contributed to renewable energy consumption in Pakistan. The causality test indicate that there is a unidirectional causality running towards macroeconomic factors to renewable energy in Pakistan, however, renewable energy Granger cause oil rent but not via other route. In addition, there is bidirectional causality between exchange rate and live stock production in Pakistan. Variance decomposition analysis shows that economic growth has a major contribution to increase renewable energy in Pakistan

Turn on the Lights: Macroeconomic Factors Affecting Renewable in Pakistan

The objective of the study is to examine the relationship between macroeconomic factors (i.e., population growth; urbanization, industrialization, exchange rate, price level, food production index and live stock production index) and renewable energy in Pakistan over a period of 1975-2012. In addition, this study uses oil rent as an intervening variable to overcome the biasness of the single equation model. The results indicate that macroeconomic factors positively contributed to renewable energy consumption in Pakistan. The causality test indicate that there is a unidirectional causality running towards macroeconomic factors to renewable energy in Pakistan, however, renewable energy Granger cause oil rent but not via other route. In addition, there is bidirectional causality between exchange rate and live stock production in Pakistan. Variance decomposition analysis shows that economic growth has a major contribution to increase renewable energy in Pakistan