An efficient cooperative lane-changing algorithm for sensor- and communication-enabled automated vehicles
- Authors: Awal, Tanveer , Murshed, Manzur , Ali, Mortuza
- Date: 2015
- Type: Text , Conference proceedings
- Full Text: false
- Description: A key goal in transportation system is to attain efficient road traffic through minimization of trip time, fuel consumption and pollutant-emission without compromising safety. In dense traffic lane-changes and merging are often key ingredients to cause safety hazards, traffic breakdowns and travel delays. In this paper, we propose an efficient cooperative lane-changing algorithm CLA for sensor- and communication-enabled automated vehicles to reduce the lane-changing bottlenecks. For discretionary lane-changing, we consider the advantages of the subject vehicle, the follower in the current lane and k (an integer) lag vehicles in the target lane to maximize speed gains. Our algorithm simultaneously minimizes the impact of lane-change on traffic flow and the overall trip time, fuel-consumption and pollutant-emission. For mandatory lane-changing CLA dissociates the decision-making point from the actual mandatory lane-changing point and computes a suitable lane-changing slot in order to minimize lane-changing (merging) time. Our algorithm outperforms the potential cooperative lane-changing algorithm MOBIL proposed by Kesting et al. [1] in terms of merging time and rate, waiting time, fuel consumption, average velocity and flow (especially at the point in front of the merging point) at the cost of slightly increased average trip time for the mainroad vehicles compared to MOBIL. We also highlight important directions for further research. © 2015 IEEE.
Comparative analysis of machine and deep learning models for soil properties prediction from hyperspectral visual band
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
- Date: 2023
- Type: Text , Journal article
- Relation: Environments Vol. 10, no. 5 (2023), p. 77
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- Reviewed:
- Description: Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
Workload-aware incremental repartitioning of shared-nothing distributed databases for scalable OLTP applications
- Authors: Kamal, Joarder , Murshed, Manzur , Buyya, Rajkumar
- Date: 2016
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 56, no. March (2016), p. 421-436
- Full Text: false
- Reviewed:
- Description: On-line Transaction Processing (OLTP) applications often rely on shared-nothing distributed databases that can sustain rapid growth in data volume. Distributed transactions (DTs) that involve data tuples from multiple geo-distributed servers can adversely impact the performance of such databases, especially when the transactions are short-lived and these require immediate responses. The. k-way min-cut graph clustering based database repartitioning algorithms can be used to reduce the number of DTs with acceptable level of load balancing. Web applications, where DT profile changes over time due to dynamically varying workload patterns, frequent database repartitioning is needed to keep up with the change. This paper addresses this emerging challenge by introducing incremental repartitioning. In each repartitioning cycle, DT profile is learnt online and. k-way min-cut clustering algorithm is applied on a special sub-graph representing all DTs as well as those non-DTs that have at least one tuple in a DT. The latter ensures that the min-cut algorithm minimally reintroduces new DTs from the non-DTs while maximally transforming existing DTs into non-DTs in the new partitioning. Potential load imbalance risk is mitigated by applying the graph clustering algorithm on the finer logical partitions instead of the servers and relying on random one-to-one cluster-to-partition mapping that naturally balances out loads. Inter-server data-migration due to repartitioning is kept in check with two special mappings favouring the current partition of majority tuples in a cluster-the many-to-one version minimising data migrations alone and the one-to-one version reducing data migration without affecting load balancing. A distributed data lookup process, inspired by the roaming protocol in mobile networks, is introduced to efficiently handle data migration without affecting scalability. The effectiveness of the proposed framework is evaluated on realistic TPC-C workloads comprehensively using graph, hypergraph, and compressed hypergraph representations used in the literature. To compare the performance of any incremental repartitioning framework without any bias of the external min-cut algorithm due to graph size variations, a transaction generation model is developed that can maintain a target number of unique transactions in any arbitrary observation window, irrespective of new transaction arrival rate. The overall impact of DTs at any instance is estimated from the exponential moving average of the recurrence period of unique transactions to avoid transient fluctuations. The effectiveness and adaptability of the proposed incremental repartitioning framework for transactional workloads have been established with extensive simulations on both range partitioned and consistent hash partitioned databases. © 2015 Elsevier B.V.
Search and tracking algorithms for swarms of robots: A survey
- Authors: Senanayake, Madhubhashi , Senthooran, Ilankaikaone , Barca, Jan , Chung, Hoam , Kamruzzaman, Joarder , Murshed, Manzur
- Date: 2016
- Type: Text , Journal article
- Relation: Robotics and Autonomous Systems Vol. 75, no. Part B (2016), p. 422-434
- Full Text: false
- Reviewed:
- Description: Target search and tracking is a classical but difficult problem in many research domains, including computer vision, wireless sensor networks and robotics. We review the seminal works that addressed this problem in the area of swarm robotics, which is the application of swarm intelligence principles to the control of multi-robot systems. Robustness, scalability and flexibility, as well as distributed sensing, make swarm robotic systems well suited for the problem of target search and tracking in real-world applications. We classify the works we review according to the variations and aspects of the search and tracking problems they addressed. As this is a particularly application-driven research area, the adopted taxonomy makes this review serve as a quick reference guide to our readers in identifying related works and approaches according to their problem at hand. By no means is this an exhaustive review, but an overview for researchers who are new to the swarm robotics field, to help them easily start off their research. © 2015 Elsevier B.V.