Channel Prioritization Research Articles

Multiple channel strategy selection: A roadmap perspective for brick-and-click retailers

We propose a brick-and-click prioritization tool that aids retailers in the transition from a multi-channel to an omni-channel strategy. The developed tool represents a strategic roadmap that goes beyond the limitations of existing literature, which either observed the transition towards omni-channel strategy theoretically or provided static, diagnostic, qualitative multiple channel classification frameworks. Quantitative measurability, benchmark potential, and predictive capacity of a multiple channel prioritization tool are introduced by applying the multiple criteria decision aiding method, dominance-based rough set approach. The output of the method is a set of “if-then” decision rules. Prescriptive in their nature, they guide managerial focus on channel aspects relevant to implementing a specific multiple channel strategy. We propose a novel attribute significance index that measures the importance of each KPI in reaching a specific multiple channel strategy. This allowed us to derive possible multiple channel pathways and optimize the retailer’s multiple channel transition strategy. The findings identified the importance of optimizing online channel sales concentration and focus, two attributes proposed by the authors to better depict the channel goal dimension in the multiple channel environment, as key preconditions for implementing the omni-channel strategy.

Using Dynamic Pruning Technique for Efficient Depth Estimation for Autonomous Vehicles

Even with the significant progress that has been achieved in monocular depth estimation in recent years, the need for better real-time inference and reduction in computing resources usage associated with the network performance is persistent. In this paper, an enquiry into the efficacy of pruning on depth estimation models is performed. Encoder-decoder model based on the ResNet-50 backbone architecture employing pruning based on channel prioritization is designed to achieve higher performance and prediction speed. This is while attempting to keep a balance in the trade-off between accuracy and performance of the network. The presented approach is trained and evaluated for outdoor scenery on the KITTI dataset to demonstrate the effectiveness and the performance improvement of the presented framework when compared to similar methods. This shows competitive accuracy when compared to state-of-the-art methods and highlights how pruning can speed up inference time by more than 16% and leading to fewer operations compared to the non-pruned model.

Multi-year planning for optimal navigation channel dredging and dredged material management

Navigation channels are critical maritime infrastructure that supports economic and recreational activities and intermodal freight supply chains, impacting broad areas of the hinterland. Maintenance of the maritime infrastructure is vital to the operations of the transportation system. This paper studies a strategic-level maritime infrastructure asset management problem: multi-year planning of navigation channel maintenance dredging and dredged material management. A mathematical model called dredging planning optimization model (DPOM) is developed to optimize channel dredging planning and dredged material management in confined disposal facilities (CDFs), accounting for practical constraints and considerations, including channel linkage and dependency, channel bundling, CDF accessibility and capacity, shoaling and navigability deterioration over time, reimbursable costs, channel economic values, etc. The problem is formulated as a mixed integer nonlinear programming (MINLP) model, with the objective of maximizing the total economic-value-weighted average navigability under fixed budget. The model can be reformulated as an equivalent mixed integer programming (MIP) model, which can be solved by the CPLEX solver, using the branch and bound algorithm. A heuristic algorithm called the dynamic planning prioritization (DPP) algorithm, is proposed in order to solve large-scale problems due to the computational complexity. DPP incorporates a dynamic ranking criterion to overcome the challenge of simultaneously handling all of the practical constraints and considerations, as well as the impact of channel prioritization on future year decisions. Finally, a real-world case study is proposed to illustrate the model results and demonstrate the effectiveness of the MIP model and the DPP algorithm.

QoS Aware Logical Channel Prioritization under Burst Resource Allocation for Uplink in LTE

QoS Aware Logical Channel Prioritization under Burst Resource Allocation for Uplink in LTE

The iCub Software Architecture: Evolution and Lessons Learned

The complexity of humanoid robots is increasing with the availability of new sensors, embedded CPUs and actuators. This wealth of technologies allows researchers to investigate new problems like whole-body force control, multi-modal human-robot interaction and sensory fusion. Under the hood of these robots, the software architecture has an important role: it allows researchers to get access to the robot functionalities focusing primarily on their research problems, it supports code reuse to minimize development and debugging, especially when new hardware becomes available. But more importantly it allows increasing the complexity of the experiments that can be implemented before system integration becomes unmanageable and debugging draws more resources than research itself. In this paper we illustrate the software architecture of the iCub humanoid robot and the software engineering best practices that have emerged driven by the needs of our research community. We describe the latest developments at the level of the middleware supporting interface definition and automatic code generation, logging, ROS compatibility and channel prioritization. We show the robot abstraction layer and how it has been modified to better address the requirements of the users and to support new hardware as it became available. We also describe the testing framework we have recently adopted for developing code using a test driven methodology. We conclude the paper discussing the lessons we have learned during the past eleven years of software development on the iCub humanoid robot.

The Effect of Product Type and Channel Prioritization on Effective Digital Marketing Performance

The Effect of Product Type and Channel Prioritization on Effective Digital Marketing Performance

Efficient Channel Utilization and Prioritization Scheme for Emergency Calls in Cellular Network

Handling of emergency calls in wireless cellular networks is one of the major issues. The main objective here is to improve quality of service by efficient channel utilization. In this paper, a new scheme called probabilistic emergency prioritization scheme (PEPS) is proposed which provides highest priority for emergency calls. The proposed method minimizes the dropping or blocking of emergency calls even if the number of emergency calls are more than 25% of the calls. Monte Carlo simulation results show that the proposed scheme works better than the existing adaptive probabilistic scheduling scheme (APS).

Channel Assignment Algorithms for OSA-Enabled WLANs Exploiting Prioritization and Spectrum Heterogeneity

SUMMARY Allowing WLANs to exploit opportunistic spectrum ac-cess (OSA) is a promising approach to alleviate spectrum congestion prob-lems in overcrowded unlicensed ISM bands, especially in highly denseWLAN deployments. In this context, novel channel assignment mecha-nisms jointly considering available channels in both unlicensed ISM andOSA-enabled licensed bands are needed. Unlike classical schemes pro-posed for legacy WLANs, channel assignment mechanisms for OSA-enabled WLAN should face two distinguishing issues: channel prioriti-zation and spectrum heterogeneity. The first refers to the fact that addi-tional prioritization criteria other than interference conditions should beconsidered when choosing between ISM or licensed band channels. Thesecond refers to the fact that channel availability might not be the same forall WLAN Access Points because of primary users’ activity in the OSA-enabled bands. This paper firstly formulates the channel assignment prob-lem for OSA-enabled WLANs as a Binary Linear Programming (BLP)problem. The resulting BLP problem is optimally solved by means ofbranch and bound algorithms and used as a benchmark to develop morecomputationally efficient heuristics. Upon such a basis, a novel channel as-signment algorithm based on weighted graph coloring heuristics and ableto exploit both channel prioritization and spectrum heterogeneity is pro-posed. The algorithm is evaluated under different conditions of AP densityand primary band availability.

A Delay Optimal Scheduling Policy for Real Time Services in WCDMA Systems

Enhanced Uplink operation of WCDMA-UMTS systems utilizes fast Node B scheduling and a short Transmission Time Interval (TTI) of 2 ms. The scheduling decisions are based on Scheduling Information (SI) sent from each User Equipment (UE) to the Node B. The weakness of the existing scheduling framework is that the SI does not include information regarding the delay sensitivity of each traffic flow, as well as the actual delay that the buffered data have experienced, especially for real-time services. In this paper, we propose the inclusion of such information in SI by introducing modifications into the respective 3GPP specifications. Thus, we propose a Delay Aware Channel Prioritization (DACP) criterion optimized for real-time services. Furthermore, we propose a DACP-based packet Scheduler (DACP-S) which is able to dynamically manage the Enhanced Dedicated Channel's (E-DCH) transmissions and offer Quality of Service differentiation. The performance of the proposed scheme is evaluated by means of system level simulations.