Towards Achieving Higher Throughput with Microchip Based Small Footprint Wireless Vibration Sensors Using Zigbee and IEEE802.15.4 Protocol

The research work of this thesis is to create a platform based on Microchip's ZigBee products with high throughput to satisfy future applications, pipeline integrity monitoring and container integrity monitoring.ZigBee is a wireless network protocol specifically designed for low data rate sensor or control networks. The default sampling rate of Microchip's ZigBee devices is 233, when payload size is set to 80 bytes. However, our future applications need a sampling rate as high as 2000. Thus, in this thesis, we present five strategies to increase the throughput of the System. The original throughput of ZigBee stack is 3.73 kb/s. After optimizations, we make the throughput of the system as high as 41.55 kb/s, which can support a sampling rate of 6520 with data compression or 3531 without data compression. The performance of the system has been improved at least 15 times than the original. This sampling rate can totally satisfy our future applications, pipeline integrity monitoring and container monitoring.Computer Science Departmen

Arbitrary slip length for fluid-solid interface of arbitrary geometry in smoothed particle dynamics

We model a slip boundary condition at fluid-solid interface of an arbitrary geometry in smoothed particle hydrodynamics and smoothed dissipative particle dynamics simulations. Under an assumption of linear profile of the tangential velocity at quasi-steady state near the interface, an arbitrary slip length $b$ can be specified and correspondingly, an artificial velocity for every boundary particle can be calculated. Therefore, $b$ as an input parameter affects the calculation of dissipative and random forces near the interface. For $b \to 0$, the no-slip is recovered while for $b \to \infty$, the free-slip is achieved. Technically, we devise two different approaches to calculate the artificial velocity of any boundary particle. The first has a succinct principle and is competent for simple geometries, while the second is subtle and affordable for complex geometries. Slip lengths in simulations for both steady and transient flows coincide with the expected ones. As demonstration, we apply the two approaches extensively to simulate curvy channel flows, dynamics of an ellipsoid in pipe flow and flows within complex microvessels, where desired slip lengths at fluid-solid interfaces are prescribed. The proposed methodology may apply equally well to other particle methods such as dissipative particle dynamics and moving particle semi-implicit methods

Decision Model for COTS Component Procurement Based on Case-based Retrieval and Goal Programming

Compared with traditional information system development methodology, COTS-based information system has the following advantages: Avoid expensive development and maintenance; frequent upgrades often anticipate organization’s need; rich functionality; mature technologies; tracks technology trends, etc. However, how to select appropriate COTS components is a complex problem. For improving the accuracy of decision-making in COTS component procurement, a two-period model is put forward. In the first period, the procurement requirement of each COTS component is compared with a COTS component case base by case-based retrieval (CBR) and the initial candidates are selected. In the second period, a (0-1) integer goal programming model is created to optimize cost and time of the whole COTS-based system, and help decision makers to decide the final candidates. Case shows that the two-period method declines the complexity of computation and increases the rationality of decisio

Quantum Approximate Optimization Algorithm Parameter Prediction Using a Convolutional Neural Network

The Quantum approximate optimization algorithm (QAOA) is a quantum-classical hybrid algorithm aiming to produce approximate solutions for combinatorial optimization problems. In the QAOA, the quantum part prepares a quantum parameterized state that encodes the solution, where the parameters are optimized by a classical optimizer. However, it is difficult to find optimal parameters when the quantum circuit becomes deeper. Hence, there is numerous active research on the performance and the optimization cost of QAOA. In this work, we build a convolutional neural network to predict parameters of depth QAOA instance by the parameters from the depth QAOA counterpart. We propose two strategies based on this model. First, we recurrently apply the model to generate a set of initial values for a certain depth QAOA. It successfully initiates depth 10 QAOA instances, whereas each model is only trained with the parameters from depths less than 6. Second, the model is applied repetitively until the maximum expected value is reached. An average approximation ratio of 0.9759 for Max-Cut over 264 Erd\H{o}s-R\'{e}nyi graphs is obtained, while the optimizer is only adopted for generating the first input of the model.Comment: 9 pages, 4 figures, 1 table

Iterative Layerwise Training for Quantum Approximate Optimization Algorithm

The capability of the quantum approximate optimization algorithm (QAOA) in solving the combinatorial optimization problems has been intensively studied in recent years due to its application in the quantum-classical hybrid regime. Despite having difficulties that are innate in the variational quantum algorithms (VQA), such as barren plateaus and the local minima problem, QAOA remains one of the applications that is suitable for the recent noisy intermediate scale quantum (NISQ) devices. Recent works have shown that the performance of QAOA largely depends on the initial parameters, which motivate parameter initialization strategies to obtain good initial points for the optimization of QAOA. On the other hand, optimization strategies focus on the optimization part of QAOA instead of the parameter initialization. Instead of having absolute advantages, these strategies usually impose trade-offs to the performance of the optimization problems. One of such examples is the layerwise optimization strategy, in which the QAOA parameters are optimized layer-by-layer instead of the full optimization. The layerwise strategy costs less in total compared to the full optimization, in exchange of lower approximation ratio. In this work, we propose the iterative layerwise optimization strategy and explore the possibility for the reduction of optimization cost in solving problems with QAOA. Using numerical simulations, we found out that by combining the iterative layerwise with proper initialization strategies, the optimization cost can be significantly reduced in exchange for a minor reduction in the approximation ratio. We also show that in some cases, the approximation ratio given by the iterative layerwise strategy is even higher than that given by the full optimization.Comment: 9 pages, 3 figure

A Feasibility-Preserved Quantum Approximate Solver for the Capacitated Vehicle Routing Problem

The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that arises in various fields including transportation and logistics. The CVRP extends from the Vehicle Routing Problem (VRP), aiming to determine the most efficient plan for a fleet of vehicles to deliver goods to a set of customers, subject to the limited carrying capacity of each vehicle. As the number of possible solutions skyrockets when the number of customers increases, finding the optimal solution remains a significant challenge. Recently, a quantum-classical hybrid algorithm known as Quantum Approximate Optimization Algorithm (QAOA) can provide better solutions in some cases of combinatorial optimization problems, compared to classical heuristics. However, the QAOA exhibits a diminished ability to produce high-quality solutions for some constrained optimization problems including the CVRP. One potential approach for improvement involves a variation of the QAOA known as the Grover-Mixer Quantum Alternating Operator Ansatz (GM-QAOA). In this work, we attempt to use GM-QAOA to solve the CVRP. We present a new binary encoding for the CVRP, with an alternative objective function of minimizing the shortest path that bypasses the vehicle capacity constraint of the CVRP. The search space is further restricted by the Grover-Mixer. We examine and discuss the effectiveness of the proposed solver through its application to several illustrative examples.Comment: 9 pages, 8 figures, 1 tabl

Efficient Temporal Butterfly Counting and Enumeration on Temporal Bipartite Graphs

Bipartite graphs model relationships between two different sets of entities, like actor-movie, user-item, and author-paper. The butterfly, a 4-vertices 4-edges $2\times 2$ bi-clique, is the simplest cohesive motif in a bipartite graph and is the fundamental component of higher-order substructures. Counting and enumerating the butterflies offer significant benefits across various applications, including fraud detection, graph embedding, and community search. While the corresponding motif, the triangle, in the unipartite graphs has been widely studied in both static and temporal settings, the extension of butterfly to temporal bipartite graphs remains unexplored. In this paper, we investigate the temporal butterfly counting and enumeration problem: count and enumerate the butterflies whose edges establish following a certain order within a given duration. Towards efficient computation, we devise a non-trivial baseline rooted in the state-of-the-art butterfly counting algorithm on static graphs, further, explore the intrinsic property of the temporal butterfly, and develop a new optimization framework with a compact data structure and effective priority strategy. The time complexity is proved to be significantly reduced without compromising on space efficiency. In addition, we generalize our algorithms to practical streaming settings and multi-core computing architectures. Our extensive experiments on 11 large-scale real-world datasets demonstrate the efficiency and scalability of our solutions

Simultaneous arthroscopic cystectomy and unicompartmental knee arthroplasty for the management of partial knee osteoarthritis with a popliteal cyst: A case report

IntroductionPopliteal cysts are secondary to degenerative changes in the knee joint. After total knee arthroplasty (TKA), 56.7% of patients with popliteal cysts at 4.9 years follow-up remained symptomatic in the popliteal area. However, the result of simultaneous arthroscopic cystectomy and unicompartmental knee arthroplasty (UKA) was uncertain.Case presentationA 57-year-old man was admitted to our hospital with severe pain and swelling in his left knee and the popliteal area. He was diagnosed with severe medial unicompartmental knee osteoarthritis (KOA) with a symptomatic popliteal cyst. Subsequently, arthroscopic cystectomy and unicompartmental knee arthroplasty (UKA) were performed simultaneously. A month after the operation, he returned to his normal life. There was no progression in the lateral compartment of the left knee and no recurrence of the popliteal cyst at the 1-year follow-up.ConclusionFor KOA patients with a popliteal cyst seeking UKA, simultaneous arthroscopic cystectomy and UKA are feasible with great success if managed appropriately