Chemical,thermal, and mechanical properties and ultraviolet transmittance of novel nano-hydroxyapatite/polyethylene terephthalate milk bottles

Polyethylene terephthalate (PET)/nano-hydroxyapatite (nHAp) composite granules were obtained using twin-screw extruder. Preforms were prepared by injection molding and then PET/nHAp bottles were produced by blow molding. For PET bottles with nHAp, the migration amounts of carboxylic acid (COOH), acetaldehyde (AA), diethylene glycol (DEG), and isophthalic acid (IPA); glass transition temperature (T_g); melting temperature (T_m); and the maximum crystallization temperature (T_cry) were measured. The load-carrying capacity, burst strength, stress cracking, and regional material distribution tests were carried out on the bottles. X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and ultraviolet transmittance analyses were conducted to explain the changes in mechanical, chemical, physical properties, and light transmission of bottles. It was found out that the COOH amount increased and the AA content decreased with increasing nHAp amount. On the other hand, no change was observed in the amounts of DEG and IPA. Although the mechanical properties such as load-carrying capacity and burst strength of the bottles have improved, it has been determined that the standard environmental stress crack resistance test procedure cannot be applied to such a composite. Experimental findings indicate that nHAp disrupts the chemical structure of PET and it isolates harmful chemicals such as AA by forming intermolecular bonds. Moreover, with the addition of up to 0.8% nHAp, PET bottles block the light transmission approximately 80% within 400–700 nm wave length zone. The study demonstrates that the PET/nHAp composite bottles can be used in the food industry, particularly in the packaging of milk and milk products which are vulnerable to light exposure.     

Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs

We propose a modified particle swarm optimization (MPSO) based method for Pseudo De-convolution of the ill-posed inverse problem namely, the space-variant image degradation (SVD). In this paper, SVD is simulated by the pseudo convolution of different sub-regions of the image with different known blurring kernels and additive random noise with unknown variance. Two heuristic modifications are proposed in PSO: 1) Initialization of the swarm and 2) Mutation of the global best. Fuzzy logic is applied for the computation of regularization parameter (RP) to cater for the sensitivity of the problem. The computation of RP is crucial due to the additive noise in the SVD image. Thus mathematical morphology (MM) is applied for better extraction of spatial activity from the distorted image. The performance of the proposed method is evaluated with different test images and noise powers. Comparative analysis demonstrates the superiority of proposed restoration, in terms of quantitative measures, over well-known existing and state-of-the-art SVD approaches.     

Siting park-and-ride facilities using a multi-objective spatial optimization model

Park-and-ride services are an important component of many public transportation systems in the United States. Locating park-and-ride facilities is an essential step when planning for these services. In this research we focus on three major siting/modeling concerns that need to be addressed when siting park-and-ride facilities: covering as much potential demand as possible, locating park-and-ride facilities as close as possible to major roadways, and siting such facilities in the context of an existing system. Unfortunately, existing models do not enable each of these concerns to be simultaneously addressed. This paper presents a multi-objective spatial optimization model for integrating these considerations. This model is applied for siting park-and-ride facilities in Columbus, Ohio. Application results show the usefulness of the developed model in supporting transit planning in an urban region.     

Development of a novel 6LoWPAN-based multipurpose sensor monitoring and notification system

In the area of wireless communication technologies, 6LoWPAN leverages the extensive capabilities of IPv6, even within the constraints imposed by resource-limited devices, particularly within wireless sensor networks (WSNs). The integration of 6LoWPAN into modern solutions for implementing the IPv6-based Internet of Things (IoT) is becoming increasingly important. In this paper, a sensor monitoring and notification system specifically designed for deployment over 6LoWPAN has been proposed. Multipurpose capability, scalability, and ease of deployability are the main features of the proposed system. Its architecture reflects the highest degree of flexibility and allows for a variety of use cases encountered in practical scenarios. In addition, a web interface has been developed as part of the comprehensive system architecture. This interface enables efficient management of the entire system and facilitates connection for new users and seamless integration of additional sensors. By encapsulating complex functions in a user-friendly interface, the system promotes accessibility, convenience, and an enhanced user experience. The proposed system overcomes the limitations of current approaches, thereby creating new opportunities. The flexibility of the proposed system allows it to be applied to various use cases.     

Parallel best-first search algorithms for planning problems on multi-core processors

The Journal of Supercomputing - The multiplication of computing cores in modern processor units permits revisiting the design of classical algorithms to improve computational performance in complex...     

Hardware/software co-design of a real-time kernel based tracking system

The probabilistic visual tracking methods using color histograms have been proven to be robust to target model variations and background illumination changes as shown by the recent research. However, the required computational cost is high due to intensive image data processing. The embedded solution of such algorithms become challenging due to high computational power demand and algorithm complexity. This paper presents a hardware/software co-design architecture for implementation of the well-known kernel based mean shift tracking algorithm. The design uses color histogram of the target as tracking feature. The target is searched in the consecutive images by maximizing the statistical match of the color distributions. The target localization is based on gradient based iterative search instead of exhaustive search which makes the system capable of achieving frame rate up to hundreds of frames per second while tracking multiple targets. The design, which is fully standalone, is implemented on a low-cost medium-size field programmable gate array (FPGA) device. The hardware cost of the design is compared with some other tracking systems. The performance of the system in terms of speed is evaluated and compared with the software based implementation. It is expected that the proposed solution will find its utility in applications like embedded automatic video surveillance systems.     

Dynamic Compensation for Control of a Rotary wing UAV Using Positive Position Feedback

This paper presents a novel compensation method for the coupled fuselage-rotor mode of a Rotary wing Unmanned Aerial Vehicle (RUAV). The presence of stabilizer bar limits the performance of attitude control of an RUAV. In this paper, a Positive Position Feedback (PPF) is introduced to increase the stability margins and allow higher control bandwidth. The identified model is used to design a PPF controller to mitigate the presence of stabilizer bar. Parameters for the linear RUAV model are obtained by performing linear system identification about a few selected points. This identification process gives complete RUAV dynamics and is suitable for designing a Stability Augmentation System (SAS) of an RUAV. The identified parameters of an RUAV model are verified using experimental flight data and can be used to obtain the nonlinear model of an RUAV. The performance of the proposed algorithm is tested using a high-fidelity RUAV simulation model, which is validated through experimental flight data. The closed-loop response of the rotorcraft indicates that the desired attitude performance is achieved while ensuring that the coupled fuselage-rotor mode is effectively compensated without penalizing the phase response.     

Multi-objective rule mining using a chaotic particle swarm optimization algorithm

In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.     

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.