Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the a mission should be aborted due to mechanical or other failure. On-board cameras provide information that can be used in the determination of potential landing sites, which are continually updated and ranked to prevent injury and minimize damage. Pulse Coupled Neural Networks have been used for the detection of features in images that assist in the classification of vegetation and can be used to minimize damage to the aerial vehicle. However, a significant drawback in the use of PCNNs is that they are computationally expensive and have been more suited to off-line applications on conventional computing architectures. As heterogeneous computing architectures are becoming more common, an OpenCL implementation of a PCNN feature generator is presented and its performance is compared across OpenCL kernels designed for CPU, GPU and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images obtained during unmanned aerial vehicle trials to determine the plausibility for real-time feature detection.

Comparison of high level FPGA hardware design for solving tri-diagonal linear systems

Reconfigurable computing devices can increase the performance of compute intensive algorithms by implementing application specific co-processor architectures. The power cost for this performance gain is often an order of magnitude less than that of modern CPUs and GPUs. Exploiting the potential of reconfigurable devices such as Field-Programmable Gate Arrays (FPGAs) is typically a complex and tedious hardware engineering task. Re- cently the major FPGA vendors (Altera, and Xilinx) have released their own high-level design tools, which have great potential for rapid development of FPGA based custom accelerators. In this paper, we will evaluate Altera’s OpenCL Software Development Kit, and Xilinx’s Vivado High Level Sythesis tool. These tools will be compared for their per- formance, logic utilisation, and ease of development for the test case of a Tri-diagonal linear system solver.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (PCNN) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose a heterogeneous computing architecture and an OpenCL implementation of a PCNN feature generator. Its performance is compared across OpenCL kernels designed for CPU, GPU, and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images to determine the plausibility for real-time feature detection.

Structure-controlled, vertical graphene-based, binder-free electrodes from plasma-reformed butter enhance supercapacitor performance

Vertical graphene nanosheets (VGNS) hold great promise for high-performance supercapacitors owing to their excellent electrical transport property, large surface area and in particular, an inherent three-dimensional, open network structure. However, it remains challenging to materialise the VGNS-based supercapacitors due to their poor specific capacitance, high temperature processing, poor binding to electrode support materials, uncontrollable microstructure, and non-cost effective way of fabrication. Here we use a single-step, fast, scalable, and environmentally-benign plasma-enabled method to fabricate VGNS using cheap and spreadable natural fatty precursor butter, and demonstrate the controllability over the degree of graphitization and the density of VGNS edge planes. Our VGNS employed as binder-free supercapacitor electrodes exhibit high specific capacitance up to 230 F g−1 at a scan rate of 10 mV s−1 and >99% capacitance retention after 1,500 charge-discharge cycles at a high current density, when the optimum combination of graphitic structure and edge plane effects is utilised. The energy storage performance can be further enhanced by forming stable hybrid MnO2/VGNS nano-architectures which synergistically combine the advantages from both VGNS and MnO2. This deterministic and plasma-unique way of fabricating VGNS may open a new avenue for producing functional nanomaterials for advanced energy storage devices.

Dielectric performance of polymer nanocomposites synthesized by atmospheric-pressure plasma-treated silica nanoparticles

In this study, atmospheric-pressure plasmas were applied to modify the surface of silane-coated silica nanoparticles. Subsequently nanocomposites were synthesized by incorporating plasma-treated nanoparticles into an epoxy resin matrix. Electrical testing showed that such novel dielectric materials obtained high partial discharge resistance, high dielectric breakdown strength, and enhanced endurance under highly stressed electric field. Through spectroscopic and microscopic analysis, we found surface groups of nanoparticles were activated and radicals were created after the plasma treatment. Moreover, a uniform dispersion of nanoparticles in nanocomposites was observed. It was expected that the improved dielectric performance of the nanocomposites can attribute to stronger chemical bonds formed between surface groups of plasma-treated nanoparticles and molecules in the matrix. This simple yet effective and environmentally friendly approach aims to synthesize the next generation of high-performance nanocomposite dielectric insulation materials for applications in high-voltage power systems.

Silica nanoparticles treated by cold atmospheric-pressure plasmas improve the dielectric performance of organic–inorganic nanocomposites

We report on the application of cold atmospheric-pressure plasmas to modify silica nanoparticles to enhance their compatibility with polymer matrices. Thermally nonequilibrium atmospheric-pressure plasma is generated by a high-voltage radio frequency power source operated in the capacitively coupled mode with helium as the working gas. Compared to the pure polymer and the polymer nanocomposites with untreated SiO2, the plasma-treated SiO2–polymer nanocomposites show higher dielectric breakdown strength and extended endurance under a constant electrical stress. These improvements are attributed to the stronger interactions between the SiO2 nanoparticles and the surrounding polymer matrix after the plasma treatment. Our method is generic and can be used in the production of high-performance organic–inorganic functional nanocomposites.

RF plasma sputtering deposition of hydroxyapatite bioceramics : synthesis, performance, and biocompatibility

Hydroxyapatite (HA) coatings have numerous applications in orthopedics and dentistry, owing to their excellent ability to promote stronger implant fixation and faster bone tissue ingrowth and remodeling. Thermal plasma spray and other plasma-assisted techniques have recently been used to synthesize various calcium phosphate-based bioceramics. Despite notable recent achievements in the desired stoichiometry, phase composition, mechanical, structural, and bio-compatible properties, it is rather difficult to combine all of the above features in a single coating. For example, many existing plasma-sprayed HA coatings fall short in meeting the requirements of grain size and crystallinity, and as such are subject to enhanced resorption in body fluid. On the other hand, relatively poor interfacial bonding and stability is an obstacle to the application of the HA coatings in high load bearing Ti6Al4V knee joint implants. Here, we report on an alternative: a plasma-assisted, concurrent, sputtering deposition technique for high performance biocompatible HA coatings on Ti6Al4V implant alloy. The plasma-assisted RF magnetron co-sputtering deposition method allows one to simultaneously achieve most of the desired attributes of the biomimetic material and overcome the aforementioned problems. This article details the film synthesis process specifications, extensive analytical characterization of the material's properties, mechanical testing, simulated body fluid assessments, biocompatibility and cytocompatibility of the HA-coated Ti6Al4V orthopedic alloy. The means of optimization of the plasma and deposition process parameters to achieve the desired attributes and performance of the HA coating, as well as future challenges in clinical applications are also discussed.

Close-quarters Quadrotor flying for a pole inspection with position based visual servoing and high-speed vision

This paper presents a 100 Hz monocular position based visual servoing system to control a quadrotor flying in close proximity to vertical structures approximating a narrow, locally linear shape. Assuming the object boundaries are represented by parallel vertical lines in the image, detection and tracking is achieved using Plücker line representation and a line tracker. The visual information is fused with IMU data in an EKF framework to provide fast and accurate state estimation. A nested control design provides position and velocity control with respect to the object. Our approach is aimed at high performance on-board control for applications allowing only small error margins and without a motion capture system, as required for real world infrastructure inspection. Simulated and ground-truthed experimental results are presented.

Changes in cervical spine bone mineral density in response to flight training

BACKGROUND High magnitude loads and unusual loading regimes are two important determinants for increasing bone mass. Past research demonstrated that positive Gz-induced loading, providing high loads in an unaccustomed manner, had an osteogenic effect on bone. Another determinant of bone mass is that the bone response to loading is site specific. This study sought to further investigate the site specific bone response to loading, examining the cervical spine response, the site suspected of experiencing the greatest loading, to high performance flight. METHODS Bone mineral density (BMD) and bone mineral content (BMC) was monitored in 9 RAAF trainee fighter pilots completing an 8-mo flight training course on a PC-9 and 10 age-height-weight-matched controls. RESULTS At completion of the course, the pilots had a significant increase in cervical spine BMD and total body BMC. No significant changes were found for the control group. CONCLUSIONS This study demonstrated that the physical environment associated with flight training may have contributed to a significant increase in cervical spine bone mass in the trainee PC-9 pilots. The increase in bone mass was possibly a response to the strain generated by the daily wearing of helmet and mask assembly under the influence of positive sustained accelerative forces.

Clinoenstatite coatings have high bonding strength, bioactive ion release, and osteoimmunomodulatory effects that enhance in vivo osseointegration

A number of coating materials have been developed over past two decades seeking to improve the osseointegration of orthopedic metal implants. Despite the many candidate materials trialed, their low rate of translation into clinical applications suggests there is room for improving the current strategies for their development. We therefore propose that the ideal coating material(s) should possess the following three properties: (i) high bonding strength, (ii) release of functional ions, and (iii) favourable osteoimmunomodulatory effects. To test this proposal, we developed clinoenstatite (CLT, MgSiO3), which as a coating material has high bonding strength, cytocompability and immunomodulatory effects that are favourable for in vivo osteogenesis. The bonding strength of CLT coatings was 50.1 ± 3.2 MPa, more than twice that of hydroxyapatite (HA) coatings, at 23.5 ± 3.5 MPa. CLT coatings released Mg and Si ions, and compared to HA coatings, induced an immunomodulation more conducive for osseointegration, demonstrated by downregurelation of pro-inflammatory cytokines, enhancement of osteogenesis, and inhibition of osteoclastogenesis. In vivo studies demonstrated that CLT coatings improved osseointegration with host bone, as shown by the enhanced biomechanical strength and increased de novo bone formation, when compared with HA coatings. These results support the notion that coating materials with the proposed properties can induce an in vivo environment better suited for osseointegration. These properties could, therefore, be fundamental when developing high-performance coating materials.

Catalyst engineering for lithium ion batteries: The catalytic role of Ge in enhancing the electrochemical performance of SnO2(GeO2)0.13/G anodes

The catalytic role of germanium (Ge) was investigated to improve the electrochemical performance of tin dioxide grown on graphene (SnO(2)/G) nanocomposites as an anode material of lithium ion batteries (LIBs). Germanium dioxide (GeO(20) and SnO(2) nanoparticles (<10 nm) were uniformly anchored on the graphene sheets via a simple single-step hydrothermal method. The synthesized SnO(2)(GeO(2))0.13/G nanocomposites can deliver a capacity of 1200 mA h g(-1) at a current density of 100 mA g(-1), which is much higher than the traditional theoretical specific capacity of such nanocomposites (∼ 702 mA h g(-1)). More importantly, the SnO(2)(GeO(2))0.13/G nanocomposites exhibited an improved rate, large current capability (885 mA h g(-1) at a discharge current of 2000 mA g(-1)) and excellent long cycling stability (almost 100% retention after 600 cycles). The enhanced electrochemical performance was attributed to the catalytic effect of Ge, which enabled the reversible reaction of metals (Sn and Ge) to metals oxide (SnO(2) and GeO(2)) during the charge/discharge processes. Our demonstrated approach towards nanocomposite catalyst engineering opens new avenues for next-generation high-performance rechargeable Li-ion batteries anode materials.

A Scalable High Throughput Firewall in FPGA

High end network security applications demand high speed operation and large rule set support. Packet classification is the core functionality that demands high throughput in such applications. This paper proposes a packet classification architecture to meet such high throughput. We have implemented a Firewall with this architecture in reconflgurable hardware. We propose an extension to Distributed Crossproducting of Field Labels (DCFL) technique to achieve scalable and high performance architecture. The implemented Firewall takes advantage of inherent structure and redundancy of rule set by using our DCFL Extended (DCFLE) algorithm. The use of DCFLE algorithm results in both speed and area improvement when it is implemented in hardware. Although we restrict ourselves to standard 5-tuple matching, the architecture supports additional fields. High throughput classification invariably uses Ternary Content Addressable Memory (TCAM) for prefix matching, though TCAM fares poorly in terms of area and power efficiency. Use of TCAM for port range matching is expensive, as the range to prefix conversion results in large number of prefixes leading to storage inefficiency. Extended TCAM (ETCAM) is fast and the most storage efficient solution for range matching. We present for the first time a reconfigurable hardware implementation of ETCAM. We have implemented our Firewall as an embedded system on Virtex-II Pro FPGA based platform, running Linux with the packet classification in hardware. The Firewall was tested in real time with 1 Gbps Ethernet link and 128 sample rules. The packet classification hardware uses a quarter of logic resources and slightly over one third of memory resources of XC2VP30 FPGA. It achieves a maximum classification throughput of 50 million packet/s corresponding to 16 Gbps link rate for the worst case packet size. The Firewall rule update involves only memory re-initialization in software without any hardware change.

Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results.

A Scalable High Throughput Firewall in FPGA

High end network security applications demand high speed operation and large rule set support. Packet classification is the core functionality that demands high throughput in such applications. This paper proposes a packet classification architecture to meet such high throughput. We have Implemented a Firewall with this architecture in reconfigurable hardware. We propose an extension to Distributed Crossproducting of Field Labels (DCFL) technique to achieve scalable and high performance architecture. The implemented Firewall takes advantage of inherent structure and redundancy of rule set by using, our DCFL Extended (DCFLE) algorithm. The use of DCFLE algorithm results In both speed and area Improvement when It is Implemented in hardware. Although we restrict ourselves to standard 5-tuple matching, the architecture supports additional fields.High throughput classification Invariably uses Ternary Content Addressable Memory (TCAM) for prefix matching, though TCAM fares poorly In terms of area and power efficiency. Use of TCAM for port range matching is expensive, as the range to prefix conversion results in large number of prefixes leading to storage inefficiency. Extended TCAM (ETCAM) is fast and the most storage efficient solution for range matching. We present for the first time a reconfigurable hardware Implementation of ETCAM. We have implemented our Firewall as an embedded system on Virtex-II Pro FPGA based platform, running Linux with the packet classification in hardware. The Firewall was tested in real time with 1 Gbps Ethernet link and 128 sample rules. The packet classification hardware uses a quarter of logic resources and slightly over one third of memory resources of XC2VP30 FPGA. It achieves a maximum classification throughput of 50 million packet/s corresponding to 16 Gbps link rate for file worst case packet size. The Firewall rule update Involves only memory re-initialiization in software without any hardware change.

Nonlinear transport in semiconducting polymers at high carrier densities

Conducting and semiconducting polymers are important materials in the development of printed, flexible, large-area electronics such as flat-panel displays and photovoltaic cells. There has been rapid progress in developing conjugated polymers with high transport mobility required for high-performance field-effect transistors (FETs), beginning(1) with mobilities around 10(-4) cm(2) V-1 s(-1) to a recent report(2) of 1 cm(2) V-1 s(-1) for poly(2,5-bis(3-tetradecylthiophen-2-yl) thieno[3,2-b] thiophene) (PBTTT). Here, the electrical properties of PBTTT are studied at high charge densities both as the semiconductor layer in FETs and in electrochemically doped films to determine the transport mechanism. We show that data obtained using a wide range of parameters (temperature, gate-induced carrier density, source-drain voltage and doping level) scale onto the universal curve predicted for transport in the Luttinger liquid description of the one-dimensional `metal'.