A Comprehensive Study about the Role of Crosslink Density on the Tribological Behavior of DLC Coated Rubber

The friction and wear behavior of coated rubber components is strongly dependent on the substrate properties. This work deals with the impact of the crosslink density, i.e., the hardness of the rubber substrate on the tribological performance of uncoated and coated rubber. The hardness of nitrile butadiene rubber (NBR) is varied altering the sulfur content. Both the uncoated and coated rubber samples are characterized in terms of surface and mechanical properties. Tribological tests comprise the examination of the macroscopic contact area and the temperature in the contact zone. It was found that the functional layer enhances the wear resistance significantly. Apparently, the wear and friction behavior of the coated rubber correlates with the hardness and the bulk properties of the substrate material

FPGA-Based Optical Surface Inspection of Wind Turbine Rotor Blades Using Quantized Neural Networks

Quantization of the weights and activations of a neural network is a way to drastically reduce necessary memory accesses and to replace arithmetic operations with bit-wise operations. This is especially beneficial for the implementation on field-programmable gate array (FPGA) technology that is particularly suitable for embedded systems due to its low power consumption. In this paper, we propose an in-situ defect detection system utilizing a quantized neural network implemented on an FPGA for an automated surface inspection of wind turbine rotor blades using unpiloted aerial vehicles (UAVs). Contrary to the usual approach of offline defect detection, our approach prevents major downtimes and hence expenses. To our best knowledge, our work is among the first to transfer neural networks with weight and activation quantization into a tangible application. We achieve promising results with our network trained on our dataset consisting of 8024 good and defected rotor blade patches. Compared to a conventional network using floating-point arithmetic, we show that the classification accuracy we achieve is only slightly reduced by approximately 0.6%. With this work, we present a basic system for in-situ defect detection with versatile usability

A neural approach to drugs monitoring for personalized medicine

Staar B, Schirmer M, Bai-Rossi C, De Micheli G, Carrara S, Chicca E. A neural approach to drugs monitoring for personalized medicine. In: 2015 International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ: IEEE; 2015.The development of fast and mobile drug detection is an important aspect of personalized medicine. It enables the quick assessment of inter-individual differences in drug metabolism and corresponding adjustments of the dose. Recent developments of amperometric biosensors using cytochrome P450 (CYP) show great promise, by lowering the detection limit to physiological range for several drugs via the usage of Multi Walled Carbon Nanotubes (MWCNT). The next challenge is to develop algorithms for processing the resulting sensor data compatible with low-power hardware, which would allow the development of portable battery-powered devices. In this work we pursue a novel approach to this problem. Here we provide a proof of principle by demonstrating how sensor data could be analyzed using a conventional multi-layer perceptron network with error-backpropagation

Fast Quality Inspection of Micro Cold Formed Parts using Telecentric Digital Holographic Microscopy

Quality inspection is an integral part of the production process and often part of the quality management agreements between manufacturer and customer. Especially when it comes to safety-relevant parts, i.e. in the automobile or medical industry, often a 100% quality inspection is mandatory. Here, we present a solution comprised of a digital holographic measurement system, as well as fast algorithms for geometric evaluation and surface defect detection that paves the way for the inspection of metallic micro cups in less than a second. By use of a telecentric lens instead of standard microscope objective, we compensate scaling effects and wave field curvature, which distort reconstruction in digital holographic microscopy. Due to limited depth of focus of the microscope objective, depth information from different object layers are then stitched together to yield 3D data of its geometry. The resulting point cloud data is automatically decomposed into simple geometric shapes in order to analyse geometric deviations. Amplitude as well as phase distribution images are then analysed for surface defects. Our approach is demonstrated by inspecting cold formed micro cups. Defects larger than 2 μm lateral resolution and 5 μm depth can be detected

Fast Quality Inspection of Micro Cold Formed Parts using Telecentric Digital Holographic Microscopy

Quality inspection is an integral part of the production process and often part of the quality management agreements between manufacturer and customer. Especially when it comes to safety-relevant parts, i.e. in the automobile or medical industry, often a 100% quality inspection is mandatory. Here, we present a solution comprised of a digital holographic measurement system, as well as fast algorithms for geometric evaluation and surface defect detection that paves the way for the inspection of metallic micro cups in less than a second. By use of a telecentric lens instead of standard microscope objective, we compensate scaling effects and wave field curvature, which distort reconstruction in digital holographic microscopy. Due to limited depth of focus of the microscope objective, depth information from different object layers are then stitched together to yield 3D data of its geometry. The resulting point cloud data is automatically decomposed into simple geometric shapes in order to analyse geometric deviations. Amplitude as well as phase distribution images are then analysed for surface defects. Our approach is demonstrated by inspecting cold formed micro cups. Defects larger than 2 μm lateral resolution and 5 μm depth can be detected

Meta-analysis of chemotherapy in head and neck cancer (MACH-NC): An update on 107 randomized trials and 19,805 patients, on behalf of MACH-NC Group

Background and purposeThe Meta-Analysis of Chemotherapy in squamous cell Head and Neck Cancer (MACH-NC) demonstrated that concomitant chemotherapy (CT) improved overall survival (OS) in patients without distant metastasis. We report the updated results.Materials and methodsPublished or unpublished randomized trials including patients with non-metastatic carcinoma randomized between 1965 and 2016 and comparing curative loco-regional treatment (LRT) to LRT + CT or adding another timing of CT to LRT + CT (main question), or comparing induction CT + radiotherapy to radiotherapy + concomitant (or alternating) CT (secondary question) were eligible. Individual patient data were collected and combined using a fixed-effect model. OS was the main endpoint.ResultsFor the main question, 101 trials (18951 patients, median follow-up of 6.5 years) were analyzed. For both questions, there were 16 new (2767 patients) and 11 updated trials. Around 90% of the patients had stage III or IV disease. Interaction between treatment effect on OS and the timing of CT was significant (p < 0.0001), the benefit being limited to concomitant CT (HR: 0.83, 95%CI [0.79; 0.86]; 5(10)-year absolute benefit of 6.5% (3.6%)). Efficacy decreased as patients age increased (p_trend = 0.03). OS was not increased by the addition of induction (HR = 0.96 [0.90; 1.01]) or adjuvant CT (1.02 [0.92; 1.13]). Efficacy of induction CT decreased with poorer performance status (p_trend = 0.03). For the secondary question, eight trials (1214 patients) confirmed the superiority of concomitant CT on OS (HR = 0.84 [0.74; 0.95], p = 0.005).ConclusionThe update of MACH-NC confirms the benefit and superiority of the addition of concomitant CT for non-metastatic head and neck cancer