About the numerous cost and processing advantages of the microcellular foam injection molding process for thermoplastics materials in the automobile industry

Today one of the goals of the automobile industry is to reduce weight. And physical foaming has already demonstrated its potential in this sector, improving the value and performance of applications under the bonnet: engine and gearbox cases, inlet air filters, cockpits, radiator baffles and so on. Around the world, the microcellular injection molding (MuCell) is used in thousands of applications in the automotive, packaging, technical molding, office machinery and electric and electronic component industries. The research opportunities purpose is to obtain even lighter pieces, with greater dimensional stability and with an excellent surface finish, in other words, perfect plastic parts. More component functionality with reduced weight, and cost control at the same time: MuCell is a process to physically foam thermoplastics, which combines technical and economic objectives. Besides weight reduction, it also provides improved dimensional stability of the moulded parts

Calibration-free and hardware-efficient neural spike detection for brain machine interfaces

Recent translational efforts in brain-machine interfaces (BMI) are demonstrating the potential to help people with neurological disorders. The current trend in BMI technology is to increase the number of recording channels to the thousands, resulting in the generation of vast amounts of raw data. This in turn places high bandwidth requirements for data transmission, which increases power consumption and thermal dissipation of implanted systems. On-implant compression and/or feature extraction are therefore becoming essential to limiting this increase in bandwidth, but add further power constraints – the power required for data reduction must remain less than the power saved through bandwidth reduction. Spike detection is a common feature extraction technique used for intracortical BMIs. In this paper, we develop a novel firing-rate-based spike detection algorithm that requires no external training and is hardware efficient and therefore ideally suited for real-time applications. Key performance and implementation metrics such as detection accuracy, adaptability in chronic deployment, power consumption, area utilization, and channel scalability are benchmarked against existing methods using various datasets. The algorithm is first validated using a reconfigurable hardware (FPGA) platform and then ported to a digital ASIC implementation in both 65 nm and 0.18MU m CMOS technologies. The 128-channel ASIC design implemented in a 65 nm CMOS technology occupies 0.096 mm2 silicon area and consumes 4.86MU W from a 1.2 V power supply. The adaptive algorithm achieves a 96% spike detection accuracy on a commonly used synthetic dataset, without the need for any prior training

Slow equivariant lump dynamics on the two sphere

The low-energy, rotationally equivariant dynamics of n CP^1 lumps on S^2 is studied within the approximation of geodesic motion in the moduli space of static solutions. The volume and curvature properties of this moduli space are computed. By lifting the geodesic flow to the completion of an n-fold cover of the moduli space, a good understanding of nearly singular lump dynamics within this approximation is obtained.Comment: 12 pages, 3 figure