Panasonic (United States)

This paper analyses the reporting requirements of upcoming European Union (EU) and UK cyber security and product security regulation. The assumption is that more regulation is forthcoming. The objective of the paper is to present ideas on how an organisation can prepare itself for reporting even when it is not known what must be reported, how and to whom. The paper asserts that it is possible to create a system which can be used for such reporting.

We investigated a method of copper plating on glass substrates wit h high smoothness and insulation properties and excellent transmission characteristics, while maintaining the smoothness of the substrate. The tin oxide deposited by the liquid phase deposition method acts as an adhesion layer between the glass substrate and the plating film, which was obtained to copper plating film with excellent adhesion. In this report, we discuss the microstructure of the plating film interface where high adhesion is obtained.

Dense optical flow estimation is complex and time consuming, with state-of-the-art methods relying either on large synthetic data sets or on pipelines requiring up to a few minutes per frame pair. In this paper, we address the problem of optical flow estimation in the automotive scenario in a self-supervised manner. We argue that optical flow can be cast as a geometrical warping between two successive video frames and devise a deep architecture to estimate such transformation in two stages. First, a dense pixel-level flow is computed with a projective bootstrap on rigid surfaces. We show how such global transformation can be approximated with a homography and extend spatial transformer layers so that they can be employed to compute the flow field implied by such transformation. Subsequently, we refine the prediction by feeding a second, deeper network that accounts for moving objects. A final reconstruction loss compares the warping of frame X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> with the subsequent frame X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t+1</sub> and guides both estimates. The model has the speed advantages of end-to-end deep architectures while achieving competitive performances, both outperforming recent unsupervised methods and showing good generalization capabilities on new automotive data sets.

In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Unlike previous works, the proposed method is based on converting fixed-point activations into vectors over the smallest GF(2) finite field followed by nonlinear dimensionality reduction (NDR) layers embedded into a DNN. Such an end-to-end learned representation finds more compact feature maps by exploiting quantization redundancies within the fixed-point activations along the channel or spatial dimensions. We apply the proposed network architectures derived from modified SqueezeNet and MobileNetV2 to the tasks of ImageNet classification and PASCAL VOC object detection. Compared to prior approaches, the conducted experiments show a factor of 2 decrease in memory requirements with minor degradation in accuracy while adding only bitwise computations.

This paper presents a nano-precision high aspect-ratio poly- and single-crystal silicon DRIE plus wet-etching fabrication platform, also known as HARPSS+, which enables single-chip integration and wafer-level-packaging of robust high-frequency timing and inertial sensors. The HARPSS+ platform is the first highly developed process providing scalable deep submicron gaps in vertical, horizontal, and 54°-slanted orientations for efficient transduction and dynamic control of high-s single crystalline silicon resonators. The platform also provides wafer-level-packaging of MEMS devices in cavities with different cavity pressures in a single bonding step. Single-chip timing and inertial measurement units (TIMU) are fabricated with high yield through the HARPSS+ platform, which features 3-axis high-frequency resonant gyroscopes with complete quadrature cancellation enabled by the slanted electrodes. High-bandwidth 3-axis accelerometers are used on the TIMU with high sensitivity provided by the nano-gaps. A high-Q BAW resonator is included as internal frequency reference for self-contained timing and inertial measurement, along with on-chip heaters for ovenization capability. Each device is packaged in cavities with different pressure levels ranging from atmosphere to sub-10Torr for optimized operation through one single bonding step. The nano-gap-enabled high-frequency devices have high environmental robustness and small form-factor with the overall TIMU being 4.5mm×5.5mm×1mm. Fabricated TIMU demonstrates high-performance with sub-10°/h gyroscopes and sub-100μg accelerometers. Low power chip-level ovenization is achievable with on-chip heaters owing to small size and vacuum packaging.

We propose an online multiple object tracking algorithm that exploits optical flow and convolutional features to handle noisy detections as well as frequent occlusion. To achieve robust tracking, we develop a data association method that deals with tracking scenarios of increasing difficulty. For easy scenarios, we use motion affinity to associate detections with objects. For ambiguous situations, we propose to use an appearance model based on convolutional features and correlation filters to complement template matching methods. For difficult cases where objects are under heavy occlusion, we carry out occlusion analysis, which exploits the relationship between targets and occluders to predict potential object locations. To deal with noisy detections, false positives are detected and removed on both raw detection and tracklet levels, while missing and inaccurate detections are recovered or corrected via short-term tracking. Experimental results on two benchmark datasets demonstrate that the proposed online algorithm performs favorably against the state-of-the-art methods.

This paper is co-authored by an informal group of experts from a broad range of backgrounds all of whom are active in standards groups, consortia and/or alliances in the Internet of Things (IoT) space. The ambition is to create mindshare on approaches to semantic interoperability and to actively encourage consensus building on what the co-authors regard as a key technical issue.

This paper presents the mathematical and harmonic analyses of output phase voltages of three-phase voltage source inverters which employ a hybrid two-dimensional-three-dimensional (2-D-3-D) space vector modulation (SVM) voltage control algorithm. The explicit time-domain representation of the harmonic components in addition to the total harmonic distortion (THD) of the output phase voltages is given in terms of system and control parameters. This paper also investigates the harmonic characteristics and THD performance against the linearity of the modulation region which helps in the harmonic performance and design studies of such inverters employing the hybrid 2-D-3-D SVM. The fundamental component of the output voltages follows the reference with lower THD in the linear modulation region rather than the overmodulation region. Experimental results are used to validate these analyses as well as the effectiveness of the hybrid 2-D-3-D SVM algorithm for generating balanced and low THD phase voltages.