Breaking the challenge of signal integrity using time-domain spoof surface plasmon polaritons

In modern integrated circuits and wireless communication systems/devices, three key features need to be solved simultaneously to reach higher performance and more compact size: signal integrity, interference suppression, and miniaturization. However, the above-mentioned requests are almost contradictory using the traditional techniques. To overcome this challenge, here we propose time-domain spoof surface plasmon polaritons (SPPs) as the carrier of signals. By designing a special plasmonic waveguide constructed by printing two narrow corrugated metallic strips on the top and bottom surfaces of a dielectric substrate with mirror symmetry, we show that spoof SPPs are supported from very low frequency to the cutoff frequency with strong subwavelength effects, which can be converted to the time-domain SPPs. When two such plasmonic waveguides are tightly packed with deep-subwavelength separation, which commonly happens in the integrated circuits and wireless communications due to limited space, we demonstrate theoretically and experimentally that SPP signals on such two plasmonic waveguides have better propagation performance and much less mutual coupling than the conventional signals on two traditional microstrip lines with the same size and separation. Hence the proposed method can achieve significant interference suppression in very compact space, providing a potential solution to break the challenge of signal integrity

Modeling Customer Experience in a Contact Center through Process Log Mining

The use of data mining and modeling methods in service industry is a promising avenue for optimizing current processes in a targeted manner, ultimately reducing costs and improving customer experience. However, the introduction of such tools in already established pipelines often must adapt to the way data is sampled and to its content. In this study, we tackle the challenge of characterizing and predicting customer experience having available only process log data with time-stamp information, without any ground truth feedback from the customers. As a case study, we consider the context of a contact center managed by TeleWare and analyze phone call logs relative to a two months span. We develop an approach to interpret the phone call process events registered in the logs and infer concrete points of improvement in the service management. Our approach is based on latent tree modeling and multi-class Naïve Bayes classification, which jointly allow us to infer a spectrum of customer experiences and test their predictability based on the current data sampling strategy. Moreover, such approach can overcome limitations in customer feedback collection and sharing across organizations, thus having wide applicability and being complementary to tools relying on more heavily constrained data

Detecting single molecules inside a carbon nanotube to control molecular sequences using inertia trapping phenomenon

Here we show the detection of single gas molecules inside a carbon nanotube based on the change in resonance frequency and amplitude associated with the inertia trapping phenomenon. As its direct implication, a method for controlling the sequence of small molecule is then proposed to realize the concept of manoeuvring of matter atom by atom in one dimension. The detection as well as the implication is demonstrated numerically with the molecular dynamics method. It is theoretically assessed that it is possible for a physical model to be fabricated in the very near future

CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention

3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a sub-network with BEV feature map as input to predict 3D lanes. Such approaches require an explicit view transformation between BEV and front view, which itself is still a challenging problem. In this paper, we propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters and can circumvent the difficult view transformation step. Specifically, we formulate 3D lane detection as a curve propagation problem by using curve queries. A 3D lane query is represented by a dynamic and ordered anchor point set. In this way, queries with curve representation in Transformer decoder iteratively refine the 3D lane detection results. Moreover, a curve cross-attention module is introduced to compute the similarities between curve queries and image features. Additionally, a context sampling module that can capture more relative image features of a curve query is provided to further boost the 3D lane detection performance. We evaluate our method for 3D lane detection on both synthetic and real-world datasets, and the experimental results show that our method achieves promising performance compared with the state-of-the-art approaches. The effectiveness of each component is validated via ablation studies as well

Thermal Characterization of Low-Dimensional Materials by Resistance Thermometers

The design, fabrication, and use of a hotspot-producing and temperature-sensing\ua0resistance thermometer for evaluating the thermal properties of low-dimensional materials are\ua0described in this paper. The materials that are characterized include one-dimensional (1D) carbon\ua0nanotubes, and two-dimensional (2D) graphene and boron nitride films. The excellent thermal performance of these materials shows great potential for cooling electronic devices and systems\ua0such as in three-dimensional (3D) integrated chip-stacks, power amplifiers, and light-emitting\ua0diodes. The thermometers are designed to be serpentine-shaped platinum resistors serving both as\ua0hotspots and temperature sensors. By using these thermometers, the thermal performance of the\ua0abovementioned emerging low-dimensional materials was evaluated with high accuracy