Multiple Model Poisson Multi-Bernoulli Mixture for 5G Mapping

In this paper, we evaluate and compare the multiple model Poisson multi-Bernoulli mixture (MM-PMBM) and the multiple model probability hypothesis density (MM-PHD) filters for mapping a propagation environment, specified by multiple types objects, using 5G millimeter-wave signals. To develop the MM-PMBM applicable to 5G scenarios, we design the density representation, data structure, and implementation strategy. From the simulation results, it is demonstrated that the MM-PMBM captures the objects and is robust to both missed detections and false alarm compared to the MM-PHD

Incremental Binarization On Recurrent Neural Networks For Single-Channel Source Separation

This paper proposes a Bitwise Gated Recurrent Unit (BGRU) network for the single-channel source separation task. Recurrent Neural Networks (RNN) require several sets of weights within its cells, which significantly increases the computational cost compared to the fully-connected networks. To mitigate this increased computation, we focus on the GRU cells and quantize the feedforward procedure with binarized values and bitwise operations. The BGRU network is trained in two stages. The real-valued weights are pretrained and transferred to the bitwise network, which are then incrementally binarized to minimize the potential loss that can occur from a sudden introduction of quantization. As the proposed binarization technique turns only a few randomly chosen parameters into their binary versions, it gives the network training procedure a chance to gently adapt to the partly quantized version of the network. It eventually achieves the full binarization by incrementally increasing the amount of binarization over the iterations. Our experiments show that the proposed BGRU method produces source separation results greater than that of a real-valued fully connected network, with 11-12 dB mean Signal-to-Distortion Ratio (SDR). A fully binarized BGRU still outperforms a Bitwise Neural Network (BNN) by 1-2 dB even with less number of layers.Comment: 5 pages, 1 figure, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019

BIOMECHANICAL CHARACTERISTICS OF THE LOWER EXTREMITY FROM GAIT INTIATION TO THE STEADY-STATE WALKING

In this study, biomechanical characteristics during the whole process of gait initiation for twenty healthy volunteers were determined by the three dimension motion analysis. Gait initiation, a transitional movement phenomenon from quiet stance to steady-state walking, involves a series of muscular activities, GRFs, movements of COP and COM and joint motions. Results showed that the location of the net COP to be most lateral during double limb stance at the beginning of gait initiation. During gait initiation, changes in anteroposterior components of GRFs were first found and then changes in vertical components followed. Hip and knee motions were found before the ankle joint motion. Walking speed, step length and stride length gradually increased until the second step. The interaction between the COM and COP is tightly regulated to control the trajectory of the COM and thereby control total body balance

Dirichlet process approach for radio-based simultaneous localization and mapping

Due to 5G millimeter wave (mmWave), spatial channel parameters are becoming highly resolvable, enabling accurate vehicle localization and mapping. We propose a novel method of radio simultaneous localization and mapping (SLAM) with the Dirichlet process (DP). The DP, which can estimate the number of clusters as well as clustering, is capable of identifying the locations of reflectors by classifying signals when such 5G signals are reflected and received from various objects. We generate birth points using the measurements from 5G mmWave signals received by the vehicle and classify objects by clustering birth points generated over time. Each time we use the DP clustering method, we can map landmarks in the environment in challenging situations where false alarms exist in the measurements and change the cardinality of received signals. Simulation results demonstrate the performance of the proposed scheme. By comparing the results with the SLAM based on the Rao-Blackwellized probability hypothesis density filter, we confirm a slight drop in SLAM performance, but as a result, we validate that it has a significant gain in computational complexity

Electric generator embedded in cellular phone for self-recharge

Nowadays, due to the development of information industry and technologies, the objective of cellular phone is not only to communicate, but also to give people various functions such as e-banking, web surfing and even excitement and fun. Because of increased usage of the cellular phone, the available time of the phone rechargeable battery is getting shorter. Therefore, in order to extend the serviceable time of the rechargeable battery, we propose self-generation system using a tubular type of permanent magnet linear self-generator (TPMLG) which can be embedded in cellular phone. The vibrational model is studied utilizing the mechanical resonance and the magnetic circuit such as permanent magnet, steel yoke and coil is designed to improve electricity generation. To investigate the electric characteristics of designed generation system, the transient finite element analysis using commercial software “MAXWELL” is performed

Low-Complexity 5G Slam with CKF-PHD Filter

In 5G mmWave, simultaneous localization and mapping (SLAM) allows devices to exploit map information to improve their position estimate. Even the most basic SLAM filter based on a Rao-Blackwellized particle filter (RBPF) combined with a probability hypothesis density (PHD) map representation exhibits high complexity. This paper proposes a new implementation method for the 5G SLAM using message passing (MP) and the cubature Kalman filter (CKF). We demonstrate that the proposed method significantly reduces the complexity while retaining the SLAM accuracy of the RBPF-PHD approach

What factors encourage the acceptance of cosmetic surgery? Differences in sociopsychological influences contingent upon cosmetic surgery experience

While numerous sociopsychological factors affect one’s acceptance of cosmetic surgery, little is known about the sociopsychological influences that lead to cosmetic surgery acceptance based on one’s prior experience with cosmetic surgery. The present study identified the differences between two groups: women with cosmetic surgery experience and women without prior cosmetic surgery experience. A research model was developed with five hypotheses to identify the four sociopsychological influences on cosmetic surgery acceptance: upward appearance comparison, awareness of an emphasis on beauty ideals, internalization of beauty ideals, and body surveillance. Data were collected from 651 South Korean women in their 20 s to 40 s and were analyzed using second-order confirmatory factor analysis and multi-group structural equation modeling. In the cosmetic surgery group, upward appearance comparison, awareness of an emphasis on beauty ideals, and body surveillance had a positive effect on cosmetic surgery acceptance. Internalization of beauty ideals and body surveillance also had a positive effect on cosmetic surgery acceptance in the no cosmetic surgery group. Additionally, the effects of upward appearance comparison, awareness of an emphasis on beauty ideals, and internalization of beauty ideals on cosmetic surgery acceptance varied significantly between the two groups. The findings add insights on the design of therapeutic programs to prevent cosmetic surgery addiction and education programs to increase body appreciation.This work was supported by Research Institute of Human Ecology, Seoul National University