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Automotive sensors are essential to autonomous driving, which performs various functions to perceive the surrounding environment. Among the various functions of the automotive sensors, the estimation of vehicle orientation is considered significant in responding to unpredictable situations in a dynamic driving environment. In this article, we propose a method of estimating the vehicle orientation using a cascaded multiple-input multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar system. The radar signal is collected by varying the orientation angle of the vehicle, and the point cloud data corresponding to the vehicle are extracted through signal preprocessing. Because the processed point cloud data are distributed along the axis of vehicle orientation, the orientation angle can be estimated by applying regression algorithms. We used the principal component analysis (PCA), decision tree, and convolutional neural network (CNN) algorithms for regression and compared their performances. The comparison of various estimation methods showed that the proposed method of using the CNN framework can accurately estimate the orientation angle of a vehicle within a root mean square error (RMSE) of 4°. [ABSTRACT FROM AUTHOR]

To perform radar-based simultaneous localization and mapping, information on a robot’s ego-motion such as its rotation angle and velocity should be considered along with radar sensor data. In this paper, we propose a method to estimate the robot’s ego-motion using only a radar sensor without any other devices. To estimate the rotation angle of a robot, we use the distribution of detected points on a two-dimensional (2D) plane. The distributions of the detected points at successive time instants are correlated with each other, and the rotation angle can be estimated based on this correlation. In addition, the moving velocity of the robot is estimated from the trend line formed by the detected points on the 2D plane. To evaluate the performance of the proposed estimation method, the information about the robot’s ego-motion received from the robot motor is compared with the information estimated from the radar sensor data. The comparison of the yaw rate obtained through each method shows that our proposed method can estimate the rotation angle of the robot within an error of 3°. In addition, the ego-velocity of the radar-equipped robot can be estimated within an error of 0.073 m/s. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for classifying human faces using a small-sized millimeter wave radar sensor. The radar sensor transmits a frequency-modulated continuous wave signal operating in the 61 GHz band and it receives reflected signals using spatially separated receiving antenna elements. Because the shape and composition of the human face varies from person to person, the reflection characteristics of the radar signal are also distinguished from each other. Therefore, training a deep neural network (DNN) using signals received from multiple antenna elements enables classification of different human faces. With our trained DNN model, eight human faces can be classified with an accuracy of 92%. We also compare the performance of the proposed method with conventional machine learning techniques (e.g., support vector machine, tree-based methods) and confirm that our method has higher classification accuracy. [ABSTRACT FROM AUTHOR]

Nowadays, various sensors are being widely used in indoor environments to improve quality of life. In particular, sensors can be installed inside a car to detect dangerous situations, such as a child or a pet being left alone in a car. In this paper, we present an effective method for monitoring people inside a vehicle by using impulse radio ultra-wideband radar. First, we identify that the waveform of the received signal varies with the arrangement of people inside the vehicle. Then, we extract features that represent the statistical characteristics of the received signal and use them as classification criteria. To determine the importance of each feature and reduce the number of features, we use a neighborhood component analysis algorithm. Next, we use ensemble learning with a decision tree as a base classifier to classify the various arrangement of people inside the car. The classification results demonstrate that our proposed method successfully estimates the position and number of people sitting inside a vehicle with an accuracy higher than 90%. Moreover, to utilize the proposed method in real time, we derive an appropriate number of features and structure for the classifier, so as to achieve high classification accuracy while keeping the computational complexity low. [ABSTRACT FROM AUTHOR]

Generally, automotive radars are installed behind bumpers, perpendicular to the ground. After mounting the radar, the mounting angle is often distorted due to external shock during driving. If the radar is tilted toward the ground or sky, its detection performance can be severely degraded, and this can pose a serious threat to the safety of drivers. Therefore, it is important that the radars should be installed at an appropriate angle. This paper proposes an effective method for estimating the tilted mounting angle of the radar in an automotive frequency modulated continuous wave radar system. First, we identify that the frequency spectrum of the received radar signal varies significantly depending on the radar tilt angle. Then, we extract features representing the statistical properties of the received radar signal and use them as criteria for classifying various signals with different tilt angles. In addition, we use the principal component analysis algorithm to reduce the dimensionality of the feature space and increase the execution speed. The classification results using the $k$ -nearest neighbor algorithm as a classifier demonstrate that our proposed method can estimate the tilt angle of the radar with an accuracy greater than 90%. [ABSTRACT FROM AUTHOR]

To achieve significantly improved sensitivity, the author propose plasmonic sensors based on extraordinary optical transmission incorporating a periodic nanoslit array in metal film with additional grooves. The author show that the peak wavelength shift in the transmission spectrum due to biomolecular film adsorption can be boosted by using the nanofocusing of light in the grooves. The sensitivities to biomolecular film of the sensor prove to be extremely improved in comparison with those of conventional sensors with the same structure and materials, but without grooves with 1.3 and 8.4 times at the top and the wall of the silt, respectively. The sensor performances for the plasmonic structures with symmetric and asymmetric grooves are investigated and the proposed sensors are shown to be able to operate for spatial multiplexing biomolecular detection. [ABSTRACT FROM AUTHOR]

A robust optimization approach for plasmonic periodic array sensor based on extraordinary optical transmission is proposed using Kriging surrogate models to reduce the effects of uncertainty in various manufacturing processes while maintaining sensor performance. For systematic design with reasonable computation cost, the author adopt the universal Kriging models whose regression function is a polynomial. The gradient index and the multiobjective genetic algorithm are chosen as a robustness measure and a global optimization tool, respectively. The figure of merit and the gradient index are set as two objective functions, and the design variables are the slit width and height, respectively. The optical properties of interest are investigated using the finite-element method. The numerical optimization results show the proposed scheme to be powerful and efficient in designing nanoslity array sensors based on extraordinary optical transmission with fabrication uncertainty. [ABSTRACT FROM AUTHOR]

This paper considers the maximization of total throughput and device lifetime for point-to-point multiple-input multiple-output communication systems, where a transmission node has a battery which exhibits non-linear battery discharge behaviors. We adopt a battery model called Peukert’s law to render the non-linear battery characteristics and formulate the battery constraint from Peukert’s law. Then, we prove that the total throughput is a strictly concave function in terms of the battery lifetime under the battery constraint. Also, we derive the optimality conditions for the total throughput maximization and device lifetime maximization problems from the strict concavity, and compute the optimal solutions by a bi-section method. Furthermore, we provide a solution based on asymptotic analysis for the case, where the eigenvalue distribution of the channel matrix is not available. In the simulation section, we conduct accurate battery discharge simulations to validate this paper. We confirm that the analysis matches well with the battery simulations, and the derived optimal scheme outperforms the baseline schemes, which neglect the non-linear battery discharge properties. Also, the proposed solution based on asymptotic analysis is shown to have almost the same performance compared with the optimal solution. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate an energy efficiency (EE) maximization problem in multiple input single output broadcast channels. The optimization problem in this system model is difficult to solve in general, since it is in non-convex fractional form. Hence, conventional algorithms have addressed the problem in an iterative manner for each channel realization, which leads to high computational complexity. To tackle this complexity issue, we propose a new simple method by utilizing the fact that EE maximization becomes identical to spectral efficiency (SE) maximization for the region of the power below a certain transmit power termed as saturation power. In order to calculate the saturation power, we first introduce upper and lower bounds of the EE performance by adopting a maximal ratio transmission beamforming strategy. Then, we propose an efficient way to compute the saturation power for the EE maximization problem. Once we determine the saturation power in advance, we can transform the EE maximization problem into a simplified sub-optimal EE problem, which can be solved by the SE maximization schemes with low complexity. The derived saturation power is parameterized by employing random matrix theory, which relies only on the second-order channel statistics. Hence, this approach needs much lower computational complexity compared with a conventional scheme, which requires instantaneous channel state information. Numerical results validate that the proposed algorithm achieves near optimal EE performance with significantly reduced complexity. [ABSTRACT FROM PUBLISHER]

A powerful design method for subwavelength-scaled phase mask with enhanced transmission is proposed. In the phase mask, the extraordinary optical transmission is further boosted up by a nanofocusing effect through a plasmonic tapered slit instead of using ordinary straight slit. The optical fields are calculated rigorously by the finite element method and the genetic algorithm is chosen as a global optimization tool. The objective function to evaluate the performance of the phase mask and its robustness are investigated at the same time by employing gradient index as another objective function. It is found that the fundamental order transmissions normalized by the gap-to-period ratio are calculated as 0.6059, 3.25, and 5.14, respectively. The numerical optimization results show the proposed method is found to be a useful design approach for subwavelength-sized phase masks with nanofocused optical field and high ratio of fundamental to first order diffraction transmission. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new beamforming design to maximize energy efficiency (EE) for multiple input single output interfering broadcast channels (IFBCs). Under this model, the EE problem is nonconvex in general due to the coupled interference and its fractional form, and thus it is difficult to solve the problem. Conventional algorithms which address this problem have adopted an iterative method for each channel realization, which requires high computational complexity. In order to reduce the computational complexity, we parameterize the beamforming vector by scalar parameters related to beam direction and power. Then, by employing asymptotic results of random matrix theory with this parametrization, we identify the optimal parameters to maximize the EE in the large system limit assuming that the number of transmit antennas and users are large with a fixed ratio. In the asymptotic regime, our solutions depend only on the second order channel statistics, which yields significantly reduced computational complexity and system overhead compared to the conventional approaches. Hence, the beamforming vector to maximize the EE performance can be determined with local channel state information and the optimized parameters. Based on the asymptotic results, the proposed scheme can provide insights on the average EE performance, and a simple yet efficient beamforming strategy is introduced for the finite system case. Numerical results confirm that the proposed scheme shows a negligible performance loss compared to the best result achieved by the conventional approaches even with small system dimensions, with much reduced system complexity. [ABSTRACT FROM PUBLISHER]

An efficient optimization approach is proposed to design plasmonic nanoslit array biosensor based on extraordinary optical transmission using Kriging surrogate models. The particle swarm optimization is adopted as a global optimization tool, which searches for optimal geometry with high sensitivity and narrow bandwidth. The design variables are the width and height of the slit, respectively, and objective function is a figure of merit defined as a ratio between peak wavelength shift and 3 dB bandwidth of optical transmission spectrum. [ABSTRACT FROM PUBLISHER]

In this paper, we analyze the error probability and ergodic capacity performance for diversity reception schemes over generalized- $K$ fading channels using a mixture gamma (MG) distribution. With high accuracy, the MG distribution can approximate a variety of composite fading channel models and provide mathematically tractable properties. In contrast to previous analysis approaches that require complicated signal-to-noise ratio (SNR) statistics, it is shown that a distribution of the received SNR for diversity reception schemes is composed of a weighted sum of gamma distributions by exploiting the properties of the MG distribution. Then, based on this result, we can derive the exact average symbol error probability and simple closed-form expressions of diversity and array gains for maximal ratio combining and selection combining. In addition, an expression of the ergodic capacity for these schemes is obtained in independent and identically distributed fading channels. Our results lead to meaningful insights for determining the system performance with parameters of the MG distribution. We show that our analysis can be expressed with any number of receiver branches over various fading conditions. Numerical results confirm that the derived error probability and ergodic capacity expressions match well with the empirical results. [ABSTRACT FROM PUBLISHER]

A signature identification algorithm is a method to obtain the cell identification information for wireless cellular systems or determine the intended user for wireless local area network. In this paper, we propose a simple and efficient signature identification algorithm on the basis of Zadoff-Chu sequence in orthogonal frequency division multiplexing systems. In addition, we prove that the proposed algorithm achieves a maximum likelihood solution if the receiver knows the channel length. Also, the exact probabilities of signature identification failures of the proposed algorithm are provided for different power delay profiles. To demonstrate efficacy of the proposed algorithm in fading channels, we derive the failure probability at high signal-to-noise ratio (SNR). Through a high SNR expression, it is shown that the proposed algorithm fully exploits frequency selective fadings. Especially, we reveal that the slope of the failure probability curves at high SNR is determined by the channel length regardless of power delay profiles. Simulation results show that the proposed algorithm outperforms conventional signature algorithms in frequency selective fading channels. Also, we confirm that our analysis matches well with the empirical results of the proposed signature identification algorithm. [ABSTRACT FROM PUBLISHER]

We propose a general principle for the guiding of light waves through heterogeneous metamaterial layers: light waves have a tendency to increase their portion guided through the medium having a larger N^2 value than the neighbors, where N^2 is defined as the product of material permittivity and permeability values. Using this principle, we discuss qualitatively the characteristics of fundamental optical modes in several metamaterial waveguide structures. [ABSTRACT FROM PUBLISHER]