This paper proposes a new method for interpreting and simplifying a black box model of a deep random forest (RF) using a proposed rule elimination. In deep RF, a large number of decision trees are connected to multiple layers, thereby making an analysis difficult. It has a high performance similar to that of a deep neural network (DNN), but achieves a better generalizability. Therefore, in this study, we consider quantifying the feature contributions and frequency of the fully trained deep RF in the form of a decision rule set. The feature contributions provide a basis for determining how features affect the decision process in a rule set. Model simplification is achieved by eliminating unnecessary rules by measuring the feature contributions. Consequently, the simplified model has fewer parameters and rules than before. Experiment results have shown that a feature contribution analysis allows a black box model to be decomposed for quantitatively interpreting a rule set. The proposed method was successfully applied to various deep RF models and benchmark datasets while maintaining a robust performance despite the elimination of a large number of rules.

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown. Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this paper. We first present the necessary and sufficient condition for unknown noise covariance estimation; these conditions are related to the rank of a matrix involving the auto and cross-covariances of a weighted sum of innovations, where the weights are the coefficients of the minimal polynomial of the closed-loop system transition matrix of a stable, but not necessarily optimal, Kalman filter. We present an optimization criterion and a novel six-step approach based on a successive approximation, coupled with a gradient algorithm with adaptive step sizes, to estimate the steady-state Kalman filter gain, the unknown noise covariance matrices, as well as the state prediction (and updated) error covariance matrix. Our approach enforces the structural assumptions on unknown noise covariances and ensures symmetry and positive definiteness of the estimated covariance matrices. We provide several approaches to estimate the unknown measurement noise covariance R via post-fit residuals, an approach not yet exploited in the literature. The validation of the proposed method on five different test cases from the literature demonstrates that the proposed method significantly outperforms previous state-of-the-art methods. It also offers a number of novel machine learning motivated approaches, such as sequential (one sample at a time) and mini-batch-based methods, to speed up the computations.

In this abstract, the principles of the development of the optimal computer- and robotic health-care systems are proposed and considered as a means to improve the health service of the aging population. The results of the implementation of a new created diagnosis system Anamnesis are analyzed and found prospective to use the modified processors for handling the predicated flow of information. It is proposed to develop a robotic system for healthcare including the cyber-physical systems for the rapid manufacturing of medicine on-demand using microwave-assisted reactors.

Automatic modulation classification (AMC) is one of the most critical technologies for non-cooperative communication systems. Recently, deep learning (DL) based AMC (DL-AMC) methods have attracted significant attention due to their preferable performance. However, the study of most of DL-AMC methods are concentrated in the single-input and single-output (SISO) systems, while there are only a few works on DL-based AMC methods in multiple-input and multiple-output (MIMO) systems. Therefore, we propose in this work a convolutional neural network (CNN) based zero-forcing (ZF) equalization AMC (CNN/ZF-AMC) method for MIMO systems. Simulation results demonstrate that the CNN/ZF-AMC method achieves better performance than the artificial neural network (ANN) with high order cumulants (HOC)-based AMC method under the condition of the perfect channel state information (CSI). Moreover, we also explore the impact of the imperfect CSI on the performance of the CNN/ZF-AMC method. Simulation results demonstrated that the classification performance is not only influenced by the imperfect CSI, but also associated with the number of the transmit and receive antennas.

Probabilistic computing algorithms require electrically programmable stochasticity to encode arbitrary probability functions and controlled stochastic interaction or correlation between probabilistic (p-) bits. The latter is implemented with complex electronic components leaving a large footprint on a chip and dissipating excessive amount of energy. Here, we show an elegant implementation with just two dipole-coupled magneto-tunneling junctions (MTJ), with magnetostrictive soft layers, fabricated on a piezoelectric film. The resistance states of the two MTJs (high or low) encode the p-bit values (1 or 0) in the two streams. The first MTJ is driven to a resistance state with desired probability via a current or voltage that generates spin transfer torque, while the second MTJ’s resistance state is determined by dipole coupling with the first, thus correlating the second p-bit stream with the first. The effect of dipole coupling can be varied by generating local strain in the soft layer of the second MTJ with a local voltage (~ 0.2 V) and that varies the degree of anti-correlation between the resistance states of the two MTJs and hence between the two streams (from 0% to 100%). This paradigm generates the anti-correlation with “wireless” dipole coupling that consumes no footprint on a chip and dissipates no energy, and it controls the degree of anti-correlation with electrically generated strain that consumes minimal footprint and is extremely frugal in its use of energy. It can be extended to arbitrary number of bit streams. This realizes an “all-magnetic” platform for probabilistic computing.

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica} Modern cities are composed of complex socio-technical systems that exist to provide services effectively to their residents and visitors. In this context, smart mobility systems aim to support the efficient exploitation of the city transport facilities as well as sustainable mobility within the urban environment. People need to travel quickly and conveniently between locations at different scales, ranging from a trip of a few blocks within a city to a journey across cities or further. At the same time, goods need to be timely delivered considering the needs of both the users and the businesses. While most of the mobility and delivery solutions can cover significant distances and multiple requests, they suffer when the requests come from the growing neighborhoods and hard-to-reach areas such as city centers, corporate headquarters, and hospitals. In the last few years, several cities indicated interest in using Autonomous Vehicles (AV) for the “last-mile” mobility services. With them, it seems to be easier to get people and goods around using fewer vehicles. In this context, Autonomous Shuttles (AS) are beginning to be thought of as a new mobility/delivery service into the city center where narrow streets are not easily served by traditional buses. They allow them to serve critical areas with minimal new infrastructure and reducing noise and pollution. The goal of this article is to present an innovative vision on the introduction of p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica} the Autonomous Shuttles-as-a service (ASaaS) concept as the key pillar for the realization of innovative and sustainable proximity mobility. Through a set of real application scenarios, we present our view, and we discuss a set of challenges, opportunities, and social implications that this way to reimage the mobility of the future introduces.

The model coil with the racetrack geometry based on second generation (2G) High Temperature Superconducting (HTS) tapes has been developed for an electrical machine where a winding pack is a stack of 2G HTS tapes. It is important to evaluate transport current AC losses and possible methods to reduce them in a winding. In a device made of 2G HTS tapes, the main the AC losses are the hysteresis ones. Only numerical simulation permits to predict them in full. The FEM model for calculation of the hysteresis losses developed before for 2G HTS power cables was modified for a stack of the 2G HTS tapes in a coil. In this paper the methods to increase a computational speed are presented. Possible ways to reduce AC losses are discussed also. The details of the model and comparisons of calculations with measurements of the AC loss are presented.

Along with the deployment of the Face Recognition Systems (FRS), concerns were raised related to the vulnerability of those systems towards various attacks including morphed attacks. The morphed face attack involves two different face images in order to obtain via a morphing process a resulting attack image, which is sufficiently similar to both contributing data subjects. The obtained morphed image can successfully be verified against both subjects visually (by a human expert) and by a commercial FRS. The face morphing attack poses a severe security risk to the e-passport issuance process and to applications like border control, unless such attacks are detected and mitigated. In this work, we propose a new method to reliably detect a morphed face attack using a newly designed denoising framework. To this end, we design and introduce a new deep Multi-scale Context Aggregation Network (MS-CAN) to obtain denoised images, which is subsequently used to determine if an image is morphed or not. Extensive experiments are carried out on three different morphed face image datasets. The Morphing Attack Detection (MAD) performance of the proposed method is also benchmarked against 14 different state-of-the-art techniques using the ISO-IEC 30107-3 evaluation metrics. Based on the obtained quantitative results, the proposed method has indicated the best performance on all three datasets and also on cross-dataset experiments.

The results presented in the paper are based on simulations on benchmark spiking neural networks. The methodology is described in the paper.

Automatic modulation classification (AMC) is an critical step to identify signal modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Convolutional neural network (CNN)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work at the corresponding condition. In this paper, a novel CNN-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from the mixed datasets under varying noise scenarios, and the CNN can extract common features from these datasets. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

Face recognition is widely employed in Automated Border Control (ABC) gates, which verify the face image on passport or electronic Machine Readable Travel Document (eMTRD) against the captured image to confirm the identity of the passport holder. In this paper, we present a robust morph detection algorithm that is based on differential morph detection. The proposed method decomposes the bona fide image captured from the ABC gate and the digital face image extracted from the eMRTD into the diffuse reconstructed image and a quantized normal map. The extracted features are further used to learn a linear classifier (SVM) to detect a morphing attack based on the assessment of differences between the bona fide image from the ABC gate and the digital face image extracted from the passport. Owing to the availability of multiple cameras within an ABC gate, we extend the proposed method to fuse the classification scores to generate the final decision on morph-attack-detection. To validate our proposed algorithm, we create a morph attack database with overall 588 images, where bona fide are captured in an indoor lighting environment with a Canon DSLR Camera with one sample per subject and correspondingly images from ABC gates. We benchmark our proposed method with the existing state-of-the-art and can state that the new approach significantly outperforms previous approaches in the ABC gate scenario.

The innovations of sixth generation wireless communication (6G) as compared to fifth generation (5G) are considered in this paper based on the analysis of related works. With the aim of improving multiple communication targets in each service, five 6G core services are identified for different target requirements. Two centricities and eight key performance indices (KPIs) are detailed to describe these services, and enabling technologies are discussed to satisfy the KPIs. A 6G architecture is proposed as an integrated system of the enabling technologies, and is then illustrated using four typical application scenarios. Potential challenges for development of 6G technology is then discussed and possible solutions are proposed. Finally, opportunities for exploring 6G are analyzed in order to guide future research.

We report a broadband microwave frequency converter based on a coherent Kerr optical micro-comb generated by an integrated micro-ring resonator. The coherent micro-comb displays features that are consistent with soliton crystal dynamics with an FSR of 48.9-GHz. We use this to demonstrate a high-performance millimeter-wave local oscillator at 48.9-GHz in the Q-band for microwave frequency conversion. We experimentally verify the microwave performance up to 40 GHz, achieving a ratio of −6.8 dB between output RF power and IF power and a spurious suppression ratio of > 43.5 dB. The experimental results show good agreement with theory and verify the effectiveness of microwave frequency converters based on coherent optical micro-combs, with the ability to achieve reduced size, complexity, and potential cost.

A coupling of wireless access via non-orthogonal multiple access and wireless backhaul via beamforming is a promising way for downlink user-centric ultra-dense networks (UDNs) to improve system performance. However, ultra-dense deployment of radio access points in macrocell and user-centric view of network design in UDNs raise important concerns about resource allocation and user association, among which notably is energy efficiency (EE) balance. To overcome this challenge, we develop a framework to investigate the resource allocation problem for energy efficient user association in such a scenario. The joint optimization framework aiming at the system EE maximization is formulated as a large-scale non-convex mixed-integer nonlinear programming problem, which is NP-hard to solve directly with lower complexity. Alternatively, taking advantages of sum-of-ratios decoupling and successive convex approximation methods, we transform the original problem into a series of convex optimization subproblems. Then we solve each subproblem through Lagrangian dual decomposition, and design an iterative algorithm in a distributed way that realizes the joint optimization of power allocation, sub-channel assignment, and user association simultaneously. Simulation results demonstrate the effectiveness and practicality of our proposed framework, which achieves the rapid convergence speed and ensures a beneficial improvement of system-wide EE.

In this chapter, we address the competent Autonomous Vehicles should have the ability to analyze the structure and unstructured environments and then to localize itself relative to surrounding things, where GPS, RFID or other similar means cannot give enough information about the location. Reliable SLAM is the most basic prerequisite for any further artificial intelligent tasks of an autonomous mobile robots. The goal of this paper is to simulate a SLAM process on the advanced software development. The model represents the system itself, whereas the simulation represents the operation of the system over time. And the software architecture will help us to focus our work to realize our wish with least trivial work. It is an open-source meta-operating system, which provides us tremendous tools for robotics related problems. Specifically, we address the advanced vehicles should have the ability to analyze the structured and unstructured environment based on solving the search-based planning and then we move to discuss interested in reinforcement learning-based model to optimal trajectory in order to apply to autonomous systems.

Over 20,000 fluorescence lifetime images from 10 patients were collected using a fibre-based custom fluorescence lifetime imaging endomicroscopy (FLIM) system. During the data collection, various measuring conditions were applied, including exposure time, optical wavelength, and lifetime extraction approaches to obtain diverse results rich in spatial and spectral resolution. The data for further processing was chosen with exposure time of 6 and 20 ns, excitation bands of 490-570 and 594-764 nm, and RLD. In addition, there are some images with sizes different than 128x128. In order to avoid any artificial errors on the lifetime images during the processing, only the lifetime images with 128x128 resolution were selected. After the selection, there were 10,155 and 11,363 frames of cancer and normal tissues respectively, and each frame contained one intensity and one corresponding lifetime image.

In this paper, we address the data sending and visualization in search-based planning using the open source software based on motion planning problems. First, we explore the computing architecture of software where we can communicate with other devices or sensors. It also is to understand the finding path problem by using the A-Start algorithm. By the way, it is integrated to ROS (Robot Operation System) and implemented in Nao Humanoid Robot based on solving the optimize the trajectories.

In this paper, we use a series expansion to calculate the sum-capacity of a massive Multiple-Input MultipleOutput (MIMO) system under propagation environment described by a dominant line-of-sight. The sum-capacity is written as Taylor’s series where each term is a function of the mean trace of k-th power of the channel matrix W. We analytically derive the mean trace of first, second, third, and fourth moments of W. Although the series is infinite, our numerical results show that only a few terms can tightly approximate the exact sum capacity. Numerical results corroborate our analytical expressions.