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This paper develops a non-coherent index modulation (IM) system in which activation patterns are characterized by multi-level block codes. We analyze performance of such a system under the maximum-likelihood (ML) receiver and when the set of activation patterns follows a multi-level code generated from asymptotically optimal alphabets. An asymptotic analysis of the pair-wise error probability (PEP) shows that the system can exploit a diversity order that is determined by the distance of the worst codeword pair in the $l_{1}$ metric, known as the Manhattan norm. We then explore the rate-diversity tradeoff for the developed non-coherent IM system as a function of the code length. Specifically, Gilbert-style bounds on the data rates for systems based on binary and ternary codes are obtained that can ensure a given diversity order. We approach the problem of packing in the $l_{1}$ metric by partitioning codes into permutation modulation codes (PMCs) and obtaining Gilbert-style bounds on PMCs. Several achievable rates for non-coherent binary and ternary IM systems, as well as a tradeoff between the information rate and codeword error probability (CEP) are also derived. Finally, simulation results are provided to corroborate the theoretical analysis. [ABSTRACT FROM AUTHOR]

We introduce the binomial line process (BLP), a novel spatial stochastic model for the characterization of streets in the statistical evaluation of wireless and vehicular networks. Existing stochastic geometry models for streets, e.g., Poisson line processes (PLP) and Manhattan line processes (MLP) lack an important aspect of city-wide street networks: streets are denser in the city center and sparse near the suburbs. Contrary to these models, the BLP restricts the generating points of the streets to a fixed radius centered at the origin of the Euclidian plane, thereby capturing the inhomogeneity of the streets with respect to the distance from the center. We derive a closed-form expression for the contact distribution of the BLP from a random location on the plane. Leveraging this, we introduce the novel Binomial line Cox process (BLCP) to emulate points on individual lines of the BLP and derive the distribution to the nearest BLCP point from an arbitrary location. Using numerical results, we highlight that the spatial configuration of the streets is remarkably distinct from the perspective of a city center user to that of a suburban user. The framework developed in this paper can be integrated with the existing models of line processes for more accurate characterization of streets in urban and suburban environments. [ABSTRACT FROM AUTHOR]

Timely and reliable information sharing among autonomous vehicles (AVs) provides a promising approach for reducing traffic congestion and improving traffic efficiency in future intelligent transportation systems. In this paper, we consider millimeter-wave (mmWave) based multi-hop vehicle-to-vehicle (V2V) communications to facilitate ultra-reliable low-latency information sharing among AVs. We propose a novel framework for performance analysis and design of relay selection schemes in mmWave multi-hop V2V communications, while taking into account the mmWave signal propagation characteristics, road topology, and traffic conditions. In particular, considering the minimum tracking distance requirement of road traffic, the headway, i.e., the distance between adjacent AVs, is modeled as shifted-exponential distribution. Moreover, we model the communication path losses using the Manhattan distance metric in the taxicab geometry, which can more accurately capture the characteristics of mmWave signal propagation in urban grid roads than conventional Euclidean distance geometry. Based on the proposed model, we investigate the latency and reliability of mmWave multi-hop V2V communications for three widely adopted relay selection schemes, i.e., random with forward progress (RFP), most forward with fixed radius (MFR), and nearest with forward progress (NFP), respectively. Furthermore, we propose a novel relay selection scheme for joint optimization of the single-hop forward progress (FP) and single-hop latency according to the AVs’ instantaneous locations and an estimate of the residual multi-hop latency. Simulation results show that, by balancing the current single-hop latency and the residual multi-hop latency for the multi-hop V2V network, the proposed relay selection scheme significantly outperforms the MFR, NFP and RFP in both multi-hop transmission latency and reliability of mmWave V2V communications. [ABSTRACT FROM AUTHOR]

Presently, many researchers and engineers have investigated autonomous driving, which has significantly influenced the revolution of artificial intelligence (AI). One of the critical challenges for autonomous driving is the inadequate precision of autonomous vehicles in detecting pedestrians, which is a major safety hazard to human beings. In this paper, a Field Programmable Gate Array (FPGA) demonstration system with a normalization-based validity index (NbVI) has been proposed for real-time pedestrian detection. The proposed algorithm can accurately detect pedestrians by calculating the Manhattan distance between the target histogram of oriented gradient (HOG) features and real-time pedestrian HOG features. In lieu of sophisticated circuit layout and substantial training burden with neuron computation circuit, the proposed detection system with adaptive features clustering is hardware-friendly and is capable of real-time pedestrian detection using fewer training images with high detection rate (up to 99.2%). Moreover, the function execution time of pedestrian detection is shortened by 25% using FPGA acceleration. [ABSTRACT FROM AUTHOR]

Helping mobile robots understand curved corridor scenes has considerable value in computer vision. However, due to the diversity of curved corridor scenes, such as curved structures that do not satisfy Manhattan assumption, understanding them remains a challenge. Curved non-Manhattan structures can be seen as compositions of spatial right angles projected into two dimensional projections, which may help us estimate their original posture in 3D scenes. In this paper, we presented an approach for mobile robots to understand curved corridor scenes including Manhattan and curved non-Manhattan structures, from a single image. Angle projections can be assigned to different clusters via geometric inference. Then coplanar structures can be estimated. Fold structures consisting of coplanar structures can be estimated, and curved non-Manhattan structures can be approximately represented by fold structures. Based on understanding curved non-Manhattan structures, the method is practical and efficient for a navigating mobile robot in curved corridor scenes. The algorithm requires no prior training or knowledge of the camera’s internal parameters. With geometric features from a monocular camera, the method is robust to calibration errors and image noise. We compared the estimated curved layout against the ground truth and measured the percentage of pixels that were incorrectly classified. The experimental results showed that the algorithm can successfully understand curved corridor scenes including both Manhattan and curved non-Manhattan structures, meeting the requirements of robot navigation in a curved corridor environment. [ABSTRACT FROM AUTHOR]

In a weighted undirected graph, a spanning/Steiner shallow-light tree (SLT) simultaneously approximates: 1) shortest distances from a root to the other vertices and 2) the minimum tree weight. The Steiner SLT has been proved to be exponentially lighter than the spanning one. In this paper, we propose a novel Steiner SLT construction method called Steiner SLT (SALT), which is efficient and has the tightest bound over all the state-of-the-art general-graph SLT algorithms. Applying SALT to Manhattan space offers a smooth tradeoff between rectilinear Steiner minimum tree and rectilinear Steiner minimum arborescence for VLSI routing. The adaption also reduces the time complexity from ${O}$ (${n} ^{2}$) to ${O}$ (${n}$ log ${n}$). Besides, several effective post-processing methods, including safe refinement and shallowness-constrained edge substitution, are proposed to further improve the result. The experimental results show that SALT can achieve not only short path lengths and wirelength but also small delay, compared to both classical and recent routing tree construction methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel visual-inertial odometry (VIO) approach that adopts structural regularity in man-made environments. Instead of using Manhattan world assumption, we use Atlanta world model to describe such regularity. An Atlanta world is a world that contains multiple local Manhattan worlds with different heading directions. Each local Manhattan world is detected on the fly, and their headings are gradually refined by the state estimator when new observations are received. With full exploration of structural lines that aligned with each local Manhattan worlds, our VIO method becomes more accurate and robust, as well as more flexible to different kinds of complex man-made environments. Through benchmark tests and real-world tests, the results show that the proposed approach outperforms existing visual-inertial systems in large-scale man-made environments. [ABSTRACT FROM AUTHOR]

Flow-based microfluidic biochips are promising with significant applications for automating and miniaturizing laboratory procedures in biochemistry. Automated design methods for flow-based microfluidic biochips are becoming increasingly important due to the advancement in both integration scale and design complexity for complicated biochemical applications. Though the multilayer soft lithography fabrication provides flexibility to route both flow and control channels in any angle, existing routing algorithms still adopt Manhattan routing metrics, which design channel in either vertical or horizontal direction only. Moreover, based on the computational fluid dynamics analysis, rectilinear channels with 90° bends have the following issues: 1) reduced the fluidic flow rate, which degrades the performance of the biochip and may even result in the erroneous outcome of the whole procedure and 2) increased pressure at the right-angle bend, which negatively affects the reliability of the biochip. To fully utilize the routing flexibility, this paper proposes the first any-angle routing algorithm for flow-based microfluidic biochip, called AARF. Computational simulation results show that compared with traditional Manhattan routing method, the proposed AARF significantly improves the total wirelength and total effective wirelength (considering the turning angles) by 17.11% and 35.91%, respectively, which prove the effectiveness of the AARF routing flow. [ABSTRACT FROM AUTHOR]