In recent years, 3D surround view systems have attracted a lot of attention in the field of advanced driver assistance systems (ADAS). However, the foundational assumption of unchanging camera poses in traditional 3D surround view systems, which is designed for single-unit vehicles, results in a failure to manage the non-rigid connections characteristic of tractor-trailer vehicles. Moreover, tractor-trailer vehicles have the feature of long bodies and large wheelbases, leading to severe distortions and abrupt changes in the rendering results of previous 3D texture mapping models. In this paper, we propose DSVT, a dynamic 3D surround view system for tractor-trailer vehicles, designed to address the aforementioned issues. Specifically, we develop a dynamic surround image stitching algorithm based on relative pose estimation, which estimates the relative poses between cameras and stitches all images together to generate a 2D panoramic image. Subsequently, a novel 3D drop model is proposed, mapping the 2D panoramic image onto the 3D model for panoramic viewing. Our system can run in real time on Nvidia AGX Orin. Experimental results in real tractor-trailer scenes show that our system can achieve more accurate and natural visual effects.

Recently, Bird’s Eye View (BEV) detection methodologies that utilize surround-view cameras have seen significant advancements in autonomous driving systems. Traditional methods, however, are constrained by their reliance on specific camera parameters, which poses challenges in generalizing across different vehicle-mounted cameras with varying poses and under adverse conditions. To address these challenges, we propose a robust BEV representation network that integrates Dual-Space Positional Encoding (DSPE) and image perception. This network is designed to enhance resilience to calibration errors and pose fluctuations, resulting in reliable detection performance on the Nuscenes dataset, even with imprecise extrinsic inputs. Our approach demonstrates competitive accuracy when compared to other methods that do not rely on temporal data, highlighting the effectiveness of our DSPE strategy in improving the robustness and accuracy of BEV detection in dynamic and challenging environments.

The trend towards denser base station deployment and multi-band operations in emerging cellular networks has made mobility management and handover (HO) optimization a formidable challenge. The challenge is further aggravated by the scarcity of practical multi-objective mobility management solutions optimizing both intra and inter frequency HO. This paper presents a holistic multi-objective mobility management solution for both intra and inter frequency HO employing multiple parameters of standardized HO events A2, A3, and A5. We formulate a multi-objective optimization problem to determine the optimal parameter settings that jointly optimize four key performance indicators: number of HO failures, HO latency, signaling overhead and number of radio link failures. We leverage soft actor-critic reinforcement learning (RL) to solve the multi-objective problem. To mitigate the risk of performance deterioration resulting from direct interactions between live network and RL-agent during training, this paper proposes a mobility management framework that develops and employs a digital twin (DT) as the training environment. To develop a cellular network DT for mobility management and HO optimization, we present a tri-pronged approach including realistic network deployment, realistic user mobility and 3GPP HO events. Results show that the proposed DT-trained RL solution for the multi-objective optimization can converge 7x faster than the brute force method with negligible loss in the value of the objective function. An analysis of the individual KPI values reveal a strong trade-off between HO signaling overhead and radio link failures.

Automotive surround-view camera systems have been commonly employed in automated driving to aid in near-field sensing and other perception tasks. Due to the large size of the body and the presence of multiple blind spots, panoramic surround-view systems are particularly crucial for tractor-trailer vehicles. However, the non-rigid body of tractor-trailer vehicles introduces pose changes between cameras, rendering traditional calibration-based methods inadequate. Additionally, cameras mounted separately on the tractor and the trailer will experience independent vibrations, resulting in undesirable shakiness in captured videos. In this paper, we propose a unified video stabilization and stitching method to address these challenges, which can smooth the unsteady frames and align the images from moving cameras. Delving into video stabilization techniques, we extend mesh-based motion model for unified stitching and leverage deep-learning based modules to handle complex real-world scenarios. Moreover, we design a new optimization framework to estimate the optimal displacements of mesh vertices, enabling simultaneous stabilization and stitching of frames. The experimental results, obtained by public datasets and videos captured from a model tractor-trailer vehicle, demonstrate that our approach outperforms previous methods and is highly effective in real-world applications.

A newly designed grounded electrical-source airborne transient electromagnetics (GREATEM) system was introduced recently. Detailed data preprocessing techniques to acquire the high-precision measured magnetic field are discussed here. Different from the previous work in which the reconstruction of the underground structure is performed in 1-D, we interpret the GREATEM data in 3-D by the volume integral equation (VIE) method in the frequency domain. Therefore, the VIE in the forward electromagnetic scattering model is formulated in the low-frequency regime. It is solved by using the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method. In the nonlinear inversion, the Born iterative method (BIM) and the conjugate gradient method are adopted to minimize the cost function. A synthetic model of GREATEM survey is used to validate the proposed 3-D forward and inversion algorithms. Then, the field data from two GREATEM surveys are used to test the effectiveness and accuracy of the proposed inversion algorithm. The reconstructed conductivity structures are consistent with geological drilling results, confirming the potential of our method for solving the 3-D GREATEM inversion problems in geophysical engineering applications. This paper represents the first application of the BCGS-FFT and BIM algorithms to a GREATEM system.

A new iterative inversion algorithm is proposed to reconstruct the electrical conductivity profile in a stratified underground medium for the grounded electrical source airborne transient electromagnetic (GREATEM) system. In forward modeling, we simplify the mathematical expressions of the magnetic fields generated by a finite line source in the layered ground to semianalytical forms in order to save the computation time. The Fréchet derivative is derived for the electromagnetic response at the receivers due to a small perturbation of the conductivity in a certain layer underground. The initial expression of the Fréchet derivative has an expensive triple integral and contains the Bessel function in the integrand. It is simplified by partially eliminating the integration along the source line and deriving the analytical expression for the integration in the vertical direction inside the perturbed layer. In the inverse solution, we use the distorted Born iterative method (DBIM). This is the first time that the DBIM is applied to data measured by the GREATEM system. Besides, the forward and inverse procedures are carried out in the frequency domain and based on the Fréchet derivative of a line source. We demonstrate the validity of our forward model, Fréchet derivative, inverse model, and the precision as well as robustness of the inversion algorithm through numerical computation and comparisons. Finally, we apply the inversion algorithm to the measured data and compare the retrieved conductivity to the actual drilling data.

One fundamental difficulty in low-frequency subsurface electromagnetic exploration is the low-frequency breakdown phenomenon in numerical computation. It makes the discretized linear system very poorly conditioned and thus difficult to solve. This issue is present in both integral equation and partial differential equation solution methods, and thus has attracted many researchers who have proposed various methods to overcome this difficulty. In this paper, we propose a new mixed spectral element method (mixed SEM) to eliminate this low-frequency breakdown problem and apply this method to solve the subsurface electromagnetic exploration problem. Since Gauss' law is now explicitly enforced in the mixed SEM to make the system matrix well-conditioned even at extremely low frequency, we can solve the linear system from dc to high frequencies. With the proposed method, we study the surface-to-borehole electromagnetic system for hydrocarbon exploration. Numerical examples show that the mixed SEM is accurate and efficient, and has significant advantages over conventional methods.

Low-frequency subsurface electromagnetic measurements are important tools for characterizing natural resources and environmental wastes. Rapid simulations of low-frequency subsurface electromagnetic measurements are still a challenge because of the large computational domain and low-frequency breakdown phenomenon. We develop an effective method to simulate these low-frequency subsurface electromagnetic measurements by using the spectral element method together with a domain decomposition method (DDM). A specific mesh has been designed based on the traveling wave nature in the air and the diffusion field nature in the underground space to greatly reduce the number of unknowns. The frequency-domain version of the Riemann solver (upwind flux) is used as an effective transmission condition to simulate the interactions between neighboring subdomains in DDM. Several numerical examples demonstrate the efficiency of the proposed approach in low-frequency subsurface electromagnetics simulations.

In order to effectively improve the efficiency and service level of port logistics, aiming to the scheduling of containers between container storage station and container terminal, this paper first develops a multi-objective scheduling model which attempts to minimize the wait time of ships in port and balance the load of the related logistics resource. Then, a discrete adaptive artificial colony algorithm is proposed to solve this model. Finally, a case study is given to demonstrate the effectiveness of the proposed model and algorithm.

This paper proposes an integrated design of active fault-tolerant H∞control in the presence of unknown actuator faults. An adaptive tracking law is designed by designing an auxiliary self-examination plant, so that actuator fault parameters and their boundaries are estimated accurately and the identification of each actuator fault is decoupled from exogenous disturbance and other actuator faults in a fault observer. With the information of fault diagnosis, especially the boundaries of estimation errors, an integrated fault-tolerant H∞control method is constructed to guarantee the overall H∞performance of the system in the presence of unknown actuator faults. Finally, the effectiveness of the proposed approach is validated by stabilizing an F-16 fighter.

Advances in digital communications and signal processing have enabled the adaptation of advanced communication techniques in wireless LAN (WLAN) standards. Among those advanced techniques, multiple-input-multipleoutput (MIMO) is now part of the high throughput (HT) WLAN standard, a.k.a. IEEE 802.11n. With the completion of the IEEE 802.11n specification, the IEEE 802.11 working group (WG) is now working on a new amendment to achieve very high throughput (VHT) in the gigabit range.. The move to higher data rates is enabled by introducing wider bandwidth, downlink multi-user MIMO (MU-MIMO), and higher modulation and coding schemes (MCS). This paper focuses the use of group membership and identifiers for managing downlink transmissions using MU-MIMO. Downlink MU-MIMO allows a WLAN access point to transmit to multiple stations simultaneously. Group Membership and identifiers are used to signal target stations together with their positions and the number of spatial streams intended for each of them in the downlink transmission. A description of the Group ID concept and its application to the VHT specification is discussed here. A method for assigning stations' position to Group IDs is introduced and its performance is examined.

The Enhanced Distributed Channel Access (EDCA) mechanism is the most commonly implemented medium access scheme in today's wireless local area network (WLAN) products. With the introduction of the downlink multi-user MIMO (DL MU-MIMO) technology to the current IEEE 802.11 specification, the EDCA mechanism has been enhanced accordingly to support multiple simultaneous transmissions to different stations. In this paper, we report the work the IEEE 802.11ac task group has done for enhancing the backoff schemes of the EDCA mechanism and point out its issues. We then present two of our approaches which we believe perform better, in terms of fairness and network throughput, than the approach adopted by the current 802.11ac draft specification. OPNET simulations were conducted for performance evaluation.

For the first time in the WLAN technology, multi-user downlink transmission will be made possible by the upcoming IEEE standard, 802.11ac. In this paper, we will report its current status, focusing on the MAC modifications and enhancements that are necessary to support the unique feature of downlink multi-user MIMO transmission. In particular, the technique of enhancing Transmit Opportunity (TXOP) for downlink MU-MIMO transmission and the revised backoff procedures for secondary ACs will be introduced and discussed. The performance of the proposed mechanisms is evaluated using simulations. The proposed TXOP Sharing mechanism has been accepted in the 802.11ac draft specification and is currently under letter ballot in the IEEE 802.11 WG.

For the first time in the WLAN technology, multi-user downlink transmission will be made possible by the upcoming IEEE standard, 802.11ac. In this paper, we will report its current status, focusing on the MAC modifications and enhancements that are necessary to support the unique feature of downlink multi-user MIMO transmission. Specifically, the technique of TXOP sharing and its related issues and solutions will be discussed and illustrated.

We present a novel clustering algorithm for tagging a face dataset (e. g., a personal photo album). The core of the algorithm is a new dissimilarity, called Rank-Order distance, which measures the dissimilarity between two faces using their neighboring information in the dataset. The Rank-Order distance is motivated by an observation that faces of the same person usually share their top neighbors. Specifically, for each face, we generate a ranking order list by sorting all other faces in the dataset by absolute distance (e. g., L1 or L2 distance between extracted face recognition features). Then, the Rank-Order distance of two faces is calculated using their ranking orders. Using the new distance, a Rank-Order distance based clustering algorithm is designed to iteratively group all faces into a small number of clusters for effective tagging. The proposed algorithm outperforms competitive clustering algorithms in term of both precision/recall and efficiency.

This paper introduces a new IEEE standard, IEEE 802.15.5,which provides mesh capability for wireless personal area network (WPAN) devices. The standard provides an architectural framework enabling WPAN devices to promote interoperable, stable, and scalable wireless mesh topologies. It is composed of two parts: low-rate WPAN mesh and high-rate WPAN mesh. In this paper, we present only low-rate WPAN mesh because it is designed to support wireless sensor networks. IEEE 802.15.5 low-rate part is a light-weight scalable mesh routing protocol that caters well to the requirements of resource-constrained wireless sensor networks. By binding logical addresses to the network topology, IEEE 802.15.5 obviates the need for route discovery. This eliminates the initial route discovery latency, saves storage space and reduces the communication overhead and energy consumption. A distributed link state scheme is further built atop the block addressing scheme to improve the quality of routes, robustness, and load balancing. The routing scheme scales well with regard to various performance metrics. The standard also provides enhanced functions such as multicast, reliable broadcast, power saving, time synchronization, route tracing and portability. We also present the performance evaluation of major functions performed with a 50-nodes tested deployed over a whole floor (100 × 140 ft2) at CUNY Engineering building. The results testify that the IEEE 802.15.5 will serve well for wireless personal area networks and wireless sensor networks.

This article presents an in-depth introduction and analysis of the low-data-rate portion of the IEEE standard 802.15.5 that targets on providing mesh capabilities to low-rate wireless personal area networks. As major contributors of this standard, we seek to share our insights and motivations of the approaches adopted in the major components of the standard instead of presenting a la carte items drawn in the specification. We wish this article helps readers of the 802.15.5 standard to better understand the rationale and intent of the protocol design. This article also compares 802.15.5 to ZigBeetrade and 6LoWPAN, for users to select the best mesh network technology for their applications.

Designed and developed for military communications in their early stage, multi-hop wireless ad hoc network is attracting interest from commercial applications and becoming a research hot spot. Although the technology has been studied for about a decade, there still exist some problems to be solved. Route discovery latency for reactive routing protocols is one of them. In this paper, we propose a dynamic RTT-based algorithm which can dramatically reduce the route discovery latency. This algorithm can be generally applied to any source selection based reactive routing protocols. Our simulation study has also shown some interesting results about packet transmission time and route discovery latency in a contention-based channel like IEEE 802.11 which have not been discussed before.

Under many circumstances, broadcasting is the only way of service discovery, device paging, and even data transfer in wireless ad hoc networks. However, broadcasting may lead to "broadcast storm" problems if it is done by simple flooding in a single common channel. The problems include redundant rebroadcast (overlaying), contention and collision. When the network density is high or the network is getting congested, the problems become severe and may directly lead to degraded performance of the network. We propose a novel broadcast scheme that is simple, highly efficient and scalable. Most importantly, it is a fully distributed scheme which introduces no control overheads (in terms of control traffic), and can adapt to network movement at no extra cost. We evaluate our scheme by OPNET simulation and compare it with existing methods.