As automotive electronic architectures grow increasingly complex and software development costs escalate, AUTOSAR plays a critical role in standardizing and enhancing the reusability of automotive controllers. However, existing AUTOSAR application layer modeling tools, such as the Simulink AUTOSAR Blockset, primarily adopt causal modeling paradigms, which constrain flexibility in capturing intricate system interactions. Additionally, their proprietary nature limits model accessibility, hindering cross-platform collaboration and multi-domain integration. Modelica, an object-oriented, equation-based modeling language, is particularly well-suited for multi-domain simulations due to its acausal modeling capabilities and strong support for component reuse. This paper proposes a Modelica-based visual modeling approach for AUTOSAR application layer models. Specifically, it establishes encapsulation rules for representing AUTOSAR constructs in Modelica and develops an open-source AUTOSAR model library, facilitating industry collaboration and accelerating rapid prototyping. A formal mathematical representation of AUTOSAR models is introduced to enhance both expressiveness and verifiability. Furthermore, a structured visual modeling methodology is presented to lower the development barrier for AUTOSAR application layer modeling. Comparative analysis with Simulink's AUTOSAR Blockset demonstrates that the proposed approach successfully integrates Modelica's multi-domain modeling capabilities into the AUTOSAR workflow while ensuring simulation consistency with Simulink. To the best of our knowledge, this work represents the first integration of AUTOSAR within Modelica's multi-domain simulation framework. Compared to Simulink, Modelica's acausal modeling paradigm enables more flexible system representations, its open ecosystem supports cross-platform collaboration, and its multi-domain integration enhances interoperability. Beyond the automotive domain, the proposed approach can also be applied to controller design in other industries, further demonstrating its potential for cross-disciplinary adoption.

With the improvement of edge-based autonomous systems such as mobile Industrial IoT (IIoT) networks, edge devices can capture and upload videos with increasing bitrates. Massive edge-computing end nodes are eager for adequate multimedia data to satisfy the requirements of real-time video services. However, existing encoding standards for video services in Web 2.0 are specifically designed for something other than IoT video streaming. We have improved our Adaptive Compression-Reconstruction (ACORN) framework to obtain ACORN+, based on compressed sensing and recent advances in deep learning. At end nodes, we compress multiple sequential video frames into a single frame to reduce video volume. Given that multiple kinds of intelligent tasks are expected to be finished on the device side, we also designed a device-cloud collaboration scheme where deep learning-based algorithms can be executed on both the device and server sides. Experiments reveal that video analytics can be conducted on compressed frames. Taking action recognition as a device-cloud collaboration use case, we find ACORN \(+\) obtains more than 3 \(\times\) speedup on compressed frames. The reconstruction algorithm in ACORN \(+\) is with 1– 4 dB improvements. Moreover, the encoding time cost and the encoded video volume are reduced by more than 4 \(\times\) under the ACORN \(+\) framework. 1

IoT devices are enabled to capture and upload videos with increasing bitrates. Massive IIoT is eager for effective video processing techniques to satisfy the requirements of real-time video services. With the emergence of 5G-unlicensed (5G-U), ultra-low latency video applications become possible. However, existing encoding standards for video services in Web 2.0, such as H.265, are not naturally designed for IIoT video streaming, leading to bandwidth pressure where 5G-U coexists with various other wireless signals. To tackle this problem and to support low-latency video utilization by IIoT video sources, we propose an Adaptive Compression-Reconstruction framework named ACORN, which is based on compressed sensing and recent advances in deep learning. At end nodes, we compress multiple sequential video frames into a single frame to reduce video volume. We design a QoE-aware parameter selection mechanism to deal with volatile network environments during compression. With learnable gated convolution layers and channel-wise soft-thresholding operators, ACORN also builds a real-time reconstruction module. Experimental results reveal that video analytics can be conducted on compressed frames. The reconstruction algorithm in ACORN is with $1-4 \mathrm{~dB}$ improvements. Moreover, both the encoding time cost and the encoded video volume are reduced by more than $4 \times$ under the ACORN framework.

With the fast evolution in the cellular communication, the unlicensed spectrum is exploited to resolve the shortage of band resources. The sharing of the unlicensed spectrum extends the applications of LTE and 5G NR techniques, especially in the industrial Internet of Things (IIoT). However, the coexistence problem among various communication technologies in the unlicensed spectrum arises great concerns due to the different communication mechanisms. The existing solutions are either not compatible with the LTE/NR standards or not flexible enough for complex and dynamic IIoT environments. In this paper, we propose a policy learning based unlicensed communication (PLUC) framework to directly learn coexistence policies from the spectrogram of frequency channels. A recurrent neural network (RNN) is built to deal with the observations from time-variant spectrogram and extract deep learning features. We further verify this framework under the duty cycle mechanism and the listen before talk mechanism in 3GPP standards, respectively. The experiments reveal the effectiveness of the proposed framework in the dynamic environment.

As the Internet of Things thrives, monitors and cameras produce tons of image data every day. To efficiently process these images, many compressed imaging frameworks are proposed. A compressed imaging framework comprises two parts, image signal measurement and reconstruction. Although a plethora of measurement devices have been designed, the development of the reconstruction is relatively lagging behind. Nowadays, most of existing reconstruction algorithms in compressed imaging are optimization problem solvers based on specific priors. The computation burdens of these optimization algorithms are enormous and the solutions are usually local optimums. Meanwhile, it is inconvenient to deploy these algorithms on cloud, which hinders the popularization of compressed imaging. In this article, we dive deep into the random projection to build reconstruction algorithms for compressed imaging. We first fully utilize the information in the measurement procedure and propose a combinatorial sparse random projection (SRP) reconstruction algorithm. Then, we generalize the SRP to a novel distributed algorithm called Cloud-SRP (CSRP), which enables efficient reconstruction on cloud. Moreover, we explore the combination of SRP with conventional optimization reconstruction algorithms and propose the Iterative-SRP (ISRP), which converges to a guaranteed fixed point. With minor modifications on the naive optimization algorithms, the ISRP yields better reconstructions. Experiments on real ghost imaging reconstruction reveal that our algorithms are effective. And simulation experiments show their advantages over the classical algorithms.

Snapshot compressive imaging (SCI) is a promising approach to capture high-dimensional data with low dimensional sensors. With modest modifications to off-the-shelf cameras, SCI cameras encode multiple frames into a single measurement frame. These correlated frames can then be retrieved by reconstruction algorithms. Existing reconstruction algorithms suffer from low speed or low fidelity. In this paper, we propose a novel reconstruction algorithm, namely, Shearlet enhanced Snapshot Compressive Imaging (SeSCI), which exploits the sparsity of the image representation in both frequency domain and shearlet domain. Towards this end, we first derive our SeSCI algorithm under the alternating direction method of multipliers (ADMM) framework. We then propose an efficient solution of SeSCI algorithm. Moreover, we prove that the improved SeSCI algorithm converges to a fixed point. Experimental results on both synthetic data and real data captured by SCI cameras demonstrate the significant advantages of SeSCI, which outperforms the conventional algorithms by more than 2dB in PSNR. At the same time, the SeSCI achieves a speed-up more than 100× over the state-of-the-art algorithm.