Ternary Content Addressable Memory (TCAM) is a high-speed, parallel-search memory that enables fast lookups for both exact-match and wildcard-match rules. TCAM serves as the standard hardware implementation of flow tables in Software-Defined Networking (SDN) switches, delivering line-rate packet classification to enforce fine-grained forwarding policies. Due to its high-cost and power-hungry design, TCAM faces a scalability challenge in accommodating large-scale rule sets in modern backbone networks. Inspired by hierarchical cache architectures in modern memory systems, TCAM-based rule-caching systems incorporate Random Access Memory (RAM) as a cost-effective, large-capacity auxiliary memory. Specifically, TCAM caches heavy-hitting rules to capture most packets from hot flows, while RAM maintains the complete rule set to handle cache-miss packets. Meanwhile, an update strategy is employed to manage the replacement of cached rules between TCAM and RAM. However, prior rule-caching systems have either failed to eliminate cross-rule dependencies or have restricted their match patterns to prefix rules only. To address these limitations, we propose AWEsome-Cache, a unified framework designed to fundamentally eliminate cross-rule dependencies for wildcard rules with arbitrary match patterns. The key idea is to construct isolation rules for hot flows by specifying the minimum number of wildcard bits in their best-match rules, thereby eliminating overlaps with all direct dependent rules. AWEsome-Cache further develops efficient replacement algorithms for TCAM updates, enabling adaptation to dynamic traffic locality. Experimental evaluations on both prefix and non-prefix rule sets show that AWEsome-Cache achieves cache-hit rates comparable to state-of-the-art baselines while reducing TCAM occupancy by 75.9%.

Ternary Content Addressable Memory (TCAM) is a specialized high-speed memory that enables fast parallel lookups for both exact-match rules and wildcard-match rules. TCAM has become a standard hardware component in Software-Defined Networking (SDN) switches to implement flow tables for packet classification. However, limited TCAM storage capacity poses a significant scalability challenge for SDN to enforce fine-grained policy-based forwarding. To this end, TCAM-based rule-caching systems are proposed by combining TCAM with Random Access Memory (RAM). Specifically, TCAM caches heavy-hitting rules to capture packets from hot flows, while RAM accommodates the complete ruleset for other cache-miss packets. However, previous rule-caching systems either failed to eliminate cross-rule dependencies or restricted their applications to prefix rules only. In this work, we propose AWEsome-Cache, a unifying framework to fundamentally eliminate cross-rule dependencies for wildcard rules with arbitrary matching patterns. The rationale behind AWEsome-Cache is to concretize a minimum number of wildcard bits in the best-match rule, thereby pruning its overlapping match fields with all direct dependent rules. AWEsome-Cache also develops replacement algorithms during TCAM updates to adapt to dynamic traffic locality. Experiments with prefix and non-prefix rules show that AWEsome-Cache outperforms baselines in achieving a comparable cache-hit rate but requiring 75.9% less TCAM occupancy.

Heavy truck drivers driving trucks under the situation of drowsiness can cause serious traffic accidents. In this paper, a heavy truck driver's drowsiness detection method using wearable electroencephalographic (EEG) based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, heavy truck driver's drowsiness detection and the early warning strategy. Firstly, a homemade wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsiness driving. Secondly, the neural networks with Inception module and improved AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the heavy truck driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsiness driving. The results show that using neural network with Inception module reached 95.59% correct classification in a one second time window and using improved AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for heavy truck driving safety.

Due to the lack of observation of rear moving objects by the passenger or driver of a vehicle, accidents happen frequently when they open the door of the vehicle. In this paper, we propose a novel vehicle open door safety system based on cyclist detection using fisheye camera and Improved Deep Convolutional Generative Adversarial Nets (IDCGANs). First of all, a fisheye camera is automatically turned on and captures the rear information of the vehicle when the vehicle is stopping. After that, a simple and effective method based on longitude coordinate is used to correct the distorted images. Second, Improved Deep Convolutional Generative Adversarial Nets is used to generate sample for data training. As the limited training datasets of cyclist and lots of annotation information which takes much time and efforts, we use Improved Deep Convolutional Generative Adversarial Nets to generate synthesized images of cyclist and use them as the training data for cyclist detectors. At last, Faster R-CNN detector is employed to train and detect the cyclist. The system was tested on realistic experiments and reached 87.2% precision rate and 95.3% recall rate. The feasibility of the proposed system for vehicle open door safety is demonstrated through simulation and test results.

In this paper, we present a steering system for a space-saving four-wheel steering and four-wheel drive (4WS4WD) electric vehicle (EV) with higher maneuverability and flexibility. The proposed system consists of three main parts, namely, an improved two-front-wheel steering (2FWS) mechanism, an omnidirectional independent steering (OIS) mechanism integrated with steer-by-wire, and a control strategy for the space-saving steering system of an EV. First, the 2FWS mechanism of the proposed 4WS4WD EV is designed to control the front wheels via the redesigned steering system when the vehicle is traveling at high speeds. Second, a retrofitted OIS mechanism is proposed to achieve an angle range of -35° ~ +90°, which is a solid basis for zero radius turning (ZRT) and lateral parking (LP) motion. The driver can control the OIS to turn the four wheels independently, which is assisted by steer-by-wire technologies. Finally, the control strategy for the space-saving steering system of the EV is redefined for the integrated 2FWS and OIS, which can easily handle the EV for high-speed driving or high-maneuverability turning, such as ZRT and LP motion. This system was field tested on a homemade 4WS4WD EV, and the final system simulation and performance evaluation demonstrated the validity of the proposed steering system for the space-saving 4WS4WD EV.

In this paper we present a vehicle safety method for reversing speed control based on obstacle detection and sparse representation. The proposed system consists of three main steps, namely, binocular camera obstacle detection and segmentation, obstacle tracking and recognition, and vehicle reversing speed control algorithm. First of all, a binocular camera system is used to detect obstacles as a vehicle is reversing. Using disparity computation and triangulation, we can get all objects' distance information in the rear of a vehicle. Second, the framework of particle filter and sparse representation are used to track and identify the main obstacles such as human or animal bodies, vehicles, or any other objects. Finally, the vehicle reversing speed control algorithm, which controls the electronic throttle opening and automatic braking prior to collisions, makes the reversing control safer and more reliable. This system has been field tested on a Dodge sport utility vehicle by which the final performance evaluation demonstrates the validity of the proposed vehicle reversing speed control.

Sparse representation based classification (SRC) could not well classify the sample belonging to different classes distribute on the same direction. To solve the problem, a Volterra kernel sparse representation based classification (Volterra-SRC) algorithm is proposed in this paper. Firstly, the original face images are divided into non overlapped patches and then mapped into a high dimensional space by utilizing the Volterra kernels. During the training stage, following by the Fisher criteria, the objective function is defined to obtain the optimal Volterra kernels via maximizing inter-class distances and minimizing intra-class distances simultaneously. During the testing stage, a voting procedure is introduced in conjunction with a sparse representation based classification to decide to which class each individual patch belongs. Finally, the aggregate classification results of all patches in a face are used to determine the overall recognition outcome for the given face image. We demonstrate the experiments on ORL and Extended Yale B benchmark face databases and show that our proposed Volterra-SRC algorithm consistently outperforms the original SRC and the proposed has some advantages and robustness in case of small train number samples.