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Since vision-based methods are vulnerable to challenging environmental conditions such as bad whether, shadows, occlusions and illumination, robustness is important. In this paper, a robust algorithm for detecting and tracking multiple lanes with arbitrary shape is proposed. We extend previous lane detection and tracking processes from the spatial domain to the temporal-spatial domain using our more robust and general multiple lane model. Distinct from traditional image pre-processing procedures, we adopt slice images generated from image sequence decomposition and reconstruction that contains important structured historical information. In this domain, we propose assumptions to reduce the algorithm’s complexity and computation further. First, instead of common binarization, a more general lane marker detector is proposed with weak constraints to obtain cleaner binary image. Then, the lane marker candidates are used as the initialization of following particle filtering tracking process. We propose a confidence map computed from binary slice images by using an improved distance transform for particles sampling and weights calculation. The multi-lane model supports the use of multiple independent particle filters for tracking each lane boundary separately. Finally, a range of experimental results on testing dataset indicate that the proposed algorithm is effective with high performance and comparing with other methods, it has better tolerance to challenging environments. [ABSTRACT FROM AUTHOR]

Spike sorting technologies support neuroscientists to access the neural activity with single-neuron or single-action-potential resolutions. However, conventional spike sorting technologies perform the feature extraction and the clustering separately after the spikes are well detected. It not only induces many redundant processes, but it also yields a lower accuracy and an unstable result especially when noises and/or overlapping spikes exist in the dataset. To address these issues, this paper proposes a unified optimization model integrating the feature extraction and the clustering for spike sorting. Unlike the widely used combination strategies, i.e., performing the principal component analysis (PCA) for spike feature extraction and the K-means (KM) for clustering in sequence, interestingly, this paper finds the solution of the proposed unified model by iteratively performing PCA and KM-like procedures. Subsequently, by embedding the K-means++ strategy in KM-like initializing and a comparison updating rule in the solving process, the proposed model can well handle the noises and overlapping interference as well as enjoy a high accuracy and a low computational complexity. Finally, an automatic spike sorting method is derived after taking the best of the clustering validity indices into the proposed model. The extensive numerical simulation results on both synthetic and real-world datasets confirm that our proposed method outperforms the related state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Obtaining the accurate and real-time state of surrounding objects is essential for automated vehicle planning and decision-making to ensure safe driving. In complex traffic scenarios, object occlusion, clutter interference, and limited sensor detection capabilities lead to false alarms and missed object detection, making it challenging to ensure the stability of tracking and state prediction. To address these challenges, in this study, we developed a robust multi-object detection and tracking method for moving objects based on radar and camera data fusion. First, the radar and camera perform target detection independently, and the detection results are correlated in the image plane to generate a random finite set with an object type. Then, based on the Gaussian mixture probability hypothesis density algorithm framework, the tracking process is improved using elliptic discriminant thresholds, an attenuation function, and simplified pruning methods. The experimental results demonstrate that the improved algorithm can accurately estimate the number and state of targets in object occlusion, measurement loss scenarios, and achieve robust continuous multi-object tracking. The proposed method could guide the design of safer and more efficient intelligent driving systems. [ABSTRACT FROM AUTHOR]

Recent support for unified memory and demand paging has improved graphics processing unit (GPU) programmability and enabled memory oversubscription. However, this support introduces high overhead when page faults occur. Therefore, when the GPU memory fills to capacity, an important issue is how to select eviction candidates. The widely used policy, LRU, and the advanced replacement policies, RRIP and CLOCK-Pro, suffer from inefficiency when dealing with thrashing access patterns. They also incur significant overhead due to managing metadata at page level. In this article, we propose hierarchical page eviction (HPE), a new replacement policy for GPUs with unified memory. Aided by page walk hit information, HPE manages a page set chain dynamically. It uses statistics to classify applications into three categories and selects an appropriate eviction strategy for each category. It also applies dynamic adjustment to switch the eviction strategy when necessary. The simulation results show that, on average, HPE achieves 1.34 × and 1.16 × speedup (up to 2.81 × ) over LRU when the oversubscription rate is 75% and 50%, respectively. HPE also outperforms RRIP and CLOCK-Pro. [ABSTRACT FROM AUTHOR]

High performance implementation of matrix multiplication is essential for scientific computing. The memory access procedure is quite possible to be the bottleneck of matrix multiplication. The widely used GotoBLAS GEMM implementation divides the integral matrix into several partitions to be assigned to different cores for parallelization. Traditionally, each core deploys a DMA transfer to access its own partition in the DRAM memory. However, deploying an independent DMA transfer for each core cannot efficiently exploit the inter-core locality. Also, multiple concurrent DMA transfers interfere with each other, further reducing the DRAM access efficiency. We observe that the same row of neighboring partitions is in the same DRAM page, which means that there is significant locality inherent in the address layout. We propose the coordinated DMA to efficiently exploit the locality. It invokes one transfer to serve all cores and moves data in a row-major manner to improve the DRAM access efficiency. Compared with a baseline design, the coordinated DMA improves the bandwidth by 84.8 percent and reduces DRAM energy consumption by 43.1 percent for micro-benchmarks. It achieves higher performance for the GEMM and Linpack benchmark. With much less hardware costs, the coordinated DMA significantly outperforms an out-of-order memory controller. [ABSTRACT FROM AUTHOR]

In recent years, the signal processing opportunities with the multi-channel recording and the high precision detection provided by the development of new extracellular multi-electrodes are increasing. Hence, designing new spike sorting algorithms are both attractive and challenging. These algorithms are used to distinguish the individual neurons’ activity from the dense and simultaneously recorded neural action potentials with high accuracy. However, since the overlapping phenomenon often inevitably arises in the recorded data, they are not accurate enough in practical situations, especially when the noise level is high. In this paper, a spike feature extraction method based on the wavelet packets’ decomposition and the mutual information is proposed. This is a highly accurate semi-supervised solution with a short training phase for performing the automation of the spike sorting framework. Furthermore, the evaluations are performed on different public datasets. The raw data are not only suffered from multiple noises (from 5% level to 20% level) but also includes various degrees of overlapping spikes at different times. The clustering results demonstrate the effectiveness of our proposed algorithm. In addition, it achieves a good anti-noise performance with ensuring a high clustering accuracy (up to 99.76%) compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]