With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

A kind of low-molecular weight organic gelator (LMOG) bearing hydrazine linkage and end-capped by alkoxy-substituted phenyl, namely 1, 4-bis[(3, 4-bisoctyloxyphenyl)hydrozide]phenylene (BPH-8), was used to facilely fabricate superhydrophobic surfaces by drop-casting...

Recently, pre-trained Transformer based language models, such as BERT, have shown great superiority over the traditional methods in many Natural Language Processing (NLP) tasks. However, the computational cost for deploying these models is prohibitive on resource-restricted devices. One method to alleviate this computation overhead is to quantize the original model into fewer bits representation, and previous work has proved that we can at most quantize both weights and activations of BERT into 8-bits, without degrading its performance. In this work, we propose MKQ-BERT, which further improves the compression level and uses 4-bits for quantization. In MKQ-BERT, we propose a novel way for computing the gradient of the quantization scale, combined with an advanced distillation strategy. On the one hand, we prove that MKQ-BERT outperforms the existing BERT quantization methods for achieving a higher accuracy under the same compression level. On the other hand, we are the first work that successfully deploys the 4-bits BERT and achieves an end-toend speedup for inference. Our results suggest that we could achieve 5.3x of bits reduction without degrading the model accuracy, and the inference speed of one int4 layer is 15x faster than a float32 layer in Transformer based model.

The coded aperture snapshot spectral imager (CASSI) is a computational imaging system that acquires a three dimensional (3D) spectral data cube by a single or a few two dimensional (2D) measurements. The 3D data cube is reconstructed computationally. Binary on-off random coded apertures with square pixels are primarily implemented in CASSI systems to modulate the spectral images in the image plane. The design and optimization of coded apertures have been shown to improve the imaging performance of these systems significantly. This work proposes a different approach to code design. Instead of using traditional squared tiled coded elements, hexagonal tiled elements are used. The dislocation between the binary hexagonal coded apertures and the squared detector pixels is shown to introduce an equivalent grey-scale spatial modulation that increases the degrees of freedom in the sensing matrix, thus further improving the spectral imaging performance. Based on the restricted isometry property (RIP) of compressive sensing theory, this paper proves that optimal coded aperture patterns under a hexagonal lattice obey blue noise spatial characteristic, where "on" elements are placed as far from each other as possible. In addition, optimal coded apertures used in different snapshots are complementary to each other. This paper also proves the superiority of the hexagonal blue noise coded aperture over the traditional coded apertures with squared tiled elements. Based on a set of simulations, the proposed hexagonal tiled coded apertures are shown to effectively improve the imaging performance of CASSI systems compared to that of traditional coded apertures on rectangular lattices.