Fully convolutional network based ship plate recognition

Abstract Ship plate recognition is challenging due to variations of plate locations and text types. This paper proposes an effcient Fully Convolutional Network based Plate Recognition approach FCNPR, which uses a CNN (Convolutional Neural Network) to locate ships, then detects plate text lines with the fully convolutional network (FCN). The recognition accuracy is improved with integrating the AIS (Automatic Identification System) information. The actual FCNPR deployment demonstrates that it can work reliably with a high accuracy for satisfying practical usages

DCNN based real-time adaptive ship license plate recognition (DRASLPR)

Abstract Ship license plate recognition is challenging due to the diversity of plate locations and text types. This paper proposes a DCNN-based (deep convolutional neural network) online adaptive real-time ship license plate recognition approach, namely, DRASLPR, which consists of three steps. First, it uses a Single Shot MultiBox Detector (SSD) to detect a ship. Then, it detects the ship license plate with a designed detector. Third, DRASLPR recognizes the ship license plate. The proposed DRASLPR has been deployed at Dongying Port, China and the running results show the effectiveness of DRASLPR

The Wt1(+/R394W) Mouse Displays Glomerulosclerosis and Early-Onset Renal Failure Characteristic of Human Denys-Drash Syndrome

Renal failure is a frequent and costly complication of many chronic diseases, including diabetes and hypertension. One common feature of renal failure is glomerulosclerosis, the pathobiology of which is unclear. To help elucidate this, we generated a mouse strain carrying the missense mutation Wt1 R394W, which predisposes humans to glomerulosclerosis and early-onset renal failure (Denys-Drash syndrome [DDS]). Kidney development was normal in Wt1(+/R394W) heterozygotes. However, by 4 months of age 100% of male heterozygotes displayed proteinuria and glomerulosclerosis characteristic of DDS patients. This phenotype was observed in an MF1 background but not in a mixed B6/129 background, suggestive of the action of a strain-specific modifying gene(s). WT1 encodes a nuclear transcription factor, and the R394W mutation is known to impair this function. Therefore, to investigate the mechanism of Wt1 R394W-induced renal failure, the expression of genes whose deletion leads to glomerulosclerosis (NPHS1, NPHS2, and CD2AP) was quantitated. In mutant kidneys, NPHS1 and NPHS2 were only moderately downregulated (25 to 30%) at birth but not at 2 or 4 months. Expression of CD2AP was not changed at birth but was significantly upregulated at 2 and 4 months. Podocalyxin was downregulated by 20% in newborn kidneys but not in kidneys at later ages. Two other genes implicated in glomerulosclerosis, TGFB1 and IGF1, were upregulated at 2 months and at 2 and 4 months, respectively. It is not clear whether the significant alterations in gene expression are a cause or a consequence of the disease process. However, the data do suggest that Wt1 R394W-induced glomerulosclerosis may be independent of downregulation of the genes for NPHS1, NPHS2, CD2AP, and podocalyxin and may involve other genes yet to be implicated in renal failure. The Wt1(R394W) mouse recapitulates the pathology and disease progression observed in patients carrying the same mutation, and the mutation is completely penetrant in male animals. Thus, it will be a powerful and biologically relevant model for investigating the pathobiology of the earliest events in glomerulosclerosis

LSTM-based service migration for pervasive cloud computing

Abstract Service migration in pervasive cloud computing is important for leveraging cloud resources to execute mobile applications effectively and efficiently. This paper proposes a LSTM (long and short-term memory model) based service migration approach for pervasive cloud computing, i.e., LSTM4PCC, which supports an accurate prediction of cloud resources. LSTM4PCC makes a prediction for cloud resource availability with a LSTM network and establishes a service migration mechanism in order to optimize service executions. We evaluate LSTM4PCC and compare it with the ARIMA (AutoRegressive Integrated Moving Average) approach in terms of prediction accuracy. The results show that LSTM4PCC performs better than ARIMA