Interactions of stimulus quality and semantic context on N400 in visual word recognition

The joint effects of stimulus quality and semantic context in visual word recognition were examined with event-related potential (ERP) recordings. In one-character Chinese word recognition, we manipulated stimulus quality at two degradation levels (highly vs. slightly degraded) and semantic context at two priming levels (semantically related vs. unrelated). In a prime–target–probe trial flow, ERPs were recorded to the target character which was presented in either high or slight degradation and which was preceded by either a semantically related or unrelated prime character. The target character was then followed by a probe character which was either identical to or different from the target character. Subjects were instructed to make target–probe matching judgments. The ERP results demonstrated a degradation by priming interaction, with larger N400 semantic priming effects for slightly degraded targets. Moreover, the degradation effects were observed on the P200, N250, and N400. These findings provided evidence for the cascaded model of visual word recognition such that the visual processing cascaded into the semantic stage and thus interacted on the N400 amplitude. The results were compared to an earlier study with a null ERP degradation by priming interaction. The ramifications of these results for models of visual word recognition are discussed.

A Cascaded Model Based on EfficientDet and YOLACT++ for Instance Segmentation of Cow Collar ID Tag in an Image

In recent years, many imaging systems have been developed to monitor the physiological and behavioral status of dairy cows. However, most of these systems do not have the ability to identify individual cows because the systems need to cooperate with radio frequency identification (RFID) to collect information about individual animals. The distance at which RFID can identify a target is limited, and matching the identified targets in a scenario of multitarget images is difficult. To solve the above problems, we constructed a cascaded method based on cascaded deep learning models, to detect and segment a cow collar ID tag in an image. First, EfficientDet-D4 was used to detect the ID tag area of the image, and then, YOLACT++ was used to segment the area of the tag to realize the accurate segmentation of the ID tag when the collar area accounts for a small proportion of the image. In total, 938 and 406 images of cows with collar ID tags, which were collected at Coldstream Research Dairy Farm, University of Kentucky, USA, in August 2016, were used to train and test the two models, respectively. The results showed that the average precision of the EfficientDet-D4 model reached 96.5% when the intersection over union (IoU) was set to 0.5, and the average precision of the YOLACT++ model reached 100% when the IoU was set to 0.75. The overall accuracy of the cascaded model was 96.5%, and the processing time of a single frame image was 1.92 s. The performance of the cascaded model proposed in this paper is better than that of the common instance segmentation models, and it is robust to changes in brightness, deformation, and interference around the tag.

A review of OBN processing: challenges and solutions

In the last decade, a significant shift in the marine seismic acquisition business has been made where ocean bottom nodes gained a substantial market share from streamer cable configurations. Ocean bottom node acquisition (OBN) can acquire wide azimuth seismic data over geographical areas with challenging deep and shallow bathymetries and complex subsurface regimes. When the water bottom is rugose and has significant elevation differences, OBN data processing faces a number of challenges, such as denoising of the vertical geophone, accurate wavefield separation, redatuming the sparse receiver nodes from ocean bottom to sea level and multiple attenuation. In this work, we review a number of challenges using real OBN data illustrations. We demonstrate corresponding solutions using processing workflows comprising denoising the vertical geophones by using all four recorded nodal components, cross-ghosting the data or using direct wave to design calibration filters for up- and down-going wavefield separation, performing one-dimensional reversible redatuming for stacking QC and multiple prediction, and designing cascaded model and data-driven multiple elimination applications. The optimum combination of the mentioned technologies produced cleaner and high-resolution migration images mitigating the risk of false interpretations.

Performance Analysis of Cascaded Hybrid Symmetric Encryption Models

Over a few years, there is rapid increase of exchange of data over the net has brought data confidentiality and its privacy to the fore front. Data confidentiality can be achieved by implementing cryptography algorithms during transmission of data which confirms that data remains secure and protected over an insecure network channel. In order to ensure data confidentiality and privacy, cryptography service encryption is used which makes data in unreadable form while the reverse process rearranges data in readable form and known as decryption. All encryption algorithms are intended to provide confidentiality to data, but their performance varies depending on many variables such as key size, type, number of rounds, complexity and data size used. In addition, although some encryption algorithms outperform others, they have been found to be prone to particular attacks. This paper reviews and summarizes the various common hybrid cascaded n-tier encryption models. Additionally, this paper compares and analyzes the performance of common hybrid cascaded 2-tier and 3-tier encryption models obtained during simulation based on encryption/decryption time, avalanche effect and throughput. The models compared with AES are 2-tier models (AES-TWOFISH, AES-BLOWFISH, TWOFISH-AES, BLOWFISH-AES, AES-SERPENT and SERPENT-TWOFISH) and 3-tier models (DES-BLOWFISH-AES, AES-TWOFISH-SERPENT and SERPENT-TWOFISH-AES). The hybrid cascaded model like AES-TWOFISH, AES-BLOWFISH and SERPENT-TWOFISH-AES are better hybrid models with respect to throughput and avalanche effect.

Design and Performance Analysis of a Cascaded Model Predictive Controller and Command Governor for Fuel-Efficient Control of Heavy-Duty Trucks

This paper presents the design and analysis of a predictive ecological control strategy for a heavy-duty truck that achieves substantial fuel savings while maintaining safe following distances in the presence of traffic. The hallmark of the proposed algorithm is the fusion of a long-horizon economic model predictive controller (MPC) for ecological driving with a command governor (CG) for safe vehicle following. The performance of the proposed control strategy was evaluated in simulation using a proprietary medium-fidelity Simulink model of a heavy-duty truck. Results show that the strategy yields substantial fuel economy improvements over a baseline, the extent of which are heavily dependent on the horizon length of the CG. The best fuel and vehicle-following performance are achieved when the CG horizon has a length of 20–40 s, reducing fuel consumption by 4–6% when compared to a Gipps car-following model.

Pig Face Recognition Model Based on a Cascaded Network

HighlightsA pig face recognition model that cascades the pig face detection network and pig face recognition network is proposed.The pig face detection network can automatically extract pig face images to reduce the influence of the background.The proposed cascaded model reaches accuracies of 99.38%, 98.96% and 97.66% on the three datasets.An application is developed to automatically recognize individual pigs.Abstract. The identification and tracking of livestock using artificial intelligence technology have been a research hotspot in recent years. Automatic individual recognition is the key to realizing intelligent feeding. Although RFID can achieve identification tasks, it is expensive and easily fails. In this article, a pig face recognition model that cascades a pig face detection network and a pig face recognition network is proposed. First, the pig face detection network is utilized to crop the pig face images from videos and eliminate the complex background of the pig shed. Second, batch normalization, dropout, skip connection, and residual modules are exploited to design a pig face recognition network for individual identification. Finally, the cascaded network model based on the pig face detection and recognition network is deployed on a GPU server, and an application is developed to automatically recognize individual pigs. Additionally, class activation maps generated by grad-CAM are used to analyze the performance of features of pig faces learned by the model. Under free and unconstrained conditions, 46 pigs are selected to make a positive pig face dataset, original multiangle pig face dataset and enhanced multiangle pig face dataset to verify the pig face recognition cascaded model. The proposed cascaded model reaches accuracies of 99.38%, 98.96%, and 97.66% on the three datasets, which are higher than those of other pig face recognition models. The results of this study improved the recognition performance of pig faces under multiangle and multi-environment conditions. Keywords: CNN, Deep learning, Pig face detection, Pig face recognition.