BCNet: A Novel Network for Blood Cell Classification

Aims: Most blood diseases, such as chronic anemia, leukemia (commonly known as blood cancer), and hematopoietic dysfunction, are caused by environmental pollution, substandard decoration materials, radiation exposure, and long-term use certain drugs. Thus, it is imperative to classify the blood cell images. Most cell classification is based on the manual feature, machine learning classifier or the deep convolution network neural model. However, manual feature extraction is a very tedious process, and the results are usually unsatisfactory. On the other hand, the deep convolution neural network is usually composed of massive layers, and each layer has many parameters. Therefore, each deep convolution neural network needs a lot of time to get the results. Another problem is that medical data sets are relatively small, which may lead to overfitting problems.Methods: To address these problems, we propose seven models for the automatic classification of blood cells: BCARENet, BCR5RENet, BCMV2RENet, BCRRNet, BCRENet, BCRSNet, and BCNet. The BCNet model is the best model among the seven proposed models. The backbone model in our method is selected as the ResNet-18, which is pre-trained on the ImageNet set. To improve the performance of the proposed model, we replace the last four layers of the trained transferred ResNet-18 model with the three randomized neural networks (RNNs), which are RVFL, ELM, and SNN. The final outputs of our BCNet are generated by the ensemble of the predictions from the three randomized neural networks by the majority voting. We use four multi-classification indexes for the evaluation of our model.Results: The accuracy, average precision, average F1-score, and average recall are 96.78, 97.07, 96.78, and 96.77%, respectively.Conclusion: We offer the comparison of our model with state-of-the-art methods. The results of the proposed BCNet model are much better than other state-of-the-art methods.

Low I/O Intensity-aware Partial GC Scheduling to Reduce Long-tail Latency in SSDs

This article proposes a low I/O intensity-aware scheduling scheme on garbage collection (GC) in SSDs for minimizing the I/O long-tail latency to ensure I/O responsiveness. The basic idea is to assemble partial GC operations by referring to several determinable factors (e.g., I/O characteristics) and dispatch them to be processed together in idle time slots of I/O processing. To this end, it first makes use of Fourier transform to explore the time slots having relative sparse I/O requests for conducting time-consuming GC operations, as the number of affected I/O requests can be limited. After that, it constructs a mathematical model to further figure out the types and quantities of partial GC operations, which are supposed to be dealt with in the explored idle time slots, by taking the factors of I/O intensity, read/write ratio, and the SSD use state into consideration. Through a series of simulation experiments based on several realistic disk traces, we illustrate that the proposed GC scheduling mechanism can noticeably reduce the long-tail latency by between 5.5% and 232.3% at the 99.99th percentile, in contrast to state-of-the-art methods.

A Survey on TLS-Encrypted Malware Network Traffic Analysis Applicable to Security Operations Centers

Recently, a majority of security operations centers (SOCs) have been facing a critical issue of increased adoption of transport layer security (TLS) encryption on the Internet, in network traffic analysis (NTA). To this end, in this survey article, we present existing research on NTA and related areas, primarily focusing on TLS-encrypted traffic to detect and classify malicious traffic with deployment scenarios for SOCs. Security experts in SOCs and researchers in academia can obtain useful information from our survey, as the main focus of our survey is NTA methods applicable to malware detection and family classification. Especially, we have discussed pros and cons of three main deployment models for encrypted NTA: TLS interception, inspection using cryptographic functions, and passive inspection without decryption. In addition, we have discussed the state-of-the-art methods in TLS-encrypted NTA for each component of a machine learning pipeline, typically used in the state-of-the-art methods.

Industrial-Scale Production of High-Quality Graphene Sheets by Millstone Grinders

Graphene exhibits a variety of unprecedented innate properties and has sparked great interest in both fundamental science and regarding prospective commercial applications. To meet the ever-increasing demand for high-quality graphene sheets, an industrial-scale, reliable, environmental-friendly, low-cost production process is required. However, large-scale production high quality graphene remains elusive. Here we demonstrate a scalable mechanical cleavage method for large-quantity production of high quality large-area and few-layer graphene sheets by introducing a millstone grinding process. The average thickness of the graphene sheets is around 5 nm. This procedure is simpler than the state-of-the-art methods that allows for scalable preparation of graphene dispersion in hundreds of litres by mechanical cleavage of graphite, and the yield is 30-40%. The size of the prepared graphene sheets can be tuneable from few micrometres to tens of micrometres by varying the dimension of raw graphite, which is larger than that produced by the state-of-the-art methods. Moreover, comparing to conductive agents, the conductivity of wafers containing graphene can be increased by one order of magnitude, suggesting a high potential of the prepared graphene sheets for the application as conductive agent in lithium battery cathodes. This allows the requirements of different sizes graphene sheets for industry applications in different fields.

Enhancing the Accuracy of Health Care Internet of Medical Things in Real Time using CNNets

This paper presents an efficient system using a deep learning algorithm that recognizes daily activities and investigates the worst falling cases to save elders during daily life. This system is a physical activity recognition system based on the Internet of Medical Things (IoMT) and uses convolutional neural networks (CNNets) that learn features and classifiers automatically. The test data include the elderly who live alone. The performance of CNNets is compared against that of state-of-the-art methods, such as activity windowing, fixed sample windowing, time-weighted windowing, mutual information windowing, dynamic windowing, fixed time windowing, sequence prediction algorithm, and conditional random fields. The results indicate that CNNets are competitive with state-of-the-art methods, exhibiting enhanced IoMT accuracy of 98.37%, which is the highest among the proposed solutions using the same dataset.

Dual-Stream Guided-Learning via a Priori Optimization for Person Re-identification

The task of person re-identification (re-ID) is to find the same pedestrian across non-overlapping camera views. Generally, the performance of person re-ID can be affected by background clutter. However, existing segmentation algorithms cannot obtain perfect foreground masks to cover the background information clearly. In addition, if the background is completely removed, some discriminative ID-related cues (i.e., backpack or companion) may be lost. In this article, we design a dual-stream network consisting of a Provider Stream (P-Stream) and a Receiver Stream (R-Stream). The R-Stream performs an a priori optimization operation on foreground information. The P-Stream acts as a pusher to guide the R-Stream to concentrate on foreground information and some useful ID-related cues in the background. The proposed dual-stream network can make full use of the a priori optimization and guided-learning strategy to learn encouraging foreground information and some useful ID-related information in the background. Our method achieves Rank-1 accuracy of 95.4% on Market-1501, 89.0% on DukeMTMC-reID, 78.9% on CUHK03 (labeled), and 75.4% on CUHK03 (detected), outperforming state-of-the-art methods.

A Rapid Model (COV_PSDI) for Winter Wheat Mapping in Fallow Rotation Area Using MODIS NDVI Time-Series Satellite Observations: The Case of the Heilonggang Region

Rapid and accurate monitoring of spatial distribution patterns of winter wheat over a long period is of great significance for crop yield prediction and farmland water consumption estimation. However, weather conditions and relatively long revisit cycles often result in an insufficient number of continuous medium-high resolution images over large areas for many years. In addition, the cropland pattern changes frequently in the fallow rotation area. A novel rapid mapping model for winter wheat based on the normalized difference vegetation index (NDVI) time-series coefficient of variation (NDVI_COVfp) and peak-slope difference index (PSDI) is proposed in this study. NDVI_COVfp uses the time-series index volatility to distinguish cultivated land from background land-cover types. PSDI combines the key growth stages of winter wheat phenology and special bimodal characteristics, substantially reducing the impact of abandoned land and other crops. Taking the Heilonggang as an example, this study carried out a rapid mapping of winter wheat for four consecutive years (2014–2017), and compared the proposed COV_PSDI with two state-of-the-art methods and traditional methods (the Spectral Angle Mapping (SAM) and the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA)). The verification results revealed that the COV_PSDI model improved the overall accuracy (94.10%) by 4% compared with the two state-of-art methods (90.80%, 89.00%) and two traditional methods (90.70%, 87.70%). User accuracy was the highest, which was 93.74%. Compared with the other four methods, the percentage error (PE) of COV_PSDI for four years was the lowest in the same year, with the minimum variation range of PE being 1.6–3.6%. The other methods resulted in serious overestimation. This demonstrated the effectiveness and stability of the method proposed in the rapid and accurate extraction of winter wheat in a large area of fallow crop rotation region. Our study provides insight for remote sensing monitoring of spatiotemporal patterns of winter wheat and evaluation of “fallow rotation” policy implementation.

Next-Generation Electronics and Sensing Technology

This Special Issue is dedicated to several aspects of next-generation electronics and sensing technology and contains eight papers that focus on advanced sensing devices, sensing systems, and sensing circuits that focus on the state-of-the-art methods for sensing technologies [...]