Search

Thousands of items are stored in libraries, archives and supermarkets, it usually takes a long time to find what we want in thousands of items. How to achieve precise localization is particularly important. We present a fine-grained Radio Frequency Identification (RFID) positioning method to help us find the items we want. In order to achieve high-precision positioning of RFID tags, we propose Hyperbolic and Hologram Composite (HHC) localization algorithm, which determines the positioning candidate set by hyperbolic intersection, thereby reducing the positioning delay, and then use the holographic positioning to filter the candidate set. We use multi-frequency holography positioning technology to reduce the influence of the reader’s own inaccurate positioning, and use multipath suppression technology to reduce the interference of multipath in the environment, finally achieves higher precision positioning. The experimental results show that the average calculation time of one positioning of our HHC positioning system is 10.3 ms, and 90% of the positioning error margin is below 4.8 cm, which realizes high-precision and low-delay positioning.

Heart failure (HF) is a disease that is harmful to human health. Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. To improve the diagnostic accuracy of HF staging, this study evaluates the performance of deep learning-based models on combined features for its categorization. We proposed a novel deep convolutional neural network-Recurrent neural network (CNN-RNN) model for automatic staging of heart failure diseases in real-time and dynamically. We employed the data segmentation and data augmentation pre-processing dataset to make the classification performance of the proposed architecture better. Specifically, this paper use convolutional neural network (CNN) as a feature extractor instead of training the entire network to extract the characteristics of the electrocardiogram (ECG) signals and form a feature set. We combine the above feature set with other clinical features, feed the combined features to RNN for classification, and finally obtain 5 classification results. Experiments shows that the CNN-RNN model proposed in this paper achieved an accuracy of 97.6%, the sensitivity of 96.3%, specificity of 97.4% and proportion of 97.1% for two seconds of ECG segments. We obtained an accuracy, sensitivity, specificity and proportion of 96.2%, 96.9%, 95.7%, and 94.3% respectively for five seconds of ECG duration. The model can be used as an aid to help clinicians confirm their diagnosis.

Background and objective Segmentation of rectal cancerous regions using 2D Magnetic Resonance Imaging (MRI) images is a critical step in radiation therapy. The shape of rectal cancer has significant variations and the shape of some surrounding organs is similar to that of rectal cancer; these conditions significantly affect the segmentation accuracy of rectal cancer and lead to incorrect segmentation. Therefore, automatic segmentation of rectal cancer is urgently needed, and it is a great challenge. For this task, the existing deep learning-based approaches have two shortcomings: 1) The U-Net network plays an important role in the field of medical segmentation. However, the designs of encoders and decoders in traditional U-Net networks are relatively simple and cannot extract good features, resulting in incorrect segmentation results. 2) Conventional neural networks extract high-level features that often do not include sufficient high-resolution contour information, resulting in ambiguity in contour segmentation. In this paper, we propose an improved U-Net network based on contour prediction, aiming at effective segmentation of rectal cancer. Methods We designed a new U-Net network by improving the traditional U-Net network. We made four improvements: 1) We replaced the encoders with the SENet network. 2) A global pooling layer was added after the last encoder. 3) We added the Spatial and Channel Squeeze & Excitation (SCSE) attention mechanism module to each decoder. 4) We concatenated the output results of each decoder. In addition, the model implemented content segmentation and contour segmentation for rectal cancer in parallel, so that both the content and contour information was learned by the network to enhance the segmentation accuracy. Results Our data were obtained from the Shanxi Provincial Cancer Hospital and included 3773 2D MRI rectal cancer images. The proposed method achieved an Mean Intersection over Union of 0.894 (MIoU) on the test set. Compared with state-of-the-art methods, our method had the best performance on the test set, and its MIoU metric was 0.123 higher than that of the second-best model. At the same time, the effectiveness of the improvements to our method was demonstrated through ablation experiments. Conclusions Our method can help radiologists to segment effectively, save their time and energy, and enable them to focus on cases that are not easily segmented because of the complex shape of rectal cancer.

Nowadays, in clinical medicine diagnosticians usually use DNA microarray datasets for diagnosis and classification of cancer. However, DNA microarray datasets typically have very large number of genes and less number of samples, therefore, before diagnosis and classification of cancer it is quite requisite to select most relevant genes. In this paper, we have developed a two phase classification model in which most relevant genes are selected by integrating ReliefF with Recursive Binary Gravitational Search Algorithm (RBGSA) in the help of a classifier of Multinomial Naive Bayes. The RBGSA recursively transforms a very raw gene space to an optimized one at each iteration while not degrading the accuracy. We evaluate our model by comparing it with 6 other known methods on 6 different microarray datasets of cancer. Comparison results show that our model gets substantial improvements in accuracy over other methods.

Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. Most existing methods share common drawbacks, that is, spatial and frequency diversity cannot be considered at the same time or channel probe is expensive. In this paper, we propose a channel prediction scheme for backscatter networks. The scheme consists of two parts: the monitoring module, which uses the data of the acceleration sensor to monitor the movement of the node itself, and uses the link burstiness metric &beta, to monitor the burstiness caused by the environmental change, thereby determining that new data of channel quality are needed. The prediction module predicts the channel quality at the next moment using a prediction algorithm based on BP (back propagation) neural network. We implemented the scheme on readers. The experimental results show that the accuracy of channel prediction is high and the network goodput is improved.

The Li 0.35 Zn 0.3 Fe 2.35 O 4 micro-belts were prepared by cotton template for the first time. The nickel-coated carbon fibers were obtained by the electroless plating method. The formation mechanism of the ferrite micro-belt was studied. The microwave absorption properties of the two layers absorbers containing Li 0.35 Zn 0.3 Fe 2.35 O 4 micro-belts and nickel-coated carbon fibers composites were investigated in the frequency range of 30–6000 MHz. The absorbers of the Li 0.35 Zn 0.3 Fe 2.35 O 4 micro-belts/nickel-coated carbon fibers composites have much better microwave absorption properties than the nickel-coated carbon fibers absorbers, and the microwave absorption properties of the composites are influenced by the content of the absorber.

SiC powders were synthesized from the Si/C system in a nitrogen atmosphere by combustion synthesis. The carbon fiber/SiC composite with different mixture ratios have been prepared by as-prepared material. The structure, morphology and properties of the composites are characterized with IR, XRD, scanning electron microscope (SEM), Network Analyzer. The complex permittivity ( e ′ r − j e ″ r ) and reflection loss (dB) of the composites have been measured at different microwave frequencies in S-band and C-band (30–6000 MHz) employing vector network analyzer model PNA 3629D vector. The effect of the mol ratio of carbon fiber/SiC on the microwave loss properties of the composites is investigated. A possible microwave absorbing mechanism of carbon fiber/SiC composite has been proposed. The carbon fiber/SiC composite can find applications in suppression of electromagnetic interference (EMI), and reduction of radar signature.

The objective of this study is to develop microwave absorbers using both dielectric and magnetic lossy materials. Polypyrrole (PPY) is used as dielectric lossy materials and carbonyl iron particles is used as magnetic lossy materials. Polypyrrole powders are prepared by in situ polymerization method. Then PPY–carbonyl iron composite with different mixture ratios have been prepared by as-prepared material. The structure, morphology and properties of the composites are characterized with IR, XRD, scanning electron microscope (SEM), Net-work Anlyzer. The complex permittivity ( e ′ r − j e ′ ′ r ) and reflection loss (dB) of the composites have been measured at different microwave frequencies in S-band and C-band (30–6000 MHz) employing vector network analyzer model HP 8722ET vector. The effect of the mass ratio of PPY–carbonyl iron on the microwave loss properties of the composites is investigated. A possible microwave absorbing mechanism of PPY–carbonyl iron composite has been proposed. The PPY–carbonyl iron composite can find applications in suppression of electromagnetic interference (EMI), and reduction of radar signature.

Packet corruption caused by collision is a critical problem that damages the performance of wireless networks. Traditional medium access control (MAC) protocols resort to collision avoidance to improve the efficiency of channel utilization. According to our investigation, however, collision avoidance causes time delay, which also hurts channel utilization, especially in the dense networks or intensive traffic. Discovering the ability to tolerate collisions with the correlatable preamble at the physical layer implementations of wireless networks, we propose CWM in this paper, a MAC protocol that advocates simultaneous accesses from multiple senders to a shared channel, allowing collisions instead of avoiding them. With the correlatable sequences, CWM addresses the challenges in achieving collision tolerance, such as precise sender alignment and control of the transmission concurrency. We implement the experiments on USRP verifying the feasibility of our protocol and implement CWM in 802.15.4 networks and evaluate its performance with 100 nodes on MATLAB. The results demonstrate that our protocol significantly improves the spatial reuse of the wireless channel by at least 23% compared with the state-of-the-art protocols.

Recently, wireless sensor networks with mobile sinks (mWSN) have attracted massive attention attributed to the benefits of mobility. The updating of routing, however, will generate a lot of control packets due to the movements of mobile sinks in mWSN, which will consume a lot of energy and reduce the life of network. This paper proposed a data gathering algorithm for large-scale mWSN. In the proposed algorithm, the entire network was divided into several gathering scopes. Each gathering scope locally updates the routing structure according to the path extension ratio which can effectively reduce the control packets generated by the updating of routing. To decrease the frequency of updating and the energy consumption of network, anchor nodes chains are adopted. Extensive simulation results show that the proposed algorithm can significantly reduce the control packets and prolong the lifetime of network when compared with other algorithms.

RFID is a practical technology that supports extensive applications such as warehouse management, asset tracking, and transportation and logistics. Identifying the missing tags in categorized RFID systems is of practical importance for a variety of applications but is not yet thoroughly investigated. Traditional missing tag identification approaches can not solve the problem efficiently because they do not distinguish categories, leading to long scanning time and high energy consumption. To achieve time and energy efficiency, this paper proposes three protocols. First, we formally formulate the problem of missing tag identification in categorized RFID systems. And then a compact structure is proposed to active tags in protocols which carries a large volume aggregated tag ID information. To further improve the utilization rate of a frame, we design a rehash scheme to change empty slots and collision slots into singleton slots. We evaluate the performance of our protocols with extensive simulations. Compared with the physical-layer missing tag identification protocol (P-MTI), which is the state-of-the-art missing tag identification technology, our best protocol reduces the execution time up to 99.8%.

This paper presents a new blind equalization algorithm based on the second-order statistics (SOS). The equalization of the single-input multiple-output(SIMO) system can be obtained by fractionally sampled(FS). Due to fractionally sampling, the equivalent channel matrix possesses a particular structure which enables us to estimate the channel based only on the information contained in the covariance matrix at zero delay. According to different delay, the covariance matrix can be used to seek equalizer matrix with different delay, then take use of the minimum mean square error(MMSE) criterion to optimize the best delay covariance matrix of the SOS equalizer. Simulation results showed that the equalizer with optimized delay has better performance than other algorithms.