An IoT-Based Breeding Egg Identification and Coding System for Selection of High-Quality Breeding Geese.

Lightweight target detection for the field flat jujube based on improved YOLOv5

Purpose In order to resolve the situation of high missed diagnosis rate and high misdiagnosis rate of the pathological analysis of the gastrointestinal endoscopic images by experts, we propose an automatic polyp detection algorithm based on Single Shot Multibox Detector (SSD). Method In the paper, SSD is based on VGG-16, the fully connected layer is changed to a convolutional layer, and four convolutional layers with successively decreasing scales are added as a new network structure. In order to verify the practicability, it is not only compared with manual polyp detection but also with Mask R-CNN. Results Multiple experimental results show that the mean Average Precision (mAP) of the SSD network is 95.74%, which is 12.4% higher than the manual detection and 5.7% higher than the Mask R-CNN. When detecting a single frame of image, the detection speed of SSD is 8.41 times that of manual detection. Conclusion Based on the traditional pattern recognition algorithm and the target detection algorithm using deep learning, we select a variety of algorithms to identify and classify polyps to achieve efficient detection results. Our research demonstrates that deep learning has a lot of room for development in the field of gastrointestinal image recognition.

Nowadays, the internet of things (IOT) has attracted the attention of the researchers. As a key technology of the Internet of Things, RFID (Radio Frequency Identification) has also attracted the interest of industry and academia. One of the most important issues for the applications of the IOT based on RFID is security. In order to promote the development of IOT based on RFID, first of all, we briefly introduce the IOT system components and work process based on RFID, and then we deeply analyze the security threats in the IOT based on RFID, by the means of these analysis we discuss the current countermeasures for the security threats of RFID. Aiming at the RFID system, the corresponding technologies are raised such as Tag killing, Tag sleeping and blocker Tag; and the authentication based on HASH is the main method aiming at the communication process.

With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system.

Evaluation of design alternatives of End-Of-Life products using internet of things

Single Shot MultiBox Detector (SSD) is an effective method for multi-targets detection. However, due to the small scale and less information, small targets detection is a difficult assignment for SSD. In order to address this issue, we design an framework based on SSD, namely feature fusion and enhancement for SSD. First, considering that the feature information contained in different scale feature maps is different, especially the shallow feature maps which contains more detailed information and lacks semantic information, we propose a method of fusing different scale feature maps to introduce abundant object information. Secondly, although the feature maps has multiple channels, the importance of different channels' feature to the detected object is different. We introduce the feature enhancement module for effective feature enhancement. Training on the PASCAL VOC2007 and VOC2012, the proposed method has an mAP of 79.07%, which is 1.4% higher than SSD, and an FPS of 39 at the input size of 300 x300.

The ability to detect small targets and the speed of the target detector are very important for the application of remote sensing image detection, and in this paper, we propose an effective and efficient method (named CISPNet) with high detection accuracy and compact architecture. In particular, according to the characteristics of the data, we apply a context information scene perception (CISP) module to obtain the contextual information for targets of different scales and use k-means clustering to set the aspect ratios and size of the default boxes. The proposed method inherits the network structure of Single Shot MultiBox Detector (SSD) and introduces the CISP module into it. We create a dataset in the Pascal Visual Object Classes (VOC) format, annotated with the three types of detection targets, aircraft, ship, and oiltanker. Experimental results on our remote sensing image dataset as well as the Northwestern Polytechnical University very-high-resolution (NWPU VRH-10) dataset demonstrate that the proposed CISPNet performs much better than the original SSD and other detectors especially for small objects. Specifically, our network can achieve 80.34% mean average precision (mAP) at the speed of 50.7 frames per second (FPS) with the input size 300 × 300 pixels on the remote sensing image dataset. On extended experiments, the performance of CISPNet in fuzzy target detection in remote sensing image is better than that of SSD.

Even invented and applied initially during the World War II, RFID (Radio Frequency IDentification) technologies [1] have attracted much attention recently. Precisely speaking, RFID technologies have been applied very widely in some proprietary or closed systems, for example, animal control [2], portal control (access badges), etc. in last decades. The main advantages of RFID application are, storing item data in an electronic way even for further update, data access by electromagnetic wave in a wireless manner, and allowing quick multiple accesses to RFID tags. Based on the diverse applications, different spectrum bands are allocated, for example, LF (125 134.2 kHz and 140 148.5 kHz) for animal control, HF (13.56MHz) for electronic ticket, and UHF (868 MHz-928 MHz) for logistics, etc. Most of the frequencies are located in the ISM (Industrial, Scientific and Medical) bands [1]. However, RFID was emphasized again mainly because of the need of supply chain [3]. By proposing a standard for the format of electronic data used for goods items, of which EPC (Electronic Product Code) [4] is an example, the products can be registered at once when they are shipped out from the factories in one country, and be released when they are checked out at the counter of a supermarket in the other country in the world. These products might have been transferred through Customs of many countries and carried by different traffic means. When being through these check points, the related data stored in the tags are updated. This is called “product tracking” and is to be carried out in an “Internet of Thing (IOT)” [5]. This Chapter is to have a review on the technology theme – how to provide low-cost RFID Tags, when RFID technology is to be applied into the logistics area where the RFID tags are supposed to be not re-usable and to be as “zero-cost” as possible. Generally speaking, there are three major parts composing a RFID Tag’s total cost, namely, antenna, chip and assembly for them. The cost of antenna, in addition to the design phase, is mainly dependent on the manufacturing process. Therefore, manufacturing process should be focused if antenna’s cost, then the tag, is concerned. This is the theme of this Chapter. Not like the other antenna applications, for example, wireless LAN or mobile phone, in which antennas need to be compliant to the end products’ appearance by following the market trend. In the tag antenna industry, on the contrary, it does not need to design or modify the tag antenna often. The tag antenna just needs to electrically match the chip used in the beginning of design. It is not necessary for tag providers to prepare a wide product spectrum in the market. Again, not like the mobile phone industry, RFID tag’s players just

One key technique in Internet of Things(IoT) is localization, which has been an indispensable context for most of the applications in IoT. In this paper, we introduce an indoor location detecting platform based on the passive RFID (Radio Frequency Identification) technology. The major advantage of our platform is that it can be adopted to meet different detection granularity requirement based on the flexible RFID antenna design. Second, the only thing for a user to carry with is a tiny RFID tag on his/her shoes which is light weight and no charging power problems. Thirdly it will be shown that our design is easy to be augmented into normal artifacts in an unobtrusive way for the indoor environment. Two versions of the RFID-based location detection systems are introduced. One is called U-tile (Ubiquitous tile), in which passive RFID reader is embedded into the tiles. Another improved version is called Indoor-tracer, which adopts anti-collision RFID and more light weight comparing with the previous version U-tile so that it can embedded into various artifacts like bags and drawers. The performances of both two prototype systems were evaluated and the mutual-aware applications for remote family based on IoT were developed by using the platform.

Internet of Things has created high opportunities for industries to enhance their manufacturing, servicing and supplying capabilities. Industrial Internet of Things (IIoT) will add innovation and smartness in industrial capabilities. Internet of Things helps the industry to generate the critical and decidedly big data. Generated big data will be analyzed by various data mining algorithms and will generate knowledge out of data. Generated knowledge will create smooth decision making for smart machines by machine learning. Radio frequency identification (RFID) and a quick response code (QR code)-based object tagging has created an easy identification environment for various things in the industry. Most security algorithms are implemented in the Industrial Internet of Things for the purpose of authentication and access control mechanism. IoT has created revolution in the healthcare industry, in terms of data availability and analytics. Similarly, many IoT devices and IoT-based services are implemented in various industries for their day-to-day tasks. In this paper, we have studied various recent applications of IoT in industry in terms of machine learning, security, and services and challenges faced by industries in the deployment of IoT-based services. Mainly, this paper will summarize the current state of an Industrial Internet of Things in a systematic manner and rate of data stream by sliding window and tumbling window, respectively.

The Internet of Things (IoT) envisions an ecosystem where smart and interconnected objects can sense surrounding changes, communicate with each other, process information and take active roles in decision making. Optimizing supply chain performance is a primary concern of manufacturing and logistics organizations. Radio Frequency Identification (RFID) is helping organizations to build automated and interconnected smart environment by object identification and tracking, motivating the first step towards an IoT-enabled world. This chapter attempts to understand extant literature studying applications of RFID in implementing the IoT in supply chain management. We categorize extant literature, firstly, based on research methodology and secondly, based on supply chain processes. We find that presently academic activity is around conceptualizing the usability of RFID in the IoT with limited analytical and empirical evidence. Supply chain processes such as demand planning, procurement, retail shelf space management and product returns are prospective areas for interesting future research.

Upcoming 5th-generation (5G) systems incorporate physical objects (referred to as things), which sense the presence of components such as gears, gadgets, and sensors. They may transmit many kinds of states in the smart city context, such as new deals at malls, safe distances on roads, patient heart rhythms (especially in hospitals), and logistic control at aerodromes and seaports around the world. These serve to form the so-called future internet of things (IoT). From this futuristic perspective, everything should have its own identity. In this context, radio frequency identification (RFID) plays a specific role, which provides wireless communications in a secure manner. Passive RFID tags carry out work using the energy harvested among massive systems. RFID has been habitually realized as a prerequisite for IoT, the combination of which is called IoT RFID (I-RFID). For the current scenario, such tags should be productive, low-profile, compact, easily mountable, and have eco-friendly features. The presently available tags are not cost-effective and have not been proven as green tags for environmentally friendly IoT in 5G systems nor are they suitable for long-range communications in 5G systems. The proposed I-RFID tag uses the meandering angle technique (MAT) to construct a design that satisfies the features of a lower-cost printed antenna over the worldwide UHF RFID band standard (860–960 MHz). In our research, tag MAT antennas are fabricated on paper-based Korsnäs by screen- and flexo-printing, which have lowest simulated effective outcomes with dielectric variation due to humidity and have a plausible read range (RR) for European (EU; 866–868 MHz) and North American (NA; 902–928 MHz) UHF band standards. The I-RFID tag size is reduced by 36% to 38% w.r.t. a previously published case, the tag gain has been improved by 23.6% to 33.12%, and its read range has been enhanced by 50.9% and 59.6% for EU and NA UHF bands, respectively. It provides impressive performance on some platforms (e.g., plastic, paper, and glass), thereby providing a new state-of-the-art I-RFID tag with better qualities in 5G systems.

In recent years, convolutional neural network (CNN)-based methods have been extensively explored for synthetic aperture radar (SAR) target detection. Nevertheless, the convolutional sampling locations of CNNs cannot accurately fit vehicle targets due to the fixed sampling mechanism in the convolutional kernel. In this paper, we focus on the vehicle target detection task in SAR images and propose a novel rectangle-invariant rotatable convolution (RIRConv) to determine more accurately the convolutional sampling locations for vehicle targets. Specifically, this paper considers the shape characteristic of vehicle targets in SAR images, which always retain a rectangular shape despite having varying sizes, aspect ratios, and rotation angles. The proposed RIRConv equips three additional learnable attribute parameters, namely, width, height, and angle attributes, to adaptively adjust the sampling locations in the convolutional kernel according to the targets. In addition, the RIRConv applies a modulation mechanism to focus on the sampling locations that significantly affect the output. Finally, the RIRConv is introduced into the single-shot multibox detector (SSD) to realize SAR vehicle target detection. In this way, the feature representation capability of SSD for vehicle targets can be enhanced, thus leading to higher detection performance. Notably, the proposed RIRConv is “plug-and-play” and can also be used with other existing advanced technologies to achieve higher detection performance. The experiments based on the measured miniSAR data validate the effectiveness of the proposed method.

The current and projected rapid increase in usage of the radio frequency identification (RFID) tags will yield new uses and applications in an Internet of things (IoT) environment. This chapter will discuss the implementation of software-defined radio (SDR) to aggregate, analytically process, and efficiently transmit data from RFID tags through a satellite backhaul solution. In the infrastructure proposed below, the first layer SDR aggregates RFID data at a tower level, the second layer SDR aggregates tower data at a low Earth orbit (LEO) satellite level, and then this aggregated data are backhauled to the gateway for the third layer SDR processing and data analytics. This chapter will discuss the RFID/SDR current and future capabilities, SDR design trades, RFID/ SDR system prototype test results, and satellite implementation scenarios.

Internet of Things (IoT) devices are basic units in edge computing. Denial of service (DoS) attack is a great threat to low-cost IoT devices, even worse in privacy-preserving authentication protocols for radio frequency identification (RFID) tags. During DoS attacks, the attacker consumes the legal states in a target tag by continuous scanning and further observes its behavior in authentication to break privacy. Due to lack of adequate energy and computing power, it is hard for passive backscattering RFID tags to defend against DoS attacks. In this work, we cast a new insight on the DoS attacks to RFID tags and leverage the malicious scanning behavior as a new energy source. In this way, a passive tag gains more and more energy and can afford more and more complex cryptography computation under a DoS attack. Finally, the victim tag with adequate energy achieves to complete the complicated public key cryptographic computations and defend against the DoS attack. We further propose a protocol, namely the RUND protocol. We define the tracking privacy model and introduce in the notion of tracking interval to define the tracking privacy other than indistinguishable privacy. To guarantee the security and tracking privacy properties of our protocol, we propose 3 rules as designing guidelines. The analysis shows that our approach can achieve $O$ (1) efficiency while providing DoS-defending privacy-preserving authentication. Furthermore, with proper parameters our approach can save about 40% boosting time at least, compared to the directly charging for public key cryptographic computation method.