Distributed ledger technology is becoming popular these days because of its high confidentiality, decentralization, and nontampering. It is suitable for replacing centralized security disadvantaged point transfer systems. Low-power wide area network (LPWAN) is capable for long-range communication with low-power consumption. The iconic features like wide area coverage and long battery-powered duration make it best to combine with large-scale IoT application deployment. In both industry and academic field, such combination of LPWAN and point transfer system is highly attended. However, the ledger management system generates too much data that low-bandwidth network such as LPWAN can hardly handle; meanwhile, the processing power’s requirement for small IoT devices is challenging. Towards addressing these issues, we design a packet transmission optimizing mechanism for a ledger-based point transfer system (LPTS) in LPWAN to reduce overall data traffic and build a simulator to evaluate its performance. Moreover, we have implemented the system and evaluated in field experiment.

In recent years, terrorist attacks have been spreading worldwide and become a public hazard to human society. The suspicious object detection system is an effective way to prevent terrorist attacks in public places. However, traditional systems face two main challenges: First, they need to conduct security checks at the entrance one by one, which leads to crowding; second, they rely heavily on screeners’ ability to understand security images, which can easily lead to misjudgment. To address these issues, we propose an AI-based W-band suspicious object detection system for moving persons that can perform a two-stage walkthrough screening for suspicious objects in an open area to maintain high throughput. The 1st screening uses millimeter wave radar and cameras to automatically screen suspects who may have concealed suspicious objects in an open area. The 2nd screening involves security personnel using a hybrid imager with active and passive imaging capabilities to identify the specific suspicious objects carried by the suspect. Convolutional neural network (CNN) based artificial intelligence (AI) technology will be used to improve the accuracy and speed of suspicious object detection. We performed an experiment to validate the proposed system. The usability and safety of the system are demonstrated by recognition rate (aka accuracy rate) or both recall and precision rate. In addition, in the process of improving the suspicious object recognition rate by AI techniques, we use generative adversarial network to help build a suspicious object database and successfully validate the effectiveness of the method and the factors affecting the suspicious object recognition rate to optimize the system.

This paper presents radio propagation characteristics in a typical indoor space in a 300 GHz Sub-THz broadband channel. We clarified path loss and cross-polarization characteristics in multiple spaces using a linearly polarized horn antenna with 24.6 dBi (E-plane beamwidth 4.5°) and 8.7 dBi (E-plane beamwidth 35°) and a newly developed RHCP circularly polarized patch array antenna which has a gain of 3.4 dBi (E-plane beamwidth 10°) at 300 GHz. We also clarified the effect of shielding by the human body. Furthermore, a comprehensive analysis using ray tracing confirms the accurate agreement of the root mean square delay spread.

In the 5th generation mobile communication system (5G), there is a strong demand for higher speed and higher capacity. The beamforming technique using phased-array antennas is effective for long-distance radio propagation and for reducing interference signals in the high SHF (super high frequency) band because the phase shift of the antenna array elements can be controlled to produce a narrow-band beam and to control the directivity. We have developed the 28 GHz array antenna for small cells and evaluated the beamforming characteristics using an RF analog phase shifter control scheme. In this paper, we verify the computer simulation and evaluation results of the beamforming characteristics of a newly developed 4x4 phased array antenna.

近年,電灯線を利用した通信技術の開発が行われている.しかし,電灯線の本来の目的は機器へのエネルギー供給であり,この中で通信を確立するためには,電灯線の伝送線路及び電磁環境に適した通信方式を用いる必要がある.本論文では,まず,電灯線通信における伝送特性解析と電磁環境の推定を行い,電灯線モデムの適用を想定する上で,代表的な家電機器を考慮した伝送特性の一例を示した.更に,この結果に基づき,周波数10～450kHz,搬送波出力10mW/10kHz以下,伝送速度9.6kbit/sという条件下での通信品質の改善策を検討した.改善策として,マルチバンド分割受信によってインピーダンス変動の影響を軽減し,また,電磁ノイズが小さくなる信号伝送周波数帯域の上端での電力密度の低下を防ぐMESA-SS方式を提案して,評価を行った.その結果,従来のSS方式に比べ同じビット誤り率を得るに必要なS/Nが6dB小さくできること,時間的な特性変動を考慮しない平均的な条件下では40mの距離においてビット誤り率を10^<-5>以下にできることがわかった.

This paper describes the results of designing and evaluating prototype 30 dBi and 47 dBi lens horn antennas with WR-3.4 as input/output interface and conical horn antenna as primary radiator. The 30 dBi lens horn antenna showed good agreement between the antenna gain measured by the 3-antenna method at 1 m, which is the far field region, and the calculation results, confirming the correctness of the lens design method. For the 47 dBi lens horn antenna, the antenna gains measured by the comparative method at 1 m and 2 m in the near field region was compared with the calculated antenna gain in the near field region.

Liquid Water Content (LWC) is a pivotal parameter that describes the mass of the water in a cloud in a specified amount of dry air, crucial for research in cloud physics and meteorology. This study explored a novel approach to estimating the LWC of cloud layers from radar observational data by utilizing dual-frequency radar (35 GHz and 95 GHz) in tandem with Bayesian Neural Network (BNN). The dual-frequency radar utilizes differential attenuation between the two distinct frequencies to directly assess the LWC in clouds, with the variance proportionate to the overall LWC in the surveyed volume. However, due to atmospheric perturbations and other factors, standalone radar observations might not suffice for high-precision LWC evaluations. To address this, we incorporated BNN, capable of handling the inherent uncertainties in radar data and offering a more accurate estimation for LWC. Preliminary results demonstrate that the combination of dual-frequency radar and BNN can effectively assess the LWC of cloud layers, showcasing superior accuracy and resolution compared to conventional methods. This methodology provides a potent tool for a deeper understanding of cloud physical processes and further refinement of climate models.

Transmission latency presents a significant challenge when operating remote equipment, such as a robotic arm. To address this, we developed a platform and conducted experiments to reduce transmission latency to near-zero levels. These experiments employed Long Short-Term Memory (LSTM) to anticipate future motion trends, leveraging both the controller's movement variables and Electromyography (EMG) data from the operator's arm muscles. Our findings indicate the potential to decrease transmission latency by approximately 500ms. Additionally, our research confirms a direct correlation between prediction accuracy and the brevity of prediction time, suggesting that shorter prediction times yield more accurate results when using EMG. In the context of video transmission for a remotely located robotic arm, we applied video prediction techniques using the Predictive Coding Network (PredNet) to counter network latency. Our results suggest that these predictive methods can effectively compensate for a latency period of 300ms, thereby highlighting their potential for reducing transmission latency in remote robotic operations.

For next-generation logistics systems using autonomous vehicles and drones, spoofing of the GNSS location data induces serious problems. Although signal-based anti-spoofing has been studied, it is difficult to apply to current commercial GNSS modules in many cases. We investigate possibilities to detect spoofing of GNSS location data using multiple sensing devices and a decision tree classifier. Multiple features using the GNSS, beacons, and the IMU are defined and create a model to detect spoofing. Experimental results using learning-based classifier indicates the higher performances and generalization capability. The results also show that distance from beacons is useful to detect GNSS spoofing and indicate prospects of installation for the future drone highways.

Low Power Wide Area Network (LPWAN) is a long-range low-power wireless communication network. Its features, such as wide network coverage and low power consumption of terminals, make it suitable for large-scale deployment of IoT applications. The points transfer system, especially points transfer system in LPWAN, as a typical third-party payment application, is being closely attended by both industry and academia. Recent studies have shown that distributed ledger technology, because ofits characteristics such as high confidentiality, non-tampering, and decentralization, is a good solution to problems such as low-security performance due to centralized storage for a points transfer system. However, the distributed ledger will generate a large amount of data traffic in recording the transactions of network participants, which is a challenge for resource-constrained IoT devices. To address these issues, we propose an optimized packet transmission mechanism for a ledger-based points transfer system in LPWAN. Simulation results show that our proposed mechanism can well reduce the packet transmission ofthe whole system and meet the requirements of LPWAN. Moreover, we update the reader with information about distributed ledger and standardization-related activities in this paper.

To reduce safety violations in wide-area ranges, there is a need for highly functional multicamera surveillance systems. We introduce a multicamera trajectory forecasting surveillance network system based on a content-oriented suspicious object network system. This system uses multiple cameras in detection and recognition to track persons among different areas and is capable of retracking people. Each camera node has a processing unit and uses information-centric networking technology to build a content-oriented IoT network. We use field-recorded data to support the simulation, and the evaluation result indicates that our trajectory forecasting method is more efficient than conventional surveillance systems.

Accident prevention for trucks, cranes and work vehicles at construction sites is important. Here, we use high-precision surveillance cameras with 4K cameras as IoT terminals to implement safe workplace environments. In this research, the system transmits photographic images from trucks, cranes, and other construction equipment fitted with 4K cameras at the work site to a database via 5G wireless networks, and uses AI to assess the interactions and movements of workers in the database. We introduce a system that makes it possible to avoid crashes by informing truck and crane drivers, and notifying automatic driving trucks and crane cars of that information. It is conceivable that uplink traffic could become congested due to many vehicles equipped with 4K cameras simultaneously transmitting images over the 5G uplink. Here, as a basic 5G characteristic evaluation, 4K images from trucks or cranes moving at a low speed are transmitted to the database according to moving speed and distance to the 5G terminal connecting the base station and IoT device. Based on the error environment, we report the relation with the error of the system that judges and identifies the surrounding environment using AI when video quality is deteriorated.