Leveraging the mobility and flexibility of unmanned aerial vehicles (UAVs), the proposed edge and trustworthy artificial intelligence (AI) UAV system (ETAUS) offers a comprehensive approach to air quality monitoring, complementing fixed monitoring stations. We propose a new convolutional neural network (CNN) model that utilizes hyperspectral imaging (HSI) data as input, enabling ETAUS to directly and accurately classify air quality index (AQI) levels without relying on a back-end AI computing platform. Additionally, ETAUS employs an FPGA-based system architecture, allowing for the integration of a neural engine, cryptographic hardware modules, and hardware protection matrices into a single FPGA device for accelerated processing, edge AI, and trustworthy AI functionalities. Based on its self-adaptivity, edge AI models, cryptographic hardware modules, and protection matrices can also be dynamically loaded into the system to support diverse functional requirements. Experiments have demonstrated that using our proposed CNN model and HSI data, the accuracy of AQI level classification can be achieved to 86.38%. ETAUS can achieve a speedup of$2.28\times $to$36.9\times $in terms of frames per second (FPS) for AQI-level classification compared to microprocessor-based and embedded GPU-based designs. ETAUS can also enhance energy efficiency by$2.7\times $compared to embedded GPU solutions, such as NVIDIA Jetson Nano. To support all the cryptographic functions and protection matrices, system adaptivity in ETAUS can significantly increase resource utilization while decreasing power consumption by up to 2.79%.

This work presents the ETAUS, an Edge and Trustworthy AI UAV System, as a mobile sensing platform for air quality monitoring. ETAUS employs an FPGA device as the main hardware computing architecture rather than relying solely on a microprocessor or integrating with GPUs to meet real-time processing demands and achieve adaptivity and scalability. ETAUS contains a neural engine that can execute our customized AI model for air quality index (AQI) level classification and a pre-trained model for detecting objects containing private information. ETAUS also incorporates a de-identification process, cryptographic functions, and protection matrices to safeguard information and individuals' privacy. Furthermore, cryptographic functions and protection matrices are implemented as reconfigurable modules, which can accelerate processing, protect data privacy, and be reconfigured as needed. Experiments have demonstrated ETAUS can achieve a speedup of 3.15x to 72.46x for AQI level classification compared to microprocessor-based and GPU-based designs. ETAUS can also enhance energy efficiency by 5.02x compared to embedded GPU solutions such as NVIDIA Jetson Nano. To support all the cryptographic functions and protection matrices, system adaptivity in ETAUS can significantly increase resource utilization while decreasing power consumption by up to 2.79%.

Open and accessible public utilities such as mass public transit systems are some of the vexing venues that are vulnerable to several criminal acts due to the large volumes of commuters. Existing forms of threat or event detection for the rail-based transit systems are either not working in real-time or do not provide complete coverage. In this paper, we present RISECURE1, an open-source system, that uses real-time social media mining to aid in the early detection of such possible events within a rail-based/metro system. The system leverages dynamic query expansion to keep track of any new emerging information about any particular incident. The Real Time Incident panel of the proposed system provides a comprehensible representation of the evolution of threatening transit events, which are further shown in the storyline modal for each respective station. The alert notification module of the system is capable of monitoring threats to the rail-based/metro systems in real-time. We demonstrate the system by including case studies involving incidents occurring within the Washington DC Metropolitan Area Transit Authority (WMATA) metro system to justify the effectiveness of our approach.

Mobile phones have become a necessity of modern life and are used for various tasks, including audio calls, text messaging, Web surfing, video streaming, gaming, and payments. Most of these tasks consume significant battery power, which makes public charging services increasingly important. Therefore, there are many public places and private stores have started to provide free charging services. However, there are several issues that restrict the popularity of public charging service. First, people cannot easily find charging spots. Second, there is no effective way to monitor charging status and manage the chargers. Third, a free charging service increases business expenses. To address these issues, we developed a Internet of Things (IoT)-based wireless charging service system, which is called AnyCharge. There are five major components of AnyCharge: 1) a Wi-Fi enabled wireless charger; 2) an IoT gateway; 3) a cloud-based management platform; 4) a secure Wi-Fi auto-connection algorithm; and 5) a mobile app. The charger is connected to an IoT gateway through Wi-Fi using our secure auto-connection algorithm, and the gateways are linked to the cloud server using message queue telemetry transport. The administrators can monitor and control chargers using the management platform. In addition, Android and iOS apps have been created to allow users to locate free chargers and find the shortest route to the nearest charging spot. We initiated a large-scale experimental deployment in Taiwan, Thailand, Singapore, and Japan. More than 200 chargers have been installed in different places, including restaurants, hospitals, telecom stores, and hotels. Statistics shows that there is a strong demand for public charging services.

Telecommunication fraud is one of the most prevalent crimes nowadays, and causes most property loss of victims. The criminals of telecommunication fraud are highly organized, concealed and transnational, making investigators difficult to track and capture the suspects. In this paper, we propose a Telecom Fraud Analysis Model (TFAM) which can unveil the underlying structure of fraud groups and identify the roles of the fraudsters. The links between suspects are built using flight information, and co-offending records. Social network analysis techniques are applied to analyze group structures as well as influences of each member. We collect a real telecom fraud dataset with 113 fraudsters whose fraudulent activities spread across four countries and 17 cities. Experimental results demonstrate that our method can successfully identify the key roles and discover the hidden structure of the fraud groups.

In advanced technologies, on-chip-variation (OCV) has accounted for a large proportion of clock skew, which limits the performance of a circuit. To mitigate the OCV problem, a mesh structure has been widely used in high-performance designs. Unfortunately, clock mesh structure also causes large power consumption and large power-ground surge current. Therefore, recently, several approaches have been proposed to apply resonant clock to reduce power consumption. However, previous works often suffer from area overhead because of the need to insert large decoupling capacitors. In this paper, we propose a novel resonant clock mesh structure, called ping-pong mesh, to overcome these drawbacks. Our ping-pong mesh contains two submeshes, each of which plays the role of the decoupling capacitor of the other, and the clocks in two submeshes operate in completely opposite phases. Our ping-pong mesh has the following two advantages: 1) a ping-pong mesh does not need additional decoupling capacitors as in previous works and 2) a ping-pong mesh can reduce the power-ground surge current about half of previous works. Benchmark data consistently show that our ping-pong mesh does work well in practice.

Examination is a traditional way to assess learners’ learning status, progress and performance after a learning activity. Except the test grade, a test sheet hides some implicit information such as test concepts, their relationships, importance, and prerequisite. The implicit information can be extracted and constructed a concept map for considering (1) the test concepts covered in the same question means these test concepts have strong relationships, and (2) questions in the same test sheet means the test concepts are relative. Concept map has been successfully employed in many researches to help instructors and learners organize relationships among concepts. However, concept map construction depends on experts who need to take effort and time for the organization of the domain knowledge. In addition, the previous researches regarding to automatic concept map construction are limited to consider all learners of a class, which have not considered personalized learning. To cope with this problem, this paper proposes a new approach to automatically extract and construct concept map based on implicit information in a test sheet. Furthermore, the proposed approach also can help learner for self-assessment and self-diagnosis. Finally, an example is given to depict the effectiveness of proposed approach.

The routing quality on RDLs in 3D ICs is affected by the micro-bump location seriously. In this paper, we propose a micro-bump assignment method using order relation to minimize the crossing problem and reduce the detours in RDLs. Experimental results show that our approach can obtain an assignment result with 100% routability and minimal wirelength in global routing.

As Internet use has proliferated, Web-based learning systems have become more and more popular. Numerous researchers have spent a great deal of effort to facilitate the promotion of high quality Web-based learning environments, such as intelligent Web-based learning systems and adaptive learning. To facilitate such researches, students' behavioral patterns must be observed and experimentally analyzed. However, building a Web-based learning system and the requisite collecting of behavioral patterns usually takes a great deal of time and effort. To solve this problem, this paper proposes a learning behavioral model based on Colored Petri Nets (CPN) to model and generate students' behavioral patterns. To verify the viability of the proposed model, this paper compares actual data collected from elementary school students with the behavioral pattern generated by the proposed model. The results prove: (1) The generated behavioral pattern approaches actual student behavior; (2) The generated behavioral pattern serves as adequate test data to test whether the predicted learning content of an intelligent e-learning system is appropriate; and (3) The proposed model is capable of recommending the appropriate learning content for students utilizing e-learning systems.

In this paper, the performance of two-dimensional optical codes with cross-correlation functions of at most one, such as the carrier-hopping prime code (CHPC), is analyzed without the traditional chip-synchronous assumption. In addition to the Gaussian approximation method, we apply a combinatorial technique for a more accurate analysis, especially when there exists only a small number of simultaneous users. It is known that an optical hard-limiter can be placed at the front end of a receiver to reduce the effect of multiple-access interference. We also derive the hard-limiting error probability under the chip-asynchronous assumption. Finally, the performance improvement of the CHPC with the hard-limiting and chip-asynchronous assumptions is studied.