Published papers

We propose a 3-dimensional(3D) CO2 concentration analyzing system to monitor the ventilation of the closed space. this system employs a 3D scan to reduce the labor associated with creating a 3D space model. This research uses multiple CO2 sensors and considers the model to map point CO2 concentration data to the spatial CO2 distribution. We experimented with evaluating the accuracy of the model in the actual bus space.

Crowdedness of buses is playing an increasingly important role in the disease control of COVID-19. The lack of a practical approach to sensing the crowdedness of buses is a major problem. This paper proposes a bus crowdedness sensing system which exploits deep learningbased object detection to count the numbers of passengers getting on and off a bus and thus estimate the crowdedness of buses in real time. In our prototype system, we combine YOLOv5s object detection model with Kalman Filter object tracking algorithm to implement a sensing algorithm running on a Jetson nano-based vehicular device mounted on a bus. By using the driving recorder video data taken from real bus, we experimentally evaluate the performance of the proposed sensing system to verify that our proposed system system improves counting accuracy and achieves real-time processing at the Jetson Nano platform.

The risk of hardware Trojan (HT) attack in the design step of the integrated circuit (IC) development process has become a 'hot spot' in hardware security. Despite some solutions presented in the literature, there are still limitations such as high time complexity, low HT detection accuracy, and the inability to locating HTs. This paper presents a novel HT detection and diagnosis method based on machine learning (ML) to identify the HT-related gates/nets maliciously inserted into the IC gate-level netlists at design stage. We partition the circuit into n sectors and extract five HT-related features from the netlists of each sector to construct a five-dimensional vector of that sector. Then, these n five-dimensional feature vectors are trained using support vector machine (SVM) to obtain the learned model. We have evaluated our method on the ISCAS'85 benchmark circuits. The experimental results show that our method can achieve 100% true positive rate (TPR), more than 90% true negative rate (TNR), complete 95% average precision and F-measure, average accuracy exceeds 80%. Moreover, it can also report the possible implantation location of a Trojan instance.

Crowdedness sensing of buses is playing an important role in the disease control of COVID-19 and bus resource scheduling. This research analyzes the relationship between carbon dioxide concentration, bus environment and the number of passengers by linear regression. Our prototype system collects the data of bus environment and carbon dioxide concentration to estimate the number of passengers in real time. By collecting the sensing data from a shuttle bus of university campus, we experimentally evaluate the feasibility and sensing performance of the crowdedness estimation model.

This paper demonstrates that the fractal dimension of frame preamble serves as a new radiometric feature that can be used together with other known radiometric features to enhance the identification accuracy in wireless device identification. We first propose a fractal dimension estimation scheme for direct-sequence spread spectrum (DSSS) frame preamble, then provide theoretical analysis to reveal how the fractal dimension is primarily determined by the device hardware imperfections, and thus prove that the fractal dimension serves as an intrinsic radiometric feature. We further show simulation results to verify our theoretical modeling of the fractal dimension and also numerically evaluate the effects of device hardware imperfections and wireless channels on the fractal dimension. Finally, by jointly applying the fractal dimension and the five features reported in the literature, we conduct extensive experiments to demonstrate that the fractal dimension can lead to a further improvement of the state-of-the-art result in the radiometric feature-based device identification.

Physical layer identification is an emerging technique that exploits physical layer features to identify wireless devices. The identification accuracy and the device quantity that can be identified at most are significant for the identification scheme. Existing works primarily focus on the feature correlation analysis for multifeature selection without investigating the least upper bound (supremum) of the performance of a single feature. The supremum indicates the limit of the performance, which is another sight for evaluating the quality of features and improving the performance of the identification scheme. Therefore, this paper first investigates the supremum of the performance of the most commonly used physical layer feature, i.e., carrier frequency offset (CFO). Specifically, we offer a rigorous mathematical analysis and derive the closed-form expression of the supremum of identification accuracy based on the max-min distance analysis (MMDA) criterion. And then, the supremum of the number of distinguishable devices is also analyzed. Finally, we conducted a simulation study to verify the theoretical analysis result.

Existing studies on deep learning-based physical layer identification have mainly exploited raw in-phase/ quadrature (IQ) samples or power spectral density (PSD) samples as inputs independently. The raw IQ and PSD samples represent the information in the time and frequency domains, respectively. It has been observed from the results of existing studies that identification using raw IQ samples outperforms that using PSD in low signal-to-noise ratio (SNR) regimes, and that identification using PSD outperforms that using raw IQ in high SNR regimes. In this paper, we propose to use the fusion of raw IQ and PSD samples to enhance deep learning-based physical layer identification. In particular, we design three general fusion frameworks, i.e., input, feature, and decision fusions, and integrate them with three typical deep neural network architectures of fully connected neural network, convolutional neural network, and recurrent neural network to form fusion identification schemes. We conduct experiments using 50 off-the-shelf Wi-Fi devices to validate the concerned fusion schemes and investigate their performance gains in identification and model training. Our experimental results verify that the proposed fusion identification schemes can achieve comparable or superior identification performances to the state-of-the-art schemes in the entire SNR regime. Moreover, for the considered fusion schemes, we further investigate the impacts of fusion strategies, deep-learning networks, and SNR conditions on the identification performance and training time.

We present a controller to Node-RED translator (CNT), a tool for developing Node-RED systems that are safe and easy to update continuously. CNT introduces the discrete controller synthesis to conventional Node- Redsystems to provide a guarantee of flow correctness (safety). Flow correctness, which essentially means that the nodes fire at the timing intended by the designer, is crucial in smart city systems that require frequent application updates with no room for design errors. In this work, we propose a method for developing a Node- Redsystem using CNT along with its accompanying algorithm. We also report the results of experiments that demonstrate the usefulness of the proposed method.

This poster introduces DeepCounter, an automotive sensing system to providing a fine-grained spatio-temporal distribution on the amount of disposed garbage bags. In the system, deep learning based image processing is used to automatically count the number of collected garbage bags from the video taken by a camera mounted on the rear of a garbage truck. A prototype system is implemented and experimental evaluation validates the feasibility of our proposal using realistic garbage collection videos in Fujisawa city, Japan.

© 2018 Elsevier B.V. Due to the advance in information and communication technologies, smart cities have become an increasingly appealing vision. One indispensable basis in the research, development and implementation of smart cities is urban sensing technology. Automotive sensing is a novel urban sensing technology that equips sensors to vehicles, like buses, taxi cabs or garbage trucks and utilizes their mobility to help to conduct urban sensing tasks. In this paper, we present Cruisers, an automotive sensing platform for smart cities, based on the following ideas: a) Garbage collecting trucks are used to host sensors, b) Cellular communication network is used to deliver sensed data to servers, and c) Proxy server(s) are adopted to convert the format of sensed data to required ones, such as sampling rate, spatial interval or pub/sub protocol. To validate the feasibility of Cruisers, we implement a real system that consists of 66 customized sensing modulars integrating typical environmental and mobility sensors and deploy them into the garbage collecting trucks at Fujisawa, a medium-sized city in Japan. Performance evaluation is conducted to investigate the performance of Cruisers in terms of scalability, data publishing delay and spatial coverage.