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We used surface-enhanced Raman spectroscopy (SERS) for the rapid and sensitive detection and quantification of caffeine in solution. Such a technique incorporated into a portable device is finding wide applications in trace chemical analysis in various fields, including law enforcement, medicine, environmental monitoring, and food quality control. To realize such applications, we are currently developing portable and handheld trace chemical analyzers based on SERS, which are integrated with a sensor embedded with activated gold nanoparticles in a porous glass matrix. In this study, we used this gold SERS-active substrate to measure aqueous solutions of the drug caffeine as a test chemical to benchmark sensor performance by defining sensitivity (lowest measured concentration (LMC) and estimated limit of detection (LOD)), determining concentration dependence and quantification capabilities by constructing calibration curves; by evaluating the effects of pH values of 3, 7, and 11; and by examining the reproducibility of the SERS measurements. The results demonstrate that the SERS sensor is sensitive, with caffeine detected at an LMC of 50 parts per billion (ppb) with an LOD of 0.63 ppb. The results further show that the sensor is very stable and can be used to make reproducible measurements, even under extremely acidic to basic pH conditions. Vibrational assignments of all observed SERS peaks are made and reported for the first time for caffeine on a gold substrate.

In this study, a miniaturized high fundamental frequency quartz crystal microbalance (QCM) is fabricated for sensor applications using a wet etching technique. The vibration area is reduced in the fabrication of the high frequency QCM with an inverted mesa structure. To reduce the complexity of the side wall profile that results from anisotropic quartz etching, a rectangular vibration area is used instead of the conventional circular structure. QCMs with high Q values exceeding 25,000 at 47 MHz, 27,000 at 60 MHz, 24,000 at 73 MHz and 25,000 at 84 MHz are fabricated on 4 × 4 mm2 chips with small vibration areas of 1.2 × 1.4 mm2. A PMMA-based flow cell is designed and manufactured to characterize the behavior of the fabricated QCM chip in a liquid. Q values as high as 1,006 at 47 MHz, 904 at 62 MHz, 867 at 71 MHz and 747 at 84 MHz are obtained when one side of the chip is exposed to pure water. These results show that fabricated QCM chips can be used for bio- and chemical sensor applications in liquids.

In this study, a new type of highly sensitive fiber-optic Fabry–Perot interferometer (FFPI) is proposed with a high sensitivity on a wide refractive index (RI) measurement range based on internal reflection mirrors of micro-cavity. The sensor head consists of a single-mode fiber (SMF) with an open micro-cavity. Since light reflections of gold thin films are not affected by the RI of different measuring mediums, the sensor is designed to improve the fringe visibility of optical interference through sputtering the gold films of various thicknesses on the inner surfaces of the micro-cavity, as a semi-transparent mirror (STM) and a total-reflection mirror (TRM). Experiments have been carried out to verify the feasibility of the sensor’s design. It is shown that the fabricated sensor has strong interference visibility exceeding 15 dB over a wide measurement range of RI, and the sensor sensitivity is higher than 1160 nm/RIU, and RI resolution is better than 1.0 × 10−6 RIU.

Device-to-device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel algorithm for dynamic task offloading between the active MDs and the idle MDs in a D2D–MEC (mobile edge computing) system by deploying multi-agent deep reinforcement learning (DRL) to minimize the long-term average delay of delay-sensitive tasks under deadline constraints. Our core innovation is a dynamic partitioning scheme for idle and active devices in the D2D–MEC system, accounting for stochastic task arrivals and multi-time-slot task execution, which has been insufficiently explored in the existing literature. We adopt a queue-based system to formulate a dynamic task offloading optimization problem. To address the challenges of large action space and the coupling of actions across time slots, we model the problem as a Markov decision process (MDP) and perform multi-agent DRL through multi-agent proximal policy optimization (MAPPO). We employ a centralized training with decentralized execution (CTDE) framework to enable each MD to make offloading decisions solely based on its local system state. Extensive simulations demonstrate the efficiency and fast convergence of our algorithm. In comparison to the existing sub-optimal results deploying single-agent DRL, our algorithm reduces the average task completion delay by 11.0% and the ratio of dropped tasks by 17.0%. Our proposed algorithm is particularly pertinent to sensor networks, where mobile devices equipped with sensors generate a substantial volume of data that requires timely processing to ensure quality of experience (QoE) and meet the service-level agreements (SLAs) of delay-sensitive applications.

The cocktail party problem can be more effectively addressed by leveraging the speaker’s visual and audio information. This paper proposes a method to improve the audio’s separation using two visual cues: facial features and lip movement. Firstly, residual connections are introduced in the audio separation module to extract detailed features. Secondly, considering the video stream contains information other than the face, which has a minimal correlation with the audio, an attention mechanism is employed in the face module to focus on crucial information. Then, the loss function considers the audio-visual similarity to take advantage of the relationship between audio and visual completely. Experimental results on the public VoxCeleb2 dataset show that the proposed model significantly enhanced SDR, PSEQ, and STOI, especially 4 dB improvements in SDR.

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Seat-level positioning of a smartphone in a vehicle can provide a fine-grained context for many interesting in-vehicle applications, including driver distraction prevention, driving behavior estimation, in-vehicle services customization, etc. However, most of the existing work on in-vehicle positioning relies on special infrastructures, such as the stereo, cigarette lighter adapter or OBD (on-board diagnostic) adapter. In this work, we propose iLoc, an infrastructure-free, in-vehicle, cooperative positioning system via smartphones. iLoc does not require any extra devices and uses only embedded sensors in smartphones to determine the phones’ seat-level locations in a car. In iLoc, in-vehicle smartphones automatically collect data during certain kinds of events and cooperatively determine the relative left/right and front/back locations. In addition, iLoc is tolerant to noisy data and possible sensor errors. We evaluate the performance of iLoc using experiments conducted in real driving scenarios. Results show that the positioning accuracy can reach 90% in the majority of cases and around 70% even in the worst-cases.

The road friction coefficient is a key parameter for autonomous vehicles and vehicle dynamic control. With the development of autonomous vehicles, increasingly, more environmental perception sensors are being installed on vehicles, which means that more information can be used to estimate the road friction coefficient. In this paper, a nonlinear observer aided by vehicle lateral displacement information for estimating the road friction coefficient is proposed. First, the tire brush model is modified to describe the tire characteristics more precisely in high friction conditions using tire test data. Then, on the basis of vehicle dynamics and a kinematic model, a nonlinear observer is designed, and the self-aligning torque of the wheel, lateral acceleration, and vehicle lateral displacement are used to estimate the road friction coefficient during steering. Finally, slalom tests and DLC (Double Line Change) tests in high friction conditions are conducted to verify the proposed estimation algorithm. Test results showed that the proposed method performs well during steering and the estimated road friction coefficient converges to the reference value rapidly.