In this work, a novel platform for surface-enhanced Raman spectroscopy (SERS)-based chemical sensors utilizing three-dimensional microporous graphene foam (GF) decorated with silver nanoparticles (AgNPs) is developed and applied for methylene blue (MB) detection. The results demonstrate that silver nanoparticles significantly enhance cascaded amplification of SERS effect on multilayer graphene foam (GF). The enhancement factor of AgNPs/GF sensor is found to be four orders of magnitude larger than that of AgNPs/Si substrate. In addition, the sensitivity of the sensor could be tuned by controlling the size of silver nanoparticles. The highest SERS enhancement factor of ∼5 × 10⁴ is achieved at the optimal nanoparticle size of 50 nm. Moreover, the sensor is capable of detecting MB over broad concentration ranges from 1 nM to 100 μM. Therefore, AgNPs/GF is a highly promising SERS substrate for detection of chemical substances with ultra-low concentrations.

Optical fire sensors, sometimes called “volumetric” sensors, are complementary to conventional point sensors such as smoke and heat detectors in providing people with early warnings of fire incidents. Cameras combined with image processing software hold the promise of detecting fire incidents more quickly than point sensors and can also provide size, growth, and direction information more readily than their conventional counterparts. In this paper, we present QuickBlaze, a flame and smoke detection system based on vision sensors aimed at early detection of fire incidents for open or closed indoor and outdoor environments. We use simple image and video processing techniques to compute motion and color cues, enabling segmentation of flame and smoke candidates from the background in real time. We begin with color balancing, then separate smoke and flame detection streams operate on the image. Both streams identify candidate regions based on color information then perform morphological image processing on the candidates. The smoke detection stream then filters candidate regions based on turbulence flow rate analysis, and the flame detection stream filters based on growth and flow rate information. QuickBlaze does not require any offline training, although manual adjustment of parameters during a calibration phase is required to cater to the particular camera’s depth of view and the surrounding environment. In an extensive empirical evaluation benchmarking QuickBlaze against commercial fire detection software, we find that it has a better response time, is 2.66 times faster, and better localizes fire incidents. Detection of fire using our real-time video processing approach early on in the burning process holds the potential to decrease the length of the critical period from combustion to human response in the event of a fire.

In the new Internet of Things (IoT) era, embedded Field-Programmable Gate Array (FPGA) technology is enabling the deployment of custom-tailored embedded IoT solutions for handling different application requirements and workloads. Combined with the open RISC-V Instruction Set Architecture (ISA), the FPGA technology provides endless opportunities to create reconfigurable IoT devices with different accelerators and coprocessors tightly and loosely coupled with the processor. When connecting IoT devices to the Internet, secure communications and data exchange are major concerns. However, adding security features requires extra capabilities from the already resource-constrained IoT devices. This article presents the FAC-V coprocessor, which is an FPGA-based solution for an RISC-V processor that can be deployed following two different coupling styles. FAC-V implements in hardware the Advanced Encryption Standard (AES), one of the most widely used cryptographic algorithms in IoT low-end devices, at the cost of few FPGA resources. The conducted experiments demonstrate that FAC-V can achieve performance improvements of several orders of magnitude when compared to the software-only AES implementation; e.g., encrypting a message of 16 bytes with AES-256 can reach a performance gain of around 8000× with an energy consumption of 0.1 $\mathsf{\mu }$J.

Road fatality rates are very high, especially in developing and middle-income countries. One of the main causes of road fatalities is not using motorcycle helmets. Active law enforcement may help increase compliance, but ubiquitous enforcement requires many police officers and may cause traffic jams and safety issues. In this paper, we demonstrate the effectiveness of computer vision and machine learning methods to increase helmet compliance through automated helmet violation detection. The system detects riders and passengers not wearing helmets and consists of motorcyclist detection, helmet violation classification, and tracking. The architecture of the system comprises a single GPU server and multiple computational clients that cooperate to complete the task, with communication over HTTP. In a real-world test, the system is able to detect 97% of helmet violations with a 15% false alarm rate. The client-server architecture reduces cost by 20-30% compared to a baseline architecture.