Preface to the Special Issue on “Heterogeneous Photocatalysts: From Fundamentals to Innovative Applications”

Topics in Catalysis -     

Flexible Transparent Reduced Graphene Oxide Sensor Coupled with Organic Dye Molecules for Rapid Dual‐Mode Ammonia Gas Detection

Flexible chemical sensors utilizing chemically sensitive nanomaterials are of great interest for wearable sensing applications. However, obtaining high performance flexible chemical sensors with high sensitivity, fast response, transparency, stability, and workability at ambient conditions is still challenging. Herein, a newly designed flexible and transparent chemical sensor of reduced graphene oxide (R‐GO) coupled with organic dye molecules (bromophenol blue) is introduced. This device has promising properties such as high mechanical flexibility (>5000 bending cycles with a bending radius of 0.95 cm) and optical transparency (>60% in the visible region). Furthermore, stacking the water‐trapping dye layer on R‐GO enables a higher response as well as workability in a large relative humidity range (up to 80%), and dual‐mode detection capabilities of colorimetric and electrical sensing for NH₃ gas (5–40 ppm). These advantageous attributes of the flexible and transparent R‐GO sensor coupled with organic dye molecules provide great potential for real‐time monitoring of toxic gas/vapor in future practical chemical sensing at room conditions in wearable electronics.     

Secure Multi-hop Data Transmission in Cognitive Radio Networks Under Attack in the Physical Layer

Wireless Personal Communications - Cognitive radio networks (CRNs) have a shortcoming in that attackers can increase their ability to disturb secondary users (SUs). This paper focuses on jamming...     

Real-time face detection and lip feature extraction using field-programmable gate arrays.

This paper proposes a new technique for face detection and lip feature extraction. A real-time field-programmable gate array (FPGA) implementation of the two proposed techniques is also presented. Face detection is based on a naive Bayes classifier that classifies an edge-extracted representation of an image. Using edge representation significantly reduces the model's size to only 5184 B, which is 2417 times smaller than a comparable statistical modeling technique, while achieving an 86.6% correct detection rate under various lighting conditions. Lip feature extraction uses the contrast around the lip contour to extract the height and width of the mouth, metrics that are useful for speech filtering. The proposed FPGA system occupies only 15050 logic cells, or about six times less than a current comparable FPGA face detection system.     

A low-power readout circuit for nanowire based hydrogen sensor

This paper presents a fully integrated lock-in amplifier intended for nanowire gas sensing. The nanowire will change its conductivity according to the concentration of an absorbing gas. To ensure an accurate nanowire impedance measurement, a lock-in technique is implemented to attenuate the low frequency noise and offset by synchronous demodulation or phase-sensitive detection (PSD). The dual-channel lock-in amplifier also provides both resistive and capacitive information of the nanowire in separate channels. Measurement results of test resistors and capacitors show a 2% resolution in the resistance range 10-40 kΩ and a 3% resolution in the capacitance range 0.5-1.8 nF. Moreover, a 28.7-32.1 kΩ impedance variation was measured through the lock-in amplifier for a single palladium nanowire that was exposed to a decreasing hydrogen concentration (10% H₂ in N₂ to air). The chip has been implemented with UMC 0.18 μm CMOS technology and occupies an area of 2 mm². The power consumption of the readout circuit is 2 mW from a 1.8 V supply.     

Hydrogen Generation from CO2 Reforming of Biomass-Derived Methanol on Ni/SiO2 Catalyst

Topics in Catalysis - CO2 reforming of methanol for producing hydrogen was experimentally carried out in a fixed-bed reactor on 10%Ni/SiO2. The 10%Ni/SiO2 was completely reduced during H2...     

A semi-interactive panorama based 3D reconstruction framework for indoor scenes

We present a semi-interactive method for 3D reconstruction specialized for indoor scenes which combines computer vision techniques with efficient interaction. We use panoramas, popularly used for visualization of indoor scenes, but clearly not able to show depth, for their great field of view, as the starting point. Exploiting user defined knowledge, in term of a rough sketch of orthogonality and parallelism in scenes, we design smart interaction techniques to semi-automatically reconstruct a scene from coarse to fine level. The framework is flexible and efficient. Users can build a coarse walls-and-floor textured model in five mouse clicks, or a detailed model showing all furniture in a couple of minutes interaction. We show results of reconstruction on four different scenes. The accuracy of the reconstructed models is quite high, around 1% error at full room scale. Thus, our framework is a good choice for applications requiring accuracy as well as application requiring a 3D impression of the scene.     

Perspectives on crowdsourcing annotations for natural language processing

Crowdsourcing has emerged as a new method for obtaining annotations for training models for machine learning. While many variants of this process exist, they largely differ in their methods of motivating subjects to contribute and the scale of their applications. To date, there has yet to be a study that helps the practitioner to decide what form an annotation application should take to best reach its objectives within the constraints of a project. To fill this gap, we provide a faceted analysis of crowdsourcing from a practitioner’s perspective, and show how our facets apply to existing published crowdsourced annotation applications. We then summarize how the major crowdsourcing genres fill different parts of this multi-dimensional space, which leads to our recommendations on the potential opportunities crowdsourcing offers to future annotation efforts.