Theoretical Study on Thin Film Dye Sensitized Photovoltaic Solar Cells

This thesis presents two models of a dye-sensitized solar cell (DSC): diffusion model and electrical model. The main purpose is to investigate interfacial charge transfer and charge transport within the semiconductor/electrolyte layer under illuminated conditions. These two interrelated models confirm that diffusion is the major driving force for electron and ion transport, while the drift of electrons is negligible. The diffusion model was utilized to simulate the temperature influence on the overall efficiency of DSC with a consideration of the voltage loss at titanium dioxide (TiO2)/ transparent conductive oxide (TCO) interface. It reveals that low temperature conditions have serious detrimental effects on the DSCs' performance. Further the electrical model was used to analyze the effect of diffusion/drift, dye loading, and electrode thickness on DSC performance. The predicted optimal electrode thickness ranges between 10-15 μm which is consistent with the thickness (10 μm) used in experimental studies published in the literature

The sustainable approach to the green space layout in highdensity urban environment: a case study of Macau peninsula

AbstractBased on a case study of Macau peninsula, this paper explores a sound approach to the urban green space development in high-density urban environment that could enhance the sustainability of the city

Pedestrian Accessible Infrastructure Inventory: Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types

In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure segmentation workflow is designed and optimized, which is capable of efficiently processing multi-sourced geospatial data including LiDAR data and satellite imagery data. We used an expanded definition of pedestrian infrastructure inventory which goes beyond the traditional transportation elements to include street furniture objects that are important for accessibility but are often omitted from the traditional definition. Our contributions lie in producing the necessary knowledge to answer the following two questions. First, which data representation can facilitate zero-shot segmentation of infrastructure objects with SAM? Second, how well does the SAM-based method perform on segmenting pedestrian infrastructure objects? Our findings indicate that street view images generated from mobile LiDAR point cloud data, when paired along with satellite imagery data, can work efficiently with SAM to create a scalable pedestrian infrastructure inventory approach with immediate benefits to GIS professionals, city managers, transportation owners, and walkers, especially those with travel-limiting disabilities, such as individuals who are blind, have low vision, or experience mobility disabilities

Sonochemical surface functionalization of exfoliated LDH:effect on textural properties, CO2 adsorption, cyclic regeneration capacities and subsequent gas uptake for simultaneous methanol synthesis

To improve CO2 adsorption, amine modified Layered double hydroxide (LDH) were prepared via a two stage process, SDS/APTS intercalation was supported by ultrasonic irradiation and then followed by MEA extraction. The prepared samples were characterised using Scanning electron microscope-Energy dispersive X-ray spectroscopy (SEM-EDX), X-ray Photoelectron Spectroscopy (XPS), X-ray diffraction (XRD), Temperature Programmed Desorption (TPD), Brunauer-Emmett-Teller (BET), and Thermogravimetric analysis (TGA), respectively. The characterisation results were compared with those obtained using the conventional preparation method with consideration to the effect of sonochemical functionalization on textural properties, adsorption capacity, regeneration and lifetime of the LDH adsorbent. It is found that LDHs prepared by sonochemical modification had improved pore structure and CO2 adsorption capacity, depending on sonic intensity. This is attributed to the enhanced deprotonation of activated amino functional groups via the sonochemical process. Subsequently, this improved the amine loading and effective amine efficiency by 60% of the conventional. In addition, the sonochemical process improved the thermal stability of the adsorbent and also, reduced the irreversible CO2 uptake, CUirrev, from 0.18 mmol/g to 0.03 mmol/g. Subsequently, improving the lifetime and ease of regenerating the adsorbent respectively. This is authenticated by subjecting the prepared adsorbents to series of thermal swing adsorption (TSA) cycles until its adsorption capacity goes below 60% of the original CO2 uptake. While the conventional adsorbent underwent a 10 TSA cycles before breaking down, the sonochemically functionalized LDH went further than 30 TSA cycles

Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement

We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.Comment: In IJCV 201

Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting

Radio frequency fingerprinting (RFF) is a promising device authentication technique for securing the Internet of things. It exploits the intrinsic and unique hardware impairments of the transmitters for RF device identification. In real-world communication systems, hardware impairments across transmitters are subtle, which are difficult to model explicitly. Recently, due to the superior performance of deep learning (DL)-based classification models on real-world datasets, DL networks have been explored for RFF. Most existing DL-based RFF models use a single representation of radio signals as the input. Multi-channel input model can leverage information from different representations of radio signals and improve the identification accuracy of the RF fingerprint. In this work, we propose a novel multi-channel attentive feature fusion (McAFF) method for RFF. It utilizes multi-channel neural features extracted from multiple representations of radio signals, including IQ samples, carrier frequency offset, fast Fourier transform coefficients and short-time Fourier transform coefficients, for better RF fingerprint identification. The features extracted from different channels are fused adaptively using a shared attention module, where the weights of neural features from multiple channels are learned during training the McAFF model. In addition, we design a signal identification module using a convolution-based ResNeXt block to map the fused features to device identities. To evaluate the identification performance of the proposed method, we construct a WiFi dataset, named WFDI, using commercial WiFi end-devices as the transmitters and a Universal Software Radio Peripheral (USRP) as the receiver. ..

Realizing In-Memory Baseband Processing for Ultra-Fast and Energy-Efficient 6G

To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-efficient baseband processors. Traditional complementary metal-oxide-semiconductor (CMOS)-based baseband processors face two challenges in transistor scaling and the von Neumann bottleneck. To address these challenges, in-memory computing-based baseband processors using resistive random-access memory (RRAM) present an attractive solution. In this paper, we propose and demonstrate RRAM-implemented in-memory baseband processing for the widely adopted multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) air interface. Its key feature is to execute the key operations, including discrete Fourier transform (DFT) and MIMO detection using linear minimum mean square error (L-MMSE) and zero forcing (ZF), in one-step. In addition, RRAM-based channel estimation module is proposed and discussed. By prototyping and simulations, we demonstrate the feasibility of RRAM-based full-fledged communication system in hardware, and reveal it can outperform state-of-the-art baseband processors with a gain of 91.2$\times$ in latency and 671$\times$ in energy efficiency by large-scale simulations. Our results pave a potential pathway for RRAM-based in-memory computing to be implemented in the era of the sixth generation (6G) mobile communications.Comment: arXiv admin note: text overlap with arXiv:2205.0356

Study of Ammonia Concentration Characteristics and Optimization in Broiler Chamber during Winter Based on Computational Fluid Dynamics

Poultry breeding is one of the most significant components of agriculture and an essential link of material exchange between humans and nature. Moreover, poultry breeding technology has a considerable impact on the life quality of human beings, and could even influence the survival of human beings. As one of the most popular poultry, broiler has a good economic benefit due to its excellent taste and fast growing cycle. This paper aims to improve the efficiency of raising broilers by understanding the impact of ammonia concentration distribution within a smart broiler breeding chamber, and the rationality of the system’s design. More specifically, we used computational fluid dynamics (CFD) technology to simulate the process of ammonia production and identify the characteristics of ammonia concentration. Based on the simulation results, the structure of the broiler chamber was reformed, and the ammonia uniformity was significantly improved after the structural modification of the broiler chamber and the ammonia concentration in the chamber had remained extremely low. In general, this study provides a reference for structural optimization of the design of broiler chambers and the environmental regulation of ammonia