123 research outputs found
PrivacyGuard: A VPN-Based Approach to Detect Privacy Leakages on Android Devices
Full text linkI hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii The Internet is now the most important and ecient way to gain information, and mobile devices are the easiest way to access the Internet. Furthermore, wearable devices, which can be considered to be the next generation of mobile devices, are becoming popular. The more people rely on mobile devices, the more private information about these people can be gathered from their devices. If a device is lost or compromised, much private information is revealed. Although today’s smartphone operating systems are trying to provide a secure environment, they still fail to provide users with adequate control over and visibility into how third-party applications use their private data. The privacy leakage problem on mobile devices is still severe. For example, according a field study [1] done by CMU recently, Android applications track users ’ location every three minutes in average
SUSTAINABILITY REPORTING IN SINGAPORE: SETTING THE BOUNDARIES FOR WHAT IS "MATERIAL"
No full textBACHELOR'SBachelor of Laws (Honours) (LL.B.
Author Correction: Digital automation of transdermal drug delivery with high spatiotemporal resolution
Full text linkAuthor Correction to "Digital automation of transdermal drug delivery with high spatiotemporal resolution
Deep learning bird song recognition based on MFF-ScSEnet
No full textBird diversity plays an important role in ecological balance, and bird song identification is of great practical significance. The spectrum generated by feature extraction shows good performance on classification. However, the information extracted by the filter in the process of spectrogram generation can cause information loss, which limits the learning ability of birdsong recognition. This study proposes a feature fusion network (MFF-ScSEnet) to solve this problem. The audios of the birdsong extracted the Mel-spectrogram with low-frequency feature advantage by the Mel-filter, and the Sinc-spectrogram with timbral feature advantage by the Sincnet-filter, respectively, and perform the early fusion strategy. The ScSEnet attention module is introduced into the backbone network ResNet18 to enhance the sound ripple information of the spectrogram, reduce the influence of spectrogram noise information on the recognition and improve the recognition performance of the network. Based on the feature fusion network MFF-ScSEnet in this paper, the accuracy of the experimental results on the self-built birdsong dataset (Huabei_dataset), the public datasets of Urbansound8K and Birdsdata reached 96.28%, 98.34%, and 96.66%, respectively. The results indicated that the method proposed in this paper is superior to the recent and latest birdsong recognition method
Biomass Burning Greatly Enhances the Concentration of Fine Carbonaceous Aerosols at an Urban Area in Upper Northern Thailand: Evidence From the Radiocarbon‐Based Source Apportionment on Size‐Resolved Aerosols
No full textTo study the role of biomass burning (BB) in air pollution at upper‐northern Thailand, the source apportionment of size‐resolved carbonaceous aerosols from Chiang Mai was carried out based on the radiocarbon (14C) analysis. The fraction of modern carbon (F14C) was generally decreased with particle size increasing and with the highest and lowest values of 0.90 ± 0.04 and 0.61 ± 0.04, respectively. Elemental carbon, regardless of emission sources, and BB‐derived organic carbon (OCbb) showed unimodal size distribution patterns with peaks at 0.43–0.65 μm. Fossil‐fuel derived‐OC (OCf) displayed a bimodal mode with the major peak at 2.1–10 μm, and the minor one at 0.43–0.65 μm. The biogenic secondary organic aerosols (BSOA) showed a typical fine‐mode unimodal size distribution pattern during the high BB (HBB) season, and a bimodal mode during the low BB season. The BSOA concentration increased by 189% ± 80% due to the interaction with open BB plums during HBB season, which was quantified by a 14C‐involved random forest model. Besides, the concentration of biogenic primary organic aerosols also showed a significant increment during the HBB season, especially in sub‐microns. Our results highlight the critical importance of controlling open fires to reduce air pollutants and the potential exposure risk
Rethinking Power Efficiency for Next-Generation Processor-Free Sensing Devices
No full textThe last decade has seen significant advances in power optimization for IoT sensors. The conventional wisdom considers that if we reduce the power consumption of each component (e.g., processor, radio) into μW-level of power, the IoT sensors could achieve overall ultra-low power consumption. However, we show that this conventional wisdom is overturned, as bus communication can take significant power for exchanging data between each component. In this paper, we analyze the power efficiency of bus communication and ask whether it is possible to reduce the power consumption for bus communication. We observe that existing bus architectures in mainstream IoT devices can be classified into either push-pull or open-drain architecture. push-pull only adapts to unidirectional communication, whereas open-drain inherently fits for bidirectional communication which benefits simplifying bus topology and reducing hardware costs. However, open-drain consumes more power than push-pull due to the high leakage current consumption while communicating on the bus. We present Turbo, a novel approach introducing low power to the open-drain based buses by reducing the leakage current created on the bus. We instantiate Turbo on I2C bus and evaluate it with commercial off-the-shelf (COTS) sensors. The results show a 76.9% improvement in power efficiency in I2C communication
Real-Time Interpretation Model of Reservoir Characteristics While Underbalanced Drilling Based on UKF
No full textThis study presents a novel interpretation model for reservoir characteristics while underbalanced drilling (UBD), by incorporating an unscented Kalman filter (UKF) algorithm in a three-phase variable mass flow model of oil, gas, and liquid. In the model, the measurement parameters are simplified to bottomhole pressure and liquid outlet flow, for decreasing the amount of the computation and time. By taking into account real-time measurements, the permeability and reservoir pressure along the well can be continuously updated. Three cases including single-parameter and double-parameter estimations have been simulated, and the performance is tested against the extended Kalman filter (EKF). The results show that single-parameter estimation of reservoir permeability or pressure achieves superior performance. The filtered values of bottomhole pressure and outlet flow trace the measured values in real time. When a new section of a reservoir is opened, the estimated reservoir permeability or pressure can always be quickly and accurately returned to its true value. However, it is not possible for the double-parameter estimation to obtain good results; its interpretation accuracy is low. UKF is superior to EKF in both estimation accuracy and convergence speed, which further illustrates the superiority and accuracy of the novel interpretation model based on UKF. Benefits from this model are seen in accurate bottomhole pressure and reservoir characteristic predictions, which are of major importance for safety and economic reasons during UBD and follow-up completion operations
An Optimization Model for Large–Scale Wind Power Grid Connection Considering Demand Response and Energy Storage Systems
No full textTo reduce the influence of wind power output uncertainty on power system stability, demand response (DRPs) and energy storage systems (ESSs) are introduced while solving scheduling optimization problems. To simulate wind power scenarios, this paper uses Latin Hypercube Sampling (LHS) to generate the initial scenario set and constructs a scenario reduction strategy based on Kantorovich distance. Since DRPs and ESSs can influence the distribution of demand load, this paper constructs a joint scheduling optimization model for wind power, ESSs and DRPs under the objective of minimizing total coal cost, and constraints of power demand and supply balance, users’ demand elasticity, thermal units’ startup-shutdown, thermal units’ output power climbing and wind power backup service. To analyze the influences of ESSs and DRPs on system wind power consumption capacity, example simulation is made in a 10 thermal units system with a 1000 MW wind farm and 400 MW energy storage systems under four simulation scenarios. The simulation results show that the introduction of DRPs and ESSs could promote system wind power consumption capacity with significantly economic and environment benefits, which include less coal consumption and less pollutant emission; and the optimization effect reaches the optimum when DRPs and ESSs are both introduced
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