Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Office System with Face Detection at the Edge

The rapid increase in the number of Internet connected devices has placed a high level of demand on both the network bandwidth and the processing power currently available to the centralized physical datacentres that embody the 'Cloud'. To alleviate such a demand and relieve the network capacity, the Edge Computing paradigm was recently proposed. Such a paradigm evangelizes the processing of data locally, closer to users and so avoiding the slow communication of data to centralized datacentres. The realization of such a paradigm requires the evaluation of smart applications on various potential energy efficient devices by understanding their processing and storage limits, while also looking for efficient methods to improve their capabilities. In this paper, we develop and evaluate an end to end smart office application on a representative Edge device, the latest Raspberry-Pi, while utilizing existing Cloud on Demand services connected through an Android application and Amazon Alexa Skill. The developed solution monitors various environmental conditions and is able to recognize users using facial recognition. We will evaluate the requirements of such an application on an Edge device and explore methods to reduce the data stored and processed, while evaluating the impact this has on the detection rate. In particular, we evaluate the effectiveness of increasing the image compression levels by measuring the level of compression used versus the time to create trained data and the face detection rate it produces. Our experimentation shows favourable results for the Edge system implementation, while also supporting the possibility of the hybridisation of Cloud and local processing to achieve complex tasks while minimising network use.
A comparison of grid-type map-building techniques by index of performance

An index of performance (IOP) designed to quantitatively express the match between a sensor-built map and a precisely measured reference map is introduced. The IOP computes a single value representing the correlation between the sensed object positions in the grid and the actual object positions. With the IOP, it is easy to compare the accuracy of different map-building methods as well as the effect of different parameters within a certain method. Two grid-type map-building algorithms were compared by means of the proposed IOP. One algorithm takes panoramic snapshots while the mobile robot is standing and uses a probabilistic distribution to update the grid. The other algorithm, called histogramic in-motion mapping, is based on rapid sampling of the sensors during motion. <<ETX>>
The effectiveness of virtual reality training in reducing the risk of falls among elderly people

Background Virtual reality (VR) training using modern game consoles is an innovative rehabilitation method for fall-prone elderly people. The aim of this study was to assess the effectiveness of VR training using the “Xbox 360 Kinect” in people over 60 years of age. Materials and methods The study involved 23 people, including 19 women and 4 men (mean age 75.74±8.09 years). The following functional tests were employed as research instruments: the 6-minute walking test (6MWT), the Dynamic Gait Index (DGI), the tandem stance test (TST), the tandem walk test (TWT), and the Beck Depression Inventory (BDI). A “spring hand dynamometer” was also used. The participants underwent 30-day VR training using an Xbox 360 Kinect. They trained 3 times a week, with each exercise lasting 30 minutes. Results The 6MWT (P<0.001), the DGI (P=0.008), the TST (P<0.001), the TWT (P=0.002), and the BDI (P<0.001) outcomes were significantly improved. There were differences in the results for the strength of the “pressing muscles” in the right (P=0.106) and left (P=0.043) hands of the participants. Both participants under 80 years of age and those aged 80 years and over had visibly better results on the 6MWT (P<0.001 and P=0.008, respectively), the TST (P<0.001 and P=0.008, respectively), and the BDI (P=0.003 and P=0.012, respectively). Conclusion Training based on VR increases the possibilities of motor training and can help reduce the risk of falls by improving the static and dynamic balance.
Improved Tx-to-Rx Isolation of Radar Transceivers Using Integrated Full Duplexer with PLL

The isolation between transmitter and receiver for the radars is ultimately important since the transmitting radar signals can be penetrated the receiver directly in case a bad isolation is formed. This paper describes the isolation between the transceiver and the design of an integrated full duplexer using the phase locked loop (PLL). Although the antenna impedance varies arbitrarily, the PLL tracks the impedance variation in real time, leading to improvement of isolation between the transmitter and the receiver of radars. The full duplexer reduces the transmitter leakage up to 45 dB using the balance network along with the PLL in measurement.
Deep Learning for Fusion of APEX Hyperspectral and Full-Waveform LiDAR Remote Sensing Data for Tree Species Mapping

Deep learning has been widely used to fuse multi-sensor data for classification. However, current deep learning architecture for multi-sensor data fusion might not always perform better than single data source, especially for the fusion of hyperspectral and light detection and ranging (LiDAR) remote sensing data for tree species mapping in complex, closed forest canopies. In this paper, we propose a new deep fusion framework to integrate the complementary information from hyperspectral and LiDAR data for tree species mapping. We also investigate the fusion of either “single-band” or multi-band (i.e., full-waveform) LiDAR with hyperspectral data for tree species mapping. Additionally, we provide a solution to estimate the crown size of tree species by the fusion of multi-sensor data. Experimental results on fusing real APEX hyperspectral and LiDAR data demonstrate the effectiveness of the proposed deep fusion framework. Compared to using only single data source or current deep fusion architecture, our proposed method yields improvements in overall and average classification accuracies ranging from 82.21% to 87.10% and 76.71% to 83.45%, respectively.
Evaluating and Ensuring the Cybersecurity of Power Line Remote Monitoring Systems

The article analyzes cyber threats for power line remote monitoring systems using UAVs. Possible attack vectors of cyber attacks on the UAV equipment used for power line monitoring are shown. An improved method for the evaluation of security threats and their associated risks is provided, which allows to estimate the robustness of the monitoring systems to cyber attacks. A set of measures is described, aimed at increasing the sustainability of the power line monitoring system to cyber attacks by increasing the resources necessary to carry out attacks, both in terms of energy and equipment required.
A Fusion Information Embedding Method for User Identity Matching Across Social Networks

Aiming at the deficiencies of low accuracy and difficulty in obtaining data for existing cross social network user identity matching algorithms, an algorithm of user identity matching across social networks based on information fusion representation was proposed. Firstly, the seed nodes were used to transform the cross-network problem into a single network problem by using the network merging algorithms. Then the username information was turned to vectors and merged with the topology vector. Finally, with the network representation learning method, account nodes' vectors with information of usernames and topology were acquired for the mission of user identity matching. Experimental results showed that the average F1 measure reached 79.7%, which is improved by 7.3%-28.8% compared with traditional machine learning algorithms and the existing other two algorithms. It can be seen that our algorithm can effectively improve the performance of user identity matching.
Individual Analysis Model and Related Research in Social Networks

As the rapid development of mobile Internet, social networking platforms make it possible for people to create, communicate and share information with each other whenever and wherever they are. The mode and effect of information propagation within social networks can be directly determined by users and their interactions. Individual analysis, as the key part of social network analysis, helps to understand the sophisticated mechanism of information dissemination from the perspective of users. Existing work all analyzes individual users primarily based on their behaviors or characteristics embedded in social networking data. In this paper, we have summarized a novel individual analysis model to unify tag recommendation, influence measurement and sentiment analysis with regard to the same user, which is convenient for social scientists to further study. We also introduce some classic and modified algorithms derived from the proposed models respectively, and illustrate how these algorithms map to models. Furthermore, our unified model can offer a reference and point out the optimization directions for individual analysis in social networks.
Efficiency Analysis of a Buck Synchronous Converter under Snubber and Gate Drive Techniques

This work presents an analysis of the efficiency of a Buck Synchronous converter considering the influence of snubber networks and the gate drives operation with different output powers and switching frequencies. The quantification of the converter efficiency will be carried out by experimentally measured efficiency curves, those curves relate the performance of the system vs the output power. The results will be presented and discussed.
Impedance-Based Sensitivity-Criterion for Grid-Connected Three-Phase Inverters

Utilization of renewable energy sources such as solar and wind power requires affordable, reliable and efficient methods for grid connection. Most often, grid-parallel inverters are applied. As the number of inverter-connected resources in the power grid increases, they start to have a significant effect on the dynamics of the power system. The inverters have been shown to produce harmonic oscillations. To avoid producing or amplifying the harmonic content in the power grid currents and voltages, sensitivity of the inverter against external distortion should be analyzed. This paper provides a method for sensitivity analysis of the inverter. The sensitivity is analyzed by applying a forbidden region in the complex plane, which is based on the maximum-peak criterion that is often applied in the field of control engineering. The system is defined to have robust stability if the minor-loop gain formed by the inverter output impedance and the grid impedance stays out from the forbidden region.
Ship-Based Exploration of Submarine Gas Hydrates and Environmental Monitoring with Multibeam Echo Sounders

Submarine gas hydrate deposits exist in almost all continental slopes. However, the dimensions and storage conditions of these deposits are insufficiently documented. Economically interesting deposits are often associated with gas flares in the water column which can be detected by multibeam echo sounders. During the production of methane and a possible future long-term storage of carbon dioxide in the deposits, ship-based multibeam techniques are also important for environmental monitoring and site safety verification. Developed as a result of the German research project SUGAR (Submarine Gas Hydrate Reservoirs), the ELAC SeaBeam 3030/3050 multibeam echo sounders provide a wide functional scope for the identification of gas flares: The online visualization of high-resolution water column image (WCI) data, automatic object detection, frequency-modulated signals and an automatic cyclical steering of the transmitted swaths during stationary vessel operations are very helpful to detect gas flares. ELAC SeaBeam 3050 systems have been successfully applied on scientific cruises regarding the detection of submarine gas hydrates, the analysis of gas flares caused by volcanic gas discharge and the investigation of abandoned drilling wells.
Smart Object for Physical Rehabilitation Assessment

Currently there are fewer systems that provide the patient monitoring during the physical rehabilitation process by physiotherapists, which may lead to less adequate diagnostic techniques for the patient's physical condition. In this work an IoT healthcare system designed for physical rehabilitation that includes a smart crutch with sensors, embedded processing and communication is reported. Based on the smart crutch it is possible to continuously monitor the evolution of the gait and balance making correlations with rehabilitation outcomes. Additionally, a mobile application for smart phone or tablet was developed to assure advanced processing, data visualization but also data synchronization with a remote database. Experimental results are also included in the paper.
Inexhaustible Battery: An Energy Harvester Based Battery for IoT Applications

We present the first energy harvester-based battery for use in sensor systems as a direct drop-in solution. The device is packaged in a standard CR2477 form factor, consisting of a vibration-based Aluminum nitride energy harvester and a power management IC (PMIC). This battery functions like a regular button cell and is able to charge up when the environmental vibration is as low as 0.85g and as high as 10.17g, with a steady DC voltage of 3.8V, which can be programmed in the PMIC. Charging up the battery requires about 16s @ 1.19g before a steady DC current is produced.
TDOA-based passive localization of standard WiFi devices

Indoor location-based service has widespread applications. With the ubiquitous deployment of WiFi systems, it is of significant interest to provide location-based service using standard WiFi devices. Most of the existing WiFi-based localization techniques are based on Received Signal Strength (RSS) measurements. As the bandwidth of WiFi systems increases, it is possible to achieve accurate timing-based positioning. This work presents a WiFi-based positioning system that has been developed at CSIRO as a research platform, where target devices are located using passive sniffers that measure the Time Difference of Arrival (TDOA) of the packets transmitted by the target devices. This work describes the architecture, hardware, and algorithms of the system, including the techniques used for clock synchronizing and system calibration. It is shown experimentally that the positioning error is 23 cm in open spaces and 1.5 m in an indoor office environment for a 80MHz WiFi system. The system can be used to track standard WiFi devices passively without interfering with the existing WiFi infrastructure, and is ideal for security applications.
Bufalin-loaded vitamin E succinate-grafted-chitosan oligosaccharide/RGD conjugated TPGS mixed micelles demonstrated improved antitumor activity against drug-resistant colon cancer

Background Multidrug resistance (MDR) is the major reason for the failure of chemotherapy in colon cancer. Bufalin (BU) is one of the most effective antitumor active constituents in Chansu. Our previous study found that BU can effectively reverse P-glycoprotein (P-gp)-mediated MDR in colon cancer. However, the clinical application of BU is limited due to its low solubility in water and high toxicity. In the present study, a multifunctional delivery system based on vitamin-E- succinate grafted chitosan oligosaccharide (VES-CSO) and cyclic (arginine-glycine-aspartic acid peptide) (RGD)-modified d-alpha-tocopheryl polyethylene glycol 1000 succinate (TPGS) was prepared by emulsion solvent evaporation method for targeted delivery of BU to improve the efficacy of drug-resistant colon cancer therapy. Methods The cytotoxicity of BU-loaded micelles against drug-resistant colon cancer LoVo/ADR and HCT116/LOHP cells was measured by CCK-8 assay. The cellular uptake, Rho123 accumulation, and cell apoptosis were determined by flow cytometry. The expression of apoptosis-related protein and P-gp was measured by Western blot assay. The antitumor activity of BU-loaded micelles was evaluated in LoVo/ADR-bearing nude mice. Results BU-loaded VES-CSO/TPGS-RGD mixed micelles (BU@VeC/T-RGD MM) were 140.3 nm in diameter with zeta potential of 8.66 mV. The BU@VeC/T-RGD MM exhibited good stability, sustained-release pattern, higher intracellular uptake, and greater cytotoxicity in LoVo/ADR cells. Furthermore, the mechanisms of the BU@VeC/T-RGD MM to overcome MDR might be due to enhanced apoptosis rate and P-gp efflux inhibition. Subsequently, in vivo studies confirmed an enhanced therapeutic efficiency and reduced side effects associated with BU@VeC/T-RGD MM compared with free BU, owing to the enhanced permeation and retention effect, improved pharmacokinetic behavior, and tumor targeting, which lead to MDR-inhibiting effect in LoVo/ADR-bearing nude mice. Conclusion Our results demonstrated that VeC/T-RGD MM could be developed as a potential delivery system for BU to improve its antitumor activity against drug-resistant colon cancer.
Dual-Band 3D Frequency Selective Surface Based on Square Coaxial Waveguide

This paper presents a dual-band three dimensional (3D) frequency selective surface (FSS), which is based on the square coaxial waveguide (SCW). The proposed 3D FSS provides two propagation paths which are supported by SCW and parallel-plate waveguide (PPW) with gaps in printed copper and shorting vias. By this design, each propagation path can construct multimode resonators, thus two passbands with two transmission poles are obtained. A transmission zero located at higher frequencies improves the out-of-band rejection by the reflection. These two pass-band frequencies can be controlled independently through simple adjustment of the design parameters. A design exapmle shows dual polarizations and a stable performance under a large variation of incident angle.
Flow Noise Calculation and Suppression for Sonobuoy

The sea waves can make sonobuoy vibrate in the vertical direction. The vibration can create flow noise which will affect the work performance of the sonobuoy. It can be suppressed by adding flexible cable to the structure of the sonobuoy. In this paper, we first establish a theoretical model used in calculating the flow noise of sonobuoy to analyze the corresponding relationship between the flow noise and the vibration velocity. Then we establish a forced vibration model of the sonobuoy to analyze the effect of flexible cable on suppressing flow noise caused by vibration of the sonobuoy. According to the simulation results calculated by the flow noise theoretical calculation model, we find that the spectrum level of the flow noise increases with the increase of the flow velocity. According to the simulation result calculated by the sonobuoy vibration model, we find that choosing an appropriate elastic coefficient for the flexible cable can effectively suppress the vibration of the sonobuoy caused by the sea waves. As a consequence, the flow noise of the sonobuoy can be reduced effectively.
Recovery of Rate Constants from Rank Deficient Kinetic Data: Comparison of MCR Approaches

Measurement of rate constants of various processes is crucial both for research and for industrial design. It is typically done by fitting kinetic models to experimental data, which are mostly multivariate nowadays. These data often bear ill properties like noise and/or rank deficiency, which may ruin the relevance of the obtained results. In the present work we tested the ability of three well defined multivariate curve resolution approaches (model-free MCR-ALS, model-based MCR-ALS and full-matrix non-linear least square fit) to recover rate constants from simple simulated kinetic data when the spectra of components are almost linearly dependent. Model-free estimation of concentrations produced incorrect estimates. Model-based analysis was possible, but the quality of estimates of rate constants and convergence were strongly degraded by rank deficiency. Full-matrix non-linear least square fit displayed the best results both with respect to accuracy of the estimates and calculation performance. It proved its known value of the method of choice. The quality of the estimates depended strongly on the ratio between the rate constants: cases with highly similar and highly dissimilar rate constants were more problematic. The reasons of poor convergence of ALS algorithms and sources of bias in the estimates of rate constants are discussed.
Soft Sensors for Estimating Laundry Weight in Household Heat Pump Tumble Dryers

The laundry weight of the loaded in the drum of a laundry treatment machine is an important piece of information; laundry weight can be used to set various washing/drying cycle parameters and to optimize performances and efficiency. Unfortunately, dedicated weight sensors cannot be included in consumer laundry equipment given the related costs. For this reason, we present in this work a soft sensor approach for estimating laundry weight based on sensors already in place in a laundry treatment equipment; in particular, we consider here a heat pump tumble dryer as case study. The proposed soft sensor is based on regularization, a popular approach in Machine Learning to provide models without overfitting the training data. Different studies are provided in this work, by considering different constrains on timing and complexity of the Soft Sensor solution. The developed Soft Sensors have been tested on laboratory data provided by an industrial partner.
A revision of the genus Littorina (Mollusca: Gastropoda) in Korea

ABSTRACT Littorina Férussac, 1822 is an abundant genus of small gastropods found in the upper littoral zone of rocky seashores worldwide. Although ecologically important, shell-based species identification in this genus is challenging due to phenotypic variation in shell morphology and lack of diagnostic characters among morphologically similar species. In this study, we revised the taxonomy of Korean Littorina species using morphological characters (shell and radula) and cox1 mitochondrial DNA sequences for three Korean species: L. brevicula, L. sitkana, and L. horikawai. Results suggest that L. sitkana was erroneously reported as L. kasatka in a previous study. A new record for Littorina horikawai (Matsubayashi & Habe in Habe, 1979), previously unknown from Korea, is described, which can be distinguished from L. sitkana by the presence of alternating white and brown spiral ribs on each whorl. Comparison of the mtDNA cox1 gene sequences shows very low intraspecific variation even between geographically distant populations. A phylogenetic tree supports a close relationship between L. horikawai and L. sitkana, consistent with earlier phylogenetic studies.
Coupled Collective Matrix Factorization

Collective Matrix Factorization (CMF) makes rating prediction by jointly factorizing multiple matrices in recommender systems (RS), which also provides a unified view of matrix factorization. However, CMF does not directly involve the user attributes and item attributes that represent the intrinsic characteristics of users and items, so it fails to capture the coupling relationships within and between entities, such as users and items, which represent low-level data characteristics and complexities and drive the rating dynamics. In this work, we propose a coupled CMF (CCMF), which not only accommodates entity attributes into rating prediction, but also incorporates the couplings within and between entities into CMF. Therefore, CCMF not only captures the latent variable-based relationships between ratings and specific dimensions at high levels, but also captures the underlying driving forces, i.e., the hierarchical couplings within and between entities representing the low-level data characteristics and complexities. This work also presents a unified framework of CCMF in RS. Experimental results on two real data sets show that our proposed model outperforms the MF-based approaches.