Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis towards Synergetic Test of Wi-Fi Signal for Indoor Localization

With the fast growth of demand for the ubiquitous, precise, and instant indoor location information, the Received Signal Strength (RSS) based Wi-Fi indoor localization has been greeted with an avalanche of publicity. The studies in this field so far rarely consider the diversity of Wi-Fi signals, and thereby the RSS measures involving gross error on account of the complicated indoor environment deteriorate localization accuracy. In response to this compelling problem, we propose to use the concept of Asymptotic Relative Efficiency (ARE) to design a new synergetic test of Wi-Fi signal for indoor localization. Specifically, first of all, the Jarque-Bera (JB) test is conducted to test the normality of Wi-Fi signals at each Reference Point (RP). Second, the result of JB test is fed into the synergetic Mann-Whitney U and T test to construct the set of matching RPs corresponding to the newly-collected RSS data. Finally, the location coordinate of the target is obtained by calculating the K-nearest neighbor of matching RPs. Furthermore, the experimental results demonstrate that the proposed approach is featured with higher localization accuracy compared with the existing Wi-Fi indoor localization approaches.
Sensorless Predictive Control for PMSM Using MRAS Observer

This article presents a sensorless MPC (Model Predictive Control) for a PMSM (Permanent Magnet Synchronous Motor) in which the rotor speed is provided by a MRAS (Model Reference Adaptive System) observer, and general control strategy is FOC (Field Oriented Control) type. The results of the numerical simulations show the superiority of the MPC control system compared to the FOC type control system in the case of parameter variation and rapidly varying torque load with a random component. The relatively low complexity of the number of arithmetic operations required to implement the estimation and control algorithms recommends the implementation of an embedded system in real time in future works.
Assessing the efficacy of appearance-focused interventions to prevent skin cancer: a systematic review of the literature

Abstract Purpose. Ultraviolet (UV) radiation is associated with adverse health effects. Although the immediate appearance values of tanning seem to outweigh the long-term health consequences, interventions emphasising the negative effects of UV exposure on appearance may be effective in modifying such behaviour. Method. A systematic review assessing the efficacy of appearance-focused interventions in modifying UV exposure and/or protection. Relevant publications were identified from a comprehensive search strategy of 16 electronic bibliographic databases from inception to March 2009, hand searches of six subject relevant journals and references lists of identified articles. Results. Data were extracted from 12 studies that were found to be eligible. Significant effects were found for UV protection behaviour in favour of the interventions. Though the interventions only had moderate success in modifying UV exposure behaviour, plausible explanations were provided. There was no clear pattern to suggest that quality of the studies or type of intervention was associated with effectiveness of the interventions. Conclusion. Appearance-focused interventions represent a promising method for modifying tanning behaviour. There is scope to evaluate the generalisability of these interventions on different populations and regions.
An Analogue Baseband Chain for a Magnetic Tunnel Junction Based RF Signal Detector

This work presents an analogue baseband (BB) chain for a magnetic tunneling junction (MTJ) based radiofrequency (RF) signal detector fully integrated in a hybrid CMOS-MTJ technology. The BB chain contains a 6th-order $\mathrm{g}_{\mathrm{m}} - \mathrm{C}$ low-pass filter (LPF), a BB amplifier, a comparator, and a current bank. According to measurement results, the 6th-order LPF with a cut-off frequency of 10 MHz consumes a very low DC power of 2.41 mW. Its DC power consumption per pole of 0.4 mW is the lowest among the state-of-the-art LPFs. The LPF can be also switched on and off very fast within 110 ns. With the fast switch-ability and the low power consumption, the LPF outperforms the state of the art. Furthermore, the complete BB chain can transform a 2.5 $\mathrm{mV}_{\mathrm{PP}}, 5$ Mbps BB signal into digital data with a bit error rate fewer than 10−6. The BB chain consumes 2.85mW including all bias circuits. To achieve power efficiency, the BB chain is designed to operate under an aggressive duty-cycling mode. The switch-on time of the BB chain is within 200 ns.
On Interference Pikes in Poisson Networks

The temporal dynamics of interference power is studied in Poisson networks with slotted random access and Rayleigh fading. Specifically, we analyze the occurrence of high interference events (pikes) and the time in between (valleys). Our main insight is that pikes arrive in bursts. This observation may help in the design of reliability techniques.
Integrating Machine Learning Technology to Data Analytics for E-Commerce on Cloud

This paper presents the technical challenges faced bye-commerce players and how cloud computing has been designed and developed to handle these technical challenges. Once these technical challenges have been handled, another challenge of building data analytics on cloud platforms come out. This paper also highlights the main reasons of driving organizations to build data analytics on cloud. Besides, this paper exhibits how to integrate machine learning models to the data analytic processes, for making more sophisticated analysis for e-commerce activities. The platform Amazon SageMaker is adopted to illustrate how machine learning models were integrated in the data analytic processes. The public cloud platform Amazon Web Services is used to present how machine learning models were integrated in the practices with a real-life c-commerce case.
Localization by inversion of the Taylor Expansion of the received power

This paper presents a localization algorithm based on the identification of a linear expression connecting a vector of mean received powers to a vector of Cartesian coordinates. The linear expression is based on a Taylor expansion of the received power collected on a set of measurement points. The terms of the Taylor expansion are then integrated in a transfer matrix able to predict a vector of received power from a composite vector of Cartesian coordinates. Using the pseudo inverse of this matrix it is then possible to find the Cartesian coordinates of any unknown reception point, from the vector of mean received powers at this point.
Node activity monitoring in heterogeneous networks using energy sensors

In Heterogeneous Networks, small cells are usually deployed without operator supervision. Their proper operation highly depends on their self-adaptation capability, especially in dense HetNets where various small cells coexist in the same macrocell. This capability requires the small-cell base stations to continuously sense the radio environment, so they can dynamically adapt their operational setting (e.g. transmission power, carrier/channel selection, etc.) to the environmental conditions. In this work we propose a new method for a small base station to monitor the activity of the rest of nodes in the macrocell. We consider a centralized sensing procedure based on the fusion of the energy levels measured by the users of the small cell at their locations. In particular, we present an efficient algorithm that enables the small base station to monitor the activity of the rest of nodes. In addition, the algorithm also provides the gain of the channels between the nodes and the users of the small cell.
Analysis of Link Loss in Wireless Mesh Networks Based on Field Trials in Disaster Communication Scenarios

In this paper, we analyse received signal strength data from large-scale field trials, simulating realistic user behavior during a disaster event in build-up areas. Based on a simple framework, we identify the overall behavior of the link loss versus distance, as well as the characteristics of random local variability in the link loss. Particularly, we analyse the cross-correlation in the random variation between pairs of links, in dependence of the separation angle between them. Given the extent of the data set, we believe the findings have statistical significance and can be used as a reference for calibrating models in simulation of mesh network performance. To this end, we discus the main contributing effects to link loss variability, including environment and body loss shadowing.
Joint Supervised Dictionary and Classifier Learning for Multi-View SAR Image Classification

A new multi-view sparse representation classification (SRC) algorithm based on joint supervised dictionary and classifier learning (MSRC-JSDC) is proposed for synthetic aperture radar (SAR) image classification. Unlike most existing sparse representation methods for SAR image classification, MSRC-JSDC learns a supervised sparse model from training samples by utilizing sample label information, rather than directly employs a predefined one. Moreover, a supervised classifier is jointly designed during dictionary learning, which can further promote the classification performance compared with unsupervised reconstruction based classifier. In the meantime, to enhance the representation capability of the sparse model, classification error is back propagated to the dictionary learning procedure to optimize dictionary atoms. In order to extract more recognition information from collected SAR images, a multi-view strategy is applied in testing stage. A new sparse constraint is introduced into multi-view sparse representation procedure so that both inner correlation and complementary information among multiple views can be extracted. This is helpful for alleviating the influence of SAR image’s sensitivity on classification performance in such challenging scenarios as large depression variation and noise corruption. Extensive experiments on the moving and stationary target acquisition and recognition (MSTAR) database demonstrate that the proposed method is more robust and performs better than some state-of-the-art approaches.
Computer-aided Diagnosis of Ambulatory Electrocardiograms via ASRS: Active-Selection-Random-Selection

Recently more and more ambulatory electrocardiograms (AECG) were put in clinical usage, casting heavy diagnosis workloads on cardiologists. Aiming to reduce cardiologists’ workloads as much as possible, in this paper, we propose an active learning framework, called Active-Selection-Random-Selection (ASRS), which combines convolutional neural network (CNN) as the classifier, and margin sampling as active selection criterion. ASRS trains a model from scratch without the need of a large database, and thus will be very useful in practice. The experimental results show that with SVEB and VEB’s F-1 scores as the evaluation metrics, using ASRS can save more than 90% of the total workloads compared to the standard practice of using the first five minutes of data for annotation.
A Machine Learning Approach to the Identification of Dynamical Nonlinear Systems

The aim of this paper is to present a general machine learning approach to the identification of nonlinear systems, using the observed input-output finite datasets. The approach is derived representing the input and output signals in the feature space by the principal component analysis (PCA), thus transforming the nonlinear time dependent identification problem to the regression of a nonlinear input-output function. To face this problem an effective machine learning technique based on particle-Bernstein polynomials has been used to model the input-output relationship that describes the system. The approach has been validated by identifying two real world nonlinear systems, in the fields of speech signals and nonlinear audio amplifiers.
$5\times240$ Gb/s WDM DD Transmission Over 80 km With Spectral Efficiency of 5.25 bit/s/Hz

With the exponential growth of the data traffic, dense wavelength-division-multiplexing (DWDM) is required for future data center interconnect (DCI) applications with increasing demand for spectral efficiency (SE). In this letter, we propose an improved Kramers-Kronig algorithm including DC-offset adjustment and phase clipping after Hilbert transformation. Using this scheme, we experimentally demonstrate a single-polarization WDM direct detection (DD) transmission over 80 km with a record SE of 5.25 bit/s/Hz by transmitting 5 single-side-band (SSB) 256-QAM modulated channels at 30 Gbaud on 35-GHz grid in the C-band.
Energy management and speed profile optimization for hybrid electric trams

Hybrid electric trams equip with additional on-board energy storage devices to improve the performance of power sources. Both of optimal energy management and velocity control are important to maximum the energy efficiency for catenary-supercapacitors hybrid trams. An optimal control model has been developed to minimize energy consumption from traction substations with supercapacitors voltage limitations. Multiple phases model transformation and solving methods are implemented to obtain the optimal voltage trajectory and speed profile simultaneously. Finally, the effect of trip time on energy consumption is assessed.
Volumetric Surface-guided Graph-based Segmentation of Cardiac Adipose Tissues on Fat-Water MR Images

Different endocrine roles of cardiac adipose tissues motivate the analysis of their volumes and compositions on large cohort image data sets. This, however, demands reliable robust methods for automated segmentations as manual segmentations are tedious costly and unreproducible. Besides the effects of noise and partial volumes, segmentation of these adipose tissues on clinical medical images is challenged by their similar intensities and features and undetectability of their boundaries. In this paper, we present a feature- and prior-based random walker graph that additionally incorporates a diffusion-based susceptible infected recovered model to guide the segmentation by the curvatures of the surface of the segmented cardiac structures. This method is trained and evaluated for segmenting epicardial, pericardial, and perivascular adipose tissues on volumetric fat-water magnetic resonance images. The obtained results demonstrate its utility for large cohort investigation of these adipose compartments and also any other segmentation task on multichannel images.
Robust Sequence Recognition Using Biologically Inspired Temporal Learning Mechanisms

The biological neural networks communicate with each other through the spike sequences fired by neurons. In this work, we propose a biologically-plausible network structure which contains spiking neurons for sequence recognition. This is a system that is consistent with the auditory coding, learning, and decoding functions. The model can reveal the neural mechanism of upstream and downstream neurons. In this model, we encode the external stimuli into the spatiotemporal spikes and further extract features of the encoded patterns by K-Singular Value Decomposition (K-SVD) algorithm. Then we use the time learning rules to learn the spike patterns and finally use downstream neurons to decode sequence order. In our experiment, two speech datasets (TIDIGIT and FSDD) have been used in evaluating the performance of the proposed speech recognition system, which are compared with other models. Experimental results show that our model can still maintain good performance in noise conditions and finally obtain a superior accuracy in sequence identification than other typical algorithms.
Introduction to Cost-Effectiveness Analysis for Clinicians

As health care expenditures continue to increase annually, pressure rises to contain spending. Clinicians are partially responsible for cost containment and can help optimize spending by utilizing more cost-effective interventions. An understanding of economic evaluations, particularly cost-effectiveness analysis, will help physicians make well-informed decisions when choosing between different treatments for their patients. This paper describes different types of economic evaluations with a focus on cost-effectiveness analysis. A hypothetical study is included to illustrate how a cost-effectiveness analysis evaluation is performed. By understanding and considering the economic value of appropriate interventions, clinicians can ensure they are providing evidence-based care for their patients and the population as a whole.
GeoTAR: A Map-Based and Traffic-Aware VANET Routing in Urban Scenario

With the high demand for vehicular communication, vehicular ad hoc network (VANET) has become a promising technology to enable Intelligent Transportation Systems (ITS) by providing efficient vehicle-to-vehicle (V2V) communications and vehicle-to-RSU (V2R) communications. Yet, due to the high mobility and unreliable channel quality, VANET has unique characteristics which pose many challenging routing issues, such as traffic awareness, line-of-sight (LOS) obstruction, infrastructure utilization. In this paper, we propose GeoTAR, a map-based and traffic-aware routing protocol, which novelly utilizes digital map and Geohash code system to collect traffic information for routing decision and designs an intersection-first restricted forwarding algorithm (IFRF) for packets forwarding with carefully considering buildings as radio obstacles. Moreover, GeoTAR takes the wired channel equipped in the Roadside Units (RSUs) into consideration when calculating the routing path. Simulation-based evaluations of GeoTAR demonstrate that GeoTAR can significantly improve VANET performance in terms of packet delivery ratio (PDR), with a comparable end-to-end delay to other geographic routing protocols.
Multifunction Fractional Inverse Filter Based on OTRA

This paper proposes a generalized topology of a fractional-order inverse filter (FOF) using operational transresistance amplifiers (OTRA) block. Seven different configurations are extracted from the introduced topology employing generalized admittances. The generalized admittances increase the flexibility to provide different types of FOFs such as inverse fractional high pass filter (FHPF), inverse fractional low pass filter (FLPF), inverse fractional bandpass filter (FBPF), and inverse fractional notch filter (FNF). Numerical and PSPICE simulation results are presented for selected cases to approve the theoretical findings. The fractional-order parameters increase design flexibility and controllability, which are validated experimentally.
A 184.6-dBc/Hz FoM 100-kHz Flicker Phase Noise Corner 30-GHz Rotary Traveling-Wave Oscillator Using Distributed Stubs in 22-nm FD-SOI

A rotary traveling-wave oscillator (RTWO) has an ability to generate multiple phases at millimeter-wave (mmW) frequencies while achieving low phase noise (PN). Unfortunately, due to transmission line (TL) dispersion, RTWOs suffer from flicker noise upconversion. In this letter, we propose a "distributed stubs" technique to mitigate this mechanism. To cancel out phase shifts due to the TL dispersion, we intentionally generate a phase difference between TL modes. The proposed 26.2–30 GHz RTWO is implemented in 22-nm FD-SOI CMOS. At 30 GHz, it achieves PN of − 107 and −128.1 dBc/Hz at 1 and MHz 10 offsets, respectively. This translates into figure-of-merits (FoM) of 183.5 and 184.6 dBc/Hz, respectively. The proposed architecture consumes 20 mW from 0.8-V supply. It achieves a flicker noise corner of 100 kHz, which is an order-of-magnitude better than currently achievable by state-of-the-art mmW RTWOs.
Hybrid RF/optical-coherence-tomography catheter for tissue monitoring during Atrial Fibrillation (AF) ablation procedures

We demonstrate the viability of an advanced 9-french (~2.8mm diameter) hybrid RF/optical catheter for atrial fibrillation (AF) ablation procedures. The catheter integrates an array of optical fibres, capable of performing non-invasive discrete optical coherence tomography (OCT), into an irrigated RF catheter, thus enabling simultaneous real-time tissue imaging and monitoring tissue ablation. Experimental results show that the use of OCT embedded in an RF ablation catheter would increase both safety and efficacy of Atrial Fibrillation (AF) ablation procedures.
Investigation of Reverse Mode Loudspeaker Performance in Urban Sound Classification

The loudspeaker is a transducer that converts electrical signals to sound. However, it is well-known that in reverse mode, it can convert sound to an electrical signal. In this paper, the reverse mode behavior is investigated through the analysis of its influence on urban sound classification accuracy by comparing the results of deep learning based classifiers. As no audio datasets recorded by loudspeakers are available, a popular traditional dataset was used and transformed into forms as they would have been recorded by reverse mode speakers. These transformations simulated the loudspeakers’ electrical responses to acoustical excitation signals based on their reverse mode transfer functions, which were derived from equivalent mechanical circuits. The details of this reverse mode modeling are also included. The transformed datasets were used during the trainings of the classifiers, and the effects of different speaker parameters and noise levels were examined and compared. The results showed that smaller, full-range speakers performed better than bigger woofers. The types of well-classified events revealed that loud, impulsive events could be classified more accurately.
Circuit-Based Hydrodynamic Modeling of AlGaN/GaN HEMTs

We have developed a simulation tool which enables us to perform circuit-based hydrodynamic modeling of realistic AlGaN/GaN TeraFET implementations (TeraFET: detector of THz radiation based on an antenna-coupled FET). The tool is based on the intrinsic hydrodynamic transport equations which are embedded into a commercially available circuit simulation environment via a novel implementation technique. For the modeling of the AlGaN/GaN HEMT devices under low bias conditions, we have derived a novel physics-based analytical model for the normalized Fermi level.
Shape Method: A New Multi-Objective Analytical Algorithm Based on Parallel Coordinate System

this paper proposes an alternative analytical algorithm for a general nonlinear multi-objective optimization problem. Rather than employing the vertical coordinate system as in the existing analytical algorithms, our algorithm adopts the parallel coordinate system. This new algorithm can guarantee to obtain exact Pareto optimum, which is independent of the relative scales of the objective functions and successful in producing representative points in the Pareto set. The composition of these properties is missing from the existing popular methods like weighted sum or ε-constraints. Furthermore, this algorithm can interact with the preferences of decision makers rapidly at any precision to obtain the expected Pareto optimum and with smaller computation expense. Finally, numerical examples are conducted to verify the effectiveness of the proposed algorithm.