Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor Localization with Spatial and Temporal Representations of Signal Sequences

Indoor localization has attracted considerable attention lately, due to its large commercial and social values in smart cities. The existing indoor localization approaches mostly rely on fingerprint techniques, and many of those leverage either spatially discrete fingerprints or temporally consecutive ones for localization, which either suffers from large errors due to signal ambiguities or high time overhead with long sequences. To achieve high accuracy with low computational cost, we propose ST-Loc, a deep neural network that extracts features from multiple representations of a single signal sequence for localization, where each representation indicates a corresponding signal structure with underlying feature correlations. Taking geomagnetism as an example, we infer location features from two different representations, e.g., spatial and temporal. In spatial representation, a signal sequence is converted to a signal heatmap, where each pixel corresponds to a spatial location and the value indicates fingerprint. Temporal representation, on the other hand, is a signal sequence with ordered readings, which provides temporal correlations. Using these different representations, we employ convolutional and recurrent networks to extract location features and fuse them to generate more distinguishing features for localization. We have conducted extensive experiments in two different trial sites, a narrow office area and a spacious food plaza. Our experimental results show that ST- Loc achieves more than 43% average localization error reduction compared with state-of-the-art competing schemes in both trial sites.
Stability and Performance Analysis of Low Power 6T SRAM Cell and Memristor Based SRAM Cell using 45NM CMOS Technology

In recent years, there has been a growing demand for low-power devices, due to the fact that the expansion of CMOS technology. Scale, the crystal size corresponds to the SOC storage phenomenon, system-on-chip (SOC), decreased by the number of transistors increased. Overall, the number of transistors in the number of transistors on a chip of information is used for various functions. They need economic, low energy consumption to promote the design capacity to increase, low power consumption and little memory because it plays an important role for the growth of the overall energy consumption device design parameters playing tight leakage power devices. Although it can be used any bit of the flip-flop - hitting the SRAM-type semiconductor SRAM: this memory is turned off to the loss of data in the conventional sense. It is used to compare the results of the memristor SRAM and SRAM. The calculation is simple memristor SRAM and SRAM based on the design parameters in 45nm technology, the Cadence tool.
Unidirectional Vienna Converter Design Based On Series-Connected Component

Along with the DC grid distribution issue, the Solid State Transformer (SST) is increasingly emerging in the field of electric power electronics, and the Hybrid SST (HSST) study is also emerging between the current AC-based Grid and future DC grid distribution. At present, several SST and HSST studies have discussed ways to implement the same topology in series to create high DC voltages. These cascade-style topologies not only make it difficult to implement controllers and designing gate drivers but also require a lot of elements, which drives up the overall system’s price. Meanwhile, IGBT and MOSFET series connections are actively being studied for high voltage topology, and various techniques are being studied to solve simultaneous turn on/off problems. This paper proposes by altering the PFC circuit used in existing LVs to switch device series-connected base for HSST and SST. In this paper, circuits were constructed using balancing resistance and passive component for voltage balancing of series-connected switches, and validity is verified through proper controller selection and formulas.
Pre-mortem interventions for donation after circulatory death and overall benefit: A qualitative study

This article explores how the type of consent given for organ donation should affect the judgement of a patient's overall benefit with regards to donation of their organs and the pre-mortem interventions required to facilitate this. The findings of a qualitative study of the views of 10 healthcare professionals, combined with a philosophical analysis inform the conclusion that how consent to organ donation is given is a reliable indicator only of the strength of evidence about views on donation and subsequent willingness to undergo pre-mortem interventions. It is not an indicator of the strength of actual desire to donate. Clinical management of living patients prior to donation after circulatory death must therefore respect the values, wishes and beliefs of the potential donation after circulatory death donor. Our participants, however, suggested that the information currently provided is sufficient to authorise donation and that this consent, however provided, was sufficient to proceed with pre-mortem interventions. Respect for autonomy underpinned this ‘all or nothing’ approach. Although the legal requirements for donation authorisation and the protection of patients without capacity are clear, practice and policy regarding consent in donation after circulatory death may be based on donation following brainstem death where the patient is already dead when the family is approached. Custom and practice in donation after circulatory death may need to be revised to protect the interests of the dying potential donor.
Predicting Blood Glucose Levels with EMD and LSTM Based CGM Data

Blood glucose monitoring is essential for diabetes management. Applying deep learning technique for blood glucose monitoring is promising, given its success in a range of healthcare and medical tasks. In this paper, we proposed a method that combines Empirical Mode Decomposition (EMD) with Long-Short Term Memory (LSTM) to achieve good experimental results in predicting patient blood glucose. We used patients' real blood glucose levels time series data to train the method proposed in this paper and to predict blood glucose for 30 minutes to 120 minutes. First, we use only blood glucose readings and timestamps in the dataset. Meanwhile, we used ADF to verify the non-stationarity of blood glucose time series. Then, we use EMD to decompose the blood glucose time series and use LSTM to train the decomposed time series to obtain a blood glucose prediction model. Finally, Mean Absolute Error (MAE) and root mean squared error (RMSE) were used to evaluate the experimental results. On the test dataset, the mean values of the MAE and RMSE are 0.4458mmol/L and 1.08mmol/L for 30mins, 0.87 and 1.27 mmol/L for 60mins, 0.85mmol/L and 1.36 mmol/L for 120mins, respectively. Experimental results show that the EMD+LSTM had better predictive performance than the LSTM when blood glucose changed dramatically. Meanwhile, it is still challenging to reach a high accuracy of predicting the long-term blood glucose.
Design of 3-bit Current Mode Flash ADC

Analog to digital converters (ADC) consists of various components namely, resistor ladder, comparator array, thermometer-to-binary encoder, etc. An error free and highspeed comparator is an important component in ADCs with high resolution and good speed. The choice of comparator as well as thermometer-to-binary encoder has a significant impact on ADC operation. There are various popular architectures of high speed comparators; dynamic latch being the most commonly used. This paper provides a comprehensive study about various dynamic latched comparator circuits, by comparing their performance, power dissipation characteristics and delay profile. The comparator circuits that are analyzed in this paper are: Preamplifier Latch Comparator, Dynamic latch with inverter buffer and Differential pair dynamic latch comparator. Their design is simulated using 180nm technology in CADENCE VIRTUOSO platform. Simulation results are tabulated, and analysis has been done with graphical representation. Based on comparison between the three simulated comparators, a 3- bit flash ADC with optimum comparator characteristics has been simulated using the same platform. Further, its design is analyzed, and results are tabulated.
Continuous Kannada Noisy Speech Recognition

ASR converts speech signal into corresponding text form. The performance of an ASR decreases under noisy environment. To overcome this problem a speech enhancement need to be performed on noisy speech before being fed to an ASR system. Speech enhancement techniques have been developed over past several decades, some of these techniques introduce musical noise. To achieve further improvement in recognition accuracy, a generalized distillation framework is used in which machines learns machines. In this paper, an ASR is implemented for noisy kannada language speech using generalized distillation framework. In this framework, a teacher machine is trained with clean speech and student machine with 4 different noise speech and teacher machine help student machine to learn by providing additional information needed. During test phase, a student machine is tested with 4 different noise speech other than used in training. A DNN acoustic model is build using a 39 dimension MFSC features and bi-gram language model is created using Kaldi Speech Recognition Toolkit. Experimental results shows that generalized distillation framework for kannada noisy speech achieved a reduction in WER compared to an HMM-GMM approach.
Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders

This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10−6, and the method is most effective at lower shaft speeds.
Multipath Routing, Spectrum and Core Allocation in Protected SDM Elastic Optical Networks

The adoption ofSpace division multiplexing (SDM) in elastic optical networks brings new challenges for spectrum allocation. This paper proposes an algorithm for routing, spectrum and core allocation based on single/multipath routing for providing path protection. The proposed solution has the advantage of using small contiguous bands divided into several paths to provide the request protected bandwidth, allowing the increase in the number of requests accepted. Results show that the proposed algorithm can decrease the blocking ratio by three orders of magnitude when compared with other existing algorithms.
Cold Test Validation of Metamaterial Based Rectangular Slow Wave Structure for High-Power Backward-Wave Oscillators

In this paper, a novel S band metamaterial based rectangular slow-wave structure (RSWS) is proposed for high-power backward-wave oscillator (BWO). The circular waveguide is loaded with R-SWS that shows negative permittivity and permeability. Since RSWS shows negative permittivity and permeability which is the key characteristics of metamaterial, it can operate below the cut-off frequency. CST Studio Suite is used to analyze high frequency characteristic of SWS. Dispersion diagram of the unit cell is observed with Eigen Mode Solver which is dedicated to simulation of closed resonant structures. Interaction impedance of the unit cell is also observed. Since the beam-wave interaction occurs with TM mode, axial and absolute electric field are observed in simulation. Slow wave structure composed of 8 unit cell is fabricated and measured in network analyzer. Cold-test measurement validates the TM mode propagation of RSWS. BWO based on this RSWS is shown to operate at 2.49 GHz with 23.8 % peak efficiency for 2 T magnetic field by using CST Particle Studio.
Coursa Venue: Indoor Navigation Platform Using Fusion of Inertial Sensors with Magnetic and Radio Fingerprinting

Navigation is one of the most essential human needs. People need to know their location anywhere and everywhere, indoors and outdoors, for example for tracking children and older people who are in extreme need of care or for providing a precise indoor location of calls to 911. The latter is especially important since more than seventy percent of calls are generated indoors. Other important fields are tracking of personnel in possibly dangerous environment, LBS/LBA applications, and social networking that rely on indoor localization. Unfortunately, broadly used satellite navigation receivers work perfectly only under open sky. Operating indoors, GPS/GNSS receivers suffer from signal attenuation when satellite signals propagate through a roof and walls of a building and from the multipath due to their reflection. As a result, accurate GNSS-based position fix is almost impossible in most indoor conditions. We present Coursa Venue solution that was developed by TDK-Invensense for infrastructure-less indoor positioning on commercial smartphones. The solution consists of two major parts: cloud-based software for fingerprinting and mobile applications for Android and iOS that provide real-time blue dot positions to users. TDK-Invensense's approach to real-time indoor positioning is based on fusion of multiple technologies. Measurements from such smartphone sensors as IMU (3D accelerometer, gyroscope), a magnetic field sensor (3D magnetometer), WiFi and BLE modules are used for hybrid indoor positioning in the navigation engine. Particle filtering is used as the fusion engine. Indoor navigation software uses such technologies as PDR, geomagnetic fingerprinting, Wi-Fi/BLE fingerprinting, and, optionally, map matching. TDK-Invensense' ‘s PDR provides prediction of user relative movement regardless of orientation and misalignment of a smartphone, whereas magnetic and radio fingerprinting serves for correction of inertial sensors error. The cloud-based component can create magnetic and radio fingerprint databases using either data collected by designated surveyors who walked inside a venue by predetermined routes, or crowdsourced data from users of a real-time mobile application collected during their everyday activity. This paper discusses the architecture of the Coursa Venue solution and demonstrates its positioning results in several venues with comparison to ground truth paths to provide statistical assessment and key performance indices.
End-to-End Multi-Modal Behavioral Context Recognition in a Real-Life Setting

Smart devices of everyday use (such as smartphones and wearables) are increasingly integrated with sensors that provide immense amounts of information about a person's daily life. The automatic and unobtrusive sensing of human behavioral context can help develop solutions for assisted living, fitness tracking, sleep monitoring, and several other fields. Towards addressing this issue, we raise the question: can a machine learn to recognize a diverse set of contexts and activities in a real-life through jointly learning from raw multi-modal signals (e.g., accelerometer, gyroscope and audio)? In this paper, we propose a multi-stream network comprising of temporal convolution and fully-connected layers to address the problem of multi-label behavioral context recognition. A four-stream network architecture handles learning from each modality with a contextualization module which incorporates extracted representations to infer a user's context. Our empirical evaluation suggests that a deep convolutional network trained end-to-end achieves comparable performance to manual feature engineering with minimal effort. Furthermore, the presented architecture can be extended to include similar sensors for performance improvements and handles missing modalities through multi-task learning on a highly imbalanced and sparsely labeled dataset.
Enhance Physical and Mental Well-Being of Game Players in an Endless Running Game

This paper presents new features added to Runner, an open-source 2D-platform running game designed for games for health research. This game can be played by using body motion through a Kinect device, and the original version of the game is focused on physical health promotion and facial expression by video camera. We present a new mode that aims at enhancing mental well-being through a smile detection mechanism. An integration between motion detection and facial expression detection is presented.
Data Transmission Method on the Basis of Reinforcement Learning

This paper considers the issues of organizing the reliable data transmission over non-stationary discrete communication channels. An analysis of existing data transmission systems with feedback is carried out, with defining the shortcomings of existing mechanisms for determining the optimal data block length in data transmission systems with feedback. Data transmission method is developed based on reinforcement learning.
Effects of Self-Heating on ${f}_{\text{T}}$ and ${f}_{\text{max}}$ Performance of Graphene Field-Effect Transistors

It has been shown that there can be a significant temperature increase in graphene field-effect transistors (GFETs) operating under high drain bias, which is required for power gain. However, the possible effects of self-heating on the high-frequency performance of GFETs have been weakly addressed so far. In this article, we report on an experimental and theoretical study of the effects of self-heating on dc and high-frequency performance of GFETs by introducing a method that allows accurate evaluation of the effective channel temperature of GFETs with a submicrometer gate length. In the method, theoretical expressions for the transit frequency (<inline-formula> <tex-math notation="LaTeX">${f}_{\text {T}}$ </tex-math></inline-formula>) and the maximum frequency of oscillation (<inline-formula> <tex-math notation="LaTeX">${f}_{\text {max}}$ </tex-math></inline-formula>) based on the small-signal equivalent circuit parameters are used in combination with the models of the field- and temperature-dependent charge carrier concentration, velocity, and saturation velocity of GFETs. The thermal resistances found by our method are in good agreement with those obtained by the solution of the Laplace equation and by the method of thermo-sensitive electrical parameters. Our experiments and modeling indicate that the self-heating can significantly degrade the <inline-formula> <tex-math notation="LaTeX">${f}_{\text {T}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${f}_{\text {max}}$ </tex-math></inline-formula> of GFETs at power densities above 1 mW/<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}^{2}$ </tex-math></inline-formula>, from approximately 25 to 20 GHz. This article provides valuable insights for further development of GFETs, taking into account the self-heating effects on the high-frequency performance.
Machine Learning for Power Allocation of a D2D Network

Resource optimization for small-cell wireless networks is more complicated than the traditional applications. The solution needs to be delivered promptly to respond the highly dynamic temporal and spatial variations. It seems that the machine learning strategy is more flexible and adaptive than the conventional optimization methods, since ML has the potential to find the implicit function relationship between arbitrary input data and output results. In this work, we focus on a generic D2D network and to show the effectiveness of ML apply to solve the power optimization problem with different optimization models. The research spans over all stages such as analysis, design, implementation, and validation. It is shown that the ML method has achieved several benchmarks in terms of QoS metrics for different optimization models.
Track-before-detect on radar image observation with an adaptive auxiliary particle filter

This paper presents a multitarget tracking solution for 2D multitarget tracking from image observations including its application on radar data and results on simulated data. Tracking from image observations rather than from detected points is often referred to as track-before-detect (TBD). The objective is to capture targets with a low signal-to-noise ratio (SNR) which would not be detected or tracked after data thresholding. The highly nonlinear filtering equations are approximated using a particle filter implementation. This nonlinearity emerges from the observation function which relies on the radar treatment chain, involving matched filtering of a chirp signal, and beamforming achieved from a linear phased array, leading to the raw data image. Amplitudes and phases of target-returned signals are considered as temporally fluctuating, random and unknown, creating non-deterministic contributions of the targets to the signal to deal with. The adaptive auxiliary particle filter is presented, and is an extension of the auxiliary particle filter to the multitarget case under some independence assumptions. Auxiliary particle filter draws strength from its ability to imitate the optimal importance density for sampling new particles with a measurement based strategy, by resampling the promising particles. While targets are dynamically assumed independent, their individual region of influence on the radar image can overlap, which creates dependencies for the likelihood computation. Treating the whole radar image containing many cells is computationally very demanding. To reduce this expense, an observation gating is performed. Eliminating measurements which are far from the predicted measurements is a point measurement reasoning, and selecting a determined number of cells in the neighborhood of the target seems to be a drastic solution. We select here the informational cells from the a priori knowledge of the signal to capture more information, including side lobes induced by the beamforming process. The filter results are compared to other track-before-detect algorithm based on Markov chain Monte Carlo (MCMC).
BlockDNS: Enhancing Domain Name Ownership and Data Authenticity with Blockchain

The Domain Name System (DNS) is one of the most fundamental infrastructures of the Internet. However, due to its design philosophy and implementation architecture, the current DNS still suffers from the centralization problem and the data authenticity problem. In this paper, we analyze these two problems and propose a blockchain-based naming system called blockDNS to solve them simultaneously. In blockDNS, domain names can be applied and transferred freely in a decentralized way. Moreover, a lightweight verification mechanism is also proposed coupled with blockDNS. The verification mechanism allows website clients to verify the authenticity of resolution results with few overheads. Simulation results show that, compared to the Simplified Payment Verification method, blockDNS can cut down the overheads for data authenticity verification from 4.955KBytes to 380Bytes. Moreover, we also present an implementation case of blockDNS, which is compatible with the current naming system and can be deployed incrementally.