Miracle-dz/UniFAR-FTUs huggingface dataset:
No-Sweat Detective: No Effort Anomaly Detection for Wi-Fi-Based Localization

At present, Wi-Fi localization is the main approach to estimating location indoors. However, age deterioration of the localization model due to dynamic environmental changes degrades its accuracy. Therefore, periodic model recalibration is inescapable. Existing methods for doing this use transfer learning and a small set of additional and supervised datasets. However, the reference points to obtain these datasets are determined either randomly or comprehensively. Such poor datasets catastrophically destabilize model recovery after recalibration because overfitting occurs. We propose a new approach that detects anomalous reference points to gain felicitous supervised datasets in order to prevent overfitting. Unsupervised datasets obtained from off-the-shelf mobile navigation applications, i.e., user logs uploaded from phones, are used. Our approach is implemented in a system we call "No-Sweat Detective". The results of an experiment in a controlled environment demonstrate that No-Sweat Detective can detect anomalies caused by environmental changes, and the results of a five-month experiment show that No-Sweat Detective has redundancy against a complex open-space environment in the real world. In addition, it could suppress model age deterioration by up to 10.9% compared to existing methods.
Analysis of Joint Hours of Work Function of Currently Married Working Couples: A Case Study of Pakistan

This study highlights various socio-economic and demographic factors, which influence the hours of work of currently married couples in Pakistan. The study is based on Pakistan Labor Force Survey data 2007-08. We have employed ordinary least square (OLS) technique in order to empirically investigate the effect of different factors on combined hours of work of couples. The study finds that education level has strong and positive impact on work hours of couples. Moreover, couples in the households belonging to urban areas work more hours than those in rural areas. Among demographic factors, age, family set up and small children (0-5years) have important effect on hours of work of couples.
Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios

Trajectory planning at high velocities and at the handling limits is a challenging task. In order to cope with the requirements of a race scenario, we propose a far-sighted two step, multi-layered graph-based trajectory planner, capable to run with speeds up to 212 km/h. The planner is designed to generate an action set of multiple drivable trajectories, allowing an adjacent behavior planner to pick the most appropriate action for the global state in the scene. This method serves objectives such as race line tracking, following, stopping, overtaking and a velocity profile which enables a handling of the vehicle at the limit of friction. Thereby, it provides a high update rate, a far planning horizon and solutions to non-convex scenarios. The capabilities of the proposed method are demonstrated in simulation and on a real race vehicle.
Design and Control of a Modular 48/400V Power Converter for the Grid Integration of Energy Storage Systems

Battery energy storage systems are increasingly used to stabilize and balance energy supply systems. For stationary applications, as UPS, electrochemical battery modules are often used, being Li - Ion and lead acid the most popular choice mostly due to power ranges, energy capacity and reversible capability. The present paper is focused in the design of a modular converter to boost the voltage of a Li - Ion battery module from 48 to 400 Volts, with a maximum power of 18 kW. The selected power converter topology is based on a DC/DC converter built by an interleaving synchronous rectifier (ISR) connected in series to a Three Phase Dual Active Bridge (TPDAB). The paper includes an study on alternative topologies, the dynamic average model for the selected approach, the control system design, the selection and implementation of a hardware prototype and the validation of the complete system using simulation under PLECs.
A Novel Switched-Capacitor Converter with Phase Shift Modulation

This paper proposes a novel switched-capacitor converter. Compared with existing switched-capacitor topologies, it has lower capacitor voltage stress than Dickson converter and lower device current stress than ladder converter. These characteristics can help high power density and high efficiency design. The corresponding phase shift modulation strategy is also proposed to achieve voltage regulation and ZVS operation. A 600W prototype is built and experiment results are presented to validate the proposed converter.
Adaptive Sensor Fault Detection and Isolation using Unscented Kalman Filter for Vehicle Positioning

There is an increasing demand for sub-meter vehicle localization for advanced safety and autonomous systems. Fault detection and isolation (FDI) for sensor systems, such as camera, LIDAR, GNSS, and V2X has been a challenge because their performances are significantly affected by weather, geographical changes, and even spoofing. In this paper, a sensor FDI using Student’s t-distribution based adaptive unscented Kalman filter is presented. The proposed filter evaluate each sensor by Hotelling’s T2 test utilizing the predicted sensor output and its covariance. This method can assess the correlation between data that is generated within the same sensor, for accurate fault detection. In addition, measurement noise is adaptively updated by identifying both the covariance and the degree of freedom of the outlier robust Student’s t-distribution. The robustness and accuracy of the localization and measurement noise estimation is confirmed through simulation and an experiment on a highway scenario. Furthermore, the result also shows that the precise FDI can be achieved without any prior information regarding sensor measurement noise. The proposed algorithm enhances the reliability of future position based systems such as autonomous control or V2V safety brake.
Dynamic Dissemination Method for Collective Perception

In future, collective perception might be essential to gather the best possible environmental awareness for an automated driving vehicle. Collective perception means that every partner shares information describing its perceived environment by means of Vehicle-to-X (V2X) communication technology. In order to avoid package loss, channel utilization shall be minimized, which is typically achieved by regulating the transmission frequency. The present work addresses the trade-off concerning optional information and message size. The conducted analyzes investigates the impact of including optional information such as correlation or higher order derivatives on the fusion accuracy. It shows that the impact of correlation on the accuracy is negligible. In contrast, the awareness of higher order derivatives such as acceleration or yaw rate improves the accuracy particularly during accelerating, respectively turning. We propose as trade-off to include higher order derivatives dependent on the estimated mode of the object. The evaluation shows that the proposed trade-off achieves comparable accuracy as disseminating the full state space, while reducing the network load.
White Light-Emitting Diodes With Ultrahigh Color Rendering Index by Red/Green Phosphor Layer Configuration Structure

Currently, white light-emitting diodes (WLEDs) have been widely applied in lighting, display, and medical fields. However, there still exists the problem of low color rendering index (CRI, <inline-formula> <tex-math notation="LaTeX">${R}_{a}$ </tex-math></inline-formula>). In this work, phosphor-converted WLEDs (pc-WLEDs) with ultrahigh <inline-formula> <tex-math notation="LaTeX">${R}_{a}$ </tex-math></inline-formula> were fabricated using Y<sub>3</sub>Al<sub>3</sub>Ga<sub>2</sub>O<sub>12</sub>:Ce<sup>3+</sup> green phosphor and CaAlSiN<sub>3</sub>:Eu<sup>2+</sup> red phosphor configuration structure. The broad emission spectrum of the green phosphor compensates for the gap of the cyan-emitting region that realizes full-spectrum white-light emission. The weight ratio of green/red phosphors was controlled to optimize the optical performances of pc-WLEDs. At the ratio of 0.15/0.012, the fabricated pc-WLED exhibits a natural white light with an ultrahigh <inline-formula> <tex-math notation="LaTeX">${R}_{a}$ </tex-math></inline-formula> of 95.2 and an incredibly small International Commission on Illumination (CIE) chromaticity coordinates deviation (<inline-formula> <tex-math notation="LaTeX">${D}_{{uv}} = -0.0034$ </tex-math></inline-formula>) at 350 mA. The corresponding correlated color temperature (CCT) and luminous efficiency (LE) are 4526 K and 62.34 lm/W, respectively. Furthermore, two separated phosphor configuration structures were constructed for pc-WLEDs. The R up/G down structure achieves the highest LE of 64.93 lm/W, and the corresponding CCT and <inline-formula> <tex-math notation="LaTeX">${R}_{a}$ </tex-math></inline-formula> are 5714 K and 95.8, respectively, chiefly due to the compensation for reabsorption effect caused by the large difference in the luminous efficacy of radiation (LER) between dichromatic phosphors. The results demonstrate that by adjusting the phosphor configuration structure, the pc-WLEDs with high color rendering can be obtained, which have great applications in high-quality lighting.
A Joint Learning Framework With BERT for Spoken Language Understanding

Intent classification and slot filling are two essential tasks for spoken language understanding. Recently, joint learning has been shown to be effective for the two tasks. However, most joint learning methods only consider joint learning using shared parameters on the surface level rather than the semantic level, and these methods suffer from small-scale human-labeled training data, resulting in poor generalization capabilities, especially for rare words. In this paper, we propose a novel encoder-decoder framework based multi-task learning model, which conducts joint training for intent classification and slot filling tasks. For the encoder of our model, we encode the input sequence as context representations using bidirectional encoder representation from transformers (BERT). For the decoder, we implement two-stage decoder process in our model. In the first stage, we use an intent classification decoder to detect the user’s intent. In the second stage, we leverage the intent contextual information into the slot filling decoder to predict the semantic concept tags for each word. We conduct experiments on three popular benchmark datasets: ATIS, Snips and Facebook multilingual task-oriented datasets. The experimental results show that our proposed model outperforms the state-of-the-art approaches and achieves new state-of-the-art results on both three datasets.
Socially-Aware Navigation of Omnidirectional Mobile Robot with Extended Social Force Model in Multi-Human Environment

We propose a new navigation method for an omnidirectional mobile robot to maneuver in a complex and populated environment. In an indoor populated environment, for example, robot has to react to such circumstances and achieve both physical and psychological navigational safety. From social researches, human motion is a hybrid system composed of holonomic and non-holonomic movements. Based on this concept, we develop a socially-aware navigation method for an omnidirectional mobile robot to achieve natural, humanlike movement. By using laser range finder and camera as sensors, robot detects geometric features and human behavior information. From the geometric features robot can construct an environment model and extract the information about distances and directions of the obstacles. Whereas with the heading and orientation information from camera, robot can model individual humans successfully. While acquiring these models as context, a suitable navigation behavior would be executed. To interact with surroundings, we develop the extended Social Force Model(ESFM) to describe the interactive force, referred to as social force, with human and environment. As a whole, the contributions of our work are twofold one being that we propose a dynamic grouping model based on human behavior using learning-based method and another being that we develop an extended Social Force Model for the system based on which a successful navigation strategy can be realized.
Energy Efficiency Optimization-Based Joint Resource Allocation and Clustering Algorithm for M2M Communication Systems

In recent years, machine-to-machine (M2M) communications have attracted great attentions from both academia and industry. In M2M communication systems, machine type communication devices (MTCDs) are capable of communicating with each other intelligently under highly reduced human interventions. Although diverse types of services are expected to be supported for MTCDs, various quality of service (QoS) requirements and network states pose difficulties and challenges to the resource allocation and clustering schemes of M2M communication systems. In this paper, we address the joint resource allocation and clustering problem in M2M communication systems. To achieve the efficient resource management of the MTCDs, we propose a joint resource management architecture, and design a joint resource allocation and clustering algorithm. More specifically, by defining system energy efficiency as the sum of the energy efficiency of the MTCDs, the joint resource allocation and clustering problem is formulated as an energy efficiency maximization problem. As the original optimization problem is a nonlinear fractional programming problem, which cannot be solved conveniently, we transform the optimization problem into power allocation subproblem and clustering subproblem. Applying iterative method-based energy efficiency maximization algorithm, we first obtain the optimal power allocation strategy based on which, we then propose a modified K-means algorithm to obtain the clustering strategy. Numerical results demonstrate the effectiveness of the proposed algorithm.
Grid-tied Inverters in Renewable Energy Systems: Harmonic Emission in 2 to 9 kHz Frequency Range

Grid distortion due to the high switching frequency of energy conversion systems has become a major concern that reduces power quality in renewable energy systems. However, the existing international standards (IEC 61000-3-2&12&16 and IEEE Std. 519) list grid harmonic limitations only up to the 40th or 50th harmonic. Due to limited knowledge of supraharmonic (Harmonics above 2 kHz) currents and voltages, an appropriate standard for this high frequency range is yet to be published. This paper presents an analysis of grid current distortion in the frequency range of 2 to 9 kHz when an active front end inverter is connected to the grid under different filter designs. New filter design approach is used to reduce or eliminate harmonics at frequencies beyond the ranges considered in conventional standards. The results show significant mitigation of high frequency harmonics in stiff grids. High frequency harmonic behavior with conventional and newly designed filters connected to a weak grid, are compared to find small amplitude current and voltage harmonics using the new filters. Furthermore, this study highlights the need for a new standard to address supraharmonics caused by grid-tied inverters.
Fast Factorized Backprojection Imaging Algorithm Integrated With Motion Trajectory Estimation for Bistatic Forward-Looking SAR

A bistatic forward-looking synthetic aperture radar (BFSAR) is increasingly in the focus of study, as it breaks through the limitations of imaging on a forward-looking terrain of a moving platform. Fast factorized backprojection (FFBP) is a reliable BFSAR imaging algorithm, with both imaging precision and efficiency being taken into consideration. In FFBP imaging, compensation of the motion errors is important to obtain a well-focused image. To accomplish an accurate motion compensation in image processing, a high-precision navigation system is needed. However, in many cases, because of the accuracy limit of such systems, motion errors are difficult to be compensated correctly, resulting chiefly in resolution decrease in the final images. To deal with such a problem, we propose an FFBP imaging algorithm integrated with motion trajectory estimation for BFSAR. First, a coarse-to-fine residual range cell migration (RCM) correction scheme is applied to ensure the residual RCM within a range resolution. Then, an optimization model with respect to motion trajectory estimation under the criterion of maximum image sharpness is built during FFBP imaging. According to the coarse-to-fine residual RCM correction scheme and the motion trajectory estimation model, a block coordinate descent technique based on steepest descent optimization method integrated with residual RCM correction is proposed to estimate the motion errors in FFBP imaging. We empirically compare the proposed method with several state-of-the-art BFSAR imaging and autofocus algorithms. Simulations on BFSAR data show that the proposed method is more accurate and has similar computational cost.
Efficient oneM2M protocol conversion platform based on NB-IoT access

The protocol is the foundation of IoT technology, which plays an important role in the IoT device interworking and interoperability. The 5G wireless communication system provides large-scale NB-IoT terminals access with F-RAN, and the vertical industry applications need support for the device management. As one of the most influential protocol in the IoT application, the oneM2M protocol not only has a complete architecture, but also has an interface with other IoT protocols. Therefore, to bridge the gap between the operator and industrial enterprises, the main contributions of this paper are as follows: Firstly, a general multi-protocol conversion method is proposed based on oneM2M platform where the protocol classification is used in different scenarios. Secondly, the F-RAN architecture of oneM2M platform is designed and implemented with NB-IoT device access. Thirdly, a multiplexing scheme to process the device information is proposed for interworking proxy entity (IPE), which improves the conversion efficiency for different protocols. Finally, the feasibility and efficiency of the scheme are verified.
Step Recognition Method Using Air Pressure Sensor

Recognizing steps taken by people walking is a technique used in a method for estimating a person’s indoor position called Pedestrian Dead Reckoning (PDR), and also plays a key role in health-care applications such as pedometers and activity trackers. Generally, accelerometers are used for step recognition, because the sensor signals exhibit clear periodic changes as the user walks. As an alternative, step recognition methods that use angular velocity or magnetic sensors to measure hip rotation have also been proposed. In this article, we propose another step recognition method that uses an air-pressure sensor. We discovered that air-pressure sensor values vary periodically as the user walks in cases such as when a smartphone is held in a hand and the user consciously swings their arms while walking, when it is placed in a sealed case attached to the waist, or when placed in a trouser pocket. We perform step recognition by applying a simple algorithm to these periodic signals, with digital filtering, extreme value detection, and thresholding. Experiments confirmed that steps were estimated with an error rate of approximately 7.5%. Performing sensing with alternate sensors in this way can help reduce the number of sensors needed in a device or system, contributing to reducing power consumption and device size. As an example, activity monitors sometimes calculate the number of steps using accelerometers, and the number of stairs ascended or descended using an air-pressure sensor. These research results could be used to implement an activity monitor that only uses an air-pressure sensor.
A Unique and Robust Technique to Eliminate Warpage for FOWLP and FOPLP During the Termal Debonding Process

The paper revolves around a method to reduce warpage, which is one of the issues commonly encountered by OSATs offering Fan-out Wafer Level Package (FOWLP). Warpage introduced by handling during thermal debonding will be the focus of this presentation and will also cover the concept of warpage reduction after debonding. The warpage adjustment concept consists of subjecting the reconstituted wafer to a temperature that is equal or higher than its glass transition temperature (Tg) for specific dwell time. The wafer should then be placed on a cold thermal chuck, and a vacuum chuck should be used to lock the profile. Contactless transport method, which proved to play a significant impact on warpage reduction for wafer level and panel level fan-out (FOWLP and FOPLP), will also be covered. The paper gives details to the common issues encountered (i.e. warpage and die shift), the test methodology, results of the experimentation, as well as the challenges encountered when applying such methodology to panel level format.
Wave Front Phase Imaging of Wafer Geometry Using High Pass Filtering for Improved Resolution

In this paper we show that Wave Front Phase Imaging (WFPI) has good enough lateral resolution and is sensitive enough to measure roughness on a silicon wafer by simply acquiring a simple image of the entire wafer. WFPI is achieved by measuring the reflected light intensity from monochromatic uncoherent light at two different planes along the optical path with the same field of view. We show that the lateral resolution in the current system is $24\ \mu\mathrm{m}$ but can be pushed to less than $5\ \mu\mathrm{m}$ by simply adding more pixels to the image sensor, and that the amplitude resolution limit is 0.3nm. Three 2-inch wafers were measured, and the roughness was revealed by applying a double Gaussian high pass filter to the global topography data.
Experimental 5G mmWave Beam Tracking Testbed for Evaluation of Vehicular Communications

Upcoming 5G technology has a great potential to enhance cellular networks especially in the field of vehicular communications. Utilizing millimeter wave (mmWave) spectrum benefits the ever-growing mobile data traffic, but the more challenging radio channel requires the use of tracking directional antennas. In this paper, we address beamforming and beam tracking to maintain highly capable and robust links to vehicles on the road or rail and in the air as well as in the context of networked indoor robotics. For this reason, a real-time, no overhead tracking method, which focuses on consistently selecting the most promising beam direction as result of a proximity search, is implemented and evaluated on a modular 5G mmWave software-defined radio (SDR) platform. To achieve a controlled environment providing very accurate ground truth of the vehicular movement, we emulate typical mobilities with a mechanical testbed, capable of providing a variety of movement dynamics in a reproducible manner. The experimental setup consists of the high performance SDR radio system operating at 28 GHz including an 8 by 8 beamforming antenna and a specifically developed mechanical testbed. The results demonstrate the feasibility of reliably providing multi-gigabit network access to vehicles with various mobilities by wireless links at mmWave frequency.