Miracle-dz/UniFAR-FTUs huggingface dataset:
CluPlaR: A Framework for Clustering Spatio-Temporal Geographic Positions

Fitness trackers, smart watches and also smartphones utilize different types of sensors to track physical activities of an active person. These measurements are often combined with geographical locations at which the sensors capture data which are then transformed to qualitative or quantitative measurements. However, for the later analysis the individual position data are often too fine-grained and, hence, need to be aggregated to locations ranging from places similar to the real world to larger regions, such as districts and whole cities. The CluPlaR (Clustering based Place Recognition) framework enables the aggregation of position data to places and their chronological visits. In this paper, we provide its conceptual foundation and corresponding algorithm allowing to aggregate chronologically collected geographic position data as they have often produced by today’s activity sensors. In detail, we describe the algorithm and corresponding configuration parameters and examine their influence on the aggregation result. The evaluation results show that the algorithm can be used to efficiently aggregate position data, but also that the result depends on the chosen aggregation parameters. In addition to the application of the CluPlaR algorithm in real application scenarios, future work should focus on the automated determination of the aggregation parameters.
Evaluation of Changes in Kinematic Measures of Three Dimensional Reach to Grasp Movements in the Early Subacute Period of Recovery from Stroke

This study examines longitudinal data of subjects initially examined in the early subacute period of recovery following a stroke with a test of reach to grasp (RTG) kinematics in an attempt to identify changes in movement patterns during the period of heightened neural recovery following a stroke. Subjects (n=8) were a convenience sample of persons with stroke that participated in an intervention trial. Baseline Upper Extremity Fugl Meyer Assessment (UEFMA) scores ranged between 31 and 52 and ages were between 49 and 83. The UEFMA and RTG test were collected prior to intervention, immediately after the intervention (approximately 18 days later post baseline) and one month after the intervention. RTG data for the uninvolved UE was collected at the one-month session. Subjects reached for objects placed on a table 10 cm from their sternums, picking them up and placing them on a target 30 cm from their acromioclavicular joints. Data was collected using an optical motion capture system. Active makers were placed on each fingertip, metacarpophalangeal, and proximal interphalangeal joint. Four additional passive markers were placed on the dorsum of the hand, the elbow, the shoulder, and the sternum. Subjects demonstrated statistically significant improvements in reaching duration, reaching trajectory smoothness, time after peak velocity and peak grip aperture. All of these measures correlated significantly with improvements in UEFMA. Clinical Relevance— Kinematic measures of reaching and grasping collected early in the subacute period of recovery from stroke may offer insight into specific aspects of the recovery of upper extremity motor function that differ from the information gleaned from clinical scales
Embedded Microchannel Cooler for Hetero-Integration of High Power GaN MMIC on Silicon

A silicon interposer with embedded microchannel cooler for high power GaN monolithic microwave integrated circuit (MMIC) integration is put forward and validated in this work. A GaN MMIC power amplifier (PA) was mounted on the silicon interposer and micro-assembled in a brass test cube for cooling performance evaluation. The thermal and hydraulic properties of deionized (DI) water for cooling, the electrical characteristics and temperature distribution of the chip were recorded in real-time by a cooling test platform. The hotspot power density of the active regions was up to 400.2 W/mm2, and the overall heat flux of the chip was 471.9 W/cm2 in the practical implementation. The validation results demonstrated that the silicon interposer provided a sufficient cooling capacity to maintain the maximum junction temperature of the chip within 160 °C at 70 °C ambient temperature and the fluid pressure drop < 210 kPa at the flow rate of 210 ~ 220 mL/min. It was ensured that the operation of high power GaN MMIC was highly efficient and safe. It is a viable thermal management for the 3rd generation semiconductor high power device integrated on silicon.
A Circuit Based Model of the Lymphatic System

The lymphatic system is one of the main, but relatively less understood, systems of the human (and animal) body. As for other systems, having a usable model of the main elements that compose this system can greatly help the process of understanding and modelling normal and pathological conditions. In this paper a simple lumped model of the lymphangion, the basic active building block of the lymphatic system, is presented and is shown to be fully autonomous, describing pulsating behaviour without the use of external periodic sources. The model is based on the circuit analogy and has been implemented both using Matlab and as a standard spice-like netlist including Verilog-A code.
Security Issues and Possible Solutions in Cloud Computing and Big Data: A Review

The traditional measures of securing the data may fail to cope with the adaptability, interoperability, and scalability of big data, cloud computing, and the contemporary technologies required. This paper comprehensively explores existing literature on big data and cloud computing to point out the security issues and solutions. Several relevant papers were collected, selected, and analyzed based on specific inclusion and exclusion criteria. The paper identifies the challenges of data integrity, confidentiality and privacy, data availability, location of data, storage, backup or recovery of data, and data authentication. Moreover, some practical strategies of fine-grained access controls, network-based intrusion prevention systems, strong data encryption, and regular audits and compliance as solutions were identified.
State Space Modelling and Stability Analysis of AC Micro-grids for Different Configurations

Three inverter based AC micro-grid, an active load and a PQ type of inverter has been modelled in Synchronous(DQ) reference frame. The integrated model of the micro-grid with PQ inverter and active load as well as with only PQ type of inverter and R-L load has been developed. As active rectifier loads present negative incremental resistance characteristics and control loop dynamics similar to that of inverters present in the micro-grid, the various modes of the active load in the integrated system have been identified using participation analysis and their effect on the inverter states has been analysed for the micro-grid having only VSI inverters and micro-grid having PQ as well as VSI inverters. The effect of changing integrator gains for the dc voltage controller of active load on the stability has been analysed for both configurations. The effect on the stability of micro-grid was observed for different loading conditions of PQ inverter of the micro-grid.
Simulation-Assisted Competing Failure Mode Analysis of SMD MOSFETs under Bending Stress

SMD packaged MOSFETs are widely used in automotive applications relying on the reduced parasitism inductance and improved thermal behavior. Under thermal imbalance and mechanical strain from printed circuit boards (PCBs), MOSFETs are likely to suffering a flex strain, resulting in a high failure risk. Considering a SMD MOSFET on PCB, its failure mode is expressed as bending induced electrical parameter variations, interconnection cracks and device solder joint cracks, which occur competitively due to different stress levels and mount situations. This paper carries out a simulation-assisted analysis method to recognize the critical failure mode of SMD MOSFETs by establishing a mechanical simulation model with device package structure and PCB layout. The proposed analysis method is illustrated and verified by 3-point bending experiments under various conditions with different device vendors.
Novel H LLC Resonant Converter with Variable Resonant Inductor

A novel Half-Bridge (HB) LLC Resonant Converter (RC) with a VRI (VRI) is proposed in this paper. The resonant inductor of a conventional LLC RC is divided into two inductors and one of which is connected to an auxiliary transformer with a full-bridge circuit of auxiliary switches. The variable inductance is achieved by controlling the duty cycle of the auxiliary switches to limit the time and voltage across the inductor participating in resonance operation. The equivalent inductances of the proposed converter can then be controlled under different loads to realize better whole-load Conversion Efficiency (CE). A HB LLC RC is designed and implemented in this paper. Experimental results show that by selecting appropriate resonant inductances for the proposed novel converter, a maximum CE improvement of about 3% at light loads and higher whole-load CE compared to a conventional LLC RC can be obtained.
A physical based fatigue model for the micro solder joint in the 3D electronic packagin

As the development of the advanced electronic packaging, the solder interconnects are expected to become smaller and smaller. At present, the characteristic size of the micro solder bump in the 3D electronic packaging is about only several dozens of microns. According to the Semiconductor Industry Association (SIA) roadmap, the solder size will decrease further into a few microns or even nanometer scale in future. At this scale, the traditional fatigue life model such as Coffin-Masson model, the Darveaux model and so on will be invalid because the solder joint is only composed of several grains which will show strong anisotropy and size effect. The new fatigue life model is seriously in need which should be established form the micro mechanism of the grain micro solder bump. In the current research, a physical based thermomechanical fatigue model is proposed using the phase transformation theory. The phased transformation theory is a classical energy approach which treating the fatigue crack initiation as a phase transformation process from the ‘micro cracks’ to the ‘macro crack’. The model is material-science based and can be applicable to solder joints of different sizes. Therefore, both the anisotropy and size effect characteristic of the micro solder bumps will be taken into account in the current research. In the developed model, a Gibbs energy function is defined to describe the fatigue crack initiation process which is governed by the elastic energy, surface energy, material defects (dislocation, grain boundary et al) energy and the diffusion energy variation. All the energy term will be deduced analytically especially for the elastic energy term and the diffusion energy term using the Eshelby equivalent inclusion theory and the mass diffusion theory. Based on the second law of thermodynamics, the fatigue crack initiation can occur only when the Gibbs energy decreases. Based on this idea, the fatigue crack nucleation can be confirmed by deriving the extreme value of the Gibbs Energy function and the fatigue crack nucleation time and the critical crack radius can all be obtained. Furthermore, the developed model can be extended to the fatigue life prediction of the micro solder bump of the advanced electronic packaging for the process of which is mainly determined by the fatigue crack nucleation process. On the other hand, as the proposed fatigue life prediction model is material-science and physical based, the relationship between the experiment results conducted on the bulk specimens and on the micro solder bump can be connected which has great engineering application as the fatigue experiment conducted on the individual small solder bump is quite difficult. At last, the proposed model is further verified by the solder fatigue experiments which shows good agreement and much of the experiment phenomenon can also be explained.
Heart Sound Classification based on Residual Shrinkage Networks

Heart sound classification is one of the non-invasive methods for early detection of the cardiovascular diseases (CVDs), the leading cause for deaths. In recent years, Computer Audition (CA) technology has become increasingly sophisticated, auxiliary diagnosis technology of heart disease based on CA has become a popular research area. This paper proposes a deep Convolutional Neural Network (CNN) model for heart sound classification. To improve the classification accuracy of heart sound, we design a classification algorithm combining classical Residual Network (ResNet) and Long Short-Term Memory (LSTM). The model performance is evaluated in the PhysioNet/CinC Challenges 2016 datasets using a 2D time-frequency feature. We extract the four features from different filter-bank coefficients, including Filterbank (Fbank), Mel-Frequency Spectral Coefficients (MFSCs), and Mel-Frequency Cepstral Coefficients (MFCCs). The experimental results show the MFSCs feature outperforms the other features in the proposed CNN model. The proposed model performs well on the test set, particularly the F1 score of 84.3 % - the accuracy of 84.4 %, the sensitivity of 84.3 %, and the specificity of 85.6 %. Compared with the classical ResNet model, an accuracy of 4.9 % improvement is observed in the proposed model.
Hardware Implementation for Determining Perfect and Non-Perfect Square Roots using Dwandwa Yoga on FPGA

This work proposes an efficient and optimized method of binary square root based on Dwandwa Yoga of Ancient Vedic Mathematics feasible for practical and real-time hardware implementations. This work demonstrates the square root of perfect and non-perfect square numbers on the Field Programmable Gate Array (FPGA) platform with an advantage of the reduced combinational delay, low device utilization (no. of slice LUTs) and area-delay product (ADP) over existing state-of-the-art square root architectures. Hardware implementation results of the proposed binary square root architecture are presented for different input-bit lengths (4-bit, 8-bit, and 16bit) on the FPGA platform. Hardware implementation results of 8-bit non-perfect square input with four-bit integer and four-bit fractional bits on Artix7-xc7a350tfbg484-3 FPGA show that the design shows 74.05% reduced no. of Slice LUTs, 46.47% lower combinational delay and 86.109% better area-delay-product (ADP) for the same accuracy with respect to existing reported state-of-the-art literature.
You can’t drive my car - a method to fingerprint individual driving styles in a sim-racing setting

The rapidly growing market of eSports has become a lucrative industry. Sim racing is one of the traditional sports that gain increasing popularity in eSports. With raising profits, lucrative sponsoring contracts, and competitive price money, incentives for fraud are also on the upraise. This is further facilitated by the COVID-19 pandemic, which led to the substitution of live eSport events by virtual formats. Particularly, in sim racing it becomes increasingly challenging to verify the contestants identity in virtual events. Here, we propose and evaluate a generic workflow to identify personal driving style by transforming raw racing data (including measurements extracted from the simulation software and connected simulator hardware) into a comparable representation (fingerprint). As data base we used an extensive collection of telemetry data recorded from the racing simulation Assetto Corsa on 3 immersive motion simulators. The set contains over 2,000 laps of seven player recorded in weekly sessions. The experiments demonstrate the feasibility to distinguish players using the proposed method on different tracks, car models, and motion simulators. Due to the extensive experiments, we could achieve a driver separation score of up to 89%. The raw data and the raw results are made publicly available on Github.
Explainable AI for Breast Cancer Diagnosis: Application and User’s Understandability Perception

With the current progress of how Artificial Intelligence (AI) is implemented in different fields, the concerns on AI technologies’ accountability, transparency, trust, and social acceptability are also raised. These concerns become even bigger when people’s well being is at stake, as in the case of AI applications implemented in healthcare. Explainable AI (XAI) or AI explanation algorithms are proposed to solve the accountability and transparency problems. However, how users perceived algorithm explanation have not yet been explored extensively. In this paper, Explainable AI approaches were implemented to the specific case in healthcare: Breast Cancer. An online survey was conducted to investigate users’ perception and understanding of the AI explanation algorithms: LIME and Anchors. We were looking at the users’ perception of Explainable AI, specifically non-expert users, and found that users’ perceived understanding was high even though the majority did not understand the explanation.
Stochastic Resonance Governs Memory Consolidation Accuracy in a Neural Network Model

The formation and recollection of memories is a multi-step neural process subject to errors. We propose a computational model of memory nodes receiving input from a colored tic-tac-toe board. We report memory errors during consolidation and reconsolidation when different noise levels are introduced into the model. The model is based on Hebbian plasticity and attempts to store the color and position of an X or O from the board. Memory nodes simulating neurons use an integrate-and-fire model to represent the correct or incorrect storage of the board information by scaling synaptic weights. We explored how baseline firing rate, which we considered analogous to noise in storing memory, impacted the creation of correct and incorrect memories. We found that a higher firing rate was associated with fewer accurate memories. Interestingly, the ideal amount of noise for correct memory storage was nonzero. This phenomenon is known as stochastic resonance, wherein random noise enhances processing. We also examined how many times our model could reactivate a memory before making an error. We found an exponentially decaying response, with a low firing rate yielding more stable memories. Even though our model incorporates only two memory nodes, it provides a basis for examining the consolidation and retrieval of memory storage based on the unique visual input of a tic-tac-toe board. Further work may incorporate different inputs, more nodes, and increased network complexity. Clinical Relevance- This model enables investigation of how the human cortex may utilize and exploit noise during information processing.
Improved Pulse Width Modulation Strategy to Reduce Harmonic Distortion in PV-CHB Converter

This paper presents a new multicarrier pulse-width modulation (PWM) strategy for a five-level solar photovoltaic (SPV) converter. The main motive is to reduce the harmonic content in the output converter voltage and higher voltage levels near the sinusoidal. This PWM strategy integrates a pulse generator function and a triangular function to form a modified triangular pattern. Traditional schemes, such as vertically shifted triangular carrier modulation strategy (V-TCMS), horizontally phase-shifted triangular carrier modulation strategy (HP-TCMS), and parabolic triangular carrier modulation strategy (PA-TCMS), are considered for comparative evaluation. A single-phase single-source magnetically isolated cascaded H bridge (CHB) configuration is used for solar grid-tied integration. It ensures a simple approach in the controller part with less proportional-integral voltage regulators. Moreover, active power control with a new modulation scheme feeds the solar power in a single-phase grid with enhanced power quality. The analytical expressions to have equivalent leakage inductances of transformer windings are presented for accurate system modeling. Moreover, the system is simulated in MATLAB and experimentally validated to showcase the feasibility of an improved pulse width modulation technique.
Entity Event Knowledge Graph for Powerful Health Informatics

This paper introduces the Entity-Event Knowledge Graph (EEKG) model for clinical data stored in graph databases. We describe how the EEKG model dramatically simplifies the representation of patient data, facilitates temporal queries, enables a 360 view of patients and promotes scalability by partitioning patient data into shards. We solved the practical problem that not all clinical data and life science knowledge can be sharded. The solution is to federate each individual shard with common shared data in a knowledge graph. One such shared data source is the UMLS (Unified Medical Language System) knowledge base, which contains genetic, drug clinical trials and Metathesaurus data that we link to individual patient records. We report on several use cases including EMR patient retrieval, matching patients with clinical trials, patient control group selection, and care quality measures.
Working paper Carbon bias in index investing

This paper presents evidence of a bias towards carbon-intensive companies in popular value-weighted stock market indices that are tracked by index funds and ETFs and serve as benchmark for active equity strategies. The average carbon bias in the U.S. Russell 1000 is close to 70% and the bias in the MSCI Europe index is about 90%. This means that the carbon intensity of the U.S. and European market indices is 70% and 90% higher than that of the U.S. and European economy, respectively. The carbon bias arises because ﬁrms operating in carbon-intensive sectors, such as mining, manufacturing, and electricity, tend to be more capital intensive and more likely to be publicly listed. These companies therefore issue more equity than ﬁrms in low-carbon sectors and receive a larger weight in the value-weighted stock market index than in the real economy. The carbon bias is problematic because it exposes institutional investors such as pension funds to carbon-transition risks and is at odds with their drive towards sustainability. We therefore explore several strategies for investors to mitigate the carbon bias in their equity allocation.
Evaluation of the optimum positioning for a multi-use and wearable pressure ulcer sensor

Pressure ulcers, also called bedsores, occur when the skin is under constant pressure for a long time and is more common in hospitalized patients. To prevent a diminish in quality of daily lives and the additional cost of clinical care, a “patient rotate system” is the standard procedure. Although there are commercial clinical platforms that suggest when and how to rotate a patient lying in bed, some of these platforms are 1) using a wearable system that has one-use accessories which increase the total cost of operation 2) rely on a system-on-a-chip that should be placed on a predetermined location which might not be the most comfortable based on the posture. This study evaluates an alternative by using a simple inertial measurement unit (IMU) hardware inside a self-designed and re-usable (disinfectable) 3d printed case placed on different anatomical regions (sternum, left and right acromion, above talus, below patella) for performance. It is suggested that, based on the regions selected, a “patient rotate system” automation is feasible with more comfortable sensor placements (e.g., on the lower limbs) without statistically significant differences (p<0.05). Clinical Relevance- The outcome of this study promises a pressure ulcer prevention system, with placing a wearable and multi-use sensor system on more comfortable and various locations on the body for a “patient rotate system”.