Miracle-dz/UniFAR-FTUs huggingface dataset:
iPonder: A Novel Approach to Multimodal Mental Health Diagnosis and Therapy for Teens via Deep Transfer Learning and Computer Vision

Suicide is the second leading cause of death for teenagers, tarnishing the future potential of the world. More teenagers die from suicide than from cancer, heart disease, stroke, and many more diseases, combined. Although the greatest mental health improvement occurs from a conversation, over 50% of suicidal teenagers stay quiet about their depression due to shame. Thus, iPonder, a cross-platform accessible mobile application for teenagers to privately discuss, confront, and overcome their depression, was created. Using a four-level diagnosis approach based on user data, responses to psychological questionnaires, choice specification, and free-response answers, iPonder constructs a hyper-personalized user profile, which can then be used to pair teenagers anonymously online with similar profiles to discuss and vent about their issues. Questionnaires include the Patient Health Questionnaire-9, General Anxiety Disorder- 7, UCLA Loneliness Scale, and Myers-Briggs Personality Type Indicator test. Using Reddit's Self-Reported Mental Health Diagnosis text-corpus with over 200,000 data points, 11 artificial neural networks were constructed to predict user risk of various mental illnesses based on their free-text-responses, using transfer learning from a token-based text embedding neural network with over 48 million parameters trained on the English Google News 7B corpus with over 4 billion data points. Additionally, two other artificial neural networks were created, trained on the Distress Analysis Interview Corpus with tremendous labeled video data regarding facial landmark tracking and audio analysis for depression-diagnosed people, to predict the risk of depression from a user-submitted video. All neural networks were evaluated on their accuracy, precision, recall, and f1-score. Besting state-of-the-art metrics on similar tasks in the relevant literature, iPonder achieves over 95% accuracy, recall, precision, and f1-score per neural network, pointing to a new age of accessible, effective, and anonymous mental health therapy in teens.
Time-Series Analysis and Forecasting of Power Consumption using Gaussian Process Regression

Reshaping the power system landscape through renewable energy sources increases the need for model-based approaches to all aspects of engineering. At the same time, the increasing availability of data opens up possibilities for the development of data-driven models and their combination with mechanistic models. In this paper, we evaluate a machine learning method called Gaussian Process Regression, which is also capable of dealing with small amounts of data. We show how to explicitly build this type of models for the load profile of a commercial building. An intuitive and physically interpretable model is achieved by incorporating domain knowledge into the structural model building process, also yielding the best performance. Moreover, the validation of the model is mainly independent of a specific choice of metric. Based on the appropriate model, we present a one day-ahead forecast of the power consumption with comparable accuracy with respect to alternative methods.
Parameter Estimation in Neutral Delay Differential Equations Using Genetic Algorithm With Multi-Parent Crossover

Neutral delay differential equations (NDDEs) are differential equations containing time lags not only in the states but also in the state derivatives. NDDEs have applications in modeling physical and biological systems. An NDDE model may consist of several parameters, some of which can be determined using available data. Various numerical techniques have been studied to estimate parameters of mathematical models. In this study, parameter estimation is posed as an optimization problem. The use of heuristic algorithms in parameter estimation has gained popularity because of its ease of implementation, requiring only function evaluations. But to our knowledge, heuristic algorithms have never been employed in estimating parameters in NDDE models. In this work, we apply Genetic Algorithm with Multi-Parent Crossover (GA-MPC) to obtain parameter estimates of three NDDE models with a discrete delay. We compare the estimates to those obtained using standard heuristic algorithms. Results show that GA-MPC is capable of consistently identifying model parameters that provide a good fit of the model to the data.
An Energy-Aware and Predictive Fuzzy Logic-Based Routing Scheme in Flying Ad Hoc Networks (FANETs)

Today, unmanned aerial vehicles (UAVs), also known as drones, have become very popular in military applications, commercial applications, and academic research. Flying ad hoc network (FANET) is a new type of ad hoc network, which groups small drones into an ad hoc form. These networks have unique characteristics, including moving in a 3D space, high mobility, frequent topological changes, limited resources, low density of nodes, and so on, which impose various challenges when designing a proper and efficient routing scheme. In this paper, we present a fuzzy logic-based routing scheme for flying ad hoc networks. The proposed routing scheme has two phases: route discovery phase and route maintenance phase. In the first phase, we propose a technique for calculating the score of each node in the network to prevent the broadcast storm problem and control the flood of the control messages, which have been broadcast to discover a new route in the network. This score is calculated based on various parameters such as movement direction, residual energy of nodes, link quality, and node stability. Moreover, in the route selection process, we design a fuzzy system to select routes with more fitness, less delay, and fewer hops for data transfer. The second phase includes two steps: preventing route failure in order to detect and modify paths at the failure threshold, and reconstructing failed routes in order to recognize and quickly replace these routes. Finally, the proposed routing scheme is implemented in NS2 to evaluate its performance and determine its efficiency. The simulation results are compared with three routing methods, namely ECaD, LEPR, and AODV. These results show that the proposed routing method outperforms other routing schemes in terms of end to end delay, packet delivery rate, route stability, and energy consumption. However, it has slightly increased the routing overhead.
BCA: A 530-mW Multicore Blockchain Accelerator for Power-Constrained Devices in Securing Decentralized Networks

Blockchain distributed ledger technology (DLT) has widespread applications in society 5.0 because it improves service efficiency and significantly reduces labor costs. However, employing blockchain DLT entails considerable energy consumption in the mining process. This paper proposes a blockchain accelerator (BCA) with ultralow power consumption and a high processing rate to address the problem. The BCA focuses on accelerating the double secure hash algorithm (SHA) 256 function required in the mining process at a system-on-chip (SoC) level. We propose three ideas, namely, multiple local memories (multimem), double-cell processing element (D-PE), and nonce autoupdate (NAU), to reduce the external data transfer time and improve the BCA hardware efficiency. We propose a cascaded multiple BCA chip model to enhance the system throughput by several-fold. Our experiments on an ASIC and FPGA prove that the proposed BCA successfully performs the mining process for multiple blockchain networks with much lower power consumption than that of the state-of-the-art CPUs and GPUs. The BCA is laid out with Renesas 65 nm technology with a chip area of <inline-formula> <tex-math notation="LaTeX">$25~mm^{2}$ </tex-math></inline-formula> and consumes <inline-formula> <tex-math notation="LaTeX">$530~mW$ </tex-math></inline-formula> at 100 <italic>MHz</italic>. The power efficiency of the layout chip is improved by 2428 and 143 times compared with that of the fastest CPU Intel i9-10940X and GPU RTX 3090, respectively.
Bidirectional DC- DC Converter with Digital Droop Parameterization

The key for decentralized battery systems is a robust and communication-less control strategy for autonomous power sharing of parallel-connected DC-DC converters. Battery systems improve the reliability and quality of power supply in renewable energy systems and enable power supply for off-grid, mobile applications, including islanded grids, home storage, and electric vehicles. In many cases, components with different electrical properties require different voltage levels. An adaptation is consequently essential and is normally implemented in DC grids for the batteries via bidirectional DC-DC converters. The power flow in both directions can thus be ensured. To achieve a power distribution in parallel connected DC-DC converters, a droop control in the form of a virtual internal resistor can be used. This paper presents a novel approach of a DC-DC converter with a digitally parameterizable droop resistor, whose voltage regulation is based on an analog operational amplifier circuit to ensure low delays and robustness. The droop resistor is adjusted with a microcontroller, which offers the possibility to apply a higher-level control for load sharing via an interface. Mathematical correlations are used to clearly define the parameters of the control. Furthermore, the circuit was completely simulated and tested in the hardware setup. The shown results verify the functionality and indicate only minor deviations. Therefore, this circuit is important for future use in distributed battery systems.
DC Pole-to-Ground Fault Analysis in a 4-Point VSC Multi-Terminal HVDC Transmission System

The protection of dc faults is a significant challenge in voltage source converters (VSC) multi-terminal high voltage direct current (HVDC) systems. DC grids, like AC grids, are susceptible to faults, hence the study of multi-terminal direct current (MTDC) grid protection with high selectivity and strong robustness is essential. This paper analyses the behavior of a 4-terminal VSC-based HVDC system under pole-to-ground dc fault conditions. At dc-fault occurrence, the IGBTs are blocked for self-protection. Anti-parallel diodes act as a bridge rectifier and feed the fault. There is a need for a thorough understanding of the system behavior when subjected to various faults such as dc-side fault conditions. This will ensure that more effective protection protocols can be developed. The response of the 4-terminal symmetrical VSC-HVDC grid to dc side faults is evaluated using dc fault on a mono-polar MTDC when a fault is applied 10km and 50km away from VSC 1 is obtained. It is observed that fault close to the converter (5 km measured ±9.8 kA) results in high fault currents compared to fault away from the converter (50 km measured ±7.8 kA).
Coordination of Aggregators for Flexibility Provision: A Conceptual Framework

Current trends in power systems include the increasing presence of renewable intermittent energy resources and important deployment of sensing and control capabilities at the premises of end users. The latter ones become important assets to off-set potential operational problems produced by the former, and can be used to provide additional flexibility resources to be traded in energy and ancillary service markets. This paper presents a conceptual framework to bridge the gap between flexible loads and ancillary service markets by means of aggregators of particular loads, and a coordinator of these aggregators which acts as a Balancing Service Provider. This coordinator communicates with lower level aggregators depending on the balancing service required by the System Operator. In the presented methodology, thermoelectric refrigerators, water booster pressure systems and chargers located at electric vehicle charging stations are aggregated and coordinated to provide balancing services such as frequency containment reserve, frequency restoration reserve and replacement reserves, depending on the capabilities in terms of response time, dynamics and available flexibility. The results show the ability of the coordinator and the lower level control algorithms to provide balancing services for different scenarios of power request, as well as how different loads can be managed to cope with multiple types of ancillary services.
A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast Uncertainty

The active distribution network management (ADNM) equipped by active distribution networks (ADNs) can enhance the resilience of the network after failure. This paper proposes a novel comprehensive post-event network restoration model and its chance-constrained variant for ADN that considering the dispatch of distributed generation (DG), energy storage system (ESS), demand response (DR), static var compensator (SVC), and network reconfiguration to fully investigate the potential of the ADNM in network restoration. In this paper, the line failure, bus failure, and the uncertainty from load and DG output forecast error are considered. Specifically, the line and bus failures are modeled by an enhanced fictitious network technique, while the uncertainty of load and DG output forecast is modeled by the chance-constrained optimization. The power flow is described by a linearized DistFlow model, and thus the deterministic and chance-constrained network restoration models are programmed by the mixed-integer linear programming (MILP). The proposed deterministic and chance-constrained restoration models are tested on the IEEE 33 bus system. Results demonstrate the effectiveness of the proposed deterministic and chance-constrained network restoration model.
A Bio-Impedance Analysis Method Based on Human Hand Anatomy for Hand Gesture Recognition

In this article, we presented a bio-impedance analysis method (BIAM) based on the human hand anatomy and propose a feasible and flexible BIAM system to obtain the bio-impedance signals for different gestures to achieve a high classification accuracy in hand gesture recognition with flexibility in electrode arrangement and fewer electrodes. To verify the proposed method, 11 gestures, including two sets: hand gestures and pinch gestures, were selected for the experiment. Based on the functional structure of the human hand, we identified appropriate electrode positions and placed five electrodes on the hand surface for bio-impedance signal measurement. Compared with the electrical impedance tomography (EIT) method, which uses a band with the same number of electrodes wrapped around the wrist, the proposed method achieved 98.7% recognition accuracies on the hand gesture set and 97.8% on the pinch gesture set, while the EIT achieved only 97.1% and 86.3%, respectively. In particular, the proposed method demonstrated the advantage of distinguishing gestures with similar muscle contractions.
What's the Buzz About? Progress and Potential of Acoustic Monitoring Technologies for Investigating Bumble Bees

Pollination is a vital ecosystem service for both natural and agricultural ecosystems. Most flowering plants-including many staples in the human diet such as fruits, vegetables and nuts- require animals for pollination. The majority is done by over 20,000 species of bees, some of which are managed commercially to provide these services. The global industry of pollination is valued at US $153 billion annually and is increasing in demand to meet the needs of a growing human population [1]. However, both managed and wild bees, especially the ecologically and economically important bumble bees (Bombus spp.), are suffering alarming declines worldwide, presenting serious implications for food security and biodiversity. Despite numerous pleas by farmers and scientists for improved management and monitoring methods [2], [3], precision techniques for these essential pollinators are still lacking.
Reactive Power Market Demonstration

The network operators in Finland are facing new challenges in reactive power control. This paper focuses on the reactive power flow at the TSO/DSO interface. The motivation is to add the control ability of reactive power by a market-based approach where so far untapped flexible inverter-based resources connected to the distribution system are used as controllable assets. This development benefits the Distribution System Operator and the work opens new business opportunities especially to asset owners and aggregators. A local reactive power market is demonstrated as a novel solution. As a part of the market concept, a forecast of reactive power at the $T$SO/DSO interface was developed and utilized by the Distribution System Operator. The reactive power demonstration was performed with two assets, a battery energy storage system and a photovoltaic power plant. The assets were controlled by an aggregator. The demonstration period was successfully performed. The work presented in this paper has been done as a part of the Finnish demonstration of the EU-SysFlex project. The presented approach also builds upon the current EU targets for the development of various market-based flexibility services. However, the development of these applications also challenges the aggregators and flexibility providers, since the control of manifold types of assets requires various technical solutions that may be case dependent. This may complicate the fluent scalability of the solutions.
Emerging Biometric Modalities and their Use: Loopholes in the Terminology of the GDPR and Resulting Privacy Risks

Technological advancements allow biometric applications to be more omnipresent than in any other time before. This paper argues that in the current EU data protection regulation, classification applications using biometric data receive less protection compared to biometric recognition. We analyse preconditions in the regulatory language and explore how this has the potential to be the source of unique privacy risks for processing operations classifying individuals based on soft traits like emotions. This can have high impact on personal freedoms and human rights and, therefore, should be subject to data protection impact assessment.
Impact of Vth Instability on Time-Resolved Characteristics of MIS-HEMT-Based GaN Power IC

The impact of dynamic threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{{\text {th}}}$ </tex-math></inline-formula>) on the real-time characteristics of GaN direct-coupled FET logic (DCFL) inverter is investigated using an in-situ voltage tracking method. Due to the electrons’ trapping/de-trapping process at the gate dielectric/AlGaN-barrier interface, a two-step positive shift of the threshold input (<inline-formula> <tex-math notation="LaTeX">${V}_{{\text {T}}}$ </tex-math></inline-formula>) is observed in the device-under-test (DUT) inverter circuit, including the start-up periods and the self-adjusted dynamic equilibrium periods. The dependence of the dynamic <inline-formula> <tex-math notation="LaTeX">${V}_{{\text {T}}}$ </tex-math></inline-formula> upon different temperatures, input voltage magnitude, and operating frequency, is systematically studied and the underlying mechanism is revealed. Compared with the dc <inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula> of the inverter of 3.6 V, the dynamic <inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula> at 1 MHz switching with 8 V input amplitude shifts to ~5 V, which poses a challenge to GaN power integrated circuit (IC).
Toward the Assessment of Intrinsic Geometry of Implicit Brain MRI Manifolds

Principal and Gaussian curvatures are commonly used intrinsic metrics for geometric morphometrics analysis: to assess morphometric changes in brain geometry (developmental neuroimaging studies), to quantify shape deformation (organ motion assessment), or to analyze shape variability across subjects in musculoskeletal studies (statistical shape analysis of bones). However, most of existing algorithms for estimating curvatures act on explicit surfaces (triangle meshes), making them time consuming and sensitive to parameterization and neighborhood size. In this paper, we present a suite of fast and parameterization-free algorithms to estimate second order morphometric parameters given an implicit representation for the surface and without any loss of accuracy. We first show results for direct comparisons of our algorithms with a suite of popular algorithms for estimating curvatures of surfaces represented by triangular meshes. Then, in the context of brain imaging, methods were validated against developmental brain MRI data in which surface based analysis has very often failed. We also provided a modified version of the algorithm that can deal with a Freesurfer output surface mesh, and which was evaluated using an adult brain with more complicated folding patterns. Our algorithm provided a more realistic measures of intrinsic curvature for the white matter (mostly ranged between ±0.07 mm−2) which confirms its robustness. As compared to mesh-based algorithms, our algorithm reduces computation times from a few minutes to only a few seconds, showing a decrease by a factor of up to 7.
Post-Manufacturing Process and Temperature Calibration of a 2-MHz On-Chip Relaxation Oscillator

In this work, the design methodology of the relaxation oscillator with the post-processing trimming option for frequency and temperature coefficient is presented. A novel oscillator core is introduced, which ensures good control linearity and insensitivity to CMOS variations of the output frequency as a prerequisite for frequency and temperature calibration. The proposed oscillator operates at 2 MHz and consumes typically about <inline-formula> <tex-math notation="LaTeX">$I_{DD}=97.8\,\,\mu \text{A}$ </tex-math></inline-formula> at the nominal temperature of 42.5°C and nominal supply voltage of <inline-formula> <tex-math notation="LaTeX">$V_{DD}=1.8$ </tex-math></inline-formula> V. The oscillator is verified experimentally with 10 samples manufactured in 180 nm CMOS process, each covering an area of 0.075 mm<sup>2</sup>. The frequency and temperature calibration was performed based on the two-point temperature measurements, resulting in a significant improvement in frequency stability. The temperature calibrated oscillator has a temperature variation ±0.30% in the temperature range from −40°C to 125°C, and the variation with the power supply ±0.07% in the supply range from 1.62 V to 1.98 V.
Thermal Analysis of an Optimized Switched Reluctance Motor for Enhanced Performance

Thermal analysis of an optimized 3-phase 12/8 switched reluctance motor is presented in this paper. The copper and core losses were constrained during the continuous operation of the motor. The average optimized torque was increased by 11 % compared to the normal model. The single-objective optimization problem was solved in which the conduction angles at four speeds were optimized. The turn on/off angles were selected as −16° and 136° respectively to meet the optimization constraint. At the speed of 1500 rpm, the hotspot on winding first hits the constraint at 128.2 s, while at 15,000 rpm, the temperature on the rotor surface first reaches the constraint at 755 s. It was observed that the temperature constraint on winding is the limiting factor for continuous operation in the low-speed region, and on the rotor surface, it limits the continuous operation in the high-speed region.