Felix-Constantin Adochiei

Autonomous takeoff and landing (ATOL) remains one of the most challenging tasks for fixed-wing unmanned aerial vehicles (UAVs), particularly in environments where Global Navigation Satellite System (GNSS) signals are degraded or unavailable. This paper presents an LiDAR-assisted terrain-relative navigation framework for the autonomous takeoff and landing of a 5 kg fixed-wing UAV operating under degraded navigation conditions. The proposed architecture integrates a downward-facing LiDAR rangefinder with barometric altitude sensing, INS/GNSS navigation, optical-flow measurements, and airspeed information within a multi-sensor fusion and flight-control framework. The system combines a Pixhawk-based autopilot with a companion-computer architecture responsible for real-time sensor processing, altitude estimation, mission supervision, and MAVLink-based communication. A dedicated filtering strategy and sensor fusion approach enable reliable terrain-relative altitude estimation during critical low-altitude flight phases, while fault-tolerant command-management mechanisms improve operational robustness in the presence of temporary communication losses and sensor disturbances. The proposed framework was validated through Software-in-the-Loop (SITL), Hardware-in-the-Loop (HITL), and real-flight experiments. Experimental results demonstrated stable and repeatable autonomous landing performance. Comparative analyses showed that the LiDAR sensor provided the most accurate and responsive terrain-relative altitude measurements during takeoff, flare, and landing operations, particularly over irregular and vegetation-covered surfaces. In contrast, barometric sensing provided greater long-term stability during cruise flight, highlighting the importance of multi-sensor fusion for reliable altitude estimation throughout the mission profile. The results confirm that LiDAR-based terrain-relative sensing significantly improves autonomous takeoff and landing performance for fixed-wing UAVs operating in GNSS-degraded environments. The proposed architecture offers a practical and low-cost solution for the autonomous takeoff and landing of fixed-wing UAVs operating in GNSS-degraded environments while demonstrating the benefits of integrating LiDAR, inertial, barometric, and GNSS measurements within a unified multi-sensor autonomous flight framework.

Major depressive disorder requires scalable, low-burden screening tools. We examined whether three-channel resting-state EEG can support reliable discrimination between major depressive disorder and healthy controls using a lightweight model compatible with portable implementations. This work makes three main contributions: (i) a compact hybrid fusion model combining raw-window Conv1D embeddings with per-channel spectral–statistical descriptors for three-channel resting-state EEG, (ii) a leakage-resistant subject-independent (cross-subject) evaluation protocol with subject-level inference via majority voting, and (iii) a preliminary external feasibility test on an independent portable three-channel cohort without fine-tuning. The proposed model fuses a Conv1D encoding of raw ≈15 s eyes-closed windows (3840 samples; 15.36 s at 250 Hz) with per-channel spectral and statistical descriptors. Training uses subject-independent splits to avoid leakage, class weighting, and data augmentation (including MixUp); hyperparameters are selected via randomized search with refinement. The model is trained on a publicly available MDD dataset and subsequently applied, without fine-tuning, on an independent acquisition of 20 subjects recorded with a portable three-channel device; we report both window-level and subject-level (majority-vote) performance. On the held-out test subjects from the public dataset, the hybrid model attains 93.43% window-level accuracy. The independent evaluation is reported as a preliminary external feasibility analysis; given the small cohort, we report subject-level performance with 95% confidence intervals to reflect uncertainty and avoid over-interpreting cross-device generalization. The model occupies approximately 40.19 MB on disk, and the architecture is compatible with post-training int8 (TFLite) quantization for resource-constrained hardware. These results, obtained on limited samples, support the feasibility of three-channel EEG for major depressive disorder detection using a lightweight hybrid architecture and motivate prospective clinical validation, on-device inference and quantization studies, and broader evaluation across centers and devices.

Deception detection with low-channel wearable EEG requires protocols that generalize across people while remaining practical for portable devices. Using the public LieWaves dataset (27 subjects recorded with a five-channel Emotiv Insight headset), we evaluate to what extent five-channel head-mounted EEG can support lie–truth discrimination under both subject-independent and subject-dependent evaluations. For the subject-independent setting, we train a compact Residual Network with Squeeze-and-Excitation blocks (ResNet-SE) model on raw overlapping windows with focal loss, light data augmentation, and grouped cross-validation by subject; out-of-fold window probabilities are averaged per session and converted to labels using a single decision threshold estimated from the cross-validated session scores. For the subject-dependent setting, we adopt an overlapping short-window Residual Temporal Convolutional Network with Squeeze-and-Excitation and Attention (Res-TCN-SE-Attention) model that fuses raw EEG with discrete wavelet transform (DWT)-based spectral and handcrafted band-power and Hjorth features, using an 80/10/10 split at the recording/session level (stratified by session label), so that all windows from a given session are assigned to a single subset; because each subject contributes two sessions, the same subject may still appear across subsets via different sessions. The subject-independent model attains 66.70% session-level accuracy with an AUC of 0.58 on unseen subjects, underscoring the difficulty of person-independent generalization from low-channel wearable EEG. Because practical deployment requires generalization to previously unseen individuals, we treat the subject-independent evaluation as the primary estimate of real-world generalization. In contrast, the subject-dependent pipeline reaches 99.94% window-level accuracy under the overlapping sliding-window (OSW) setting with a session-disjoint split (no session contributes windows to more than one subset). This near-ceiling performance reflects the optimistic nature of subject-dependent evaluation with highly overlapping windows, even when avoiding within-session train–test overlap, and should not be interpreted as a meaningful indicator of deception-detection capability under realistic deployment constraints. These results suggest limited, above-chance separability between lie and truth sessions in LieWaves using a five-channel wearable EEG under the studied protocol; however, performance remains far from deployment-ready and is strongly shaped by evaluation design. Explicit reporting of both protocols, together with clear rules for windowing, aggregation, and threshold selection, supports more reproducible and comparable benchmarking.

Anxiety disorders are commonly assessed using instruments such as HAM-A and GAD-7. These tools rely on patient self-report and clinician interpretation, which may introduce variability. This study proposes an EEG-based computational framework for estimating anxiety levels using portable EEG recordings from the Unicorn Hybrid Black device. These data were harmonized with the DASPS public dataset to ensure methodological consistency. After standardized preprocessing and multi-domain feature extraction, three classifiers—logistic regression, multilayer perceptron (MLP), and k-nearest neighbors (KNN)—were trained and evaluated. Logistic regression achieved 81.25% accuracy (F1 = 0.8247), while the MLP reached 87.5% accuracy (F1 = 0.859). ROC analysis (AUC = 0.98 for logistic regression and 0.92 for MLP) confirmed that both classifiers reliably separated non-anxious from moderate participants. Severe anxiety could not be classified, reflecting the extremely limited number of participants in this category. Predicted anxiety probabilities showed significant correlations with HAM-A scores (r up to 0.71, p < 0.01), supporting the external validity of the proposed approach.

This study presents the design and validation of an open-source framework for biomedical signal acquisition and interoperable data exchange based on the Health Level Seven—Fast Healthcare Interoperability Resources (HL7 FHIR) standard. The proposed system enables secure, wireless transmission of physiological data from distributed sensing nodes toward a locally hosted monitoring platform. The hardware architecture integrates ESP32-WROOM-32 microcontrollers for multi-parameter acquisition, the MQTT protocol for low-latency communication, and a Home Assistant (Nabu Casa, San Diego, CA, USA)–InfluxDB (InfluxData, San Francisco, CA, USA)–Grafana (Grafana Labs, New York, NY, USA) stack for real-time visualization. The novelty of this work lies in the full-stack implementation of HL7 FHIR Observations within a reproducible, open-source environment, ensuring semantic interoperability without reliance on proprietary middleware or cloud services. A case study involving multi-sensor acquisition of electrocardiographic (ECG), photoplethysmographic (PPG), temperature, and oxygen saturation signals was conducted to evaluate system performance. Validation results confirmed consistent end-to-end data flow, sub-second latency, zero packet loss, and accurate semantic preservation across all processing stages. These findings demonstrate the feasibility of implementing standardized, open, and scalable biomedical Internet of Medical Things (IoMT) systems using non-proprietary components. The proposed framework provides a reproducible foundation for future telemedicine and continuous patient-monitoring applications, aligning with FAIR data principles and the ongoing digital transformation of healthcare.

Functional electrical stimulation (FES) is an effective method for correcting foot drop and facilitating forefoot elevation during walking. As a subset of neuromuscular electrical stimulation (NMES), FES enhances functional movements by stimulating muscles to compensate for neurological deficits. Recent technological advancements focus on developing more compact, efficient, and customizable devices that cater to individual patient needs, improving rehabilitation outcomes. This study developed a hybrid device combining electrical stimulation with ankle-foot orthoses (AFO) to enhance gait rehabilitation. A customized stimulation unit with adjustable parameters was created to deliver electrical impulses. An Arduino nano microcontroller was programmed to process data from an MPU6050 sensor, which measures accelerations and ankle angles (plantar flexion and dorsiflexion). This setup allows for precise control of stimulation patterns, offering a cost-effective and adaptable solution for patients with foot drop, aligning with current scientific efforts to enhance gait rehabilitation technologies.