Search Results (112)

The Silicon Electron Multiplier (SiEM) is a novel silicon sensor concept for minimum-ionising particle (MIP) detection in which internal gain is achieved through a composite electrode structure embedded in the silicon bulk. Unlike conventional gain sensors such as Low-Gain Avalanche Detectors (LGADs), the SiEM produces internal multiplication through electrostatic field shaping rather than through doped gain layers. This architecture is also finely segmented by design, with a pixel pitch of 10$\mathrm{m}$, making it a promising candidate for small pitch radiation-hard tracking and timing applications. This work extends the initial conceptual studies by using a three-dimensional Technology Computer-Aided Design (TCAD) and Garfield++ simulation pipeline for simplified single-electrode SiEM geometries. The study evaluates how the multiplication-region geometry affects electrical response, effective charge collection, internal gain, intrinsic timing performance, and the fraction of the pixel area providing a full MIP response. The results show that enlarging the multiplication pillar can improve the effective active area while preserving timing performance comparable to the initial conceptual design. Full article

Coherent vortices alter flapping-foil energy harvesting, but wake-generated vortex properties and timing are coupled to upstream-body kinematics. We use two-dimensional immersed boundary–lattice Boltzmann simulations of a prescribed heaving–pitching NACA0015 foil at $Re=1100$. An independently prescribed Taylor vortex allows nominal encounter phase, body-fixed offset, diameter, and intensity to be varied at fixed kinematics. Production-grid ${\overline{C}}_P$ is within $0.10\%$ of the fine-grid result; the fine-grid diffusion test gives a maximum full-field velocity $L_2$ error of $0.0690\%$ against the analytical solution over $0\le t^{*}\le 4$. For the reference vortex with a pivot-centered nominal target ($D/c=v_{\theta m}/U_{\infty }=1$), nominal-encounter-aligned mean power coefficients of $0.747$, $0.862$, and $0.987$ occur at ${\psi }_e=0.10$, $0.40$, and $0.60$, respectively, compared with $0.832$ without gusts. These define the power-reducing (PR), near-baseline (NB), and power-enhancing (PE) cases. Within the sampled ranges, diameter is associated mainly with disturbance reach and duration, intensity with loading magnitude, and offset with spatial overlap and interaction timing. Power variations are consistent with the timing of vortex-modified loading relative to prescribed foil motion. In three same-sign, once-per-cycle sequences, the PR–NB–PE ordering persists despite residual-wake interactions, with sustained mean power coefficients of $0.763$, $0.916$, and $0.965$, respectively. Nominal encounter phase and foil placement should be considered jointly for repeatable or predictable vortex passages. Full article

Digital modeling of traditional Chinese musical instruments significantly lags behind that of their Western counterparts, limiting advances in cultural preservation and related research. This paper addresses score-to-audio generation for the Chinese bamboo flute (dizi), aiming to synthesize expressive audio with human-like performance nuances directly from Musical Instrument Digital Interface (MIDI) scores. Two core challenges remain in existing score-to-audio synthesis methods: first, the scarcity of large-scale paired MIDI-audio training data for traditional Chinese instruments; second, the inability of standard MIDI to encode instrument-specific expressive techniques, such as vibrato, pitch bends, and ornamentations. To address these challenges, we propose a scalable workflow that generates large-scale synthetic MIDI-audio pairs through rule-based score randomization, automated digital audio workstation (DAW) rendering with commercial sample libraries, and standardized feature extraction. Performance technique information is directly encoded into the MIDI stream using out-of-range MIDI note numbers, achieving more effective conditioning than external control methods. Our system is built upon the Musical Instrument Digital Interface–Differentiable Digital Signal Processing (MIDI-DDSP) framework. Large-scale synthetic data is used for pre-training to establish timbral consistency, while real recordings from the University of Rochester Multi-Modal Music Performance (URMP) flute corpus are employed for fine adjustment to achieve expressive dynamic variations. Synthetic data alone yields stable but less expressive outputs, whereas real data alone risks overfitting. The combined strategy achieves a realistic timbre with controllable dynamics and performance techniques. Full article

Cover song recognition typically relies on computationally expensive raw audio analysis, which limits applicability in resource-constrained or privacy-preserving scenarios. Existing audio-free alternatives use metadata or lyrics to augment rather than replace audio analysis, and the performance level achievable from compact pre-computed audio descriptors alone has not been systematically established. This paper is an empirical study of that constrained regime: how far pre-computed audio descriptors go without raw audio, and which engineering choices matter. We propose a lightweight approach based on a Siamese retrieval model operating on Million Song Dataset features and evaluated on the SecondHandSongs benchmark. The method combines temporal slope features extracted from pitch and timbre time series, confidence-weighted feature multiplication, and a hybrid delta-gradient optimization framework designed for low-dimensional feature spaces. Each component contributes measurably: temporal slope features raise the unweighted baseline from 0.370 to 0.400 MAP@10, normalization to 0.420, heuristic feature weighting to 0.520, and delta-gradient refinement to the final 0.553, to our knowledge, the best result reported under this constraint. This remains below audio-based state-of-the-art systems, which exploit fine-grained spectral detail that compact descriptors discard; in exchange, the proposed system requires much less storage and computation, enabling privacy-preserving, bandwidth-constrained, and audio-unavailable deployment scenarios. Full article

Soccer-video analysis centers on pitch-plane tracking, but camera-view depth cues such as occlusion and goal-area structure are not fully represented on the field plane. Synthetic benchmarks provide dense supervision unavailable for real broadcasts, but whether adaptation yields predictions that are reproducible across matches and operationally feasible remains unclear. We evaluate a Depth Anything V2 model adapted to SoccerNet-Depth with four components: Unaligned MDE accuracy, scale-and-shift aligned diagnostic, match-to-match reliability, and accuracy–cost trade-off. The model achieves an unaligned validation AbsRel of 0.00372. The aligned diagnostic shows that Base DAv2 retained substantial scene-depth structure, whereas SoccerNet adaptation enabled direct compatibility with the normalized target without per-frame ground-truth fitting. Relative to the VKITTI-fine-tuned reference, the adaptation improved all eight metrics in all 21 validation matches, with paired Wilcoxon tests significant after Bonferroni correction. On the challenge split, it reduced AbsRel by 34.1% versus the official baseline. The higher-resolution configuration improved the validation AbsRel by 5.9%, while the default retained a better accuracy–cost balance. At 401.57 ms per frame, the default is suited to post-match analysis, not live or near-real-time use. The study contributes a benchmark-scoped adaptation case study and protocol for foundation MDE on SoccerNet-Depth. Full article

Advanced packaging requires high-density interconnects with sub-10 μm design rules; however, conventional processes involving laser drilling and plasma desmearing increase dielectric surface roughness and degrade signal performance. A nanosecond ultraviolet (ns-UV) laser microvia process using a sacrificial metal barrier layer (SMBL) was developed to enable sub-10 μm via formation while preserving dielectric surface integrity. A Cu SMBL was introduced to block debris redeposition during laser irradiation and shield the dielectric from ion bombardment during plasma processing. By optimizing laser power, shot count, and SMBL thickness, approximately 8 μm microvias were formed in 10 μm thick Ajinomoto Build-up Film (ABF). The Cu SMBL facilitated heat dissipation, reducing the heat-affected zone and limiting lateral widening at the via entrance, resulting in improved via geometry and higher taper. The dielectric surface roughness increased significantly (>80 nm) when no SMBL was used during processing, whereas it remained nearly constant with the SMBL (Ra: 6.36 → 6.43 nm), thereby reducing current scattering at high frequencies. Adhesion of 0.46 kgf/cm was maintained after quick-via-pull testing without mechanical interlocking, with no interfacial separation observed, confirming reliable interconnect formation. Therefore, the SMBL process enables precise microvia fabrication and low-loss interconnects for high-frequency packaging. Full article

With the growing demand for precise absolute pose estimation of landers in lunar exploration missions, crater database-based navigation technology has become a core path to achieving this goal, but it faces challenges of low efficiency in large-scale data retrieval and insufficient matching robustness. To address these issues, a coarse-to-fine crater matching framework with database fast searching and robust triangle similarity matching is proposed. A Geo-KD search algorithm is designed to realize fast and accurate retrieval of craters within the field of view by combining Geohash and KD-tree. A robust triangle similarity matching algorithm is constructed through local neighborhood crater screening, triangle similarity matching, and mismatching elimination based on Random Sample Consensus (RANSAC) and Local Motion Consistency (LMC). Experiments show that the algorithm achieves an average retrieval time of 20 ms with an F₁-score of 0.8 for the global lunar database with 1.29 million craters. It has an F₁-score more than 0.746 and a single-frame matching time less than 1.005 s under lunar orbital phase, landing phase, and different camera pitch angles, outperforming other advanced algorithms and meeting on-orbit real-time requirements, providing reliable support for the absolute pose estimation of lunar probes. Full article

Ultrahigh lateral resolution (UHLR) optical coherence tomography (OCT) technology, also called optical coherence microscopy (OCM), has gained popularity, especially in the field of biomedical imaging. In these systems, high numerical aperture (NA) Microscope objectives (MO) are employed in OCM systems to offer better than 3 µm lateral resolution. However, in the implemented broadband OCM configuration, the use of complex multi-element microscope objectives can reduce the detected returned signal compared with a simpler imaging lens configuration. This reduction in detected returned signals can become an important practical limitation in many OCM applications, particularly for biomedical imaging when high imaging speed is crucial. This study investigates whether a single off-the-shelf lens can provide a practical alternative to conventional MOs, achieving higher throughput while maintaining reasonable spatial resolution. We systematically evaluated 14 commercial lenses using Zemax OpticStudio simulations, identifying an aspherized achromatic lens (Edmund Optics #85302) that best met these key criteria. To validate its feasibility for OCM, performance was tested in both Full-Field Time-Domain OCM (FF-TD-OCM) and Line-Field Spectral-Domain OCM (LF-SD-OCM) configurations. Using a broadband composite Superluminescent Diode (SLD) source (750–920 nm), we quantified the resolvable features, axial resolution, and overall light transmission. The validated system demonstrated near-diffraction-limited performance. In the LF-SD-OCM setup, it successfully resolved features as fine as Group 8, Element 6, corresponding to a 2.2 µm line pair pitch (~1.1 µm line width) and achieved a 2.86 µm axial resolution in air. A through-focus comparison further showed practically useful contrast retention around focus. Additional imaging of onion epidermal tissue and ex vivo porcine corneal tissue demonstrated that the proposed lens could provide interpretable structural images on representative biological samples. Under the tested LF-SD-OCM detection configuration, the selected lens delivered approximately 2.0 dB higher returned signal than the Mitutoyo MY10X-823 objective according to 1.59× larger received signal. Full article

This study proposes a heterogeneous fusion path planning framework for unmanned aerial vehicles (UAVs) operating in complex emergency rescue and civil environments. Existing single-mechanism metaheuristics—including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GAs)—suffer from fundamental limitations in three-dimensional kinematic path planning: PSO converges rapidly but stagnates at local optima due to population variance collapse; ACO offers robust local exploitation but incurs prohibitive cold-start overhead; GAs maintain diversity at the cost of expensive crossover operations. To address these complementary deficiencies simultaneously, the proposed framework introduces a spherical coordinate representation that reduces computational complexity and naturally enforces UAV kinematic constraints, combined with adaptive weight factors and a serial PSO-ACO fusion strategy, and subsequently incorporates adaptive weight factors. A serial fusion strategy is then introduced, wherein the sub-optimal trajectory generated by the Spherical PSO phase is mapped into the ACO pheromone field via a Gaussian Kernel Density Mapping (GKDM) mechanism, enabling the ACO phase to perform fine-grained local exploitation within a kinematically feasible corridor. Various constraints along the flight path are formulated into distinct cost functions, which cover aircraft track length, pitch angle variation, altitude difference variation, obstacle avoidance, and smoothness; the core task of the algorithm is to find the flight path with the minimum total cost. The proposed algorithm is dedicated to UAV path planning in complex emergency rescue environments (disaster-stricken areas, hazardous zones) and is further applicable to civil low-altitude logistics delivery, industrial facility inspection, ecological environment monitoring and urban air mobility (UAM) scenarios with complex obstacle constraints. It can effectively improve the safety and efficiency of UAVs in reaching rescue points, delivering emergency supplies, conducting disaster surveys, and completing various civil low-altitude operation tasks. Full article

The performance of the pitcher is the cornerstone of baseball, often determining the flow and ultimate outcome of a game. Given this centrality, understanding the mechanics of an elite pitcher and decoding their strategies are paramount for both internal optimization and competitive scouting. This study proposes an end-to-end deep learning pipeline for automatically classifying five distinct pitch types from raw broadcast footage of MLB pitcher Max Scherzer between 2015 and 2020. By formulating pitch delivery as a time-series classification problem tailored to the unique biomechanics of an elite athlete, the proposed CNN–LSTM framework integrates per-frame spatial feature extraction using an advanced CNN backbone (YOLOv8s-cls) with a two-layer long short-term memory (LSTM) network to capture subtle biomechanical cues across a standardized 20-frame delivery sequence. While skeletal pose estimation primarily focuses on tracking major joints to analyze standard pitching mechanics, the proposed pixel-based method preserves fine-grained visual cues—such as finger grip and wrist rotation—that are critical for distinguishing pitch variations. The proposed framework achieved an accuracy of 91.8% under a standard Random Split and, importantly, 84.5% under a strict Chronological Split across different seasons, validating the feasibility of automated pitch “tell” detection from broadcast video. The resulting system provides coaches and analysts with an objective, data-driven tool for generating personalized scouting reports, identifying mechanical inconsistencies, and refining pitching strategies. Full article

Reliable nondestructive evaluation of fine weave pierced carbon/carbon (C/C) composites is essential because these materials are increasingly used in critical components, yet ultrasonic inspection is often compromised by dispersion and frequency-selective filtering that distort waveforms and complicate imaging. This study aimed to experimentally characterize the anisotropic acoustic dispersion and frequency-filtering behavior of up-to-date fine weave pierced C/C composites with a pitch-based matrix. Phase velocities along the three principal directions (x, y, z) were measured over a frequency range of 0.5–5.0 MHz. Along the z-direction, phase velocity increases from 7250 m/s to 13,500 m/s with rising frequency, revealing four selective passbands. This indicates pronounced geometric dispersion and a wave-filtering effect due to the larger-scale fibers aligned in this direction. In contrast, the x- and y-directions exhibit only a single low-frequency passband dominated by the strong viscoelasticity of the matrix, with phase velocities of 8100 m/s at 0.5 MHz and 7100 m/s at 0.3 MHz, respectively. Furthermore, temperature-dependent measurements in the z-direction demonstrate a transition from viscoelastic-dominated to geometric-dominated dispersion as temperature increases. These results provide frequency-selection guidance for reliable ultrasonic nondestructive evaluation of advanced C/C composite components. Full article

Fine-pitch redistribution layers (RDLs) are key enabling technologies for fan-out wafer-level packaging (FOWLP)-based interposers used in chiplet and high-bandwidth memory (HBM) integration. In this study, a CAR-based photolithography process optimized for fine-pitch RDL fabrication was evaluated to realize 2 μm/2 μm line/space (L/S) RDL structures in an FOWLP environment. Key lithographic parameters, including exposure energy, focus offset, and thermal processing conditions, were systematically optimized to establish a stable and reproducible process window. Cross-sectional analysis confirmed the structural integrity of the electroplated RDL features formed under the optimized conditions. To assess functional feasibility, channel-level electrical simulations were performed using JEDEC-defined HBM3 signal assignments. Simulated eye diagrams indicate that the fabricated fine-pitch RDL interconnects are capable of supporting HBM3-class signal transmission with a moderate level of signal integrity. The presence of jitter and noise suggests that further optimization of RDL transmission line impedance is required. Rather than presenting a fully optimized interposer solution, this work provides an engineering-level assessment of lithographic and process constraints associated with implementing 2 μm class RDLs in FOWLP-based interposers, offering practical insight into fine-pitch RDL process window definition for advanced packaging applications. This work uniquely combines systematic CAR-based lithography optimization with cross-sectional structural validation and HBM3-class channel-level simulations to define a practical process window for 2 μm/2 μm RDLs in an FOWLP environment. Full article

Reading proficiency in early childhood is crucial for academic success and intellectual development. However, more and more children are struggling with reading. According to the last PISA study in Austria, one out of five children is dealing with reading difficulties. The reasons for this are diverse, but an application that tracks children while reading aloud and guides them when they experience difficulties could offer meaningful help. Therefore, this proposal explores a prototyping approach for a core component that tracks children’s reading using a self-supervised Wav2Vec2 model with a limited amount of data. Self-supervised learning allows models to learn general representations from large amounts of unlabeled audio, which can then be fine-tuned on smaller, task-specific datasets, making it especially useful when labeled data is limited. Our model is operating on the phonetic level with the help of the International Phonetic Alphabet (IPA). To implement this, the KidsTALC dataset from the Leibniz University Hannover was used, which contains spontaneous speech recordings of German-speaking children. To enhance the training data and improve robustness, several data augmentation techniques were applied and evaluated, including pitch shifting, formant shifting, and speed variation. The models were trained using different data configurations to compare the effects of data variety and quality on recognition performance. The best model trained in this work achieved a phoneme error rate (PER) of 14.3% and a word error rate (WER) of 31.6% on unseen child speech data, demonstrating the potential of self-supervised models for such use cases. Full article

Background: Cochlear implants (CIs) are transformative neuroprosthetics that restore speech perception for individuals with severe-to-profound hearing loss. However, temporal envelope cues are well-represented within the signal processing, while spectral envelope cues are poorly accessed by CI users, resulting in substantial deficits compared to normal-hearing individuals. This profoundly impairs the perception of complex auditory stimuli like music and vocal prosody, significantly impacting users’ quality of life, social engagement, and artistic expression. Methods: This narrative review synthesizes research on CI signal-processing limitations, perceptual and production challenges in music and singing, the role of the auditory–motor feedback loop, and strategies for improvement, including rehabilitation, technology, and the influence of neuroplasticity and sensitive developmental periods. Results: The degraded signal causes marked deficits in pitch, timbre, and vocal emotion perception. Critically, this impoverished input functionally breaks the high-fidelity auditory–motor feedback loop essential for vocal control, transforming it from a precise fine-tuner into a gross error detector sensitive only to massive pitch shifts (~6 semitones). This neurophysiological breakdown directly causes pervasive pitch inaccuracies and melodic distortion in singing. Despite these challenges, improvements are possible through advanced sound-processing strategies, targeted auditory–motor training that leverages neuroplasticity, and capitalizing on sensitive periods for auditory development. Conclusions: The standard CI signal creates a fundamental neurophysiological barrier to singing. Overcoming this requires a paradigm shift toward holistic, patient-centered care that moves beyond speech-centric goals. Integrating personalized, music-based rehabilitation with advanced CI programming is essential for improving vocal production, fostering musical engagement, and ultimately enhancing the overall quality of life for CI users. Full article

A new and transformative era in semiconductor packaging is underway, wherein, there is a shift from transistor scaling to system scaling and integration through advanced packaging. For advanced packaging, interconnect scaling is a key driver, with interconnect density requirements for chip-to-substrate microbump pitch below 5 μm and half-line pitch below 1 μm for Cu redistribution layer (RDL). Here, we present a comprehensive theoretical comparison of thermal cycling behavior in accordance with JESD22-A104D standard, intermetallic thickness evolution, and steady-state thermal analysis of Cu-microbump assembly for different bonding materials and substrates. Bonding materials studied include solder caps such as SAC105 (Sn98.5Ag1.0Cu0.5), eutectic Sn-Pb (Sn63Pb37), eutectic Sn-Bi (Sn42Bi58), Pb95Sn5, Indium, and Cu-Cu TCB structure. Effect of substrates including Si, glass and FR-4 is evaluated for various microbump structures with varying pitches (85 µm, 40 µm, 10 µm, and 5 µm) on their fatigue life. Results indicate that for Cu-microbump assemblies at an 85 µm pitch. The Pb95Sn5 exhibits the longest predicted fatigue life (3267 cycles by Engelmaier and 452 cycles by Darveaux), while SAC105 shows the shortest (320 and 103 cycles). Additionally, the Cu-Cu TCB structure achieves an estimated lifetime of approximately 7800 cycles, which is significantly higher than all solder-based Cu-microbump assemblies. The findings contribute to advanced packaging applications by providing valuable theoretical references for optimizing solder materials and structural configurations. Full article