Search Results (438)

This study introduces an innovative mixed-mode universal biquad filter implemented using multiple-input operational transconductance amplifiers (MI-OTAs). Based on the advantage of OTAs, which possess multiple inputs, the proposed mixed-mode universal filter using MI-OTAs can implement both non-inverting and inverting standard filtering functions such as low-pass, high-pass, band-pass, band-stop, and all-pass filters in voltage-mode, transadmittance-mode, current-mode, and transimpedance-mode, which is the maximum capability of mixed-mode universal filters. The natural frequency of all filtering functions can be electronically controlled. Based on the multiple-input bulk-driven MOS transistor (MOST) technique, the OTA can also operate at very low supply voltage and provide wide-input voltage swing. The technique of MOST, operating in the weak inversion region, is used to achieve the low-power consumption of OTA. The MI-OTA circuit and mixed-mode universal filter were designed and simulated using Cadence Virtuoso, utilizing TSMC’s 65-nm CMOS technology. At a 0.5 V supply voltage, the filter demonstrated a simulated power consumption of 450 nW at a natural frequency of 156 Hz. In these ranges of power consumption and natural frequency, it can be expected that the proposed filter can be built as an versatile integrated circuit for low-frequency applications such as bio-signal processing. The design parameters were successfully validated through both post-layout extractions and discrete hardware prototyping utilizing commercially available LM13700N ICs. Full article

To address the high demand for precise low-current measurements at the Sirius’ beamlines, a quad-channel high-resolution Ethernet picoammeter has been designed. The instrument can measure currents ranging from femtoampere to milliampere across eight selectable ranges, featuring integrated analog-to-digital converters (ADCs), enabling sampling rates of up to 2 ksps and synchronization capabilities. This work describes the hardware design, the hardware handling procedures to achieve sub-picoampere resolution, and the characterization results of the instrument, considering the experimental results from Sirius beamlines. The designed device provides noise performance and gain accuracy that is comparable to high-end commercial solutions, proving its suitability for critical applications like on-the-fly scanning experiments. Special attention will be given to evaluating trigger latency, synchronization outcomes, as well as the device’s installation and commissioning at beamlines. Furthermore, we will deeply explore the interplay between the trigger period, digital filter bandwidth, and front-end analog bandwidth to optimize the signal-to-noise ratio in specific applications. The hardware project is publicly available in CERN’s open hardware repository. Full article

This paper proposes a spatial analog of the compass rose, interpreted as a discrete analog of cylindrical coordinates and considered as a basis for direction-based command filtering in Unmanned Aerial Vehicle (UAV) groups. The initial formulation is the problem of determining the direction to a radio signal source using data obtained by a group of four UAVs located at different altitudes. It is shown that, under conditions where the distance to the signal source significantly exceeds the characteristic size of the UAV spatial configuration, the direction to the source is determined much more reliably than the range to it. The results of Monte Carlo simulations confirm that the angular component of the solution remains meaningful under Time Difference of Arrival (TDoA) noise, whereas range reconstruction is substantially less stable. On this basis, a transition from a continuous description to a discrete sector representation of directions is proposed. The spatial compass rose is defined as a partition of the cylinder’s surface into a finite number of elements differing in azimuth and altitude. It is shown that this representation admits a natural algebraization: discrete directions can be one-to-one mapped to elements of finite fields and, therefore, interpreted in terms of multivalued logic. The obtained result creates the basis for simplifying computational procedures related to direction-sector classification and command processing in the on-board systems of UAV groups, provided that the method is interpreted as directional classification rather than complete three-dimensional localization. Full article

Background: Robust perception involves processing heterogeneous sensory signals, such as facial expressions, vocal prosody, and language, particularly in noisy environments. In computational modeling, a key challenge is integrating these diverse inputs while actively filtering uninformative variations. While recent deep learning models address this integration through complex fusion architectures, they typically aggregate features without explicit filtering modules analogous to inhibitory control. In this study, we propose Multi-modal Information Disentanglement (MInD), a computational framework designed to test the hypothesis that algorithmic noise isolation facilitates robust multisensory integration. Methods: Drawing conceptual inspiration from cognitive theories of modularity, our model decomposes sensory inputs into amodal (modality-invariant) and modal-specific pathways. Furthermore, we introduce an adversarial noise isolation mechanism to serve as an algorithmic analog to cognitive inhibition. Given that our model operates on pre-extracted high-level features, this mechanism functions to isolate latent distributional variance—uninformative fluctuations that persist after initial feature extraction—guiding the network to separate task-relevant affective cues from irrelevant feature variance. Results: Empirical evaluations on standard emotion recognition benchmarks indicate that this purification-before-fusion strategy is associated with competitive performance and stability across multiple metrics. Notably, the framework attains these results using simple linear integration layers, suggesting that separating representations prior to fusion may reduce the computational complexity required for subsequent integration. Conclusions: These observations highlight the computational utility of algorithmic noise suppression, illustrating how cognitive inspiration can inform efficient machine learning architectures without claiming direct neurobiological validation. Full article

Passive components in integrated circuits occupy significant areas and increase production costs, driving the demand for compact alternatives. This study presents a generalized, electronically controllable positive/negative floating impedance multiplier implemented in TSMC 180 nm CMOS technology. To achieve a compact layout, the architecture utilizes custom-designed operational transconductance amplifiers (OTAs). The circuit operates on a lossless principle, scaling resistance, capacitance, and inductance values within a wide multiplication range of −100 to +100 using only a single base element. Comprehensive LTspice simulations including PVT, Monte Carlo, THD, and noise analyses verify the design’s stable operation, low distortion, and favorable noise characteristics across various filter configurations. Furthermore, practical feasibility is validated through SPICE simulations using commercial LM13700 OTA, confirming consistent behavior for real-world applications. The proposed active topology occupies a compact core area of only 5831 μm². By scaling down large passive components, this design decreases the overall system-level footprint, providing a versatile and area-efficient solution for tunable analog IC applications. It should be noted that the reported 5831 μm² corresponds to the active core only, while the effective system-level area benefit depends on the selected base impedance and the target application. Full article

Background/Objectives: Carbonic anhydrase I (CAI) is a zinc-dependent metalloenzyme whose inhibitor discovery requires both effective navigation of chemical space and explicit evaluation of coordination-credible binding hypotheses. We aimed to develop an interpretable and reproducible QSAR-to-structure workflow for CAI inhibitor discovery. The workflow links potency prediction with zinc-site plausibility and early developability to support decision-oriented prioritization of new CAI inhibitor candidates. Methods: CAI inhibitors were retrieved from ChEMBL (CHEMBL261) and modeled as $p{K}_i=9-{\mathrm{l}\mathrm{o}\mathrm{g}}_{10}(K_i [\mathrm{n}\mathrm{M}\left]\right)$. AlvaDesc v3.0.8 generated 4224 2D descriptors, which were reduced using train-only preprocessing, variance filtering, correlation pruning, and bagged-tree ranking to a top-100 panel. Five regressors (elastic net, CART, bagging, GB, and XGB) were benchmarked on a held-out test set. Potent ChEMBL seeds (K_i ≤ 10 nM) were used for a 90% 2D similarity PubChem expansion. Predicted hits were then externally validated using independently available PubChem CAI $K_i$ records. Ten novel candidates lacking CAI $K_i$ data were docked to CAI (PDB: 1AZM) via SwissDock AutoDock Vina in neutral and relevant anionic states, with pose selection constrained by a Zn-donor filter (Zn-N/O $\le 2.6$ Å). SwissADME was used to profile physicochemical space, alerts, and absorption/distribution proxies. Results: The bagging model showed the best test generalization ($R^2=0.646$; RMSE = 0.61; MAE = 0.45). PFI and SHAP converged on sulfur/heteroatom connectivity and polar–lipophilic organization as dominant potency drivers. PubChem expansion yielded 25,315 analogs and 233 candidates at predicted $p{K}_i\ge 8.0$; external validation on 145 CAI-measured hits gave $R^2=0.358$ (RMSE = 0.456; MAE = 0.320). Across 20 ligand/protomer docking runs, 12 produced canonical Zn-anchored poses (10 Zn-N; 2 Zn-O). SwissADME indicated consensus logP values from −0.65 to 3.21, 0/10 PAINS alerts, and predominantly favorable drug-likeness (8/10 with zero Lipinski violations), supporting tiered advancement. Conclusions: Integrating interpretable QSAR, external PubChem validation, coordination-aware docking, and SwissADME yields a practical triage framework for CAI inhibitor discovery. The resulting tiered shortlist identifies two Zn-N-anchored N-alkyl sulfamides (CIDs 103935964 and 112684680) and one Zn-O-anchored carboxylate control (CID 122367674) as highest-priority computational hypotheses for staged biochemical evaluation. Full article

We introduce a local phase-field framework for spin-entanglement correlations. In this framework, the relevant hidden variable is an internal scalar phase associated with each fermion and derived from two underlying real fields. The fields are assumed to evolve locally in ordinary spacetime. When a particle pair is produced at a common spacetime event, the pair acquires a shared phase-locking condition at creation; after separation, the two internal phases evolve independently and no nonlocal interaction is introduced. Spin measurements by Stern–Gerlach analyzers are modeled as local filtering operations. Each local response depends only on the internal phase carried by the particle and on the orientation of the local analyzer. The local response function A(α,λ) = cos(λ − 2α) is derived from the spinorial transformation law of the underlying real field pair and the projection geometry of the detector interaction; it is not a phenomenological ansatz. From these deterministic local responses we derive an analog correlator. The raw product moment of the continuous detector outputs evaluates to ⟨AB⟩ = −½ cos 2(α − β), which satisfies classical Clauser-Horne-Shimony-Holt (CHSH) bounds. After Pearson normalization—the operationally appropriate correlation measure for continuous analog detector outputs, justified by channel-contrast physics and scale invariance—the normalized correlator yields E(α,β) = −cos 2(α − β), matching the quantum singlet correlator in functional form. When this normalized correlator is inserted into the CHSH expression, it yields the numerical value 2√2. This result is a structural consequence of the reduced marginal variance of continuous response functions relative to the unit-variance dichotomic observables assumed in Bell’s derivation; it does not constitute a violation of Bell’s inequality. The model does not reproduce quantum singlet statistics at the level of binary detector outcomes, where the correlator takes a triangular rather than cosine form. The contribution is therefore ontological and conceptual rather than predictive. The framework preserves parameter independence and no-signaling throughout. It provides a concrete real-field ontology for spin correlations based on internal phase structure, and it demonstrates that the functional form of the quantum singlet correlation can be obtained from a strictly local deterministic description, provided that the detector responses are treated as continuous analog quantities and normalized accordingly. We compare the model with earlier phase-based approaches and discuss experimental configurations—including time-resolved and multi-stage Stern–Gerlach measurements—that could in principle probe the proposed internal-phase dynamics at the pre-registration level. Full article

Microfluidic systems are increasingly used in neuroprotection research, but their clearest value may be to show why candidate compounds fail before costly downstream models. This critical framework review examines CNS-relevant microfluidic studies through a within-program triage logic linking chemistry-aware prescreening, blood-brain barrier/neurovascular unit (BBB/NVU) filtering, and timed validation in neuronal ischemia/reperfusion models, and treats non-CNS organ-on-a-chip and analytical microfluidic studies as engineering analogies only. The available evidence most strongly supports BBB/NVU chips as exposure- and safety-aware filters and compartmentalized neuronal oxygen-glucose deprivation platforms as timing-sensitive validation tools; droplet microfluidics contributes mainly upstream through dense dose mapping, aggregation assays and counterscreens for assay interference. A compound-centered reading also suggests that apparent activity often fails for distinct reasons, including timing mismatch, poor solubility, surface adsorption, optical artifact, inadequate multicellular context, or loss of efficacy under transport-aware testing. Taken together, the literature supports a cautious, within-program triage logic in which microfluidics is used not as a universal disease model, but as an operational framework for exposing transport, barrier, timing and assay liabilities early in neuroprotective discovery. Full article

This paper introduces a novel analog-to-digital converter (ADC) employing a passive noise-shaping (NS) technique combined with a chopper-stabilized comparator, enhancing performance and reducing ripple factor while maintaining low power consumption. The NS architecture is built on a cascade-integrator feedforward (CIFF) structure, using both infinite- and finite-impulse response filters to minimize quantization and kT/C noise. Additionally, it employs a low-power two-stage chopper amplifier to compensate for the offset voltage and enhance system stability. Validated according to the 180 nm CMOS process, the proposed ADC has an effective number of bits of 10.6, a signal-to-noise-and-distortion ratio of 68.4 dB, and a signal-to-noise ratio of 59.33 dB. With a compact area of 0.17 mm² and a power consumption of 650 µW from a 1.8 V supply, the proposal is well suited to biomedical sensor applications requiring strict accuracy and low energy consumption. Full article

This work presents a design technique for a type of recursive 2D filter, specifically anisotropic filters, with a frequency response depending on orientation. This design method is based on a 1D analog low-pass prototype filter of a specified approximation type (for instance, elliptical) and imposed order and selectivity. Next, a special frequency transformation is applied to this prototype, leading to a 2D oriented filter in the analog version. Next, applying the well-known bilinear transformation on the two frequency axes, we finally derive the frequency response of the desired 2D directional filter, with a given orientation angle in the frequency plane. The obtained 2D filter is of low complexity, its matrices being of size 5 × 5, and therefore can be efficiently implemented. Moreover, the filter is parametric (tunable), its selectivity and orientation angle being adjustable through independent parameters, which appear explicitly in the filter matrices. Several design examples using the proposed method are given for specified values of parameters (selectivity and orientation angle). The main application of this type of filter is enhancing and extracting straight lines or various oriented features and details from an image, as shown in the provided simulation results. A very efficient system-level implementation is also developed, using the block filtering approach, which ensures a higher degree of parallelism and a lower arithmetic complexity. Full article

Surface Acoustic Wave (SAW) technology, long central to analog signal processing and RF filtering, is undergoing a major renewal. Driven by advances that decouple SAWs from traditional piezoelectric materials and fixed-function devices, the field is gaining unprecedented control over acoustic, optical, and electronic interactions at the micro and nanoscale. This review synthesizes these developments across four fronts: new physical mechanisms for SAW manipulation, emerging material platforms, ranging from thin films to 2D systems, along with reconfigurable device architectures and circuits, and the expanding landscape of applications they enable. Optical methods are reshaping how SAWs are generated and controlled, bypassing the limits of conventional electromechanical coupling. Coherent optical excitation of high-Q SAW cavities via Brillouin-like optomechanical interactions now grants access to modes in non-piezoelectric substrates such as diamond and silicon, while on-chip SAW excitation in photonic waveguides through backward stimulated Brillouin scattering opens new integrated sensing routes. In parallel, magneto-acoustic experiments have revealed nonreciprocal SAW diffraction from resonant scattering in magnetoelastic gratings. On the device side, ZnO thin-film transistors integrated on LiNbO₃ exploit acoustoelectric coupling to realize voltage-tunable phase shifters; UHF Z-shaped delay lines achieve high sensitivity in a compact footprint; and parametric synthesis of wideband, multi-stage lattice filters targets 5G-class performance. Atomistic simulations show that SAW propagation in 2D MXene films can be engineered via surface terminations, while aerosol jet printing and SAW-assisted particle patterning provide agile, cleanroom-light fabrication of microfluidic and magnetic components. These advances enable applications ranging from hybrid quantum systems and quantum links to lab-on-a-chip particle control, SBS-based and UHF sensing, reconfigurable RF front-ends, and soft robotic actuators based on patterned magnetic composites. At the same time, optical techniques offer non-contact probes of dissipation, and MXenes and other emerging materials open new regimes of acoustic control. Conclusively, they are transforming SAW technology into a versatile, programmable platform for mediating complex interactions in next-generation electronic, photonic, and quantum systems. Full article

Heart rate variability (HRV) is a key physiological marker for autonomic nervous system function and cardiovascular health. Photoplethysmography (PPG) is commonly used to derive HRV metrics in wearable and low-cost monitoring systems. This study presents a comparative assessment of basic HRV metrics obtained from a MAX30102 optical sensor and a custom analog circuitry with an ADS1115 analog-to-digital converter (ADC). Both measurement pathways were carefully aligned using analog high-pass and low-pass filters and a consistent digital filtering pipeline, ensuring that the frequency bands relevant to HRV were preserved. PPG signals were recorded simultaneously, and inter-beat intervals were extracted to calculate the Standard Deviation of NN intervals (SDNN), Root Mean Square of Successive Differences (RMSSD), and Percentage of successive NN intervals >50 ms (pNN50) across multiple 30-s windows. Bland–Altman analysis was employed to evaluate agreement between the two methods. Results indicate that the analog circuit with an ADS1115 achieves comparable HRV basic metrics to the MAX30102 sensor, with improved Signal-to-Noise Ratio (SNR) due to high-resolution ADC and low-noise analog amplification. These findings demonstrate that a carefully designed analog acquisition system can reliably reproduce HRV basic parameters from PPG signals, providing an alternative approach for low-cost, flexible biosensing platforms. Full article

Highly pathogenic avian influenza (HPAI) H5N1 clade 2.3.4.4b continues to circulate globally across wild birds, poultry, and an expanding range of mammalian hosts, highlighting the need for antiviral strategies that address the animal–environment–human interface. The influenza A polymerase acidic (PA) endonuclease, a key enzyme in viral transcription, represents a conserved antiviral target across host species. In this study, we present a computational prioritization framework integrating homology modeling, molecular docking, molecular dynamics simulations, and physicochemical filtering to identify candidate PA endonuclease inhibitors relevant to a One Health context. Homology models of contemporary H5N1 clade 2.3.4.4b PA sequences were constructed based on the crystallographic template 6FS8 and used for cross-host docking against a targeted ligand library. Docking analysis identified baloxavir, a reference inhibitor, and entecavir, a nucleoside analog, as compounds of interest, with entecavir demonstrating favorable binding behavior, particularly in the poultry-associated model. Molecular dynamics simulations of the poultry PA–entecavir complex indicated stable interaction over 170 ns, supported by low structural deviation and favorable binding free energy (ΔG ≈ −85 kJ/mol). Physicochemical profiling suggested that entecavir possesses properties such as high polarity and predicted aqueous solubility, which were considered within the translational filtering step of this computational workflow. However, these properties do not establish antiviral efficacy or practical suitability for field use. The study provides a structured framework for integrating cross-host structural analysis with basic translational considerations, supporting the identification of candidate compounds for further biochemical and virological evaluation within the context of H5N1 control. Full article

This paper presents a novel voltage differencing transconductance amplifier (VDTA)-based mixed-mode inverse filter capable of operating in voltage mode, transadmittance mode, transimpedance mode, and current mode using a single topology. The proposed configuration employs only three VDTAs with two resistors and three capacitors, offering low component count, high input/output impedance flexibility, and no requirement for component matching. It simultaneously realizes first-order inverse lowpass and highpass, as well as second-order inverse bandpass responses. A comprehensive non-ideal analysis, which includes the effects of VDTA parasitic impedances, determines the practical operating frequency range. The design is validated through PSPICE simulations using 0.18 μm CMOS technology, showing close alignment between theoretical predictions and simulation results, with cutoff frequencies of approximately 1.60 MHz and low power consumption of 0.972 mW. Further analyses confirm orthogonal tuning capability, acceptable temperature stability, and robustness against component tolerances. In a practical application, the proposed inverse filter is employed to implement a mixed-mode PID controller, which significantly improves transient response characteristics by reducing rise time, settling time, and steady-state error. These findings highlight the effectiveness and versatility of the proposed design for analog signal processing and control system applications. Full article

Respiratory rate (RR) is a critical indicator of physiological status, yet unobtrusive and continuous RR monitoring remains challenging, particularly in wearable applications that require soft, lightweight, and low-power sensing systems. This paper presents an integrated approach that combines a textile-embedded embroidered strain-gauge sensor with Tiny Machine Learning (TinyML) to enable real-time, on-device RR estimation. The sensing platform consists of a textile-integrated meander-pattern strain gauge and a fabric-mounted analog readout circuit, which together capture thoracic expansion during breathing. Two lightweight neural network models—a convolutional neural network (CNN) operating on raw respiratory waveforms and a dense neural network (DNN) operating on wavelet features—were developed and trained using a public strain-sensor dataset and a custom dataset collected with the textile system (TexHype dataset). Both models were optimized through 8-bit quantization and deployed to an STM32L4 microcontroller, where end-to-end on-device preprocessing, filtering, segmentation, normalization, and inference were performed. The CNN achieved the highest accuracy, with a mean absolute error (MAE) of 1.23 breaths per minute (BPM) on the TexHype dataset, but exhibited substantial inference latency (5.8–6.2 s) due to its computational complexity. In contrast, the wavelet-based DNN demonstrated lower accuracy (MAE 2.21 BPM) but achieved real-time performance with inference times of 18–96 ms, and a power overhead ($\Delta P=P_{\mathrm{active}}-P_{\mathrm{idle}}$) of approximately 3.3 mW during inference. Cross-dataset testing revealed limited generalization between different strain-sensor platforms. The findings highlight key trade-offs between accuracy, latency, and energy efficiency, and illustrate the potential of combining stretchable electronics with embedded intelligence to enable next-generation wearable respiratory monitoring systems. Full article