Search Results (3,908)

In this work, we analyze the arithmetic building blocks of DNA basecalling to motivate runtime approximate computing in bio systems-on-chip (BioSoCs). We propose and characterize a reconfigurable compressor-tree multiplier whose operating mode can be selected at runtime to trade energy for controlled arithmetic error. Using a 45 nm CMOS evaluation flow, the proposed design demonstrates a clear power–accuracy trade-off across 64 operating modes, achieving about a 58–61% reduction in multiplier power (per multiply under fixed V/f) relative to an accurate Wallace baseline, with mean relative error distance (MRED) in the 1.05–2.88% range. At the application level, we outline a first-order noise-propagation model and, consistent with prior approximate-inference studies, note that task-level quality loss is often within a few percent (up to 5%), motivating end-to-end basecalling evaluation. Application-level evaluation on a TinyX3 DNA basecaller—a compact Bonito-based model—shows that the proposed multiplier with measured REV = 0.012 and MRED = 1.98% preserves near-baseline performance, with negligible degradation in sequence identity and relative length at low perturbation levels and only gradual accuracy decline (confirming ≤ 5% accuracy drop) emerging as perturbations increase into the moderate regime. Finally, a processor-level case study using convolution microbenchmarks (kernel footprints 9–49 weights per output) shows an 11% improvement in energy per instruction and a 12% reduction in energy per MAC when integrating the proposed multiplier into an embedded RISC-V execution engine. Full article

Phototherapy is a physical treatment modality that utilizes natural or artificial light sources and harnesses radiant energy to treat diseases. Dynamic monitoring of the actual light dose received by tissues is crucial to the success of phototherapy. However, most current phototherapy devices feature bulky and complex hardware and depend on fixed parameters or surface measurements for dose estimation, failing to provide precise, real-time monitoring of light dose distribution that is tailored to individual users, specific treatment sessions, and different body regions. Furthermore, most of these devices are incapable of generating tunable and stable LED light. This study presents a preliminary diffusion equation-based proof-of-concept for a wearable, integrated dual-mode sensing system for real-time dynamic monitoring of tissue light dose and temperature change. The system, controlled by a single-chip microcontroller, rapidly extracts key tissue optical parameters via a custom multi-wavelength LED optical probe and provides real-time feedback on light dose distribution through a dynamic tissue optical simulation model. To expand the monitoring dimensions, the system innovatively integrates a thermal sensor. This sensor enables synchronous monitoring of the temperature field in the treatment area, thereby allowing for an estimation of the combined photothermal effect. The system features a compact design, user-friendly operation, fast and stable communication, and repeatable and reliable detection. With promising clinical application prospects, it holds the potential to evolve into a portable, home-use, safe, effective, wearable, and cost-effective phototherapy device. Full article

Electro-optic frequency combs (EOFCs), generated through the microwave-driven modulation of continuous-wave lasers, have emerged as a highly reconfigurable and system-compatible class of optical frequency combs with growing importance in microwave photonics, coherent communications, spectroscopy, and precision metrology. In contrast to mode-locked lasers and Kerr microresonator combs, EOFCs offer electrically programmable repetition rates, deterministic phase coherence, and intrinsic compatibility with radiofrequency electronic systems, making them particularly attractive for integrated and application-oriented implementations. As EOFCs evolve toward broader bandwidths, lower power consumption, and full on-chip integration, their achievable performance is increasingly constrained by the interplay between electro-optic physical mechanisms, modulator architectures, and material platform properties. This review establishes a unified analytical framework that systematically connects EOFC generation mechanisms, device configurations, key performance metrics, and platform-level limitations. We first summarize the fundamental electro-optic effects underpinning EOFC generation and analytically examine representative modulator architectures, including phase modulators, Mach–Zehnder modulators, and microresonator-based schemes, to clarify their respective comb-generation characteristics. Key performance determinants, such as modulation depth, bandwidth, electro-optic efficiency, and optical loss, are then discussed to elucidate their coupled influence on comb-line count, spectral flatness, output power, and phase noise. Subsequently, the performance of EOFCs implemented on major integrated platforms, including Silicon on Insulator (SOI), Indium Phosphide on Insulator (InPOI), Lithium Niobate on Insulator (LNOI), and Lithium Tantalate on Insulator (LTOI), is comparatively reviewed to highlight the material-dependent advantages and constraints. Finally, emerging directions based on heterogeneous integration and ferroelectric materials with ultrahigh electro-optic coefficients are discussed as promising pathways to overcome the current performance bottlenecks. This review provides clear physical insights and engineering guidance for the future development of high-performance, integrated EOFC systems. Full article

Over the past 200 years (1820–2020), global life expectancy has nearly tripled, increasing from 26 to 72.91 years, due to factors such as poverty reduction and public health initiatives. Today, society faces different challenges than it did centuries ago. In patient care and healthcare system priorities, the goal is to develop smart, feasible, long-lasting, cost-effective, readily available, adverse-reaction-free, adaptable, and personalized solutions that minimize patient discomfort, reduce caregiver effort, and decrease hospitalization duration and costs. In this context, biomaterials serve as versatile tools capable of performing a wide range of diagnostic, therapeutic, and theragnostic functions. Thanks to their biocompatibility, biodegradability, surface chemistry, and responsiveness, biomaterials are currently addressing issues such as patient compliance (through controlled drug-delivery systems and smart wound dressings), long transplant waiting lists, transplant rejection, non-adaptable prosthetics (artificial organs), oncology treatment efficacy (nano-formulations for theragnostics and multiple tumor targeting), and inconsistent in vitro drug-testing models (organs-on-a-chip). In this review, we focus on biomaterials’ smartness, then explore databases for efficient product design, and finally highlight their applications in the biomedical field, especially in drug delivery, tissue engineering, and regenerative medicine. Full article

Integrated optical phased array (OPA) chips enable high-speed beam steering via electronic phase control, providing a promising solution for compact pointing, acquisition, and tracking (PAT) systems. However, OPA-PAT systems must simultaneously achieve wide-field-of-view (FOV) coverage and high-precision angle-of-arrival (AOA) detection. To address this challenge, a cascaded AOA detection method based on a multi-sensor collaborative architecture is proposed. This approach utilizes a distributed detector array (DA) for coarse incident angle estimation over a wide-FOV, which then guides a two-dimensional (2D) galvanometer to steer the beam into a quadrant detector (QD) for fine measurement within a narrow-FOV. A prototype system is developed to validate the proposed cascaded algorithm. Experimental results show that within a ±20° FOV, the proposed system achieves root-mean-square errors (RMSEs) of 0.007° in azimuth and 0.01° in elevation. When integrated into an OPA-PAT terminal, static 2D closed-loop tracking is maintained with an overall tracking error better than 0.016° (RMSE). These results demonstrate that the proposed cascaded detection method can simultaneously provide wide-FOV coverage and high-precision AOA measurement, offering a practical solution for wide-FOV OPA-PAT systems. Full article

Continuous cough monitoring provides valuable insights into respiratory health; however, conventional air-conduction microphones are highly susceptible to ambient noise and raise privacy concerns. This work presents BCough, a bone-conduction-based embedded AI platform for on-device cough detection, designed and evaluated on the MAX78000 neural accelerator. The system employs a skin-contact bone-conduction sensor worn on the chest to capture body vibrations transmitted through bone and tissue, detecting cough events while minimizing environmental interference. The custom prototype integrates a bone-conduction microphone, a synchronized 6-axis IMU, power management circuitry, and an embedded neural accelerator to support real-time inference and future multimodal extensions. A compact 8-bit quantized convolutional neural network was optimized for deployment on the MAX78000 and evaluated using leave-one-subject-out cross-validation on one-second cough and non-cough segments derived from a corpus of 19,955 labeled events collected from five participants under controlled conditions. The deployed model achieved 0.80 Macro-F1, 0.81 balanced accuracy, 0.74 cough F1, and 0.89 AUC, with 15–16 ms inference latency and approximately 20 $\mathsf{\mu }$J energy per inference on chip. These results demonstrate the feasibility of low-power, privacy-preserving, bone-conduction cough detection on embedded AI hardware within an initial five-participant study. The current design is a benchtop prototype; the findings should therefore be interpreted as an initial feasibility assessment rather than evidence of robust performance across diverse users and real-world conditions. Future work will extend this platform toward miniaturized wearable implementations combining bone-conduction and inertial sensing for continuous multimodal respiratory monitoring. Full article

Conventional terahertz (THz) radio frequency (RF) frontends struggle to simultaneously balance the high performance and miniaturization of monolithic integrated designs with the excellent testability of discrete modular architectures. This paper presents a detachable 183 GHz terahertz RF frontend and completes the module design and system integration of a low-noise amplifier (LNA) and a second-order subharmonic mixer. Through optimization of the waveguide-to-microstrip transition, parasitic compensation for pad bonding, and the structural design of the chip shielding cavity, combined with a high-precision alignment scheme using positioning pins and screws, the integrated module achieves detachability, testability, and ease of maintenance. Measurement results show that across the 160–200 GHz frequency band, the amplifier achieves an average gain of 16.51 dB; the mixer exhibits a minimum conversion loss of 8.62 dB; and the full-link noise figure of the system reaches 6.68 dB. The proposed scheme effectively addresses the engineering challenges of conventional integrated architectures and provides a practical implementation pathway for terahertz communication and remote sensing detection frontends. Full article

Oral diseases remain highly prevalent worldwide and require early diagnosis and continuous monitoring to improve clinical outcomes. Conventional diagnostic methods are often invasive, time-consuming, and limited in their capacity for real-time assessment, which has driven the development of biosensor technologies for point-of-care applications. Among these, cell-based biosensors utilize living cells as sensing elements capable of responding to inflammatory mediators, bacterial toxins, metabolic products, and tumor-associated biomarkers. This narrative review summarizes the principles, cell types, detection mechanisms, and applications of cell-based biosensors in oral health. The literature was identified through a structured search of PubMed, Scopus, Web of Science, and Google Scholar using keywords related to cell-based biosensors, oral diagnostics, salivary biomarkers, periodontal disease, oral cancer, and lab-on-chip technologies. Due to the heterogeneity of biosensor designs and detection methods, the selected studies were analyzed qualitatively. Cell-based biosensors have demonstrated applications in periodontal disease detection, cariogenic biofilm monitoring, oral cancer diagnostics, cytotoxicity testing of dental materials, and salivary biomarker analysis. The integration of microfluidic and lab-on-chip systems enables real-time and multiplex detection, supporting the development of chairside diagnostic platforms in dentistry. However, challenges related to standardization, reproducibility, and clinical validation remain and must be addressed to facilitate broader implementation in routine practice. Full article

This work presents wirelessly interrogated microelectromechanical system (MEMS) capacitive sensors for continuous intraocular pressure (IOP) monitoring. The sensor uses a passive inductor–capacitor (LC) tank circuit comprising a fixed, on-chip spiral inductor and a pressure-sensitive, variable-gap capacitor with parallel-plate membrane electrodes and side anchors. The membrane is designed with dimensions of 500 µm × 500 µm × 2 µm and a capacitive transducer gap of 2.5 µm. Applied pressure deflects the top membrane, producing a corresponding capacitance variation that changes the frequency and phase response of the LC tank circuit, enabling real-time and continuous IOP monitoring over a target detection range of 0–50 mmHg and beyond. Mutual inductive coupling between the sensor and the external readout coil is investigated as a reliable readout mechanism. Full article

Background/Objectives: The rapid expansion of New Approach Methodologies (NAMs) is transforming oral biopharmaceutics by offering mechanistically rich, human-relevant tools that can reduce reliance on animal testing while improving translational confidence. Regulatory agencies, including the Food and Drug Administration (FDA) and the European Medicines Agency (EMA), are increasingly open to NAM-generated evidence, provided that methods are fit-for-purpose and scientifically justified. This review synthesizes current advances and evaluates how NAMs can be integrated across drug-development stages to enhance the prediction of oral absorption, formulation performance, and regulatory decision-making. Methods: A comprehensive literature review was conducted across classical and emerging methodologies, including in vitro permeability and solubility models, organoids, organ-on-a-chip (OoC) systems, machine learning frameworks, and mechanistic approaches such as the physiologically based pharmacokinetic (PBPK) and biopharmaceutics (PBBM) models. Emphasis was placed on physiological relevance, predictive performance, validation status, and regulatory applicability. Results: Classical tools remain essential for the Biopharmaceutics Classification System (BCS)-based biowaivers and risk-based assessments, yet they often lack physiological fidelity. NAMs provide enhanced representation of intestinal architecture, hydrodynamics, transporter activity, and metabolism. Organoids and microphysiological systems generate high-quality permeability and metabolic data, while computational NAMs enable scalable prediction of ADME properties and formulation behavior. When integrated into PBPK/PBBM models, these methods have great potential in predicting in vivo performance in humans. Evidence demonstrates that NAMs can refine, reduce, and, in specific contexts, replace animal studies without compromising scientific rigor. Conclusions: NAMs complement, rather than displace, classical biopharmaceutic tools, enabling a more mechanistic, human-centered, and ethically responsible framework for drug development. Their effective implementation will depend on continued validation, standardization, and regulatory harmonization as the field transitions toward fully NAM-supported biopharmaceutical assessment. Full article

The purpose of this research is to determine which factors contribute to extending the read range of transponders equipped with different coupling-circuit topologies operating within selected RFID frequency bands. The analysis covered transponders that varied in both the configuration of their coupling circuits and their geometric dimensions. To accomplish this, transponder models were created using the EMCoS Studio electromagnetic simulation environment. Each model was subjected to simulations that yielded the mutual inductance and the voltage induced at the chip terminals. This study examines how the impedance of the embroidered antenna, the impedance of the chip’s coupling circuit, and the magnetic flux density affect the resulting chip voltage. In several of the investigated configurations, the peak chip voltage appeared outside the frequency range normally associated with RFID systems. The frequency at which this maximum occurred was dependent on the mutual inductance value. Understanding how individual parameters influence mutual inductance makes it possible to shift the voltage peak into a target operating band. Numerical simulation results, combined with the transponder’s mathematical model, enabled the calculation of the mutual inductance and the terminal voltage—quantities that directly determine the achievable read range. This study focuses on factors such as the resonant frequencies of the antenna and coupling circuit, their impedances, and the characteristics of the magnetic field. The findings show that tuning these parameters can affect not only the location of the voltage maximum, but also its amplitude. This effect introduces additional complexity in designing and selecting suitable transponder configurations. Full article

Urinary cancers present a severe clinical challenge due to high recurrence rates. Standard intravesical therapies suffer from limited efficacy because of the urinary tract’s robust physiological defenses, namely, the dynamic washout effect during voiding and highly restrictive urothelial barriers, such as the anti-adhesive glycosaminoglycan layer and intercellular tight junctions. This review aims to explore how biomimetic engineering can overcome these obstacles by transitioning drug delivery from passive carriers to active, nature-inspired systems. We conducted a comprehensive review of the recent literature focusing on biomimetic strategies for intravesical drug delivery and urinary cancer theranostics. The analyzed approaches are categorized into chemical biomimicry (such as adhesion and camouflage) and structural/functional biomimicry (including adaptive devices and microrobots). Biomimetic strategies significantly enhance targeted drug retention and tissue penetration. Chemical biomimicry, utilizing mussel-inspired catechol chemistry and cell membrane camouflage, effectively bypasses the urothelial anti-adhesive defenses and reduces the immune clearance. Structural and functional biomimicry, such as naturally derived carriers and actively propelled magnetic or biohybrid microrobots, enables the precise spatial localization and controlled payload release in dynamic fluid environments. Furthermore, lab-on-a-chip technologies and patient-derived organoids (PDOs) offer scalable platforms for screening cargo-specific efficacies and tailoring treatments, providing a crucial bridge to personalized precision medicine. Integrating nature-inspired designs with advanced nanotechnologies provides a highly promising pathway with which to overcome the mechanical and biological barriers of the urinary tract. These biomimetic innovations hold the potential to shift the therapeutic paradigm for urinary oncology, paving the way for more efficient, targeted, and personalized precision medicine. Full article

Clonal hematopoiesis of indeterminate potential (CHIP) is an age-related condition affecting over 10–20% of individuals older than 70 years, characterized by the expansion of hematopoietic stem cell clones carrying somatic mutations in leukemia-associated driver genes in the absence of overt hematologic disease. Initially recognized as a precursor to hematologic malignancies, CHIP has since been implicated in diverse non-malignant disorders, notably increasing the risk of cardiovascular events by 40%. Recent epidemiological and experimental evidence suggests a potential disease-modifying influence of CHIP in neurodegenerative diseases, particularly Alzheimer’s disease (AD), although findings remain heterogeneous and sometimes contradictory. This review synthesizes recent evidence linking CHIP to AD risk, neuropathology, and disease progression. In this study, we summarize population-based cohort studies reporting a 36 to 54% reduction in the odds of clinical AD among CHIP carriers, alongside emerging data indicating that DNMT3A and TET2 mutations may exert divergent effects on neurodegeneration. Mechanistic insights from experimental models are examined, highlighting the ability of mutated myeloid cells to infiltrate the central nervous system and modulate neuroinflammation and amyloid clearance. We discuss conflicting findings and analyze how CHIP-driven vascular disease and stroke confound neuroprotective signals. We propose that CHIP may differentially influence AD and vascular contributions to cognitive impairment and dementia, shaping mixed dementia phenotypes. Methodological challenges, including survivor bias, competing risks, variable mutation detection thresholds, and incomplete Apolipoprotein E stratification, are discussed. Ultimately, our review clarifies that CHIP is not a simple protective factor, but a complex systemic modulator that reshapes the neurodegenerative and vascular drivers of cognitive decline, necessitating cross-disciplinary neuro-hematology collaboration to establish its role as a novel risk stratificator for improving diagnostic precision and personalizing clinical outcomes in Alzheimer’s disease. Full article

EmbeddedField-Programmable Gate Array (FPGA)-Advanced RISC Machine (ARM) systems used in industrial and Internet of Things (IoT) environments increasingly operate as network-connected edge devices. While such connectivity enables distributed processing and remote monitoring, it also exposes embedded vision nodes to security threats, including command injection, frame replay, data tampering, and abnormal communication traffic. This paper presents a hardware-assisted security architecture for an FPGA-ARM embedded vision system designed for high-speed image acquisition and network streaming. The proposed solution integrates several lightweight protection mechanisms directly into the FPGA processing pipeline, including frame replay detection, cyclic redundancy check (CRC)-based frame integrity verification, frame sequence monitoring, authenticated command execution, communication anomaly monitoring, and hardware-rooted trust primitives, such as a ring-oscillator physical unclonable function (PUF) and a pseudo-random generator. Optional secure communication is provided via a lightweight ASCON-authenticated encryption core. The architecture was implemented on a Cyclone V System-on-Chip (SoC) platform using an industrial Camera Link camera and evaluated in a low-latency image-acquisition setup operating at 100 fps, with data throughput exceeding 1 Gbps. Experimental results demonstrate that the proposed security architecture introduces only about 1.6% additional FPGA logic utilization while maintaining full real-time acquisition performance. The presented approach demonstrates that practical hardware-level security mechanisms can be integrated into FPGA-based embedded vision nodes with minimal architectural modifications and negligible performance overhead. Full article

Wireless sensor networks (WSNs) have increasing demand on lightweight, efficient, and secure encryption techniques for devices with limited resources, since traditional algorithms require high computation which make them impractical. This preliminary study presents an encryption algorithm based on chaos designed for transmitting short data, using the Lorenz system and Euler’s method for computation. It is combined with a synchronization model based on data array. It inserts iteration parameters within the ciphertext to ensure consistent key reproduction while decrypting. Within the broader context of e-health data streams, encryption efficiency is critical: continuous ECG signals generate large volumes of data that challenge real-time secure transmission, whereas individual blood pressure readings are far smaller and lightweight. While this work delimits its scope to short, low-power transmissions, simulations and hardware implementation on an nRF chip using the Enhanced ShockBurst (ESB) protocol demonstrated efficiency, with the lowest encryption speed of 0.154 ms for a 1-byte payload. Security analysis using the NIST Statistical Test Suite confirmed high statistical randomness of the generated keystream, and theoretical key-space analysis supports robustness. By focusing on short-stream encryption in preliminary form, the scheme contributes toward inclusive secure communication technologies for resource-constrained IoT healthcare systems and diverse user populations. Full article