Real-time Visualization Research Articles

MoS2–Plasmonic Hybrid Platforms: Next-Generation Tools for Biological Applications

The combination of molybdenum disulfide (MoS2) with plasmonic nanomaterials has opened up new possibilities in biological applications by combining MoS2’s biocompatibility and high surface area with the optical sensitivity of plasmonic metals. These MoS2–plasmonic hybrid systems hold great promise in areas such as biosensing, bioimaging, and phototherapy, where their complementary properties facilitate improved detection, real-time visualization, and targeted therapeutic interventions. MoS2’s adjustable optical features, combined with the plasmon resonance of noble metals such as gold and silver, enhance signal amplification, enabling detailed imaging and selective photothermal or photodynamic therapies while minimizing effects on healthy tissue. This review explores various synthesis strategies for MoS2–plasmonic hybrids, including seed-mediated growth, in situ deposition, and heterojunction formation, which enable tailored configurations optimized for specific biological applications. The primary focus areas include highly sensitive biosensors for detecting cancer and infectious disease biomarkers, high-resolution imaging of cellular dynamics, and the development of phototherapy methods that allow for accurate tumor ablation through light-induced thermal and reactive oxygen species generation. Despite the promising advancements of MoS2–plasmonic hybrids, translating these platforms into clinical practice requires overcoming considerable challenges, such as synthesis reproducibility, toxicity, stability in physiological conditions, targeted delivery, and scalable manufacturing. Addressing these challenges is essential for realizing their potential as next-generation tools in diagnostics and targeted therapies.

A Sustainability-Oriented Digital Twin of the Diamond Pilot Plant

The pharmaceutical industry is undergoing a significant transition from batch to continuous manufacturing, driven by increasing regulatory requirements and sustainability pressures. Digital twins (DTs) play a pivotal role in facilitating this transition by enabling real-time data visualisation, process optimisation, and predictive analytics. While substantial progress has been made in the development and application of DTs, particularly in industries such as energy and automotive, there remains a critical need for further research and development focused on creating sustainability-oriented digital twins tailored to pharmaceutical processes. In the pharmaceutical sector, DTs are being progressively utilised not only for real-time monitoring and analysis but also as dynamic training platforms for engineers and operators, enhancing both operational efficiency and workforce competency. This paper examines the University of Sheffield’s Diamond Pilot Plant (DiPP), a facility showcasing the future of pharmaceutical manufacturing through the integration of Industry 4.0 technologies and advanced sensors. This paper focuses on developing a data-driven model to predict energy consumption in a twin-screw granulator (TSG) within the DiPP. The model, based on second-degree polynomial regression, demonstrates strong predictive accuracy with R-squared values exceeding 0.8. By optimising energy performance indicators, this work aims to improve the sustainability of pharmaceutical manufacturing processes. This research contributes to the field of pharmaceutical manufacturing by providing a foundation for creating energy models and advancing the development of comprehensive DT.

Design and Implementation of an Emergency Environmental Monitoring System

The collection and real-time transmission of emergency environmental information are crucial for rapidly assessing the on-site situation of sudden disasters and responding promptly. However, the acquisition of emergency environmental information, particularly its seamless transmission, faces significant challenges under complex terrain and limited ground communication. This paper utilizes sensors, line-of-sight communication with unmanned aerial vehicles (UAVs), and LoRa long-distance communication to establish an integrated emergency environmental monitoring system that combines real-time monitoring, UAV-mounted LoRa gateway relaying, and backend data analysis. This system achieves real-time acquisition, seamless transmission, storage management, and visualization of environmental emergency information. First, a portable emergency environmental monitoring device was developed to collect and transmit environmental factor data. Second, a UAV-mounted LoRa gateway was designed to extend the data transmission coverage, ensuring seamless communication. Finally, multiple field experiments were conducted to evaluate the system’s performance. The experimental results indicate that the system possesses reliable capabilities for emergency data collection and transmission in complex environments, providing new technical solutions and practical support for developing and applying emergency environmental monitoring systems.

In situ structural-functional synchronous dissection of dynamic neuromuscular system via an integrated multimodal wearable patch.

Neuromuscular abnormality is the leading cause of disability in adults. Understanding the complex interplay between muscle structure and function is crucial for effective treatment and rehabilitation. However, the substantial deformation of muscles during movement (up to 40%) poses challenges for accurate assessment. To address this, we developed a wearable structural-functional sensing patch (WSFP) that enables synchronous analysis of muscle structure and function. The WSFP incorporates a soft, stretchable electrode array for high-performance electrophysiological monitoring with low contact impedance and high stability. Its innovative design absorbs skin deformation stress, ensuring stable adhesion of a flexible ultrasound transducer array, offering higher-fidelity imaging. With dynamic tissue imaging, it allows real-time visualization of muscle structure. The WSFP achieves superior accuracy in dynamic action recognition and disease assessment compared to single-modal methods, maintaining stable operation during motion for up to 72 hours. This study advances neuromuscular system analysis and improves diagnostic precision.

Novel Leech Antimicrobial Peptides, Hirunipins: Real-Time 3D Monitoring of Antimicrobial and Antibiofilm Mechanisms Using Optical Diffraction Tomography.

Antimicrobial peptides (AMPs) are promising agents for treating antibiotic-resistant bacterial infections. Although discovering novel AMPs is crucial for combating multidrug-resistant bacteria and biofilm-related infections, their clinical potential relies on precise, real-time evaluation of efficacy, toxicity, and mechanisms. Optical diffraction tomography (ODT), a label-free imaging technology, enables real-time visualization of bacterial morphological changes, membrane damage, and biofilm formation over time. Here, a computational analysis of the leech transcriptome using an advanced AI-based peptide screening strategy with ODT to identify potential AMPs is employed. Among the 19 potential AMPs identified, hirunipin 2 demonstrates potent antibacterial activity, low mammalian cytotoxicity, and minimal hemolytic effects. It demonstrates efficacy comparable to melittin, resistance to physiological salts and human serum, and a low likelihood of inducing bacterial resistance. Microscopy and 3D-ODT confirm its disruption of bacterial membranes and intracellular aggregation, leading to cell death. Notably, hirunipin 2 effectively inhibits biofilm formation, eradicates preformed biofilms, and synergizes with antibiotics against multidrug-resistant Acinetobacter baumannii (MDRAB) by enhancing membrane permeability. Additionally, hirunipin 2 significantly suppresses pro-inflammatory cytokine expression in LPS-stimulated macrophages, highlighting its anti-inflammatory properties. These findings highlight hirunipin 2 as a strong candidate for developing novel antibacterial, anti-inflammatory, and antibiofilm therapies, particularly against multidrug-resistant bacterial infections.

Real-time visualisation of fast nanoscale processes during liquid reagent mixing by liquid cell transmission electron microscopy

Liquid cell transmission electron microscopy (LCTEM) is a powerful technique for investigating crystallisation dynamics with nanometre spatial resolution. However, probing phenomena occurring in liquids while mixing two precursor solutions has proven extremely challenging, requiring sophisticated liquid cell designs. Here, we demonstrate that introducing and withdrawing solvents in sequence makes it possible to maintain optimal imaging conditions while mixing liquids in a commercial liquid cell. We succeeded in visualising a fast nanoscale crystallisation mechanism when an organic molecule of R-BINOL-CN dissolved in chloroform interacts with methanol. The scanning transmission electron microscopy images recorded in real-time during the interaction of the two volatile solvents reveal the formation of chain-like structures of R-BINOL-CN particles, whereas they coalesce to form single large particles when methanol is absent. Our approach of mixing liquids establishes a platform for novel LCTEM studies of a wide range of electron-beam-sensitive materials, including drug molecules, polymers and molecular amphiphiles.

Visualizing fiber end geometry effects on stress distribution in composites using mechanophores.

Localized stress concentrations at fiber ends in short fiber-reinforced polymer composites (SFRCs) significantly affect their mechanical properties. Our research targets these stress concentrations by embedding nitro-spiropyran (SPN) mechanophores into the polymer matrix. SPN mechanophores change color under mechanical stress, allowing us to visualize and quantify stress distributions at the fiber ends. We utilize glass fibers as the reinforcing material and employ confocal fluorescence microscopy to detect color changes in the SPN mechanophores, providing real-time insights into the stress distribution. By combining this mechanophore-based stress sensing with finite element analysis (FEA), we evaluate localized stresses that develop during a single fiber pull-out test near different fiber end geometries-flat, cone, round, and sharp. This method precisely quantifies stress distributions for each fiber end geometry. The mechanophore activation intensity varies with fiber end geometry and pull-out displacement. Our results indicate that round fiber ends exhibit more gradual stress transfer into the matrix, promoting effective stress distribution. Also, different fiber end geometries lead to distinct failure mechanisms. These findings demonstrate that fiber end geometry plays a crucial role in stress distribution management, critical for optimizing composite design and enhancing the reliability of SFRCs in practical applications. By integrating mechanophores for real-time stress visualization, we can accurately map quantified stress distributions that arise during loading and identify failure mechanisms in polymer composites, offering a comprehensive approach to enhancing their durability and performance.

Recent progress towards the development of fluorescent probes for the detection of disease-related enzymes.

Normal physiological functions as well as regulatory mechanisms for various pathological conditions depend on the activity of enzymes. Thus, determining the in vivo activity of enzymes is crucial for monitoring the physiological metabolism and diagnosis of diseases. Traditional enzyme detection methods are inefficient for in vivo detection, which have different limitations, such as high cost, laborious, and inevitable invasive procedures, low spatio-temporal resolution, weak anti-interference ability, and restricted scope of application. Because of its non-destructive nature, ultra-environmental sensitivity, and high spatiotemporal resolution, fluorescence imaging technology has emerged as a potent tool for the real-time visualization of live cells, thereby imaging the motility of proteins and intracellular signalling networks in tissues and cells and evaluating the binding and attraction of molecules. In the last few years, significant advancements have been achieved in detecting and imaging enzymes in biological systems. In this regard, the high sensitivity and unparalleled spatiotemporal resolution of fluorescent probes in association with confocal microscopy have garnered significant interest. In this review, we focus on providing a concise summary of the latest developments in the design of fluorogenic probes used for monitoring disease-associated enzymes and their application in biological imaging. We anticipate that this study will attract considerable attention among researchers in the relevant field, encouraging them to pursue advances in the development and application of fluorescent probes for the real-time monitoring of enzyme activity in live cells and in vivo models while ensuring excellent biocompatibility.

Deep learning-driven ultrasound equipment quality assessment with ATS-539 phantom data

IntroductionUltrasound equipment provides real-time visualization of internal organs, essential for early disease detection and diagnosis. However, poor-quality ultrasound images can compromise diagnostic accuracy and increase the risk of misdiagnosis. Quality assessments are often subjective, relying on the evaluator's experience and interpretation, which can vary. MethodsThis study introduces a two-stage deep learning framework designed to objectively assess ultrasound image quality using phantom data across three key parameters: ‘Dead zone’, ‘Axial/lateral resolution’, and ‘Gray scale and dynamic range’. Stage 1 automatically extracts regions of interest for each parameter, while Stage 2 employs detection or classification models to evaluate image quality within these regions. To generate an overall equipment quality score, a logistic regression model combines the weighted results from each parameter. ResultsThe classification model demonstrated high performance across datasets, achieving AUC scores of 98.6% for ‘Dead zone’, 87.7% for ‘Axial/lateral resolution’, and 96.0% for ‘Gray scale and dynamic range’. Further analysis using guideline-compliant images of individual devices showed AUC scores of 98.2%, 92.8%, and 100%, respectively. These findings highlight deep learning's potential for quantitative and objective assessments of ultrasound image quality. Ultimately, this framework provides a streamlined approach to quality management, enabling consistent quality control and efficient scoring-based evaluation of ultrasound equipment.

Illuminating cisplatin-induced ferroptosis in non-small-cell lung cancer with biothiol-activatable fluorescent/photoacoustic bimodal probes.

Ferroptosis modulation represents a pioneering therapeutic approach for non-small-cell lung cancer (NSCLC), where precise monitoring and regulation of ferroptosis levels are pivotal for achieving optimal therapeutic outcomes. Cisplatin (Cis), a widely used chemotherapy drug for NSCLC, demonstrates remarkable therapeutic efficacy, potentially through its ability to induce ferroptosis and synergize with other treatments. However, in vivo studies of ferroptosis face challenges due to the scarcity of validated biomarkers and the absence of reliable tools for real-time visualization. Biothiols emerge as suitable biomarkers for ferroptosis, as their concentrations decrease significantly during this process. To address these challenges, fluorescence/photoacoustic (PA) bimodal imaging offers a promising solution by providing more accurate in vivo information on ferroptosis. Therefore, the development of methods to detect biothiols using fluorescence/PA bimodal imaging is highly desirable for visualizing ferroptosis in NSCLC. In this study, we designed and constructed two activatable near-infrared (NIR) fluorescent/PA bimodal imaging probes specifically for visualizing ferroptosis by monitoring the fluctuations in biothiol levels. These probes exhibited excellent bimodal response performance in solution, cells, and tumors. Furthermore, they were successfully applied for real-time monitoring of biothiol changes during the ferroptosis process in NSCLC cells and tumors. Importantly, our findings revealed that the combined use of erastin and cisplatin exacerbates the consumption of biothiols, suggesting an enhancement of ferroptosis in NSCLC. This work not only provides powerful tools for monitoring in vivo ferroptosis but also facilitates the study of ferroptosis mechanisms and holds the potential to further advance the treatment of NSCLC.

Evaluation of liver fibrosis in HCV-infected patients using two-dimensional shear-wave elastography (2D-SWE) before and after antiviral treatment

Aim: Chronic hepatitis C virus infections can lead to liver fibrosis. Appropriate treatment of chronic hepatitis C may result in significant fibrosis reversal. The best method to assess liver fibrosis is an invasive hepatic biopsy. Among non-invasive options, one of the most recent methods is two-dimensional shear- wave elastography, which allows real-time visualization of liver stiffness. The purpose of this study was to analyze changes in liver fibrosis among patients with hepatitis C virus receiving direct-acting antiviral therapy. Material and methods: Five different elastographic measurements in kilopascals were performed in a group of 50 patients before direct-acting antiviral treatment, at the end of treatment, and 24 weeks after the end of treatment, using an Aixplorer® (Supersonic Imagine, France) ultrasound device. The results were correlated with biochemical serum tests, specifically the Fibrosis-4 and AspAT-to-platelet ratio indices. Results: Time-dependent alterations of all of the parameters were observed, including a significant decrease in liver stiffness in comparison to baseline values (before treatment). A moderate correlation between liver stiffness measurement values and both Fibrosis-4 and AspAT-to-platelet ratio indices was observed. Interestingly, only liver stiffness and blood platelet count changed over time, regardless of the sex and age of the patient. Conclusions: Two-dimensional shear-wave elastography combined with non- invasive serologic tests like Fibrosis-4 and AspAT-to-platelet ratio indices is a sufficient tool for evaluating liver fibrosis regression during and after direct-acting antiviral therapy.

Smart Watch for Health Monitoring System

This project presents a smart device designed to enhance personal safety and health monitoring. It integrates advanced features such as fall detection using a MPU6050 gyroscope, GPS for location tracking and sharing, heart rate and temperature monitoring with the MAX30105 sensor. An emergency help request button enables quick alerts during emergency situations. Powered by an ESP32C microcontroller, the system includes an OLED display for real-time data visualization and a lithium polymer battery for portability. This device offers a compact, reliable, and efficient solution for personal safety and health management.

An MRI-guided stereotactic neurosurgical robotic system for semi-enclosed head coils.

Magnetic resonance imaging (MRI) offers high-quality soft tissue imaging without radiation exposure, which allows stereotactic techniques to significantly improve outcomes in cranial surgeries, particularly in deep brain stimulation (DBS) procedures. However, conventional stereotactic neurosurgeries often rely on mechanical stereotactic head frames and preoperative imaging, leading to suboptimal results due to the invisibility and the contact with patient's head, which may cause additional harm. This paper presents a frameless, MRI-guided stereotactic neurosurgical robotic system. The robot features a seven-degree-of-freedom (7-DOF) remote center of motion, with five DOFs for preoperative trajectory alignment to the target lesion and two DOFs for defining the depth and twisting motion of the needle during insertion, thus to minimize tissue damage. The system employs interactive MRI guidance for real-time visualization of the puncture process, showing great potential in reducing surgery time, enhancing targeting accuracy, and improving safety. Experiments were conducted on the proposed system to evaluate signal-to-noise ratio (SNR) and geometric distortion. During the simultaneous operation and imaging, the system demonstrated less than 10.02% SNR attenuation and less than 0.1% geometric distortion, ensuring image usability. The free-space positioning accuracy of the system was evaluated using a laser tracker, revealing a tip position repeatability error within 0.3 ± 0.1mm.

Kanban Production Control For An Aluminum Profile Solutions Factory Using the Notion Digital Platform

Objective: This research aims to implement a production management system that combines the kanban method with the Notion digital platform in an industry specialized in the manufacture of structural aluminum profiles. Theoretical Framework: The structuring of production processes in industrial contexts requires efficient planning and control to ensure good results and competitiveness. Accurate production planning and control are essential for operations to achieve the desired levels of performance and customer service and deadlines. Method: The kanban methodology, widely recognized for optimizing workflow and reducing waste, is integrated with Notion, an intuitive digital tool that facilitates real-time visualization and monitoring of tasks. Results and Discussion: The results suggest that Notion and kanban are responsible for promoting a more flexible and adaptable management system, offering a potential model for industries facing similar challenges. Research Implications: The company that is the object of the research faces challenges in planning and meeting deadlines due to the confusing manual management of processes, which generates bottlenecks and rework. Originality/Value: Integration aims to reduce communication errors, improve inventory management and increase process efficiency, which are essential factors for competitiveness in unstable markets

Force-Responsive Delivery of Anticancer Drugs via a DNA Mechanical Nanovehicle.

Cellular mechanical dysregulation can lead to diseases and conditions like tumorigenesis. Drug delivery systems that recognize and respond to specific cellular mechanical characteristics are potentially useful for targeted therapy. We report here the creation of a DNA mechanical nanovehicle that is responsive to cell surface receptor-mediated tensile forces, which can then correspondingly deliver an anticancer drug in situ. These DNA mechanical nanovehicles can spontaneously anchor onto cell membranes and enable the real-time visualization of molecular tensions at intercellular junctions. Once a strong intercellular force was detected, a rapid drug release event was followed automatically. Force-triggered targeted cancer cell treatment was demonstrated both in vitro and in a cell mixture. Our results proved a novel cellular force-responsive platform that can be used for highly specific drug delivery, which may potentially lead to smart cancer therapy with enhanced efficacy and safety.

Dynamic Digital Radiography (DDR) in the Diagnosis of a Diaphragm Dysfunction.

Dynamic digital radiography (DDR) is a recent imaging technique that allows for real-time visualization of thoracic and pulmonary movement in synchronization with the breathing cycle, providing useful clinical information. A 46-year-old male, a former smoker, was evaluated for unexplained dyspnea and reduced exercise tolerance. His medical history included a SARS-CoV-2 infection in 2021. On physical examination, decreased breath sounds were noted at the right-lung base. Spirometry showed results below predicted values. A standard chest radiograph revealed an elevated right hemidiaphragm, a finding not present in a previous CT scan performed during his SARS-CoV-2 infection. To better assess the diaphragmatic function, a posteroanterior DDR study was performed in the standing position with X-ray equipment (AeroDR TX, Konica Minolta Inc., Tokyo, Japan) during forced breath, with the following acquisition parameters: tube voltage, 100 kV; tube current, 50 mA; pulse duration of pulsed X-ray, 1.6 ms; source-to-image distance, 2 m; additional filter, 0.5 mm Al + 0.1 mm Cu. The exposure time was 12 s. The pixel size was 388 × 388 μm, the matrix size was 1024 × 768, and the overall image area was 40 × 30 cm. The dynamic imaging, captured at 15 frames/s, was then assessed on a dedicated workstation (Konica Minolta Inc., Tokyo, Japan). The dynamic acquisition showed a markedly reduced motion of the right diaphragm. The diagnosis of diaphragm dysfunction can be challenging due to its range of symptoms, which can vary from mild to severe dyspnea. The standard chest X-ray is usually the first exam to detect an elevated hemidiaphragm, which may suggest motion impairment or paralysis but fails to predict diaphragm function. Ultrasound (US) allows for the direct visualization of the diaphragm and its motion. Still, its effectiveness depends highly on the operator's experience and could be limited by gas and abdominal fat. Moreover, ultrasound offers limited information regarding the lung parenchyma. On the other hand, high-resolution CT can be useful in identifying causes of diaphragmatic dysfunction, such as atrophy or eventration. However, it does not allow for the quantitative assessment of diaphragmatic movement and the differentiation between paralysis and dysfunction, especially in bilateral dysfunction, which is often overlooked due to the elevation of both hemidiaphragms. Dynamic Digital Radiography (DDR) has emerged as a valuable and innovative imaging technique due to its unique ability to evaluate diaphragm movement in real time, integrating dynamic functional information with static anatomical data. DDR provides both visual and quantitative analysis of the diaphragm's motion, including excursion and speed, which leads to a definitive diagnosis. Additionally, DDR offers a range of post-processing techniques that provide information on lung movement and pulmonary ventilation. Based on these findings, the patient was referred to a thoracic surgeon and deemed a candidate for surgical plication of the right diaphragm.

Near-Infrared Light-Activated DNA Nanodevice for Spatiotemporal In Vivo Fluorescence Imaging of Messenger RNA.

Real-time visualization of messenger RNA (mRNA) is essential for tumor classification, grading, and staging. However, the low signal-to-background ratios and nonspatiotemporal specific signal amplification restricted the in vivo imaging of mRNA. In this study, a near-infrared (NIR) light-activated DNA nanodevice (DND) was developed for spatiotemporal in vivo fluorescence imaging of mRNA. The DND was fabricated by encapsulating indocyanine green (ICG) and DNA fluorescent probes within thermosensitive liposomes and subsequently functionalizing the liposomes with aptamers. The ICG offers the "always-on" fluorescence signal, offering a feasible strategy for monitoring DND distribution. The fluorescence signal of DNA probes remains inactive ("off" state) during the delivery process. Upon targeted delivery of the DNDs to tumor cells via aptamer recognition, the thermosensitive liposomes could be dissociated by the photothermal effect induced by ICG under near-infrared irradiation, thereby facilitating the release of DNA probes. The DNA probes were activated ("turn on") by tumor-specific thymidine kinase 1 (TK1) mRNA through toehold-mediated strand displacement cascades, enabling the signal-amplified fluorescence imaging of mRNA. This study reveals the distinctive light-activated merit and remarkable fluorescence imaging of DNDs, highlighting their great potential to promote progress in spatiotemporal resolution imaging of other disease-relevant RNAs in vivo.

Prediction and prevention of crowd-crush accidents using crowd-density simulation based on unity engine

This paper presents a crowd-density simulation system developed using the Unity engine to predict and prevent crowd-crush accidents. Crowd-crush accidents can occur in various environments where large crowds gather, often leading to significant casualties. Effective tools for risk assessment, training, and decision-making are crucial in preventing such accidents. The proposed simulator aims to provide a realistic virtual environment that accurately models crowd behavior and dynamics. By analyzing crowd density, flow patterns, and bottleneck situations, the simulator enables users to identify potential hazards, evaluate evacuation strategies, and develop proactive crowd management measures. The system incorporates advanced features such as real-time crowd visualization, interactive scenario creation, and data analytics. Validation experiments demonstrate the effectiveness of the simulator in predicting crowd-crush risks and supporting decision-making processes. The proposed solution enhances crowd safety and helps prevent accidents in various public-gathering scenarios.

CIGVis: An open-source Python tool for the real-time interactive visualization of multidimensional geophysical data

As Python’s role in processing and interpreting geophysical data expands, the need for a Python-based tool tailored for visualizing geophysical data has become increasingly critical. In response, the Computational Interpretation Group Visualization tool (CIGVis) — a fully open-source Python tool optimized for researchers and licensed under the Massachusetts Institute of Technology License — has been developed. It specializes in the real-time interactive visualization of multi-dimensional geophysical data, including volumetric data of geophysical properties, meshes of geologic objects (e.g., faults, horizons, and geobodies), points and tubes of well-log data. CIGVis enables users to interact with the data through operations such as rotation, panning, magnifying, and dragging slices. It also supports a multicanvas functionality, allowing a simultaneous visualization across multiple subcanvases with a unified camera perspective. Its ease of use allows for effective visualization with just a few lines of code across all major operating systems and extends to desktop and Jupyter environments, facilitating code execution in various settings. The core functionalities of CIGVis are exemplified using standard geophysical data sets, such as the F3 data set.

Cameraless sensor fusion: developing a cost-effective driver assistance system using radar and ultrasonic sensor

Purpose This paper aims to develop a cost-effective, camera-less advanced driver assistance system (ADAS) for electric vehicles. It will use sensor fusion of ultrasonic and radar sensors to implement adaptive cruise control (ACC), blind spot detection (BSD) and reverse parking (RP). Design/methodology/approach The system was tested on an electric vehicle test bench, using strategically placed ultrasonic and radar sensors. Sensor fusion enabled accurate object detection and distance measurement. The system’s performance was evaluated through simulated obstacle scenarios, with responses monitored via a graphical user interface. Sensor and GPS data were transmitted to the cloud for potential vehicle-to-vehicle communication. Findings The sensor fusion approach effectively supported ACC, BSD and RP functions, demonstrating accuracy in obstacle detection, speed adjustment and emergency braking. The real-time system visualization confirmed reliability across various scenarios and cloud integration showed promise for future communication enhancements. Research limitations/implications Ultrasonic and radar sensors have limited range and accuracy compared to cameras. Ultrasonic sensors are less effective at longer distances and in adverse weather conditions, whereas radar can face challenges in detecting small or stationary objects. Sensor performance can be affected by environmental factors such as rain, fog or snow, which may reduce the effectiveness of both ultrasonic and radar sensors. Sensor performance can be affected by environmental factors such as rain, fog or snow, which may reduce the effectiveness of both ultrasonic and radar sensors. Practical implications Improved obstacle detection and collision avoidance contribute to overall vehicle safety. Drivers benefit from advanced features like ACC, BSD and RP without the high cost of traditional camera-based systems. The use of ultrasonic and radar sensors makes advanced driver assistance features more affordable, allowing broader adoption across various vehicle segments, including budget-friendly and mid-range models. The system’s responsiveness and obstacle detection capabilities can lead to more efficient driving, reducing the likelihood of accidents and improving traffic flow. Social implications Enhanced safety features such as ACC, BSD and RP contribute to reducing traffic accidents and injuries. By making advanced driver assistance features more affordable, the system improves vehicle safety for a broader range of drivers, including those in lower-income brackets. The introduction of such systems can raise public awareness about the benefits of ADAS technologies and their role in enhancing road safety. Originality/value This study introduces a novel ADAS system that eliminates the need for cameras by leveraging the strengths of radar and ultrasonic sensors. The approach offers a practical and innovative solution for enhancing vehicle safety at a reduced cost.