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A Lorentz force particle analyzer (LFPA) is a contactless measuring apparatus used to detect inclusions or defects within metallic materials. A laser–cantilever–magnet (LCM) sensor is a reliable method to measure the variations of Lorentz force caused by inclusions in LFPA. In this paper, digital shearography technology, whose displacement resolution is up to 30 nm, is involved in an array LCM measurement sensor to acquire the displacement distribution of the array cantilever. The experimental setup is constructed, and the three-step phase-shifting method is selected to calculate the displacement of cantilevers. The array LCM sensor is arranged in a staggered two-row configuration, and the interval of each sensor is set as 2 mm to avoid mutual disturbance through the numerical method. In the measuring experiment, a pre-experiment, which generates periodic deformation of the cantilever using a piezoelectric ceramic actuator, is first conducted to verify the reliability of the system. Then, dynamic measurement experiments under three different conditions are conducted, and the displacement distributions of the array cantilever are obtained by the phase difference map. The dynamic experimental results show that the displacement of the cantilevers is in the order of μm. Meanwhile, the variation of the Lorentz force and the deformation of the cantilever caused by defects in three different conditions are calculated by numerical simulation. Finally, the results of the experiments and numerical simulations under various conditions are compared. The displacements of the cantilevers are very close in all cases, indicating that the array LCM sensor incorporated with digital shearography technology is robust and reliable to measure the minute force changes in LFPA. [ABSTRACT FROM AUTHOR]

Driven by the need to in situ monitoring of the endogenous substances in biological matrices, a simple, rapid and sensitive optical nanoarray system has been designed for determination and visual discrimination of four important catecholamine neurotransmitters: dopamine (DA), epinephrine (EP), norepinephrine (NE) and levodopa (LD). To fabricate this platform, spherical citrate-capped gold nanoparticles (AuNPs) in the size range of 13–42 nm have been employed as sensing elements. For each catecholamine different responses toward the AuNPs was obtained indicating unique aggregation kinetics. When the sensor array exposed to different catecholamines (CAs), both the color and UV–Vis absorbance spectra changed due to the interaction of analytes and AuNPs, creating a unique fingerprint-like pattern for each analyte which can be visually detected by naked eye. To analyze response profiles, we employed unsupervised chemometric algorithms including hierarchical cluster analysis (HCA) and principal component analysis (PCA) and satisfactory class separations were achieved. Furthermore, the sensitive RGB based color difference patterns were generated providing easily monitored visual output. Based on this sensor array, the set of four catecholamine neurotransmitters have been correctly identified and classified in the spiked real urine samples. The target analytes have been well distinguished at low concentrations of 0.3 − 4 μM DA, 0.8 − 10 μM EP, 0.8 − 8 μM NE, and 0.3 − 10 μM LD using the proposed "chemical nose" strategy. We believe that the established optical sensor array can be potentially employed as an efficient discrimination tool for diagnostic and therapeutic applications in the future. [ABSTRACT FROM AUTHOR]

Herein, we report on a polyamine receptor L1 bearing pyrene fluorogenic groups for the optical assessment of the non-opioid analgesic drug ketoprofen (KP). L1, composed of a diethylenetriamine moiety linked at its extremities to the 1 position of two pyrene units via methylene linkers, produced an emission at 460 nm in 1 : 1 (v/v) water/ethanol mixture at pH 7, which can be attributed to the excimer formation between the two aromatic groups. In the presence of KP, a salt-bridging interaction between the carboxylate group of the analyte and the central ammonium group of L1 induced a redistribution of acidic protons in the polyamine chain, causing a marked increase in the emission. This optical signal was used to detect KP in aqueous media. Based on this observation, the properties of all-solid-state optodes with plasticized PVC membranes doped with L1 and deposited on an appropriate solid support material were further investigated. The best membrane contained 1 wt% of fully protonated L1, plasticized with DOS and doped with 3 equiv. of the TDMACl anion-exchanger, which detected KP in the 2 μM–0.1 mM range with a low influence of interfering ions. Furthermore, this membrane was applied for the assessment of the amount of KP in OkiTask, achieving an RSD of 2.1% and recovery of 102%. Moreover, the possibility to decrease the LOD of KP to 0.84 μM (0.21 mg L−1) through the application of L1-based fluorescent sensor arrays and chemometrics was studied. [ABSTRACT FROM AUTHOR]

The collection and analysis of acoustic emission (AE) from weld circumferential cracks are crucial for ensuring pipeline safety. However, the unclear propagation characteristics and mode features of AE present challenges in array design and mode identification. In this manuscript, a novel mode feature extraction method for circumferential crack AE signals is proposed. This method involves three primary steps: firstly, a L-shaped flexible sensor array is designed to capture raw AE signals from both axial and circumferential directions. Next, the collected signals are filtered and evaluated to extract effective multi-mode components through the Kalman filtering and recursive plot (RP). Finally, the time–frequency features and mode types are extracted through combining the Hilbert-Huang transform (HHT) and dispersion curves. Results indicate that during the crack growths, both axial and circumferential direction AE signals contain multi-mode components, specifically L(0,1), F(1,1) and F(1,2) modes, with durations spanning 30–500 μs. Additionally, the axial modes predominantly occur within 200–300 kHz range, whereas the circumferential modes span both low and high frequency bands, specifically 40–50 kHz and 200–300 kHz, respectively. Modes across different frequencies indicate distinct structural behaviours, including crack growths and plastic deformations. The proposed method provides attempts for the pipe weld AE monitoring and multi-mode analysis. [ABSTRACT FROM AUTHOR]

Precise snake venom identification is the prerequisite for clinical treatment, production of antiserum, basic research, and other applications. Unfortunately, it is still a challenging task which mainly originates from their extraordinary complicated protein composition. Differential sensing strategy was applied benefiting from its advantage in discrimination of complex mixtures. Herein, A dual-mode supramolecular fluorescent sensor array based on the heteromultivalent sensing strategy was developed. The heteromultivalent macrocyclic coassemblies with strong and pan-selective binding to proteins were employed to construct sensor units. Fluorescence intensity and anisotropy signals were integrated, which were expected to show multidimensional information of proteins, such as surface groups, protein structures, and molecular weights. Finally, a supramolecular sensor array with the ability of facile, rapid and general species-specific identification and taxonomic classification for snake venom was constructed. The sensor array also demonstrated its advantage in semiquantitative analysis and multi-level identification, suggesting its great potential for practical use. [ABSTRACT FROM AUTHOR]

There is a persistent need for effective sensors to detect rare earth element ions (REEIs) due to their effects on human health and the environment. Thus, a simple and efficient fluorescence-based detection method for REEIs that offers convenience, flexibility, versatility, and efficiency is essential for ensuring environmental safety, food quality, and biomedical applications. In this study, 6-aza-2-thiothymine-gold nanoclusters (ATT-AuNCs) and bovine serum albumin/3-mercaptopropionic acid-AuNCs (BSA/MPA-AuNCs) were utilized to detect 14 REEIs (Sc3+, Gd3+, Lu3+, Y3+, Ce3+, Pr3+, Yb3+, Dy3+, Tm3+, Sm3+, Ho3+, Tb3+, La3+, and Eu3+), resulting in the creation of a simple, sensitive, and multi-target fluorescence sensor array detection platform. We observed that REEIs exert various enhancement or quenching effects on ATT-AuNCs and BSA/MPA-AuNCs. Thus, these two probes function as double signal channels, with the different effects of REEIs serving as signal inputs. Pattern recognition methods, including hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA), were used to assess the recognition performance of the constructed sensing system. Beyond the excellent ability to recognize individual REEIs, the platform is also capable of distinguishing mixed REEIs. Also, this approach was validated by applying it to detect REEIs in purified water samples. This method not only minimizes the need for synthesizing and optimizing new probes but also offers a novel approach for the determination and identification of diverse analytes, filling a gap in the detection of a large number of REEIs simultaneously. [ABSTRACT FROM AUTHOR]

In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device--skin coupling quality in electrophysiological sleep monitoring systems hinder the comfort and reliability of night wearing. Here, we report a washable, skin-compatible smart garment sleep monitoring system that captures local skin strain signals under weak device--skin coupling conditions without positioning or skin preparation requirements. A printed textile-based strain sensor array responds to strain from 0.1 to 10% with a gauge factor as high as 100 and shows independence to extrinsic motion artifacts via strain-isolating printed pattern design. Through reversible starching treatment, ink penetration depth during direct printing on garments is controlled to achieve batch-to-batch performance variation <10%. Coupled with deep learning, explainable AI, and transfer learning data processing, the smart garment is capable of classifying six sleep states with an accuracy of 98.6%, maintaining excellent explainability (classification with low bias) and generalization (95% accuracy on new users with few-shot learning less than 15 samples per class) in practical applications, paving the way for next-generation daily sleep healthcare management. [ABSTRACT FROM AUTHOR]

A sensitized dual-response ratiometric fluorescent sensor integrated smartphone platform for accurate discrimination and detection of tetracycline (TC) homologues was fabricated based on N-CDs-Eu3+ complex. In the sensing system, N-CDs act as a sensitizer of Eu3+ and significantly enhance the fluorescence of TC-Eu3+ complex approximate 40-fold owing to the synergistic effect of antenna effect (AE) and fluorescence resonance energy transfer (FRET). A paper sensor integrated with a smartphone platform is further fabricated for on-site measurement of TC. The proposed sensing platform exhibits an obvious color change from blue to red with limit of detection (LOD) of 1.5 and 63.2 nM for spectrofluorimetry and paper sensor, respectively. In addition, to eliminate the interference from TC homologues, a simple and effective sensor array was constructed by regulating the pH of the system. The different fluorescence responses to four tetracycline homologues used widely (TC, OTC, CTC, and DOX) were examined and dealt with principal component analysis, and accurate differentiation of TC homologues was therefore achieved. This work provides an integrated method for identification and synchronous quantitative detection of TCs, and a potential application for visual and on-site detection of TCs in environmental monitoring and food safety. [ABSTRACT FROM AUTHOR]

Conventional flexible electrodes used in capacitive flexible pressure sensors require cumbersome manufacturing processes, high production costs, and harsh operating conditions. Thus, these limit the wide application of conventional flexible electrodes. To solve these problems, we reported a capacitive flexible sensor based on flexible serpentine electrodes fabricated by direct ink writing method and porous polydimethylsiloxane (PDMS) dielectric layer. The sensor has a simple structure and low production cost. It has a quick response time, wide detection range, and can remain stable for up to 200 cycles. It can also be used to monitor human movement. We also made 5 × 5 sensor arrays to detect spatial pressure distribution. This pressure sensor has wide application scenarios in the future of wearable electronics. [ABSTRACT FROM AUTHOR]

Treating ankle fractures in athletes, commonly resulting from training injuries, remains a significant challenge. Current approaches to managing both non-surgical and postoperative foot and ankle disorders have focused on integrating sensory systems into orthotic devices. Recent analyses have identified several gaps in rehabilitation strategies, especially regarding gait pattern reformation during recovery. This work aims to enhance rehabilitation effectiveness for patients with ankle injuries by controlling load distribution and monitoring joint flexion/extension angles, as well as the reactive forces during therapeutic exercises and walking. We developed an exoskeleton device model using SolidWorks 2024 software, based on data from two patients: one healthy and one with an ankle fracture. Pressure measurements in the posterior limb region were taken using the F-Socket system and a custom electromechanical sensor designed by the authors. The collected data were analyzed using the butterfly parameterization method. This research led to the development of an adaptive exoskeleton device that provided pressure distribution data, gait cycle graphs, and a diagram correlating foot angles with the duration of exoskeleton use. The device demonstrated improvement in the patients' conditions, facilitating a more normalized gait pattern. A reduction in the load applied to the ankle joint was also observed, with the butterfly parameter confirming the device's correct operation. [ABSTRACT FROM AUTHOR]

The fast progress in artificial intelligence has increased the need for wearable electronics. Herein, a piezoresistive pressure sensor consisting of three layers has been proposed. The soft sensing film has been fabricated by filling PDMS (polydimethylsiloxane) with Ag-graphene by a simple mixing method, which possesses low cost and excellent sensing properties, including high sensitivity, high conductivity, and quick response to different pressures. Furthermore, the characterizations were performed by using a scanning electron microscope to study the microstructure and surface morphologies of the Ag-graphene-PDMS film to explore its internal working mechanism. Moreover, the single sensor and sensor array were tested for many applications. The results showed that the fabricated pressure sensor effectively identified various human movements and distinguished pressures with different values and positions. As a result, this sensor has a wide range of applications, including smart wearable technologies and soft robotics. [ABSTRACT FROM AUTHOR]

To address the issue of reduced sowing depth detection accuracy caused by varying soil topography during the operation of wheat row drills, an indoor bench test device suitable for wheat row drills was developed. The device integrates a laser sensor and an array sensor for terrain and sowing depth detection. The laser sensor provides the detected sowing depth values, while the array sensor captures different terrain features. The actual sowing depth values are obtained through the indoor experimental setup. The experiment includes three types of terrain: convex, concave, and flat. The terrain slope matrix is obtained using the array sensor, and terrain feature values are extracted. The laser sensor is then used to obtain the detected sowing depth, and the actual sowing depth is manually measured. PCA analysis is conducted to correlate terrain feature values with sowing depth deviations. Results indicate that under different terrain conditions, the slope mean and slope standard deviation are the main components affecting sowing depth deviations. Compared to using a single sensor, this system enables more accurate sowing depth measurement by analyzing terrain features. The device provides valuable data support for controlling sowing depth under varying terrain conditions in subsequent operations. [ABSTRACT FROM AUTHOR]

In this letter, MEMS gas sensors-based electronic nose (e-nose) was developed and used for odorant evaluations at different parts of the city. A gas analyzer, in conjunction with sensorial analysis, was performed for different odorous samples. The design of experiments that consisted of eight experimental sets was developed to the selectivity and sensitivity of the developed sensor array following the actual environmental scenario. Advanced multivariate statistical approaches, such as linear discriminant analyses and K-means, were used to describe sample similarity and discrimination ability of the system. The e-nose data processing exhibits satisfactory discrimination between air samples with more than 97% variability. A validated partial least-square (PLS) model foresees good co-relation between e-nose measurement and gas analyzer analysis. Analysis of variance shows that the model is a good fit with significantly reduced RMSE values and high $R^{2}$ values. The finding indicates that an e-nose unit could be a low-cost solution for environmental measurement-based odorant emissions measurement. [ABSTRACT FROM AUTHOR]

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Robotic auscultation has the potential to solve problems associated with gender issues by allowing examinations that eliminate the need for physical contact between doctor and patient. Aiming toward a robotic auscultation device capable of safely acquiring chest and back auscultatory sounds simultaneously, this study aimed to develop a unique actuator-less hugging mechanism with a multi-acoustic sensor array that can be transformed to wrap around the chest and back to fit the patient's body shape. The mechanism consists of a twin-articulated arm with multi-layer gear coupling and a cam mechanism for power transmissions. The hugging motion is generated by pushing the cam mechanism by the patient. The force applied to the cam mechanism acts as the driving force for the twin-articulated arm. The trajectory of the arm changes depending on the distance that the cam mechanism is pressed, and it was designed to fit typical body types (obese, standard, and slender). Our results demonstrated that the proposed mechanism was able to be transformed for each body type, and its positional error was less than 15 mm in all body types. This means that the proposed mechanism is capable of safely acquiring chest and back auscultatory sounds whilst simultaneously fitting to various body shapes. [ABSTRACT FROM AUTHOR]

Pathogenic intracellular bacteria pose a significant threat to global public health due to the barriers presented by host cells hindering the timely detection of hidden bacteria and the effective delivery of therapeutic agents. To address these challenges, we propose a tandem diagnosis-guided treatment paradigm. A supramolecular sensor array is developed for simple, rapid, accurate, and high-throughput identification of intracellular bacteria. This diagnostic approach executes the significant guiding missions of screening a customized host-guest drug delivery system by disclosing the rationale behind the discrimination. We design eight azocalix[4]arenes with differential active targeting, cellular internalization, and hypoxia responsiveness to penetrate cells and interact with bacteria. Loaded with fluorescent indicators, these azocalix[4]arenes form a sensor array capable of discriminating eight intracellular bacterial species without cell lysis or separation. By fingerprinting specimens collected from bacteria-infected mice, the facilitated accurate diagnosis offers valuable guidance for selecting appropriate antibiotics. Moreover, mannose-modified azocalix[4]arene (ManAC4A) is screened as a drug carrier efficiently taken up by macrophages. Doxycycline loaded with ManAC4A exhibits improved efficacy against methicillin-resistant Staphylococcus aureus-infected peritonitis. This study introduces an emerging paradigm to intracellular bacterial diagnosis and treatment, offering broad potential in combating bacterial infectious diseases. In this work, authors present a supramolecular fluorescent sensor array that enables the accurate discrimination of intracellular bacteria, also demonstrating that this can be modified as a host-guest drug delivery system to combat these bacteria. [ABSTRACT FROM AUTHOR]

Highly packable and deployable electronics offer a variety of advantages in electronics and robotics by facilitating spatial efficiency. These electronics must endure extreme folding during packaging and tension to maintain a rigid structure in the deployment state. Here, we present foldable and robustly deployable electronics inspired by Plantago, characterized by their tolerance to folding and tension due to integration of tough veins within thin leaf. The primary design approach for these electronics involves a high resistance to folding and tension, achieved through a thin multilayered electronic composite, which manages the neutral axis and incorporates tough Kevlar. The fabricated electronics can be folded up to 750,000 times without malfunctions and endure pulling an object 6667 times heavier than itself without stretching. Such robust electronics can be used as a deployable robot with sensor arrays, demonstrating practical applicability, as it maintains their mechanical and electrical properties during inflation from the packaged state. [ABSTRACT FROM AUTHOR]

Pressure and temperature sensing simultaneously and independently is crucial for creating electronic skin that replicates complex sensory functions of human skin. Thin-film transistor (TFT) arrays with sensors have enabled cross-talk-free spatial sensing. However, the thermal dependence of charge transport in semiconductors has resulted in interference between thermal and pressure stimuli. We develop multimodal sensor arrays based on three-dimensional integration of an active matrix to detect temperature and pressure independently. Our approach includes a calibrated compensation to decouple temperature and pressure signals. An individual pixel device consists of a TFT-based pressure sensor layered above a TFT-based temperature sensor. The detected temperature is used to compensate for the thermal effect on TFT-based pressure sensors. We develop large-area sensor arrays to enable accurate detection of two-dimensional pressure and temperature, leveraging these technologies to demonstrate advanced robotic grippers. The grippers stably grasp and lift a cup regardless of temperature, proving their possibility in skin-like electronic applications. [ABSTRACT FROM AUTHOR]

Infrared array sensor-based fall detection and activity recognition systems have gained momentum as promising solutions for enhancing healthcare monitoring and safety in various environments. Unlike camera-based systems, which can be privacy-intrusive, IR array sensors offer a non-invasive, reliable approach for fall detection and activity recognition while preserving privacy. This work proposes a novel method to distinguish between normal motion and fall incidents by analyzing thermal patterns captured by infrared array sensors. Data were collected from two subjects who performed a range of activities of daily living, including sitting, standing, walking, and falling. Data for each state were collected over multiple trials and extended periods to ensure robustness and variability in the measurements. The collected thermal data were compared with multiple statistical distributions using Earth Mover's Distance. Experimental results showed that normal activities exhibited low EMD values with Beta and Normal distributions, suggesting that these distributions closely matched the thermal patterns associated with regular movements. Conversely, fall events exhibited high EMD values, indicating greater variability in thermal signatures. The system was implemented using a Raspberry Pi-based stand-alone device that provides a cost-effective solution without the need for additional computational devices. This study demonstrates the effectiveness of using IR array sensors for non-invasive, real-time fall detection and activity recognition, which offer significant potential for improving healthcare monitoring and ensuring the safety of fall-prone individuals. [ABSTRACT FROM AUTHOR]

Clinical motion analysis plays an important role in the diagnosis and treatment of mobility-limiting diseases. Within this assessment, relative (point-to-point) tracking of extremities could benefit from increased accuracy. Given the limitations of current wearable sensor technology, supplementary spatial data such as distance estimates could provide added value. Therefore, we propose a distributed magnetic tracking system based on early-stage demonstrators of novel magnetoelectric (ME) sensors. The system consists of two body-worn magnetic actuators and four ME sensor arrays (body-worn and fixed). It is enabled by a comprehensive signal processing framework with sensor-specific signal enhancement and a gradient descent-based system calibration. As a pilot study, we evaluated the technical feasibility of the described system for motion tracking in general (Scenario A) and for operation during treadmill walking (Scenario B). At distances of up to 60 cm, we achieved a mean absolute distance error of 0.4 cm during gait experiments. Our results show that the modular system is capable of centimeter-level motion tracking of the lower extremities during treadmill walking and should therefore be investigated for clinical gait parameter assessment. [ABSTRACT FROM AUTHOR]

Thermopile sensor arrays provide a sufficient counterbalance between person detection and localization while preserving privacy through low resolution. The latter is especially important in the context of smart building automation applications. Current research has shown that there are two machine learning-based algorithms that are particularly prominent for general object detection: You Only Look Once (YOLOv5) and Detection Transformer (DETR). Over the course of this paper, both algorithms are adapted to localize people in 32 × 32-pixel thermal array images. The drawbacks in precision due to the sparse amount of labeled data were counteracted with a novel generative image generator (IIG). This generator creates synthetic thermal frames from the sparse amount of available labeled data. Multiple robustness tests were performed during the evaluation process to determine the overall usability of the aforementioned algorithms as well as the advantage of the image generator. Both algorithms provide a high mean average precision (mAP) exceeding 98%. They also prove to be robust against disturbances of warm air streams, sun radiation, the replacement of the sensor with an equal type sensor, new persons, cold objects, movements along the image frame border and people standing still. However, the precision decreases for persons wearing thick layers of clothes, such as winter clothing, or in scenarios where the number of present persons exceeds the number of persons the algorithm was trained on. In summary, both algorithms are suitable for detection and localization purposes, although YOLOv5m has the advantage in real-time image processing capabilities, accompanied by a smaller model size and slightly higher precision. [ABSTRACT FROM AUTHOR]

A sensor array that can distinguish ochratoxins based on the fluorescence of the albumin–ochratoxin complex has been developed. This sensor array enabled the identification of ochratoxins and their mixtures in real food samples. [ABSTRACT FROM AUTHOR]

In this paper, we present the design and fabrication of a plasmonic metasurface based on titanium dioxide (TiO2) nanowire arrays integrated with plasmonic layers. The structure is engineered to produce Fano resonances within the visible spectrum, resulting from the coupling of localized surface plasmon resonances, lattice modes, and nanowire's optical modes. Experimentally, we show that by tuning the geometrical features of the metasurface, such as the length, diameter, and period of the nanowires, a high-quality factor single peak can be achieved in the reflection spectra, resulting in vivid structural colors in bright field. To our knowledge, this is the first demonstration of such vivid colors with nanowire arrays in bright field reflections. When characterized by refractive index fluids around the refractive index of water, the plasmonic metasurface also showed great potential for biochemical colorimetric sensing. The best design demonstrated a bulk sensitivity of 183 nm/RIU with high Q resonance features and linear changes in color values using image processing. [ABSTRACT FROM AUTHOR]

The following paper reports about a non-intrusive diagnostic system able to measure the frequency of azimuthal instabilities inside the discharge channel of Hall Effect Thrusters (HETs) up to several hundreds of kHz in a real-time fashion. The innovative optical assembly, formed by an optimized sensor array, can help to drastically reduce the amount of processed data and the size of the opto-electronic front-end. The performances of the diagnostic system have been tested successfully during several tests on a HET in different regimes, showing an accuracy and reliability comparable with the state-of-the-art video recording methods. In order to test the real-time fashion of the diagnostic, we have monitor the time-evolution of the azimuthal mode spectra during the startup transient of the thruster, highlighting a redistribution of the oscillation energy among the different mode to an asymptotic stationary regime. The high compactness of the developed sensor array opens new possibilities to perform simultaneously multi-channel or species-selective real-time measurements, while the reduced electronics and power consumption pave the way to miniaturized tools for on-flight diagnostics. [ABSTRACT FROM AUTHOR]

Retinomorphic systems that can see, recognize, and respond to real-time environmental information will extend the complexity and range of tasks that an exoskeleton robot can perform to better assist physically disabled people. However, the lack of ultrasensitive, reconfigurable, and large-scale integratable retinomorphic devices and advanced edge-processing algorithms makes it difficult to realize retinomorphic hardware. Here, we report the retinomorphic hardware prototype with a 4096-pixel perovskite image sensor array as core module to endow embodied intelligent vision functionalities. The retinomorphic sensor array, using a one photodetector-one transistor geometry to resemble retinal circuit with broadband, ultrahigh, and reconfigurable photoresponsivities, executes both adaptive imaging with a contrast enhancement of ~620% under a dim-lit intensity of 10 microwatts per square centimeter and an instantaneous one-dimensional feature extraction algorithm to decompose the origin visual scenarios into parsimoniously encoded spatiotemporal information. This retinomorphic system endows embodied intelligence with adaptive imaging, in situ processing, and decision-making capabilities and promises enormous potential for autonomous robot applications. [ABSTRACT FROM AUTHOR]

Non-destructive detection of maize silage quality is essential. The aim is to propose a fast and non-destructive silage pH detection method based on a colorimetric sensor array (CSA). Extended color components, a novel sensitive dye screening method, and a feature screening method were integrated and applied to enhance pH detection. Fifty color components were constructed from five color spaces and used to extract information about the response of CSA to silage. Forward and backward stepwise selection and support vector regression (SVR) were combined to create a sensitive dye screening method, which was used to determine the optimal sensitive dye. The variable combination population analysis–iteratively retains informative variables algorithm was iterated to optimize effective features. Consequently, six hundred variables were extracted from the twelve dyes, which were able to comprehensively and finely characterize the CSA response. Four sensitive dyes were screened out from the twelve dyes, which were sensitive to silage volatile compounds and accurately reflected the odor changes. Twenty-eight effective features were preferred, based on which the SVR model had R p 2 , RMSEP and RPD scores of 0.9533, 0.4186, and 4.4186, respectively; the pH prediction performance was substantially improved. This study provides technical support for the scientific evaluation of silage quality. [ABSTRACT FROM AUTHOR]

Person identification is a critical task in applications such as security and surveillance, requiring reliable systems that perform robustly under diverse conditions. This study evaluates the Vision Transformer (ViT) and ResNet34 models across three modalities—RGB, thermal, and depth—using datasets collected with infrared array sensors and LiDAR sensors in controlled scenarios and varying resolutions (16 × 12 to 640 × 480) to explore their effectiveness in person identification. Preprocessing techniques, including YOLO-based cropping, were employed to improve subject isolation. Results show a similar identification performance between the three modalities, in particular in high resolution (i.e., 640 × 480), with RGB image classification reaching 100.0%, depth images reaching 99.54% and thermal images reaching 97.93%. However, upon deeper investigation, thermal images show more robustness and generalizability by maintaining focus on subject-specific features even at low resolutions. In contrast, RGB data performs well at high resolutions but exhibits reliance on background features as resolution decreases. Depth data shows significant degradation at lower resolutions, suffering from scattered attention and artifacts. These findings highlight the importance of modality selection, with thermal imaging emerging as the most reliable. Future work will explore multi-modal integration, advanced preprocessing, and hybrid architectures to enhance model adaptability and address current limitations. This study highlights the potential of thermal imaging and the need for modality-specific strategies in designing robust person identification systems. [ABSTRACT FROM AUTHOR]

The effects of mechanical vibrations on control system stability could be significant in control systems designed on the assumption of rigid-body dynamics, such as launch vehicles. Vibrational loads can also cause damage to launch vehicles due to fatigue or excitation of structural resonances. This paper investigates a method to control structural vibrations in real time using a finite number of strain measurements from a fiber Bragg grating (FBG) sensor array. A scaled test article representative of the structural dynamics associated with an actual launch vehicle was designed and built. The main modal frequencies of the test specimen are extracted from finite element analysis. A model of the test article is developed, including frequency response, thruster dynamics, and sensor conversion matrices. A model-based robust controller is presented to minimize vibrations in the test article by using FBG measurements to calculate the required thrust in two cold gas actuators. Controller performance is validated both in simulation and on experiments with the proposed test article. The proposed controller achieves a 94% reduction in peak–peak vibration in the first mode, and 80% reduction in peak–peak vibration in the second mode, compared to the open loop response under continuously excited base motion. [ABSTRACT FROM AUTHOR]

Electric heaters are widely used owing to their portability, fast heating, single-focus heating, and energy efficiency advantages. Manufacturers provide customers with information on the power consumption and energy efficiency classes of heaters but do not provide any information on heating patterns. Knowing the heating pattern enables users to select the correct heater, which has a significant effect on comfort, health, energy efficiency, industrial process performance, plant growth, and climate change. In previous studies, two-dimensional temperature measurements were performed using sensor arrays. However, the three-dimensional heating patterns of the heaters have not been extracted, and the deformation effect of gravity on the heating patterns has not been demonstrated. In this study, a temperature measurement system with 64 temperature sensors placed at equal intervals in the xz-plane was designed and implemented. Then, the fan heater was moved along the y-axis at intervals of 10 cm from 0 to 100 cm, and three-dimensional heating patterns were obtained for different fan voltages. As part of the research objectives, the deformation effect of gravity on the heating pattern was revealed, and the shift in the maximum temperature point on the +z-axis was measured. The mathematical formula for the maximum temperature value was derived based on the fan voltage and the distance between the heater and the sensor array. The goodness-of-fit statistical values for the derived mathematical formula for the 55 temperature measurements were calculated as the root mean square error of 1.9543 and R-squared of 99.43%, demonstrating the accuracy of the presented model. [ABSTRACT FROM AUTHOR]

In the current era of advanced IoT technology, human occupancy monitoring and positioning technology is widely used in various scenarios. For example, it can optimize passenger flow in public transportation systems, enhance safety in large shopping malls, and adjust smart home devices based on the location and number of occupants for energy savings. Additionally, in homes requiring special care, it can provide timely assistance. However, this technology faces limitations such as privacy concerns, environmental factors, and costs. Traditional cameras may not effectively address these issues, but infrared thermal sensors can offer similar applications while overcoming these challenges. Infrared thermal sensors detect the infrared heat emitted by the human body, protecting privacy and functioning effectively day and night with low power consumption, making them ideal for continuous monitoring scenarios like security systems or elderly care. In this study, we propose a system using the AMG8833, an 8 × 8 Infrared Thermal Array Sensor. The sensor data are processed through interpolation, adaptive thresholding, and blob detection, and the merged human heat signatures are separated. To enhance stability in human position estimation, a dynamic sliding window adjusts its size based on movement speed, effectively handling environmental changes and uncertainties. [ABSTRACT FROM AUTHOR]

The rapid and reliable detection of pathogenic bacteria remains a significant challenge in clinical microbiology. Consequently, the demand for simple and rapid techniques, such as antimicrobial peptide (AMP)-based sensors, has recently increased as an alternative to traditional methods. Melittin, a broad-spectrum AMP, rapidly associates with the cell membranes of various gram-positive and gram-negative bacteria. It also inhibits bacterial biofilm formation in blood culture media. In our study, bacterial growth was measured using electrical vertical-capacitance sensors with interdigitated electrodes functionalized with melittin, a widely studied AMP. The melittin-immobilized vertical-capacitance sensors demonstrated real-time detection of both standard and clinically isolated bacteria in media. Furthermore, these sensors successfully detected clinically isolated bacteria in blood culture media while inhibiting bacterial biofilm formation. Melittin-immobilized vertical-capacitance sensors provide a rapid and sensitive pathogen detection platform, with significant potential for improving patient care. [ABSTRACT FROM AUTHOR]

Micro-hotplates have provided the possibilities of miniaturization, low power consumption, and high integration for widespread application in MEMS sensors, such as the MEMS-based metal oxide gas sensors. However, thermal crosstalk among micro-heating areas has greatly restricted the design of a micro-hotplate. Although the issue of thermal crosstalk is annoying between independent working areas, it can reduce power consumption to a certain extent. This paper proposed a dual working area micro-hotplate based on thermal crosstalk through the foundation of an electro-thermal analysis model. It especially proposes a strategy of introducing optimized parameters from a single working area to a dual working area. Besides, evaluation for thermal crosstalk was achieved by setting the temperature of one working area as constant and monitoring the power of the other working area when it reaches a certain temperature. The results indicated that the designed dual working area micro-hotplate can save at least a quarter of the heating power compared with the single working area micro-hotplate at the same working temperature of 300 ∘ C and other same parameter settings. Within the acceptable limits of mechanical deformation, the heating efficiency of the micro-hotplate is improved from 4.10 mW/(mm 2 . ∘ C ) to 2.99 mW/(mm 2 ∘ C ). It was demonstrated that the introduction of thermal crosstalk can significantly reduce the power consumption of the micro-hotplate, providing a viable solution for enhancing the properties of MOX gas sensor array. [ABSTRACT FROM AUTHOR]

Purpose: The paper aims to accurately measure the key motion parameters, such as velocity, azimuth and pitch angle, of the small flying object with a non-uniform curve trajectory. It proposes a measurement method and its calculation model of non-uniform curve trajectory using a photoelectric sensor array. Design/methodology/approach: First, the basic composition of the measurement system and mechanism of photoelectric sensor array are described, respectively. Second, a non-uniform curve mathematical measurement model is constructed differently from the traditional linear trajectory, taking into account the influence of gravity and air resistance. Third, the measurement error of the system is analyzed through numerical simulation. Finally, the accuracy and feasibility of the approach are verified by live-ammunition experiments. Findings: The results show that the systematic error of the hitting point coordinates can be reduced by 9% compared to the traditional linear measurement model. Consequently, this method can meet the higher measurement requirement for the key motion parameters of the small flying object under the non-uniform curve trajectory. Research limitations/implications (if applicable)- although the approach itself is generalizable, the method is unable to detect the motion parameters of multiple small flying objects. Research limitations/implications: Although the approach itself is generalizable, the method is unable to detect the motion parameters of the multiple small flying objects. Practical implications: It is evident that the proposed non-uniform curve measurement model is more precise in quantifying the essential characteristics of the small flying object, particularly in consideration of the environmental conditions. Social implications: The precise measurement of the key motion parameters of the small flying object can facilitate the enhancement of the protective performance of protective materials. Originality/value: A novel approach to measurement is proposed, which differs from the conventional uniform trajectory model. To this end, the space construction of the photoelectric sensor array is optimized. The number of the sensors is revised. [ABSTRACT FROM AUTHOR]

A HPU-23@Ru@Tb-NH2 sensor array with light-driven oxidase-mimicking activity and triple-emission fluorescence was developed. It was composed of a Tb3+-functionalized metal organic framework and Ru(bpy)32+ and applied to the simultaneous detection of Hg2+, ClO−, and PO43− via differently responsive channels. HPU-23@Ru@Tb-NH2 had a photoresponsive colorimetric response toward Hg2+ with a LOD as low as 4.18 nM. In addition, the three emissions of the HPU-23@Ru@Tb-NH2 sensor array were influenced by ClO− and PO43− to varying degrees, causing remarkably distinguishable responses for the fluorescence channels to discriminate ClO− and PO43− from each other. The detection limits of ClO− and PO43− were 12.26 µM and 0.197 nM, respectively. Therefore, this work demonstrates the feasibility of multi-emission and multi-mode sensing platform, which is able to combine the advantages of different strategies for solving the problems of various toxic substances coexisting in the environment while meeting the needs of accurate and precise results and no side interferences. [ABSTRACT FROM AUTHOR]

A colorimetric sensor array is proposed for ultrasensitive detection and identification of bacteria by using Cl− at various concentrations as sensing elements and triangular silver nanoparticles (T-AgNPs) as a single sensing nanoprobe. T-AgNPs are easily etched by Cl−. However, in the presence of bacteria, the etching process will be hindered. Different bacteria have differential protective effects on T-AgNPs due to their interactions, resulting in different etching degrees of T-AgNPs by Cl−, and visual color changes. By adjusting the antagonistic action between bacteria protection on T-AgNPs and the etching by using Cl− at various concentrations, different bacteria had their own color response patterns. Combined with linear discriminant analysis (LDA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), the bacteria could be identified. The method was also used for bacteria mixtures identification and showed high sensitivity (OD600 = 1.0 × 10−6) for V. parahaemolyticus detection. Finally, the sensor array was successfully utilized in the identification of bacteria in pure and mineral bottled water. The method is low-cost, simple, sensitive, visual, and has potential application in point-of-care testing of bacteria. [ABSTRACT FROM AUTHOR]

In this study, Ti2O3 thin films were successfully produced using magnetron sputtering. Through orthogonal gradient experiments, the impact of substrate temperature, sputtering vacuum, RF power, and sputtering duration on surface morphology, roughness, physical structure, and resistivity was investigated. Various analytical techniques were employed, including AFM and SEM for surface morphology observation, XRD and Raman for qualitative physical structure analysis, XPS for elemental valence examination, and the four‐probe method for resistivity measurements. The study identified optimal growth conditions for Ti2O3 films, demonstrating a low resistivity of 2.66 × 10−3 Ω cm under the following conditions: RF power of 200 W, sputtering vacuum of.6 Pa, substrate temperature of 600°C, and sputtering duration of 60 min. Additionally, the sensor arrays were efficiently fabricated using the Lift‐off method to evaluate the photoelectric performance of the films. A light responsiveness of approximately 6 µA/W was observed in the device when illuminated with 950 nm light for 10 s. This finding carries important implications for the use of Ti2O3 thin films in future photoelectric devices. [ABSTRACT FROM AUTHOR]

Accurately estimating the direction of arrival (DOA) of wideband signals with a sensor array is critical in communications, radar, and the Internet of Things. This paper proposes two single-source DOA estimation methods for wideband linear frequency modulation signals: time-delay mixing multiple signal classification (TDM-MUSIC) and enhanced self-mixing MUSIC (ESM-MUSIC). TDM-MUSIC employs time-delay mixing of the received signal to construct an equivalent single-frequency signal model, thereby enhancing estimation accuracy while maintaining reasonable computational efficiency. ESM-MUSIC improves the conventional self-mixing model by adding frequency correction steps, resulting in excellent DOA estimation performance at the expense of computational complexity. Unlike conventional methods that rely on approximate models, our methods establish more accurate equivalent models. A key advantage of our methods is that they allow flexible adjustment of the optimal sensor inter-element spacing in arrays based on the equivalent signal model rather than the actual signal model, simplifying engineering fabrication and reducing mutual coupling between sensors. The paper establishes the Cramér–Rao bounds for both proposed methods and demonstrates their superiority over existing methods through comprehensive numerical simulations. Further, the experiment using a TI-AWR2243 multi-sensor array radar system confirms that our methods are feasible for practical engineering applications. [ABSTRACT FROM AUTHOR]

Background: Substantial improvements in spatial resolution in brain positron emission tomography (PET) scanners have greatly reduced partial volume effect, making head movement the main source of image blur. To achieve high‐resolution PET neuroimaging, precise real‐time estimation of both head position and orientation is essential for accurate motion compensation. Purpose: A high‐resolution electromagnetic motion tracking (EMMT) system with an event‐by‐event motion correction is developed for PET‐CT scanners. Methods: EMMT is comprised of a source, an array of sensors, and a readout electronic unit (REU). The source acts as a transmitter and emits an EM dipole field. It is placed in close proximity to the sensor array and detects changes in EM flux density due to sensor movement. The REU digitizes signals from each sensor and captures precise rotational and translational movements in real time. Tracked motion in the EMMT coordinate system is synchronized with the PET list‐mode data and transformed into the scanner coordinate system by locating paired positions in both systems. The optimal rigid motion is estimated using singular value decomposition. The rigid motion and depth‐of‐interaction (DOI) parallax effect are corrected by event‐by‐event rebinning of mispositioned lines‐of‐response (LORs). We integrated the EMMT with our recently developed ultra‐high resolution Prism‐PET prototype brain scanner and a commercial Siemens Biograph mCT PET‐CT scanner. We assessed the imaging performance of the Prism‐PET/EMMT system using multi‐frame motion of point sources and phantoms. The mCT/EMMT system was validated using a set of point sources attached to both a mannequin head and a human volunteer, for simulating multiframe and continuous motions, respectively. Additionally, a human subject for [18F]MK6240 PET imaging was included. Results: The tracking accuracy of the Prism‐PET/EMMT system was quantified as a root‐mean‐square (RMS) error of 0.49∘$^{\circ }$ for 100∘$^{\circ }$ axial rotations, and an RMS error of 0.15 mm for 100 mm translations.The percent difference (%diff) in average full width at half maximum (FWHM) of point source between motion‐corrected and static images, within a motion range of ±20∘$\pm 20^\circ$ and ±$\pm$10 mm from the center of the scanner's field‐of‐view (FOV), was 3.9%. The measured recovery coefficients of the 2.5‐mm diameter sphere in the activity‐filled partial volume correction phantom were 23.9%, 70.8%, and 74.0% for the phantom with multi‐frame motion, with motion and motion compensation, and without motion, respectively. In the mCT/EMMT system, the %diff in average FWHM of point sources between motion‐corrected and static images, within a motion range of ±30∘$\pm 30^\circ$ and ±$\pm$10 mm from the center of the FOV, was 14%. Applying motion correction to the [18F]MK6240 PET imaging reduced the motion‐induced spill‐in artifact in the lateral ventricle region, lowering its standardized uptake value ratio (SUVR) from 0.70 to 0.34. Conclusions: The proposed EMMT system is a cost‐effective, high frame‐rate, and none‐line‐of‐sight alternative to infrared camera‐based tracking systems and is capable of achieving high rotational and translational tracking accuracies for mitigating motion‐induced blur in high‐resolution brain dedicated PET scanners. [ABSTRACT FROM AUTHOR]

A HPU-23@Ru@Tb-NH2 sensor array with light-driven oxidase-mimicking activity and triple-emission fluorescence was developed. It was composed of a Tb3+-functionalized metal organic framework and Ru(bpy)32+ and applied to the simultaneous detection of Hg2+, ClO−, and PO43− via differently responsive channels. HPU-23@Ru@Tb-NH2 had a photoresponsive colorimetric response toward Hg2+ with a LOD as low as 4.18 nM. In addition, the three emissions of the HPU-23@Ru@Tb-NH2 sensor array were influenced by ClO− and PO43− to varying degrees, causing remarkably distinguishable responses for the fluorescence channels to discriminate ClO− and PO43− from each other. The detection limits of ClO− and PO43− were 12.26 µM and 0.197 nM, respectively. Therefore, this work demonstrates the feasibility of multi-emission and multi-mode sensing platform, which is able to combine the advantages of different strategies for solving the problems of various toxic substances coexisting in the environment while meeting the needs of accurate and precise results and no side interferences. [ABSTRACT FROM AUTHOR]

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In this work, an integrated microfluidic microwave array sensor is proposed for the enrichment and detection of mixed biological solution. In individuals with urinary tract infections or intestinal health issues, the levels of white blood cells (WBCs) and Escherichia coli (E. coli) in urine or intestinal extracts can be significantly elevated compared to normal. The proposed integrated chip, characterized by its low cost, simplicity of operation, fast response, and high accuracy, is designed to detect a mixed solution of WBCs and E. coli. The results demonstrate that microfluidics could effectively enrich WBCs with an efficiency of 88.3%. For WBC detection, the resonance frequency of the sensing chip decreases with increasing concentration, while for E. coli detection, the capacitance value of the sensing chip increases with elevated concentration. Furthermore, the measurement data are processed using machine learning. Specifically, the WBC measurement data are subjected to a further linear fitting. In addition, the prediction model for E. coli concentration, employing four different algorithms, achieves a maximum accuracy of 95.24%. Consequently, the proposed integrated chip can be employed for the clinical diagnosis of WBCs and E. coli, providing a novel approach for medical and biological research involving cells and bacteria. [ABSTRACT FROM AUTHOR]

Standardization is paramount for the production and quality control of medicinal plants and their derivatives. One method of achieving standardization is through chemical fingerprinting. Among various techniques, colorimetric sensor arrays are deemed a reliable method for the quality control and diagnosis of medicinal plants. Unlike conventional methods such as DNA barcoding and spectroscopy, chemical fingerprinting offers a different analysis of the plant's unique chemical patterns. Colorimetric sensor arrays were chosen for their cost-effectiveness, ease of use, and rapid results. The objective of this study was to devise an effective method for the classification of ten different commercial brands of thyme and to compare them with three reference original thymes using a colorimetric sensor array technique coupled with mathematical data analysis methods. The findings indicated that colorimetric sensor arrays could effectively represent noticeable color changes between different essential oil samples based on Color Difference Maps (CDMs). The subsequent application of multivariate pattern recognition methods improved the discrimination ability between the samples. Principal Component Analysis (PCA) revealed the discrimination of three classes of the samples with 93% of the total variance, highlighting the distinct separation between different thyme origins. Hierarchical Cluster Analysis (HCA) elucidated the similarity of the studied commercial samples to the reference samples of thyme essential oil, demonstrating clear grouping patterns. Furthermore, Partial Least Squares Discriminant Analysis (PLS-DA) indicated that the majority of commercial samples were as similar as the thymus vulgaris samples, with fitting and validation accuracy of 95% and 91%, respectively. This analysis effectively discriminated the commercial thyme essential oil samples, highlighting their notable similarities to specific reference samples and clarifying the alignment or deviation from different reference standards. Lastly, Linear Discriminant Analysis (LDA) confirmed previous results and differentiated the Thymus samples from zataria multiflora, with 100% fitting and 91% cross-validation accuracy. These methods together provided a comprehensive analysis, each contributing unique insights into the classification and potential standardization of thyme quality detection. Among the used sensing elements, 4 top sensor elements were identified using the factor loadings for the first three PCs of PCA analysis and discrimination ability. While this study primarily focuses on classification, it lays a strong foundation for future standardization efforts in quality control of thyme. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea is a globally prevalent concern with significant health impacts, especially when coupled with comorbidities. Accurate detection and localization of airway obstructions are crucial for effective diagnosis and treatment, which remains a challenge for traditional sleep monitoring methods. Here, we report a catheter-based flexible pressure sensor array that continuously monitors soft tissue pressure in the upper airway and facilitates at the millimeter level. The sensor's design and versatile 3D femtosecond laser fabrication process enable adaptation to diverse materials and applications. In vitro testing demonstrates high sensitivity (38.1 Ω/mmHg) and excellent stability. The sensor array effectively monitors distributed airway pressure and accurately identifies obstructions in an obstructive sleep apnea animal model. In this work, we highlight the potential of this catheter-based sensor array for long-term, continuous upper airway pressure monitoring and its prospective applications in other medical devices for pressure measurement in human body cavities. Obstructive sleep apnea can be ameliorated through accurate detection and localization of airway obstructions. Here, the authors present a flexible, catheter-based pressure sensor array that enables continuous high-sensitivity upper airway pressure assessment and obstructions localization. [ABSTRACT FROM AUTHOR]

Urinary Tract Infections (UTIs) are a prevalent medical condition affecting male felines that can lead to severe discomfort, behavioural changes, and even fatality if not promptly diagnosed and treated. This paper aims to address the limitations of traditional diagnostic methods by integrating Internet of Things (IoT) sensors with machine learning algorithms to predict UTIs in male felines. The study utilizes a multi-modal sensor array to continuously monitor various physiological and behavioural parameters, such as acidity of urine pH levels, heart rate, territorial marking, and eating habits. Observations were categorized into several states, ranging from normal conditions to severe abnormalities, including death. A machine learning model was trained on the collected data to identify early signs of UTIs. The model demonstrated a high predictive accuracy in identifying urinary tract infections before the manifestation of severe symptoms, thus providing a promising avenue for early intervention. The integrated system offers a non-invasive, real-time monitoring solution that could significantly improve the management and the treatment outcomes of UTIs in male feline. [ABSTRACT FROM AUTHOR]

The safety of food and beverages has emerged as an urgent concern, as adulterated food and drink can seriously affect human health when consumed. Within this context, the spirits industry stands out as a sector that requires particular attention, as spirits such as whisky, gin, and vodka possess distinct flavor and aroma profiles intrinsically linked to their alcohol composition. To prevent adulteration and ensure the authenticity of spirits, it is essential to identify the purity of specific aroma compounds. In this work, we employed an electronic nose system with an Al algorithm to extract the scent profiles of various whisky spirits, generating their unique aromatic signatures. The AI algorithm demonstrated exceptional performance in classifying different whisky types, achieving an accuracy of 93% to 94%, depending on the type of whisky. Among the models tested, the convolutional neural network-long short-term memory (CNN-LSTM) model consistently outperformed other architectures, including traditional recurrent neural network (RNN), CNN, and LSTM models. The CNN-LSTM model exhibited the highest accuracy and lowest loss, highlighting its superior capability in capturing the complex aroma patterns of whisky spirits. This study represents a significant step forward in ensuring the integrity of spirits; a method for the rapid identification of spirits purity is also provided. [ABSTRACT FROM AUTHOR]

In this article we explore the benefits of matching sensing characteristics to actuation and dynamics in the context of spatially distributed sensorimotor architectures, motivated by recently discovered connections in blowfly flight physics and visual physiology. Within the proposed framework, we present novel semidefinite programs with linear matrix inequality constraints which yield directions encoded in the sensory output that maximize the smallest unstable Hankel singular value of the system. This is a coordinate-invariant metric that minimizes the control energy required to stabilize an unstable system and maximizes the achievable robustness to unstructured additive uncertainty over all possible controllers. We also reformulate the problem to achieve a prescribed speed of response, which can be applied to stable and unstable systems. We adapt a maximally robust controller synthesis method from previous work which provides a tool for validation. We additionally present an $H_{\infty }$ controller formulation which allows for a trade-off between minimization of actuator effort and robustness versus disturbance rejection and tracking capability, providing design flexibility over the maximally robust controller.