Search Results (43)

Mechanical speed sensors increase cost and may reduce the reliability of permanent magnet synchronous motor (PMSM) drives under harsh conditions. This paper proposes a validation-protected physics-consistent probabilistic neural estimator for sensorless PMSM speed estimation using only online-deployable signals: the previous estimated speed, measured d/q-axis currents, and commanded d/q-axis voltages. A multi-output probabilistic network predicts the speed distribution and auxiliary residual-compensation variables. Mechanical consistency, electrical consistency, and regularization losses are imposed during training, while a validation-protected rule selects, for each random seed, the checkpoint with the lower validation RMSE from the paired baseline and physics-trained candidates. Experiments use a frozen multi-seed protocol covering locked holdout evaluation, independent comparison, and disturbance tests. Across Datasets 8–11, the frozen predictive distributions yielded Gaussian NLL values from 3.190 to 3.239, 100% empirical coverage of the nominal 95% prediction intervals, and mean interval widths of approximately 35.9 rad/s, indicating conservative rather than well-calibrated uncertainty. On locked Dataset 7, Physics-safe reduced the mean RMSE from 3.415 to 3.277 and the inter-seed standard deviation from 0.290 to 0.052. Results on Datasets 8–11 show that the method is not universally mean-error optimal; its recurring advantage is lower inter-seed variability and more reproducible training outcomes. A local sensitivity analysis on Dataset 4 confirmed seed- and loss-weight-dependent physics-training outcomes, supporting the need for validation protection without implying globally optimal loss weights. On the specified desktop CPU using ONNX Runtime, the complete recursive estimator step required 40.154 microseconds, below the adopted 100-microsecond sampling interval, supporting estimator-level computational feasibility. Full article

This paper presents a low-cost embedded monitoring system for real-time RMS voltage and RMS current acquisition in three-phase electrical networks. The proposed architecture is based on distributed Arduino Nano acquisition nodes equipped with ACS712 Hall-effect current sensors and isolated voltage transformers, while a Raspberry Pi 4 Model B is used as a centralized data acquisition and processing unit through the I²C communication protocol. The embedded acquisition nodes implement timer-controlled analog signal sampling using the internal 10-bit ADC of the ATmega328P microcontroller, allowing real-time acquisition of electrical waveforms for RMS computation. Unlike conventional low-cost IoT electrical monitoring systems focused mainly on basic parameter visualization and wireless communication, the proposed platform emphasizes synchronized three-phase RMS monitoring and experimental validation accuracy under real operating conditions. The proposed monitoring architecture is experimentally benchmarked against a FLUKE 435 professional power quality analyser used as a high-accuracy reference instrument. Experimental results demonstrate that the proposed low-cost embedded architecture can provide RMS voltage and RMS current measurements with acceptable accuracy for educational applications, experimental electrical platforms, and distributed IoT-based monitoring systems. The presented system does not aim to implement a fully IEC 61000-4-30-compliant power quality analyser but rather to validate the feasibility of low-cost embedded RMS monitoring architectures for real-time electrical applications. Full article

Batteries, especially useful for portable instruments, are the most widely used alternative to direct current. Their operating principle is based on irreversible or reversible chemical reactions, which are called primary (non-rechargeable) or secondary (rechargeable), respectively. Primary batteries never completely discharge and their residual charge depends on the energy demand of the instrument in which they are used. Even if correctly disposed of, therefore, and with the possibility of recycling the constituent materials and purchasing and using consciously, only 2 of the 3Rs, namely “Recycle” and “Reuse”, are respected; recovering the residual charge would also allow compliance with the last of the 3Rs, namely “Reduce”. Direct energy recovery methods have been proposed but the simplest method to “reduce”, analogously to what is done with secondary batteries, remains being recharging, which, unfortunately, is a risky operation, as it can cause the battery to explode or leak corrosive solution. Following our previous research, in which we proposed a method for measuring the residual charge of alkaline batteries, we now propose a method for charging the batteries whose residual charge we measured in our previous work. In this research, a hand-built external circuit, Arduino UNO R3, was used to generate the charging pulses and as a controller and measuring instrument and an Open-Source protocol for the recharging process. The results demonstrated the feasibility of the method, as the voltage of 50% of the batteries increased and, further, only 18% of the batteries leaked and none exploded. Full article

High-channel-count neural recording requires wireless links with high throughput, low power, and compact implementation, yet commercial protocols and phase-locked loop (PLL)-based transmitters often trade data rate against power and complexity. We present a low-power, PLL-less wideband on–off keying (OOK) neural-signal transmitter fabricated in a 180 nm CMOS process. The transmitter employs a free-running inductor–capacitor voltage-controlled oscillator (LC-VCO), a Gilbert mixer for OOK modulation and reverse isolation, and a current-reuse stacked power amplifier. It consumes 8 mA from a 3.3 V supply (26.4 mW), demonstrates modulation and receiver frame acquisition at a maximum raw input rate of 90 Mbps, corresponding to a 180 Mbps Manchester-coded line rate, and tunes from 3.266 to 3.445 GHz. End-to-end bit error rate (BER) was measured at raw rates of 15 and 31.2 Mbps, corresponding to encoded rates of 30 and 62.4 Mbps; the latter matches the in vivo data stream. The transmitter was integrated with a 128-channel recording chip and evaluated in freely moving adult C57 mice. Wireless hippocampal spike and local field potential (LFP) recordings, wired-system comparison, and event-locked LFP analysis support its feasibility for untethered neural recording. Full article

Conventional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe (P&O), suffer from steady-state oscillations and slow convergence under rapidly varying environmental conditions, leading to suboptimal energy extraction and unnecessary switching activity. To address these limitations, we propose a predictive control strategy in which the DC–DC converter control signal is adaptively updated only when significant deviations are detected between measured and model-predicted voltage and current values. The approach leverages power-of-two quantized Artificial Neural Networks ($2^{Q}ANNs$), enabling highly accurate inference with extreme weight quantization (2–3 bits) while remaining suitable for MPPT. A dataset-driven evaluation using year-long climatic records from geographically distinct locations indicates annual energy yields of up to 99.90% of the ideal maximum under the adopted modeling assumptions. Under the adopted fixed-condition evaluation protocol, compared with conventional P&O implementations, the proposed method requires 20–40× fewer internal control updates to approach the same efficiency region. Additionally, a robustness experiment with perturbed voltage and current measurements further shows that the recurrent $2^{Q}ANN$ controllers remain above 98% aggregated efficiency even under the strongest tested sensing-noise condition, without retraining. Finally, post-place-and-route FPGA implementation estimates on a highly resource-constrained device indicate that the resulting architecture supports low-resource edge-oriented implementation. Full article

Portable devices are powered in direct current (DC) or by batteries (primary battery), accumulators (secondary battery), and now supercapacitors, which can also be used for energy storage. The European Portable Battery Association states that approximately 239,000 tons of batteries were placed on the market in the European Economic Area (EEA) plus Switzerland in 2022. Even if they were all disposed of correctly respecting the 3R paradigm (Reduce, Reuse and Recycle), non-rechargeable batteries create an environmental problem because they do not discharge completely with an obvious waste of energy. Secondary batteries and supercapacitors can be recharged because they use reversible chemical/physical processes while primary batteries cannot be recharged because they are based on irreversible redox reactions; nevertheless, it is possible to try to recover their residual charge if this is higher than a threshold beyond which the reactions can be reversible. The most used batteries are alkaline zinc/manganese dioxide and they are non-rechargeable; an inappropriate recharge attempt can lead to serious harm to the operator and the environment. This paper describes a simple Arduino-based circuit and the protocol to measure and graph the residual charge of an alkaline battery in order to establish if it can be recharged. The circuit, design, the Arduino Uno R3 sketch (i.e., microprocessor software) and the full protocol are here presented under the open source license (Copyright Creative Commons Public license, CC BY-NC-ND 4.0 EN) so that they could become a pilot system and then a commercial product. The residual charge of 158 batteries, obtained after discharging those that, by eye, appeared damaged, was measured. Results evidenced that 49% of batteries had a residual voltage, under low load, between 1.2 and 1.6 V, making them good candidates for a recharge attempt. Full article

This paper presents the design and experimental validation of a hardware-enforced charging profile selection framework for low-voltage electric all-terrain vehicles (ATVs), implemented on a programmable Delta battery charger operating within a voltage range of 0–120 V and a current range of 0–30 A. Unlike conventional programmable chargers that rely primarily on software-defined configuration or battery management system (BMS)-negotiated parameter setting, the proposed system enforces predefined constant-current–constant-voltage (CC–CV) charging profiles at the hardware execution layer. Vehicle identification is performed using CANopen-based identifiers, while relay-based selection, controlled via Modbus RTU, physically routes the charger output to fixed CC–CV control paths, thereby structurally reducing the risk of misconfiguration and unintended parameter changes. The system integrates layered control using embedded ESP32 nodes, a redPLC supervisory controller, and NodeRED-based orchestration, combined with real-time measurement, logging, and visualization using a time-series database and Grafana dashboards. Experimental validation is conducted using lithium-ion battery packs configured at four nominal voltage levels (24 V, 48 V, 60 V, and 72 V). The results confirm correct automatic profile selection, deterministic relay-based routing, and stable CC–CV charging behavior across repeated charging sessions. Rather than proposing a new charging algorithm, this work contributes a safety-by-design execution-layer charging architecture that complements higher-level smart charging and management protocols and is particularly suited for closed, heterogeneous fleet environments where deterministic behavior, robustness against configuration errors, and transparent verification of charging processes are critical. Full article

This paper presents the design and implementation of a custom-built LiFePO₄ battery monitoring system that offers real-time visibility into the status of individual battery cells. The system is based on a Battery Management System (BMS) architecture and is implemented by measuring the voltage, current, and temperature of each cell in a multi-cell pack. These key parameters are essential for ensuring safe operation, prolonging battery life, and optimizing energy usage in off-grid or mobile power systems. The system architecture is based on an ESP32 microcontroller that interfaces with INA219 and DS18B20 sensors to continuously measure individual cell voltage, current, and temperature. Data are transmitted wirelessly via Wi-Fi to a remote time-series database for centralized storage, analysis, and visualization. Experimental validation, conducted over a 15-day period, demonstrated stable system performance and reliable data transmission. Analytically, the findings indicate that utilizing an advanced smart charger for precise cell balancing and improving the physical layout for cooling led to superior thermal performance. Even when load current nearly tripled to 110 mA, the system maintained a stable cell operating temperature range of 29.8 °C to 30.3 °C. This result confirms significantly reduced cell stress compared to previous iterations, which is critical for enhancing battery health and lifespan. The application of this project aimed to demonstrate how a combination of open hardware components and lightweight network protocols can be used to create a robust, cost-effective battery monitoring solution suitable for integration into smart energy systems or remote IoT infrastructures. Full article

Huanglongbing (HLB), associated with Candidatus Liberibacter asiaticus (CLas), has severely impacted global citrus production, with no economically viable control measures currently available. This study explored microwave treatment at 2450 MHz as an innovative physical method for HLB control, combining pathogen elimination efficacy with metabolomic analysis. In controlled experiments, 36 HLB-infected citrus plants were treated with 500 W or 250 W microwave irradiation and underwent 10 cycles, achieving up to 99.83% reduction CLas titer. Non-targeted metabolomic analysis identified 15 significantly altered metabolites, including upregulated beta-caryophyllene and lysophosphatidylinositols, and downregulated 5′-S-methyl-5′-thioadenosine. The results indicate that microwave treatment effectively suppressed CLas while simultaneously triggering citrus physiological metabolic changes. These findings suggest that microwave treatment could serve as a sustainable alternative to chemical controls. However, further optimization of parameters, such as wavelengths, voltages, currents, and safety protocols, will be essential for practical field implementation. Full article

Human embryonic stem cell-derived cardiomyocytes (hESC-CMs) tend to show a mixed population of action potential (AP) types, including atrial-like (A-like) and ventricular-like (V-like) APs. In the present study, we investigated the membrane currents underlying these two AP types in hESC-CMs. These were generated using standard (Std) and retinoic acid (RA)-based differentiation protocols. Patch clamp methodology was used to correlate AP morphology with major cardiac ion currents by applying alternating current and voltage clamp protocols to each cell, and to measure L-type Ca²⁺ current (I_Ca,L) and Na⁺-Ca²⁺ exchange current (I_NCX) in detail, whereas Ca²⁺ transients were measured ratiometrically using Indo-1. A- and V-like APs were found in both Std and RA-treated hESC-CMs and the AP plateau amplitude (AP_plat), as a measure of fast phase-1 repolarization, appeared the best AP criterion to separate these two AP types. Traditional voltage clamp experiments revealed a significantly smaller I_Ca,L density in RA-treated hESC-CMs, as well as larger densities of the transient outward and delayed rectifier K⁺ currents (I_to1 and I_K, respectively), without changes in the inward rectifier K⁺ current (I_K1). The AP_plat showed strong and moderate correlations with the densities of I_Ca,L and I_K, respectively, in the absence of a clear-cut correlation with the density of I_to1. Using pre-recorded, typical A- and V-like APs, AP clamp demonstrated that the I_Ca,L-mediated Ca²⁺ influx during the V-like AP in Std hESC-CMs is 3.15 times larger than the influx during the A-like AP in RA-treated hESC-CMs. Ca²⁺ transients of A-like hESC-CMs have a lower diastolic and systolic level, as well as a lower amplitude, than those of Std hESC-CMs, while their duration is shorter due to enhanced SERCA activity. In conclusion, I_Ca,L is an important determinant of the differently shaped A- and V-like APs in hESC-CMs. Furthermore, the Ca²⁺ homeostasis differs between A- and V-like hESC-CMs due to the smaller I_Ca,L and enhanced SERCA activity during A-like APs, resulting in a strongly reduced Ca²⁺ influx, which will cause a substantial reduction in I_NCX, further contributing to the shorter A-like APs. Full article

Iron tailings-based ultra-high-performance concrete (UHPC) was developed using iron tailings as aggregates, with steel fiber incorporation ranging from 0% to 2.5%. This study investigates the effects of steel fiber dosage and curing age on the electrical, electrothermal, and thermal conductivity properties of iron tailings-based UHPC. A comprehensive evaluation protocol was implemented to quantify resistivity, electrothermal conversion efficiency, and heat transfer characteristics, providing a systematic understanding of the material’s multifunctional properties. Results demonstrate that steel fiber incorporation significantly reduces electrical resistivity, achieving optimal conductivity at 1.5% fiber content. Electrothermal analysis under a 60 V applied voltage revealed maximum heating efficiency (ΔT = 32.5 °C/30 min for UHPC cured for 7 days and ΔT = 8.0 °C/30 min for UHPC cured for 28 days) at 1.5% fiber content. Thermal conductivity measurements identified a non-monotonic relationship with steel fiber content, initially increasing and then decreasing, with maximum thermal conductivity observed at 1.5% fiber content. This trend aligns with the observed resistivity behavior, suggesting a strong correlation between electrical and thermal properties. Fiber distribution within the iron tailings-based UHPC matrix revealed that steel fiber dispersion significantly affects material properties, with 1.5% fiber content achieving optimal percolation network formation for electrical current flow and heat transfer. Full article

When chips perform numerous computational tasks or process complex instructions, they generate substantial heat, potentially affecting their long-term reliability and performance. Thus, accurate and effective temperature measurement and management are crucial to ensuring chip performance and lifespan. This paper presents a multi-channel temperature measurement system based on a diode temperature-sensitive array thermal test chip (TTC). The thermal test chip accurately emulates the heat power and thermal distribution of the target chip, providing signal output through row and column address selection. The multi-channel temperature measurement system centers around a microcontroller and includes voltage signal acquisition circuits and host computer software. It enables temperature acquisition, storage, and real-time monitoring of 16 channels in a 4 × 4 array thermal test chip. During experiments, the system uses a constant current source to drive temperature-sensitive diodes, collects diode output voltage through multiplexers and high-precision amplification circuits, and converts analog signals to digital signals via a high-speed ADC. Data transmission occurs via the USB 2.0 protocol, with the host computer software handling data processing and real-time display. The test results indicate that the system accurately monitors chip temperature changes in both steady-state and transient thermal response tests, closely matching measurements from a semiconductor device analyzer, with an error of about 0.67%. Therefore, this multi-channel temperature measurement system demonstrates excellent accuracy and real-time monitoring capability, providing an effective solution for the thermal design and evaluation of high power density integrated circuits. Full article

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device’s continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%. Full article

Dose optimization in computed tomography (CT) is crucial, especially in CT fluoroscopy (fluoro-CT) used for real-time navigation, affecting both patient and operator safety. This study evaluated the impact of spectral X-ray filtering using a tin filter (Sn filter), and a method called partial-angle computed tomography (PACT), which involves segmentally switching off the X-ray tube current at the ambient dose rate ${\dot{H}}^{*}(10)$ at the interventional radiologist’s (IR) position. Measurements were taken at two body regions (upper body: head/neck; lower body: lower legs/feet) using a 120 kV X-ray tube voltage, 3 × 5.0 mm CT collimation, 0.5 s rotation speed, and X-ray tube currents of 43 Eff.mAs (without Sn filter) and 165 Eff.mAs (with Sn filter). The study found significant dose reductions in both body regions when using the Sn filter and PACT together. For instance, in the upper body region, the combination protocol reduced ${\dot{H}}^{*}(10)$ from 11.8 µSv/s to 6.1 µSv/s (p < 0.0001) compared to the protocol without using these features. Around 8% of the reduction (about 0.5 µSv/s) is attributed to the Sn filter (p = 0.0005). This approach demonstrates that using the Sn filter along with PACT effectively minimizes radiation exposure for the IR, particularly protecting areas like the head/neck, which can only be insufficiently covered by (standard) radiation protection material. Full article

The Internet of Things (IoT) is a system that enables wirelessly linked devices to be tracked and managed remotely. It uses Ethernet protocols and the principles behind wireless sensor networks. Soil moisture monitoring, hydraulic pressure monitoring, soil testing, preventing trespassing through motion detection, and conserving energy are only some of the agricultural and irrigational operations that are the subject of this research. The implementation shown in this work breaks down larger systems into several smaller ones. A subsystem incorporates a vibration warning sensor, pump, and the ability to monitor soil moisture and hydraulic pressure to detect movement in and around the associated field. The second method will be utilized to deter intruders by picking up on their presence when they move within range of the necessary field barrier. Sensors for measuring current and voltage will be included for energy management regulation. It will be utilized for controlling the system. The main system will receive data through ZigBee from the first and second subsystems, monitor them, and then transfer them to the network router via ZigBee, where the necessary data will be shown on a website home page alongside the appropriate Ethernet protocols and current operating data. Full article