Abstract This work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless implantable brain machine interface (iBMI). The architectural trade-offs and implications of the proposed method are quantitatively analyzed in terms of compression ratio (CR) and spike information preservation. For the latter, we used metrics such as root-mean-square error and correlation coefficient (CC) between the original and recovered signals to assess the effect of neuromorphic compression on the spike shape. Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection. We demonstrate that a data CR of 15–265 per channel can be achieved by transmitting address-event pulses for two different biological datasets. The CR further increases to 200– 50 K per channel, 50 × more than in prior works, by the selective transmission of event pulses corresponding to neural spikes. A CC of ≈0.9 and spike detection accuracy of over 90% were obtained for the worst-case analysis involving 10 K -channel simulated recording and typical analysis using 100 or 384-channel real neural recordings. We also analyzed the collision handling capability for up to 10K channels and observed no significant error, indicating the scalability of the proposed pipeline. We also present initial results to show the ability of intention decoders to work directly on the events generated by the neuromorphic front-end.

Control Systems have taken essential parts widely from industrial world to military equipment. Control systems in the industrial world determine the quality, speed, cost and efficiency of the manufacturing process known as the Process Control System (PCS). The core of the Control System is conditioning (actuators) and reading conditions (sensors). By synergizing those parts with the coordination of the controller, the process value (PV) can be obtained according to the condition of the set point (SP). To get the proper control characteristics, i.e. the fastest possible response, the lowest possible oscillation and offset (error steady state/ESS), an understanding of the concept of a closed loop control system with a continuous scheme is developed. In this regard, the Automatic Control Laboratory under the Pneumatic and Hydraulic Laboratory at the Department of Mechanical Engineering - POLBAN considered to provide students with an introduction to Automatic Instrumentation and Control. The briefing covered the introduction, mechanism and operation of Automatic Instrumentation and Control systems. Therefore, tools were required as a medium for achieving the above competencies, one of which was the modernization of pressure regulators in order to strengthen the Laboratory. Through this research, a rejuvenation and modernization of the control system were carried out by applying a proportional, integral and differential (PID) control system. It was based on a programmable logic controller (PLC) equipped with a human machine interface (HMI) to make it easier to enter set points and PID parameters, to be practical in operation and effective in monitoring and data acquisition. The result of the pressure regulator modernization had the ability to include servo control process (getting a certain value) and regulatory control process (maintaining a certain value) with a continuous closed loop scheme. The modernized pressure control system could be utilized as a prototype demonstration of the system in an actual environment to study and observe the Proportional, Integral and Differential (PID) control system along with the process of determining the parameters Kp, Ti and Td as Practicum equipment.

Traditional tactile sensors are single-function, and it is difficult to meet the needs of applications in complex environments. This paper describes the development and applications of flexible tactile sensors with cilia based on magnetoelectric composites made of neodymium iron boron (NdFeB) microparticles with a silver (Ag) nanoshell in polydimethylsiloxane (PDMS). These sensors adopt the inherent magnetism of NdFeB microparticles and the excellent conductivity of the Ag coating. Self-assembly of the composites of NdFeB@Ag/PDMS under the combined effect of intrinsic magnetism and external magnetic field yields cilia that are sensitive to forces with a high conductivity of 36479.33 S/m. The resulting sensors can measure forces in the range of 0.02-0.05 N and recognize surrounding magnetic fields whose magnitudes are larger than 10 mT. These sensors have been used to perform texture recognition, Braille recognition, and object recognition to yield accuracies of 98%, 100%, and 94.58%, respectively. This research based on NdFeB@Ag magnetoelectric microparticles presents a convenient approach to construct tactile sensors with randomly distributed surficial microstructures, leading to the prevalence of low-cost but highly sensitive tactile sensors for humanoid, human machine interface, and healthcare.

Background/Objectives: Tactile gnosis derives from the interplay between the hand's tactile input and the memory systems of the brain. It is the prerequisite for complex hand functions. Impaired sensation leads to profound disability. Various invasive and non-invasive sensory substitution strategies for providing feedback from prostheses have been unsuccessful when translated to clinical practice, since they fail to match the feeling to genuine sensation of the somatosensory cortex. Methods: Herein, we describe a novel surgical technique for upper-limb-targeted sensory reinnervation (ulTSR) and report how single digital nerves selectively reinnervate the forearm skin and restore the spatial sensory capacity of single digits of the amputated hand in a case series of seven patients. We explore the interplay of the redirected residual digital nerves and the interpretation of sensory perception after reinnervation of the forearm skin in the somatosensory cortex by evaluating sensory nerve action potentials (SNAPs), somatosensory evoked potentials (SEPs), and amputation-associated pain qualities. Results: Digital nerves were rerouted and reliably reinnervated the forearm skin after hand amputation, leading to somatotopy and limb maps of the thumb and four individual fingers. SNAPs were obtained from the donor digital nerves after stimulating the recipient sensory nerves of the forearm. Matching SEPs were obtained after electrocutaneous stimulation of the reinnervated skin areas of the forearm where the thumb, index, and little fingers are perceived. Pain incidence was significantly reduced or even fully resolved. Conclusions: We propose that ulTSR can lead to higher acceptance of prosthetic hands and substantially reduce the incidence of phantom limb and neuroma pain. In addition, the spatial restoration of lost-hand sensing and the somatotopic reinnervation of the forearm skin may serve as a machine interface, allowing for genuine sensation and embodiment of the prosthetic hand without the need for complex neural coding adjustments.

Automation is increasingly gaining attention as the global industry moves toward intelligent, unmanned approaches to perform hazardous tasks. Although the integration of autonomous technologies has revolutionized various industries for decades, the mining sector has only recently started to harness the potential of autonomous technology. Lately, the mining industry has been transforming by implementing automated systems to shape the future of mining and minimize human involvement in the process. Automated systems such as robotics, artificial intelligence (AI), the Industrial Internet of Things (IIOT), and data analytics have contributed immensely towards ensuring improved productivity and safety and promoting sustainable mineral industry. Despite the substantial benefits and promising potential of automation in the mining sector, its adoption faces challenges due to concerns about human–machine interaction. This paper extensively reviews the current trends, attempts, and trials in converting traditional mining machines to automated systems with no or less human involvement. It also delves into the application of AI in mining operations from the exploration phase to the processing stage. To advance the knowledge base in this domain, the study describes the method used to develop the human–machine interface (HMI) that controls and monitors the activity of a six-degrees-of-freedom robotic arm, a roof bolter machine, and the status of the automated machine. The notable findings in this study draw attention to the critical roles of humans in automated mining operations. This study shows that human operators are still relevant and must control, operate, and maintain these innovative technologies in mining operations. Thus, establishing an effective interaction between human operators and machines can promote the acceptability and implementation of autonomous technologies in mineral extraction processes.

The integration of artificial intelligence into daily life significantly enhances the autonomy and quality of life of visually impaired individuals. This paper introduces the Visual Impairment Spatial Awareness (VISA) system, designed to holistically assist visually impaired users in indoor activities through a structured, multi-level approach. At the foundational level, the system employs augmented reality (AR) markers for indoor positioning, neural networks for advanced object detection and tracking, and depth information for precise object localization. At the intermediate level, it integrates data from these technologies to aid in complex navigational tasks such as obstacle avoidance and pathfinding. The advanced level synthesizes these capabilities to enhance spatial awareness, enabling users to navigate complex environments and locate specific items. The VISA system exhibits an efficient human–machine interface (HMI), incorporating text-to-speech and speech-to-text technologies for natural and intuitive communication. Evaluations in simulated real-world environments demonstrate that the system allows users to interact naturally and with minimal effort. Our experimental results confirm that the VISA system efficiently assists visually impaired users in indoor navigation, object detection and localization, and label and text recognition, thereby significantly enhancing their spatial awareness and independence.

The article considers in providing barrier between the production fluid and environment. Subsea test tree has 2 ball valves which can prevent blow out in case of abnormal pressure reachs to surface, also isolate well, unlatch and semi subor drill ship can relocate on safe zone. To control the operation of the formation tester, it is necessary to equip it with monometers located in separate sections of the formation tester system. When working with these formation testers, it was necessary to regularly obtain, over a certain period of time, the pressure values (both behind the column and inside) that arise during its operation, which presents certain difficulties, especially when operating this system in offshore conditions. The system has couple of unique features that will tie into the surface well test system and rig. The emergency shut down system is a vital part of the surface welltest equipment. It allows the flow of the well to be stopped in the event of problems occurring at surface and relocate platform into safe zone. Prior to running the equipment, testing should be carried out to allow hardware failures to be tested, including loss of trigger line pressure, loss of subsea electronic module, loss of enhanced data acquisition system surface card and loss of a human machine interface. These tests may be performed by physically disconnecting each device in turn and confirming the expected behavior. It is recommended that the testing should also include a test where the trigger line pressure is raised, triggerlLine rapid vent option enabled, and then the trigger line pressure reduced below the trigger pressure to initiate a triiger line rapid vent – this may be done without the valve mechanism. Keywords: subsea; shear rams; well; hydraulic; lubricator valves; platform.

EMG-based body–machine interface for targeted trunk muscle activation

Spinal cord injury (SCI) patients experience long-term deficits in motor and sensory functions. While brain-machine interface (BMI) has shown great promise for restoring neurological functions after SCI, spinal cord-machine interface (SCMI) offers unique advantages, such as more defined somatotopy and the compact organization of neural elements in the spinal cord. In the current study, we aim to demonstrate the feasibility of sensing and evoking compound action potentials (CAPs) via electrode implantation in spinal cord axonal bundles, an essential prerequisite for advancing SCMI development. To do so, we designed microelectrode arrays (MEA) optimized for recording and stimulation in the spinal cord. For sensory mapping, the MEAs were inserted into the lumbar dorsal column (i.e., the fasciculus gracilis) to determine somatotopic representations corresponding to tactile stimulation across lower body regions and assess proprioceptive signals with varying hip positions. For stimulations, at the L3 level, we delivered electrical pulses both rostrally, along ascending afferent tracts (dorsal column), and caudally, down descending corticospinal tract. We successfully captured axonal CAPs from the dorsal columns with high spatial precision that corresponded to known dermatomal somatotopy. Proprioceptive changes produced by abduction at the hip resulted in modulation of discharge frequency in the dorsal column axons. We demonstrated that stimulation pulses emitted by a caudally placed electrode could be propagated up the ascending fibers and be intercepted by a rostrally placed electrode array along the same axonal tracts. We also confirmed that electrical pulses can be directed down descending corticospinal tracts resulting in specific activations of lower limb muscles. These findings set a critical groundwork for developing closed-loop, bidirectional SCMI systems capable of sensing and modulating spinal cord activity.

In this study, we have developed a compact and ultrathin wideband antenna system with stable broadside radiation patterns for brain-machine interface applications. The antenna system operates in the ultrawideband (UWB) frequency range and employs a deionized (DI) water-infilled superstrate to achieve efficient radiation in the broadside direction. The antenna was constructed using a thin Taconic TRF-43 substrate, which has a relative permittivity (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon _{r}$ </tex-math></inline-formula>) of 4.3 and a loss tangent (tan<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>) of 0.0035. By incorporating a modified rectangular slot on the ground plane and a tapered stepped microstrip feedline, we achieved a broad frequency response. The overall system consists of a compact <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times 9\times 0.7$ </tex-math></inline-formula> mm antenna, along with system dummies such as batteries, sensors, and electronic components, all enclosed in a biocompatible casing manufactured via 3-D printing. The design and analysis of the system were performed using computer simulation technology (CST) and Sim4Life simulation tools. To validate our findings, we built a prototype and conducted measurements using a brain phantom made of semi-solid artificial tissue-emulating (ATE) material. Our results demonstrate that the antenna exhibits a −10-dB bandwidth of 129% from 3 to 14 GHz, with a peak gain of −19 dBi at 3 GHz while maintaining the desired broadside radiation characteristics.

The article presents the architecture and operational principles of an unmanned ground vehicle (UGV) built on a system-engineering approach suitable for integration into civilian and military automotive platforms. The UGV system comprises two interdependent parts: a transmitting side that forms a coordinated control-command flow via an operator trainer and a human–machine interface, and a receiving side that performs secure validation, command routing, execution, and generates a backward telemetry data stream. The architecture implements a conflict-free, layered, three-level hierarchical control scheme separating mission-level command logic (upper level), autonomous functions and middleware coordination (middle level), and deterministic closed-loop real-time control (lower level). A real-time controller on the vehicle executes actuator control using cascaded subordinate regulation, where the inner control loop regulates velocity and is embedded into the outer position/tracking loop to ensure accuracy, stability, and safety of the electromechanical drives. PID-based real-time closed-loop contours are demonstrated for steering actuator, throttle valve drive, brake servo unit, and accelerator linkage, providing non-conflicting cooperative regulation of all primary motion-control effectors. The system is designed by modular principles at hardware and software levels using standard automotive/industrial buses and open-architecture interfaces, enabling scalability, role reconfiguration, fault-tolerant operation, and future autonomy upgrades. The development roadmap includes transitioning the validated architecture from the ZIL-131 prototype chassis to new-generation heavy-duty and light military trucks equipped with automatic transmission, such as KrAZ-Spartan platforms and NATO-standard vehicles, increasing deployment flexibility, autonomy depth, and reducing personnel exposure and risks in field and combat scenarios. Keywords: UGV, remote control, modular architecture, hierarchical control, PID controller, telemetry, position loop, speed loop, real-time control, mission computer, subordinate regulation.

Recently, robotic wheelchairs commanded by human-machine interfaces (HMIs) have gained recognition for enhancing the quality of life of people with physical disabilities. In this sense, implementing sensors that allow for accurate and comfortable user intention recognition remains challenging. Additionally, posture monitoring for pressure ulcer prevention in wheelchair users is often overlooked. In this study, three HMIs are proposed to recognize the user’s intention using information linked to head and neck movements, and visually evoked potentials to control an electric-powered wheelchair in four directions: forward, left, right, and back. These HMIs incorporate technologies, such as an inertial measurement unit (IMU)-based system, pressure sensors based on polymeric optical fiber (POF), and steady-state visual-evoked potential (SSVEP)-based brain-computer interface (BCI) to generate control commands. The POF-based pressure sensors also allow for posture classification. The HMIs were evaluated functionally, and the user’s experience (UX) was considered from healthy subjects. The head-motion-based system obtained the highest accuracy (ACC) rate (~0.99) and less workload. In contrast, the BCI reached the highest satisfaction and usability, whereas the neck-motion-based system achieved the lowest latency (~28 ms). The posture classification system achieved an acceptable ACC (~0.80), latency (~117 ms), and good perception. These results have great implications for the design of wheelchair systems to improve the independence of people with reduced mobility using information from multiple sources and for posture monitoring toward the prevention of pressure ulcer generation.

ABSTRACT Given the variety of equipment and controllers used in the textile industry, an efficient monitoring system is necessary. Current solutions are sometimes costly and rigid when merging different machine types and communication protocols. Our proposed supervisory control and data acquisition (SCADA) system, which is based on the internet of things (IoT), is affordable and efficient. It comprises a single master programmable logic controller (PLC) central processing unit (CPU) module that interfaces with several slave controllers via various communication protocols. Differentiable communication modules ensure efficient data aggregation and allow for protocol translation. Data is temporarily saved in database blocks by the master PLC and is shown in real‐time on the human machine interface (HMI) screen attached to it. An Arduino gateway connects the master PLC to a server for centralized data storage that may be accessed via a web application. Compared to earlier approaches, ours produced a 35% improvement in data aggregation efficiency and a 50% decrease in costs. This work presents an innovative step towards low‐cost, efficient central monitoring systems in the textile sector, with potential applicability in other industries requiring integrated machine and controller monitoring.

In order to address the challenge of low efficiency in removing the thin shells from cassia seeds, a bionic grinding machine was developed for the removal of thin shells from cassia seeds during pharmaceutical processing, inspired by the hand-rubbing motion observed in traditional manual methods. The machine features a grinding-suction disk assembly integrated with a negative pressure feeding system. Flow rate calculations were performed to support the design, although specific parameters require further elaboration. A single-chip microcontroller (SCM)-based control system enables real-time parameter adjustment via a human–machine interface, ensuring precise control throughout the grinding process. After prototype fabrication, single-factor experiments established parameter ranges for orthogonal testing. The optimal parameter combination was identified as a grinding layer gap of 2.4 mm, a rotational frequency of 170 r/min, and a grinding duration of 12 s, resulting in a grinding degree of 9.07% and a shell removal cleanliness of 4.51%. Image-based surface feature analysis confirmed the machine’s effectiveness in removing thin shells, supporting its applicability in pharmaceutical seed processing. This study introduces a novel and efficient mechanized approach for cassia seed thin shell removal, which enhances the efficiency of thin shell removal and provides meaningful support for the advancement of pharmaceutical seed processing technologies. Key words: cassia seed; grinding; automatic control system; test; light reflectivity DOI: 10.25165/j.ijabe.20251806.9532 Citation: Yuan X Y, Huang C J, Gao X W, Tong S Y, Li Y F, Yi S J. Bionic grinding machine design for thin shell removal from cassia seeds. Int J Agric & Biol Eng, 2025; 18(6): 269–280.

Self-powered sensing technologies have sparked a revolution in electric devices. Furthermore, ultrathin characteristics are highly desirable for on-skin and wearable devices to achieve superior conformability on complex 3-dimensional surfaces, which facilitates improved wearing comfort and detection accuracy. However, developing self-powered sensors with ultrathin and conformal features without complicated fabrication processes remains a formidable challenge. Herein, we present an ultrathin self-powered sensor with high conformability, fabricated by a liquid-phase transferring approach. The sandwich-like sensor is spin-coated layer by layer on a water-soluble substrate. Upon immersion in water and complete dissolution of the sacrificial layer, the sensor can be transferred to a variety of surfaces with diverse morphologies. The ultrathin sensor shows long-term stability. When the 45-μm-thick sensor is transferred to human skin, robotic hands, insole, flat plates with fine bevels, cylinders, undulating surfaces, and leaf textures, the fingerprint and surface details of the objects are vividly reflected on the sensor surface, attesting to its exceptional conformability. Driven by the triboelectric effect, the self-powered sensor and its array exhibit good sensitivity and rapid response time, enabling tactile sensing functions for pressure, material species, surface roughness detection, and motion state. The proposed design strategies for ultrathin self-powered sensors hold immense promises in wearable devices, robotics, and human–machine interfacing.

Speech impairments in conditions such as amyotrophic lateral sclerosis (ALS) and muscular dystrophy significantly limit communication with patients, profoundly impacting their daily life and social participation. Communication support through decoding technology based on electrocorticography (ECoG) is thus critically important to assist the day-to-day communication of patients with speech impairments. This study proposes an electrocorticography (ECoG)-based communication support system to enhance communication for individuals with speech impairments, such as those caused by amyotrophic lateral sclerosis (ALS) or muscular dystrophy. In our system, we classify ECoG signals recorded during movement attempt tasks involving the upper limbs, such as hand grasping. This classification enables the control of a virtual keyboard, allowing for text-based communication. Specifically, predicted movement attempts are associated with keyboard functions, such as moving the cursor or pressing the select button. In this study, we propose the ECoG Dual Context Network (ECoG DCNet), designed to efficiently embed information between electrodes while preserving the length of temporal sequences to capture long-range dependencies. To validate our approach, we used a clinical dataset from a single paralyzed subject with ALS, focusing on the ECoG recorded during movement attempt tasks. The proposed method outperformed baseline methods in terms of classification accuracy. Notably, our method achieved an accuracy of 75.3%, outperforming the widely-used EEGNet by 30%. Moreover, Gradient-weighted Class Activation Mapping visualizations suggested that the proposed method focuses on features that could be neurophysiologically relevant.

We designed an efficient stretchable magnetoelectric film for self-powered energy harvesting devices under low-intensity magnetic fields. This soft wearable platform enables real-time gesture recognition.

Conventional human-machine interface (HMI) systems generally take surface electromyography as the only signal source, which often faces the problem of multi-channel crosstalk. To enhance HMI performance, this study introduces a novel three-degree-of-freedom HMI system for surgical robot control, which integrates surface electromyography signals and inertial measurement unit signals. A cost-effective wearable HMI hardware system is designed to address the high price and poor portability of existing commercial equipment, which also has a higher signal-to-noise ratio of 35.9 dB. Based on the proposed simultaneous and proportional control strategy, ten subjects were recruited to conduct an online experiment involving four upper limb postures to assess the real-time performance of the HMI system. Additionally, six able-bodied participants participated in a validation experiment to statistically verify and rigorously evaluate the implemented HMI system. The experimental results demonstrate that our HMI system can accurately decode the angles of the wrist and metacarpophalangeal joints in both single-degree-of-freedom and three-degree-of-freedom movements, achieving stable control and robust online performance. Furthermore, the proposed HMI system was applied to a flexible endoscopic robot to realize remote simultaneous and proportional control, and the results indicate that it can perform well in the practical application. The current study proves the feasibility and practicability of multi-source data fusion for HMI, providing a new direction.

This study proposes an Improved Deep Transfer Network (IDTN) to enhance decoding accuracy, calibration efficiency, and adaptability of intracortical brain machine interface (iBMI) systems while reducing the reliance on new labeled samples. IDTN integrates two core components: Structural Joint Discriminative Maximum Mean Discrepancy (SJDMMD) and Kernel Norm Improved Multi-Gaussian Kernel (KNK). SJDMMD extends the standard MMD framework by incorporating a structure-enhanced soft label weighting mechanism that simultaneously minimizes intra-class distributional shifts and maximizes inter-class margins for precise cross-domain alignment. KNK employs multi-Gaussian kernels with kernel norm regularization to enhance high-dimensional feature representations and sharpen inter-class boundaries, thereby improving the effectiveness of SJDMMD. Evaluated on neural datasets from two rhesus macaques, IDTN achieved superior performance in both intra- subject and inter-subject transfer scenarios, consistently outperforming state-of-the-art methods in decoding accuracy. IDTN also exhibited consistent decoding stability across daily recording sessions. Ablation studies further confirm that SJDMMD improves inter-class separability and intra-class coherence, while KNK contributes to more effective kernel mapping in complex feature spaces. These findings underscore the effectiveness of structure-aware transfer learning for neural decoding. They also highlight the potential of IDTN for deployment in real-world iBMI applications, particularly in data-limited or cross-subject environments.

An ultra-low-power (ULP) six-class keyword spotting (KWS) ASIC is presented in this article, which can be used in always-on speech-based human–machine interface applications. The ASIC is composed of a sampling frequency and resolution adaptive (SFRA) dual-mode analog-to-digital quantizer and a power-gated KWS engine with a multiplier-less processing element (PE) array. The dual-mode quantizer mainly operates as a 1.5-bit delta quantizer (DQ), providing inherent robustness against dc drift. It adaptively switches to high-resolution successive-approximation register (SAR) quantization mode upon detecting a sound event based on the DQ output. In the KWS engine, the multiplier-less PE array is reused for both feature extraction and gated recurrent unit (GRU)-based keyword classification. To unveil the tradeoffs between power consumption and flexibility, two versions of the classifier have been implemented, with ROM-based on-chip weight memory (WM) and SRAM-based WM. Fabricated in 180-nm CMOS technology, the proposed KWS ASIC with ROM and SRAM-based WM achieves six-class classification accuracies of 87.3% and 90.1%, respectively, on the Google Speech Command dataset (GSCD) while consuming 174 and 756 nW long-term average (LTA) power with a decision latency of 14 ms at a clock frequency of 256 kHz.