The research paper proposes a new solution to combat river pollution, a major global issue that poses a significant threat to the environment and human health. The proposed solution is a river-cleaning machine that will use a unique technique to remove floating waste and chemicals from the river. The device will be designed to operate in different water conditions and can be transported to different locations. It will be able to remove floating waste, plastics, and chemical pollutants, which are the major pollutants of the rivers. The research paper presents the results of an experiment that evaluated the efficacy of the river cleaning machine model in removing floating debris, plastics, and toxic chemicals from water bodies. The results showed that the river cleaning machine was highly effective in removing floating debris and toxic chemicals from the water. The study concludes that this model has the potential to revolutionize the way we clean our rivers and improve the lives of aquatic species and those who depend on river water.

A model-predictive controller (MPC) for synchronous generator-based microgrids is introduced to enhance voltage, frequency, and transient stability in fast timescales. The centralized controller uses mathematical models of power system dynamics to predict the evolution of system states over a finite horizon based on information from local state observers and relays. The MPC dynamically adjusts the setpoints of existing primary controls to maximize system stability. To address the computational challenges of a real-time MPC implementation due to nonlinear power system dynamics and short sampling intervals, a trajectory linearization technique is applied to the MPC formulation. The proposed controller is validated via a hardware-in-the-loop (HIL) testbed. The testbed includes an implementation of the controller in hardware, which interfaces with a high-fidelity microgrid simulation model running in a realtime simulator. State observers based on the Extended Kalman Filter (EKF), which provide the controller with estimates of the system state, are also implemented. The HIL testbed is used to verify the real-time operation of the controller and evaluate its performance under modeling uncertainties.

Semiconductor manufacturing has been extensively exploiting machine-learning (ML) to process equipment sensory data (ESD) for near-real time anomaly detection (AD). ESD characteristics are highly diversified and data lengths vary among processing steps and cycles. Cleansing ESD with minimum distortion (CMD) to fit the fixed-length input requirement by ML-based AD is critical to AD effectiveness and is challenging. This paper presents a novel CMD method of four innovations: i) statistical mode-based equalization of step data lengths for the least number of step data length changes, ii) importance indicator value (IIV) of a data sample based on its relative difference with the subsequent sample, and iii) step data segmentation into groups based on samples of significant IIVs and  the least-entropy-group-to-cleanse-first rule, and iv) cleansing the least IIV sample(s) in the selected group for step data length equalization. CMD application to ESD demonstrates its characteristics preservation property. Simulation experiments are on an integration of data cleansing with an unsupervised ML-based AD system, STALAD. Comparisons with two benchmark methods over AD scenarios of small-scale drifts and shifts show that CMD not only is superior in facilitating accurate detection by STALAD but also helps detect anomaly much earlier than using the two benchmarks.

The anterior cruciate ligament (ACL) tear is one of the most common knee injuries causing instability to the knee joint. Current methods of diagnosis fail to meet the ergonomic, reliability and reproducibility requirements. Thus, the wearable sensors are gaining momentum to overcome current challenges. This paper aims at proposing a wearable capacitive based sensor system that shows a good potential in substituting the currently used methods for the diagnosis of ACL rupture. The developed sensor system measures the internal tibial rotation of the knee. It is compact and lightweight. Being cable free, it can be worn as a patch, without impeding the freedom of movement of the physician. Moreover, it can be powered with a battery or wireless. Both methods make it compact, ergonomic, easy for the patient to wear and for the doctor to use. To analyze the suitability of the developed sensing system, data from a knee simulator setup and three healthy volunteers (2 Males and 1 Female) are compared and analyzed. In all the patients, above 15° for every 5° angle variation, a relative change of capacitance with respect to its initial value of 0.01 is observed. These results are comparable with the knee simulator’s data with a max RMSE of 0.002. Below 15° the system was additionally able to measure a gender-based difference of rotation due to the higher flexibility of ligaments in females. For them the sensitivity below and above 15° is comparable, for male the sensitivity below 15° is lower. The results show that the developed system has good potential in substituting the currently used method for the diagnosis of ACL rupture and paves the way toward the continuous observation in free movement of knee laxity.

Time series of optical image allow to derive land surface phenology metrics. These metrics are only complete with a statement about their uncertainty. A source of uncertainty is the radiometry of the sensor. We focused on Sentinel-2 MSI data since quantitative estimates of radiometric uncertainties are available. Uncertainties were propagated within a Monte-Carlo framework into the Normalized Difference Vegetation Index (NDVI), three-band Enhanced Vegetation Index (EVI) and Green Leaf Area Index (GLAI) derived from radiative transfer model inversion. In addition, we studied the effect of propagated uncertainties on scene pre-classification. Propagation was carried out for 34 scenes acquired for a crop growing season over an agricultural region in Switzerland using the TIMESAT approach for crop phenology estimation. Propagated uncertainties had little impact on the classification except for spectrally mixed pixels. Effects on the spectral indices and GLAI were more pronounced. In detail, GLAI was more uncertain due to the ill-posedness of radiative transfer model inversion (median relative uncertainty for all crop pixels and Sentinel-2 scenes: 4.4\%) than EVI (2.7\%) and NDVI (1.1\%). In crop phenology, phenological metrics exhibited largest uncertainties in the case of GLAI. The magnitude of uncertainty in the metrics depends on the error correlation between the scenes, which we assumed to be either zero (uncorrelated) or one (fully correlated) since the actual degree of correlation is unknown. If uncertainties are fully correlated, uncertainties in phenological metrics are small (2 to 3 days) but can take values up to greater 10 days under the uncorrelated assumption.

This paper addresses the challenges associated with the high winding and core loss in the Integrated Transformer-Inductor (ITL). To overcome these challenges, we propose an improved winding design of the ITL by utilizing idle shielding layers for inductor integration within the matrix transformer. This method offers full printed circuit board (PCB) utilization, where all layers are consumed as winding, resulting in a significant reduction in the winding loss of the ITL. Moreover, we propose an improved core structure of the ITL that offers better flux distribution of the leakage flux within the magnetic core. This method reduces the core loss by more than 50\% compared to the conventional core structure. We demonstrate the effectiveness of our proposed concepts by presenting the design of the ITL used in a high-efficiency, high-power-density 3-kW 400-V-to-48-V LLC module. The proposed converter achieves a peak efficiency of 98.7\% and a power density of 1050 W/in3.

Ferritin is a 12 nanometer (nm) diameter iron storage protein complex that is found in most plants and animals. A substantial body of evidence has established that electrons can tunnel through and between ferritin protein nanoparticles and that it exhibits Coulomb blockade behavior, similar to quantum dots and nanoparticles. This evidence can be used to understand the behavior of these particles for use in nanoelectronic devices, for biomedical applications and for investigation of quantum biological phenomena. Ferritin also has magnetic properties that make it useful for applications such as memristors and as a contrast agent for magnetic resonance imaging. This article provides a short overview of this evidence, as well as evidence of ferritin structures in vivo and of tunneling in those structures, with an emphasis on ferritin structures in substantia nigra pars compacta (SNc) neurons. Potential biomedical applications that could utilize these ferritin protein nanoparticles are also discussed.

Accepted for full oral presentation at the 16th Conference on Artificial General Intelligence, taking place in Stockholm, 2023. We integrate foundational theories of meaning with a mathematical formalism of artificial general intelligence (AGI) to offer a comprehensive mechanistic explanation of meaning, communication, and symbol emergence. This synthesis holds significance for both AGI and broader debates concerning the nature of language, as it unifies pragmatics, logical truth conditional semantics, Peircean semiotics, and a computable model of enactive cognition, addressing phenomena that have traditionally evaded mechanistic explanation. By examining the conditions under which a machine can generate meaningful utterances or comprehend human meaning, we establish that the current generation of language models do not possess the same understanding of meaning as humans nor intend any meaning that we might attribute to their responses. To address this, we propose simulating human feelings and optimising models to construct weak representations. Our findings shed light on the relationship between meaning and intelligence, and how we can build machines that comprehend and intend meaning.

Abstract-Using wearable robotics to modulate step width in  normal walking for enhanced mediolateral balance has not been  demonstrated in the field. We designed a bilateral hip exoskeleton  with admittance control to power hip abduction and adduction to  modulate step width. Objective: As the first step to show its potential, the objective of  this study was to investigate how human’s step width reacted to  hip exoskeleton’s admittance control parameter changes during  walking. Methods: Ten non-disabled individuals walked on a treadmill at  a self-selected speed, while wearing our bilateral robotic hip  exoskeleton. We used two equilibrium positions to define the  direction of assistance. We studied the influence of multiple  stiffness values in the admittance control on the participants’ step  width, step length, and electromyographic (EMG) activity of the  gluteus medius. Results: Step width were significantly modulated by the change  of stiffness in exoskeleton control, while step length did not show  significant changes. When the stiffness changed from zero to our  studied stiffness values, the participants’ step width started to  modulate immediately. Within 4 consecutive heel strikes right  after a stiffness change, the step width showed a significant change.  Interestingly, EMG activity of the gluteus medius did not change  significantly regardless the applied stiffness and powered  direction. Conclusion: Tuning of stiffness in admittance control of a hip  exoskeleton, acting in mediolateral direction, can be a viable way for controlling step width in normal walking. Unvaried gluteus  medius activity indicates that the increase in step width were  mainly caused by the assistive torque applied by the exoskeleton. Significance: Our study results pave a new way for future  design and control of wearable robotics in enhancing mediolateral  walking balance for various rehabilitation applications.

Tactile perception has been a hot topic of research in robotics. Robots sense the shape, material, distributed force, slip during contact, and use the multi-modal contact information to control grasping and manipulation. For vision-based tactile sensors, the contact representation and extraction determine the quality of the raw tactile information, and therefore serve a significant role in the robot perception system. This article highlights for the first time the importance of raw representation and extraction in visuotactile perception, and proposes a new multicolor CMP method for enhancing the performance of vision-based tactile sensors. Based on the principle of continuous marker pattern (CMP), the multicolor CMP method is optimized in the pattern and algorithm design. Regarding information representation, we present a new type of marker pattern based on RGB triangles and a preferred layout. In terms of information extraction, we propose a series of extraction strategies with the adaptive growing algorithm (AGA) and the spin-search algorithm (SSA) as the cores. The experiments reveal that the multicolor CMP method achieves improved precision and reliability compared to the former CMP method.

This paper presents a novel contactless field excitation~(CFE) system based on wireless power transfer~(WPT), which utilizes the existing voltage source inverter~(VSI) of the motor drive for electrically excited synchronous motors~(EESMs). In conventional CFE systems, an extra high-frequency converter is required to excite the field winding.  In this paper, it is proposed to utilize existing voltage switching harmonics of the VSI for exciting the field winding while the low-frequency modulated component is used to drive the motor. In the proposed system, a novel variable carrier phase shift method~(VCPSM) is developed to achieve constant input excitation voltage for the WPT part independently from the motor operation. In addition, a hybrid frequency detuning control method is introduced to adjust the field current.  For experimental validation, a small-scale prototype with 100~V DC-link and 60~kHz switching frequency is established. It is observed that the field current could be kept almost constant at 5~A under different motor driving conditions operations regarding modulation index and fundamental frequency.  Also, it is shown that the field current could be reduced by detuning the switching frequency. In brief, without an additional active converter and only with a software update, a cost-effective CFE system for EESMs can be easily implemented.

The next generation of Internet services, such as Metaverse, rely on mixed reality (MR) technology to provide immersive user experiences. However, limited computation power of MR headset-mounted devices (HMDs) hinders the deployment of such services. Therefore, we propose an efficient informationsharing scheme based on full-duplex device-to-device (D2D) semantic communications to address this issue. Our approach enables users to avoid heavy and repetitive computational tasks, such as artificial intelligence-generated content (AIGC) in the view images of all MR users. Specifically, a user can transmit the generated content and semantic information extracted from their view image to nearby users, who can then use this information to obtain the spatial matching of computation results under their view images. We analyze the performance of full-duplex D2D communications, including the achievable rate and bit error probability, by using generalized small-scale fading models. To facilitate semantic information sharing among users, we design a contract theoretic AI-generated incentive mechanism. The proposed diffusion model generates the optimal contract design, outperforming two deep reinforcement learning algorithms, i.e., proximal policy optimization and soft actor-critic algorithms. Our numerical analysis experiment proves the effectiveness of our proposed methods.

Understanding the success of augmentative exoskeletons is critical to their impact on society. Many exoskeletons are developed to address impairments stemming from neuromotor pathology, which  provides guidance for understanding their abilities; however, for augmentative exoskeletons that  assist able-bodied users, a clear metric of success remains an open question. In this study, we leverage  an approach from economics–the Vickrey second-price auction–to quantify the economic value  added by lower-limb exoskeletons during uphill walking. In our protocol, participants describe the  monetary compensation needed to continue walking uphill for consecutive bouts of two minutes. To incentivize truthful descriptions of participant’s “price to walk,” their compensation amounts were  provided as bids in Vickrey auctions before each bout. We compared these data across different  conditions to determine the marginal value of the exoskeleton weight, assistance, and weight +  assistance. Our approach found that the total value of the exoskeleton and its assistance was modest  ($3.40/hr, SD: $3.35/hr); while most participants found the assistance itself valuable ($19.76/hr, SD:  $19.43/hr), this value was nearly offset by the cost of wearing the exoskeleton weight (-$18.59/hr, SD:  $18.28/hr). Our approach and results demonstrate that economic value can be a powerful tool to  develop exoskeletons that maximize user benefit.

Nearly 15% of the global population is affected by disabilities impacting mobility. Monitoring foot pressure distribution during gait is a fundamental aspect of evaluating rehabilitation. Wearable systems provide a portable alternative to stationary equipment monitoring gait without laboratory space limitations. However, wearable sensors in some applications present challenges in the calibration, sensitivity, and human-sensor interface, requiring application-specific sensors. This study aimed to develop wearable sensors where the structural and material properties can characterise the sensitivity and range of measurement during the design phase. We developed wearable piezoresistive sensors using additive manufacturing to create mechanical metamaterials with embedded pressure-sensing capabilities. The sensors were fabricated in TPU using SLS and graphene ink infusion processes. Three structural designs were developed for different measuring ranges (0 – 50 N, 0 – 100 N, and 0 – 150 N) using body-centred cubic lattices constructed via pyramid unit cells. Two graphene infusion processes were evaluated. We tested the sensors’ mechanical and piezoresistive behaviour, measuring the compressive force, strain, and electrical resistance across the sensor. We analysed the influence of structural dimensions and the infusion process on the piezoresistive behaviour. The measuring range was affected mainly by tuneable structural dimensions. The infusion process influenced the piezoresistive sensitivity and affected the linearity response. The results indicate the characterisation of the sensitivity of piezoresistive sensors based on structural parameters and material properties. Mechanical metamaterials could embed pressure sensing in wearables, allowing for customisation based on design parameters using additive manufacture and graphene inks.

Liver vessels generated from computed tomography are usually pretty small, which poses big challenges for satisfactory vessel segmentation, including 1) the scarcity of high-quality and large-volume vessel masks, 2) the difficulty in capturing vessel-specific features, and 3) the heavily imbalanced distribution of vessels and liver tissues. To advance, a sophisticated model and an elaborated dataset have been built. The model has a newly conceived Laplacian salience filter, highlighting vessel-like regions and suppressing other liver regions, to shape the vessel-specific feature learning and to balance vessels against others. It is further coupled with a pyramid deep learning architecture to capture various levels of features, hence the enhancement of feature formulation. Experiments show that this model markedly outperforms the state-of-the-art approaches, achieving a relative improvement of Dice score by at least 1.63% compared to the existing best model on available datasets. More promisingly, the averaged Dice score produced by existing models on the newly con- structed dataset is as high as 0.728 ± 0.067, which is at least 19.1% higher than that obtained from the existing best dataset under the same settings. These observations suggest that the proposed Laplacian salience, along with the elaborated dataset, can be helpful for liver vessel seg- mentation.

This paper considers the investment coordination problem for the long term transmission capacity expansion in a situation where there are multiple regional Transmission Planners (TPs), each acting in order to maximize the utility in only its own region. In such a setting, any particular TP does not normally have any incentive to cooperate with the neighboring TP(s), although the optimal investment decision of each TP is contingent upon those of the neighboring TPs. A game-theoretic interaction among the TPs does not necessarily lead to this overall social optimum. We, therefore, introduce a social planner and call it the Transmission Planning Coordinator (TPC) whose goal is to attain the optimal possible social welfare for the bigger geographical region. In order to achieve this goal, this paper introduces a new incentive mechanism, based on distributed optimization theory. This incentive mechanism can be viewed as a set of rules of the transmission expansion investment coordination game, set by the social planner TPC, such that, even if the individual TPs act selfishly, it will still lead to the TPC’s goal of attaining overall social optimum. Finally, the effectiveness of our approach is demonstrated through several simulation studies.

Considered as basic structures of next-generation energy system, environment-friendly and flexible microgrid (MG) systems are potential solutions to deal with stochastic renewable energy sources (RESs). The essence of management is to maximize the benefits of available resources through coordination and optimization, which also applies to energy systems. Adaptable energy management approaches provide the possibility to construct effective and various energy interaction. The purpose of this paper is to present a systematic review of energy management in MG systems, and further to propose an energy management model. Therefore, this paper first comprehensively reviews recent research studies on MG, particularly in the interconnected mode (called as multi-microgrid, MMG). Based on the analysis of relative literatures, a STEER (stability, touch, efficiency, evenness, and resilience) strategy is proposed and formulated from the perspective of energy utilization for energy management. Then, critical technologies in energy management are reviewed and summarized from the aspects of control, communication, optimization, prediction, and evaluation. Furthermore, energy management system is illustrated from the perspectives of system function, management framework and operation logic, based on a four-layer hierarchical architecture. Eight main prospects on the future trend of energy management in MG and MMG are also presented.

In this paper, we focus on intelligent reflecting surface (IRS) assisted multiuser simultaneous wireless information and power transfer (SWIPT)  systems with hardware impairments (HWIs), which result from the aggregate effect of in-phase and quadrature-phase imbalance (IQI), power amplifier non-linearity, quantization distortion, phase noise, etc. Improper Gaussian signaling (IGS) is employed to combat the signal distortion incurred by HWIs and the interference from other users. We aim to maximize the minimum achievable information rate among all users by jointly optimizing the active beamforming vectors, passive reflecting coefficients, and power splitting coefficients, subject to the minimum harvested energy requirements of these users and the total power budget at the transmitter. Due to the intricate coupling between the reflecting coefficients and beamforming vectors, we propose a two-level iterative optimization algorithm to solve this non-convex problem. Based on the above, we also study the weighted rate-energy region maximization problem. Numerical results show that the use of IGS significantly outperforms traditional proper Gaussian signaling (PGS) in the considered systems and also affirm the effectiveness of the proposed optimization algorithm.