Employing high switching speed wide bandgap (WBG) devices improves the efficiency of industrial drive inverters, but can result in motor overvoltages for shorter lengths of cable between the inverter and motor. These overvoltages are caused by high frequency impedance mismatches resulting in reflection of the voltage pulse edges of the inverter output. Forwards and backwards travelling reflections interfere and in the worst case cause double the DC-link voltage to appear across the motor terminals. In this paper this doubling of the motor voltage stress is observed with only a 10 metre cable. To mitigate the motor overvoltage a novel method using a differential mode coupled inductor between the paralleled half bridges and the phase output is proposed. By adding a delay time between the half bridges at every switching event a quasi-three-level output is produced which can actively cancel reflected voltage waves, eliminating the cause of motor overvoltages. The method can be utilised for three phase inverters, and when using paralleled devices only requires one additional inductor per phase. A design process for the coupled inductors is given which aims to minimise the circulating current between the half bridges and therefore give minimal increase in conduction losses due to imbalanced current sharing. These additional conduction losses and other limitations of the proposed method are analysed. An inverter utilising this proposed method is implemented and compared to a filter designed for overvoltage mitigation. The novel method achieves near perfect overvoltage mitigation with much smaller additional volume and much lower losses than the filter. Furthermore the losses when using the active mitigation method are very similar to using the inverter with no overvoltage mitigation.

A document by Hao Cheng . Click on the document to view its contents.

While treating sensorimotor impairments, a therapist may provide physical assistance by guiding their patient’s limb to teach a desired movement. In this scenario, a key aspect is the compliance of the interaction, as the therapist can provide subtle cues or impose a movement as demonstration. One approach to studying these interactions involves haptically connecting two individuals through robotic interfaces. Upper-limb studies have shown that pairs of connected individuals estimate one another’s goals during tracking tasks by exchanging haptic information, resulting in improved performance dependent on the ability of one’s partner and the stiffness of the virtual connection. In this study, our goal was to investigate whether these findings generalize to the lower-limb during an ankle tracking task. Pairs of healthy participants (i.e., dyads) independently tracked target trajectories with and without connections rendered between two ankle robots. We tested the effects of connection stiffness as well as visual noise to manipulate the correlation of tracking errors between partners. In our analysis, we compared changes in task performance across conditions while tracking with and without the connection. We found that tracking improvements while connected increased with connection stiffness, favoring the worse partner in the dyad during hard connections. We modeled the interaction as three springs in series, considering the stiffness of the connection and each partners’ ankle, to show that improvements were likely due to a cancellation of random tracking errors between partners. These results suggest a simplified mechanism of improvements compared to what has been reported during upper-limb dyadic tracking.

The paper gives an overview of Semiconductor-based THz sources and receivers.

Individuals using passive prostheses typically rely heavily on their biological limb to complete sitting and standing tasks, leading to slower completion times and increased rates of osteoarthritis and lower back pain. Powered prostheses can address these challenges, but have control methods that divide sit-stand transitions into discrete phases, limiting user synchronization across the motion and requiring long manual tuning times. This paper extends our preliminary work using a thigh-based phase variable to parameterize optimized data-driven impedance parameter trajectories for sitting, standing, and walking, with only two classification modes. We decouple the stand-to-sit and sit-to-stand equilibrium angles through a knee velocity-dependent scaling term, reducing the model fitting error by approximately half compared to our previous results. We then experimentally validate the controller with three individuals with above-knee amputation performing sitting and standing transitions to/from three different chair heights. We show that our controller implemented on a powered knee-ankle prosthesis produced biomimetic joint mechanics, resulting in significantly reduced sit/stand loading asymmetry and time to complete a 5x sit-to-stand task compared to participants’ passive prostheses. Integration with a previously developed walking controller also allowed sit/walk transitions between different chair heights. The controller’s biomimetic assistance may reduce the overreliance on the biological limb caused by inadequate passive prostheses, helping improve mobility for people with above-knee amputations.

A document by Ali Alhaj Abbas . Click on the document to view its contents.

A document by Aman Batra . Click on the document to view its contents.

In the realm of radio frequency identification (RFID) technology, the integration of reconfigurable intelligent surfaces (RISs) has opened up new possibilities for real-time remote data capturing and seamless connectivity. By manipulating the electromagnetic properties of the environment, RIS enables the control of electromagnetic wave propagation and allows for virtual line-of-sight (LOS) in cases where physical LOS is blocked. This has tremendous implications for the future of RFID applications, particularly with the emergence of chipless RFID technology. In this regard, this paper develops free-space path loss models for RIS-assisted RFID wireless communications. The proposed models in this study have taken into account several crucial physical factors, including tag radar cross-section (RCS), the physical properties of the RIS, and the near-field/far-field effects of the RIS. To further validate the theoretical findings, we have conducted experimental measurements using a fabricated RIS. Numerical simulations were also utilized to validate the models and verify our findings. The channel measurements have demonstrated good agreement with the proposed path loss models, further bolstering the potential of RIS-assisted RFID wireless communications.

The concept of sensing environmental parameters in the air has recently gained attention through drone-based, insect-based, or wind-dispersal-based mechanisms. Interestingly, wind-dispersal mechanisms require no driving mechanical system and are solely powered by wind energy to scan and sense selected areas of interest. In this paper, we propose a low-cost system of wind-dispersal fliers that are coded and localized based on passive UHF RFID technology. For the flier's mechanical design, a 2D structure is first designed, inspired by winged seeds, consisting of four wings with asymmetrical cross-sections to provide better mechanical stability during descent and enhanced integration with UHF RFID tags. The 2D structure is then transformed into 3D configurations by tilting the blades at different angles and utilizing different curvatures. By introducing porosity in the 3D design and optimizing the blades' angles and curvature, a low terminal velocity of 1.64\,m/s is achieved based on measurements. The fliers are fabricated using Laser Sintering from Polyamide 12 (PA12) with a low thickness of $200 \,\mu \textrm{m}$. For system demonstration, four distributed reader antennas are employed to interrogate a coded flier equipped with a passive RFID tag that uses a UHF Gen2 chip. For system evaluation, various scenarios encompassing distinct tags and flier trajectories are evaluated using a Received Signal Strength Indicator (RSSI) algorithm, specifically tailored to meet our system requirements. The average localization error varies across the evaluated scenarios, providing insights into the performance of the system under different conditions.

Depression clinical interview corpora are essential for advancing automated depression diagnosis. While previous studies have used written speech material in controlled settings, these materials do not accurately represent spontaneous conversational speech. Additionally, self-reported measures of depression are subject to bias, making the data unreliable for training models for real-world scenarios. This study introduces a new corpus of depression clinical interviews collected directly from a psychiatric hospital, containing 113 recordings with 52 healthy and 61 depressive patients. The subjects were examined using the Montgomery-Asberg Depression Rating Scale (MADRS) in Chinese. Their final diagnosis was based on medical evaluations through a clinical interview conducted by a psychiatry specialist. All interviews were audio-recorded and transcribed verbatim, and annotated by experienced physicians. This dataset is a valuable resource for automated depression detection research and is expected to advance the field of psychology. Baseline models for detecting and predicting depression presence and level were built, and descriptive statistics of audio and text features were calculated. The decision-making process of the model was also investigated and illustrated. To the best of our knowledge, this is the first study to collect a depression clinical interview corpus in Chinese and train machine learning models to diagnose depression patients.

The study presents a new approach for solving the sensor subset selection problem using set encoding and genetic algorithms, aiming to minimize the number of sensors while maintaining accurate spatial estimation. The proposed method, tested on groundwater data from the Savannah River Site, introduces novel crossover and mutation methods, outperforming a previous greedy method with an R2 higher than 0.98 for reduced sensor counts.

This paper would introduce new method to approach magnetism in multidimensional structure of atoms.

The data center markets in emerging economies are being built at a furious pace. When high availability is required, as it always is in the modern digital economy, the placement of geo-distributed data centers may be influenced by factors such as technician shortage and under-developed infrastructure, both of which are typical in emerging economies. Although the data center availability subject in general has been well studied, it remains unclear how rapid and unbalanced economic development in emerging economies may affect the availability of geo-distributed data centers and their cost of ownership. In this paper, we incorporate the unbalanced availability of infrastructure and technician into the data center placement. The problem is first formulated as a mixed integer nonlinear program (MINLP).To solve this potentially large scale problem, we transform it into a QCQP, capable of handling heterogeneous workloads. The resulting problem can then be efficiently solved by off-the-shelf optimization toolboxes. With real-life data in China, we show how unbalanced development of infrastructure and technician shortage may affect the placement of data centers, and analyze the tradeoff between cost and availability. Our results indicate that technician shortage and unbalanced network infrastructure will lead to increased cost and distinct data center placement strategies.

This publication devises a method which formulates a national strategy for the renovation of single family houses by treating each era of initial construction independent of all the others, and applies a universally adoptable procedure to evaluate the cost efficiency of carbon emission reduction. A multi-objective optimization was conducted which varied the building envelope and energy systems while optimizing for annual cost and carbon emissions. The optimization was carried out in oemof.solph and PyGMO on typical German building stocks from 11 different construction eras between 1860 and 2020. The buildings were modelled in TEASER using TABULA building stock data. The results indicate that post-war era construction has the greatest improvement potential by saving over 16 tons of CO2/yr with respect to its business as usual case. The recommended solutions for each construction era have an investment cost to emission reduction ratio which is 25% better than those of the current efficiency subsidies.

Operation Icebridge (OIB) 2016 level 1b Airborne Ku-band Synthetic Aperture Radar waveforms, concurrent with both OIB lidar data and Environment and Climate Change Canada in situ data, over snow on arctic sea ice, near Eureka, Canada. Footprint-scale (~16 m × 4.5 m) snow depths are estimated and compared to in situ measurements. Relative return powers and energies of the air-snow and snow-ice interface are estimated, as well as footprint-scale roughness and slope. Quasi-specular scattering (higher return powers/energies) are observed for smoother, flatter footprints. In addition to this, satellite data is discussed, including Cryosat-2 SARIn.

Federated learning (FL) has gained increasing attention due to its ability to collaboratively train while protecting client data privacy. However, vanilla FL cannot adapt to client heterogeneity, leading to a degradation in training efficiency due to stragglers, and is still vulnerable to privacy leakage. To address these issues, this paper proposes RingSFL, a novel distributed learning scheme that integrates FL with a model split mechanism to adapt to client heterogeneity while maintaining data privacy. In RingSFL, all clients form a ring topology. For each client, instead of training the model locally, the model is split and trained among all clients along the ring through a pre-defined direction. By properly setting the propagation lengths of heterogeneous clients, the straggler effect is mitigated, and the training efficiency of the system is significantly enhanced. Additionally, since the local models is blended, it is less likely for an eavesdropper to obtain the complete model and recover the raw data, thus improving data privacy. The experimental results on both simulation and prototype systems show that RingSFL can achieve better convergence performance than benchmark methods on independently identically distributed (IID) and non-IID datasets, while effectively preventing eavesdroppers from recovering training data.

Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), digital twin, Metaverse and other related digital technologies have attracted much attention in recent years. These new emerging technologies are changing the world significantly. This research introduces a fusion model, i.e. Fusion Universe (FU), where the virtual, physical, and cognitive worlds are merged together. Therefore, it is crucial to establish a set of principles for the fusion model that is compatible with our physical universe laws and principles. This paper investigates several aspects that could affect immersive and interactive experience; and proposes the fundamental principles for Fusion Universe that can integrate physical and virtual world seamlessly.

The integration of sensing and communications (ISAC) has brought considerable benefit in predictive beamform?ing. However, it is dedicated to the line-of-sight channels, and the effect of the spatial degree of freedom (DoF) of multipath channels on ISAC performance has not been revealed thus far. This compact letter proposed a novel ISAC-assisted beam?tracking solution in multipath channels. Based on the reflected echoes, the kinematic parameters are measured and the extended Kalman filtering (EKF) is designed for angle prediction. The fine beam tracking method is also proposed based on the tracking results of the EKF. Simulation results show that the proposed method has a remarkable superiority over the conventional feedback-based one. In addition, the angle parameters observed from radar echoes are not always the optimal alignment direction, which still has nearly a one-third gap from the global optimal performance in a strong multipath environment, and this gap can be bridged by the proposed fine beam tracking method.