9,895 research outputs found

    Robot skill learning system of multi-space fusion based on dynamic movement primitives and adaptive neural network control

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    This article develops a robot skill learning system with multi-space fusion, simultaneously considering motion/stiffness generation and trajectory tracking. To begin with, surface electromyography (sEMG) signals from the human arm is captured based on the MYO armband to estimate endpoint stiffness. Gaussian Process Regression (GPR) is combined with dynamic movement primitive (DMP) to extract more skills features from multi-demonstrations. Then, the traditional DMP formulation is improved based on the Riemannian metric to encode the robot's quaternions with non-Euclidean properties. Furthermore, an adaptive neural network (NN)-based finite-time admittance controller is designed to track the trajectory generated by the motion model and to reflect the learned stiffness characteristics. In this controller, a radial basis function neural network (RBFNN) is employed to compensate for the uncertainty of the robot dynamics. Finally, experimental validation is conducted using the ROKAE collaborative robot, confirming the effectiveness of the proposed approach. In summary, the presented framework is suitable for human-robot skill transfer method that require simultaneous consideration of position and stiffness in Euclidean space, as well as orientation on Riemannian manifolds

    Common synaptic inputs and persistent inward currents of vastus lateralis motor units are reduced in older male adults

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    Abstract: Although muscle atrophy may partially account for age-related strength decline, it is further influenced by alterations of neural input to muscle. Persistent inward currents (PIC) and the level of common synaptic inputs to motoneurons influence neuromuscular function. However, these have not yet been described in the aged human quadriceps. High-density surface electromyography (HDsEMG) signals were collected from the vastus lateralis of 15 young (mean ± SD, 23 ± 5 y) and 15 older (67 ± 9 y) men during submaximal sustained and 20-s ramped contractions. HDsEMG signals were decomposed to identify individual motor unit discharges, from which PIC amplitude and intramuscular coherence were estimated. Older participants produced significantly lower knee extensor torque (p < 0.001) and poorer force tracking ability (p < 0.001) than young. Older participants also had lower PIC amplitude (p = 0.001) and coherence estimates in the alpha frequency band (p < 0.001) during ramp contractions when compared to young. Persistent inward currents and common synaptic inputs are lower in the vastus lateralis of older males when compared to young. These data highlight altered neural input to the clinically and functionally important quadriceps, further underpinning age-related loss of function which may occur independently of the loss of muscle mass

    Speech-based automatic depression detection via biomarkers identification and artificial intelligence approaches

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    Depression has become one of the most prevalent mental health issues, affecting more than 300 million people all over the world. However, due to factors such as limited medical resources and accessibility to health care, there are still a large number of patients undiagnosed. In addition, the traditional approaches to depression diagnosis have limitations because they are usually time-consuming, and depend on clinical experience that varies across different clinicians. From this perspective, the use of automatic depression detection can make the diagnosis process much faster and more accessible. In this thesis, we present the possibility of using speech for automatic depression detection. This is based on the findings in neuroscience that depressed patients have abnormal cognition mechanisms thus leading to the speech differs from that of healthy people. Therefore, in this thesis, we show two ways of benefiting from automatic depression detection, i.e., identifying speech markers of depression and constructing novel deep learning models to improve detection accuracy. The identification of speech markers tries to capture measurable depression traces left in speech. From this perspective, speech markers such as speech duration, pauses and correlation matrices are proposed. Speech duration and pauses take speech fluency into account, while correlation matrices represent the relationship between acoustic features and aim at capturing psychomotor retardation in depressed patients. Experimental results demonstrate that these proposed markers are effective at improving the performance in recognizing depressed speakers. In addition, such markers show statistically significant differences between depressed patients and non-depressed individuals, which explains the possibility of using these markers for depression detection and further confirms that depression leaves detectable traces in speech. In addition to the above, we propose an attention mechanism, Multi-local Attention (MLA), to emphasize depression-relevant information locally. Then we analyse the effectiveness of MLA on performance and efficiency. According to the experimental results, such a model can significantly improve performance and confidence in the detection while reducing the time required for recognition. Furthermore, we propose Cross-Data Multilevel Attention (CDMA) to emphasize different types of depression-relevant information, i.e., specific to each type of speech and common to both, by using multiple attention mechanisms. Experimental results demonstrate that the proposed model is effective to integrate different types of depression-relevant information in speech, improving the performance significantly for depression detection

    Effects of type of emission and masking sound, and their spatial correspondence, on blind and sighted people’s ability to echolocate

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    Ambient sound can mask acoustic signals. The current study addressed how echolocation in people is affected by masking sound, and the role played by type of sound and spatial (i.e. binaural) similarity. We also investigated the role played by blindness and long-term experience with echolocation, by testing echolocation experts, as well as blind and sighted people new to echolocation. Results were obtained in two echolocation tasks where participants listened to binaural recordings of echolocation and masking sounds, and either localized echoes in azimuth or discriminated echo audibility. Echolocation and masking sounds could be either clicks or broad band noise. An adaptive staircase method was used to adjust signal-to-noise ratios (SNRs) based on participants’ responses. When target and masker had the same binaural cues (i.e. both were monoaural sounds), people performed better (i.e. had lower SNRs) when target and masker used different types of sound (e.g. clicks in noise-masker or noise in clicks-masker), as compared to when target and masker used the same type of sound (e.g. clicks in click-, or noise in noise-masker). A very different pattern of results was observed when masker and target differed in their binaural cues, in which case people always performed better when clicks were the masker, regardless of type of emission used. Further, direct comparison between conditions with and without binaural difference revealed binaural release from masking only when clicks were used as emissions and masker, but not otherwise (i.e. when noise was used as masker or emission). This suggests that echolocation with clicks or noise may differ in their sensitivity to binaural cues. We observed the same pattern of results for echolocation experts, and blind and sighted people new to echolocation, suggesting a limited role played by long-term experience or blindness. In addition to generating novel predictions for future work, the findings also inform instruction in echolocation for people who are blind or sighted

    Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

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    The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

    Edge-emitting mode-locked quantum dot lasers

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    Edge-emitting mode-locked quantum-dot (QD) lasers are compact, highly efficient sources for the generation of picosecond and femtosecond pulses and/or broad frequency combs. They provide direct electrical control and footprints down to few millimeters. Their broad gain bandwidths (up to 50 nm) for ground to ground state transitions as discussed below, with potential for increase to more than 200 nm by overlapping ground and excited state band transitions) allow for wavelength-tuning and generation of pico- and femtosecond laser pulses over a broad wavelength range. In the last two decades, mode-locked QD laser have become promising tools for low-power applications in ultrafast photonics. In this article, we review the development and the state-of-the-art of edge-emitting mode-locked QD lasers. We start with a brief introduction on QD active media and their uses in lasers, amplifiers, and saturable absorbers. We further discuss the basic principles of mode-locking in QD lasers, including theory of nonlinear phenomena in QD waveguides, ultrafast carrier dynamics, and mode-locking methods. Different types of mode-locked QD laser systems, such as monolithic one- and two-section devices, external-cavity setups, two-wavelength operation, and master-oscillator power-amplifier systems, are discussed and compared. After presenting the recent trends and results in the field of mode-locked QD lasers, we briefly discuss the application areas

    Direct measurement of coating thermal noise in the AEI 10m prototype

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    A thermal noise interferometer for the characterization of thermal noise in high reflectivity mirrors has been commissioned and first direct measurements of coating thermal noise have been performed. This serves as an important step in the improvement of current and future gravitational wave detectors

    Efficient abstraction of clock synchronization at the operating system level

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    Distributed embedded systems are emerging and gaining importance in various domains, including industrial control applications where time determinism – hence network clock synchronization – is fundamental. In modern applications, moreover, this core functionality is required by many different software components, from OS kernel and radio stack up to applications. An abstraction layer devoted to handling time needs therefore introducing, and to encapsulate time corrections at the lowest possible level, the said layer should take the form of a timer device driver offering a Virtual Clock to the entire system. In this paper we show that doing so introduces a nonlinearity in the dynamics of the clock, and we design a controller based on feedback linearization to handle the issue. To put the idea to work, we extend the Miosix RTOS with a generic interface allowing to implement virtual clocks, including the newly designed controller that we call FLOPSYNC-3 after its ancestor. Also, we introduce the resulting virtual clock in the TDMH [20] real-time wireless mesh protocol

    Time Reversal Enabled Fiber-Optic Time Synchronization

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    Over the past few decades, fiber-optic time synchronization (FOTS) has provided fundamental support for the efficient operation of modern society. Looking toward the future beyond fifth-generation/sixth-generation (B5G/6G) scenarios and very large radio telescope arrays, developing high-precision, low-complexity and scalable FOTS technology is crucial for building a large-scale time synchronization network. However, the traditional two-way FOTS method needs a data layer to exchange time delay information. This increases the complexity of system and makes it impossible to realize multiple-access time synchronization. In this paper, a time reversal enabled FOTS method is proposed. It measures the clock difference between two locations without involving a data layer, which can reduce the complexity of the system. Moreover, it can also achieve multiple-access time synchronization along the fiber link. Tests over a 230 km fiber link have been carried out to demonstrate the high performance of the proposed method
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