The 3D position estimation of pedestrians is a vital module to build the connections between persons and things. The traditional gait model-based methods cannot fulfill the various motion patterns. And the various data-driven-based inertial odometry solutions focus on the 2D trajectory estimation on the ground plane, which is not suitable for AR applications. TLIO (Tight Learned Inertial Odometry) proposed an inertial-based 3D motion estimator that achieves very low position drift by using the raw IMU measurements and the displacement predict coming from a neural network to provide low drift pedestrian dead reckoning. However, TLIO is unsuitable for mobile devices because it is computationally expensive. In this paper, a lightweight learned inertial odometry network (LLIO-Net) is designed for mobile devices. By replacing the network in TLIO with the LLIO-Net, the proposed system shows similar accuracy but significantly improved efficiency. Specifically, the proposed LLIO algorithm was implemented on mobile devices and compared the efficiency with TLIO. The inference efficiency of the proposed system is 2-12 times that of TLIO.

Enabling global Internet access is challenging for cellular-based Internet of Things (IoT) due to the limited range of terrestrial network services. One viable solution is to deploy IoT over satellite systems for coverage extension. However, operating a hybrid satellite-terrestrial network (STN) might incur high satellite bandwidth consumption and excessive service latency. Aiming to reduce the content delivery latency from the Internet-connected gateway to the users, this work proposes a wireless two-tier cache-enabled model with full-duplex transmissions where content caches are deployed at the satellite and ground station. A closed-form solution for the successful delivery probability (SDP) of the files is derived considering the requested content distributions and channel statistics. Then, the SDP performance under common caching policies can be conveniently evaluated. The results are also used to optimize cache placement under caching capacity constraints. Numerical results demonstrate the performance improvements of the proposed system over those of single-tier cache-aided and half-duplex transmission systems.

Current teleguidance methods include verbal guidance and robotic teleoperation, which present tradeoffs between precision and latency versus flexibility and cost. We present a novel concept of “human teleoperation” which bridges the gap between these two methods. A prototype teleultrasound system was implemented which shows the concept’s efficacy. An expert remotely “teloperates” a person (the follower) wearing a mixed reality headset by controlling a virtual ultrasound probe projected into the person’s scene. The follower matches the pose and force of the virtual device with a real probe. The pose, force, video, ultrasound images, and 3-dimensional mesh of the scene are fed back to the expert. In this control framework, the input and the actuation are carried out by people, but with near robot-like latency and precision. This allows teleguidance that is more precise and fast than verbal guidance, yet more flexible and inexpensive than robotic teleoperation. The system was subjected to tests that show its effectiveness, including mean teleoperation latencies of 0.27 seconds and errors of 7 mm and 6◦ in pose tracking. The system was also tested with an expert ultrasonographer and four patients and was found to improve the precision and speed of two teleultrasound procedures.

Most pooling methods in state-of-the-art speaker embedding networks are implemented in the temporal domain. However, due to the high non-stationarity in the feature maps produced from the last frame-level layer, it is not advantageous to use the global statistics (e.g., means and standard deviations) of the temporal feature maps as aggregated embeddings. This motivates us to explore stationary spectral representations and perform aggregation in the spectral domain. In this paper, we propose attentive short-time spectral pooling (attentive STSP) from a Fourier perspective to exploit the local stationarity of the feature maps. In attentive STSP, for each utterance, we compute the spectral representations through a weighted average of the windowed segments within each spectrogram by attention weights and aggregate their lowest spectral components to form the speaker embedding. Because most energy of the feature maps is concentrated in the low-frequency region in the spectral domain, attentive STSP facilitates the information aggregation by retaining the low spectral components only. Moreover, due to the segment-level attention mechanism, attentive STSP can produce smoother attention weights (weights with less variations) than attentive pooling and generalize better to unseen data, making it more robust against the adverse effect of the non-stationarity in the feature maps. Attentive STSP is shown to consistently outperform attentive pooling on VoxCeleb1, VOiCES19-eval, SRE16-eval, and SRE18-CMN2-eval. This observation suggests that applying segment-level attention and leveraging low spectral components can produce discriminative speaker embeddings.

In order to solve the problem of searching a radioactive source in a wide area, we developed a mobile CsI detector. This paper presented the performance of the detector during the spectra collection investigation. The 1 s spectrum collected by the detector was low-count spectrum and it is hard to distinguish whether it contains radioactive source signals. A rapid detection method of radioactive source based on low-count gamma spectra was proposed. Principal component analysis (PCA) was the key technology of the method. According to the PCA, the source information was efficiently extracted. With the method, the detect sensitivity and accuracy of the detector were optimized.

This paper concentrates on tracking multiple targets using an asynchronous network of sensors with different sampling rates. First, a timely fusion approach is proposed for handling measurements from asynchronous sensors. In the proposed approach, the arithmetic average fusion of the estimates provided by local cardinalized probability hypothesis density filters is recursively carried out according to the network-wide sampling time sequence. The corresponding intersensor communication is conducted by a partial flooding protocol, in which either cardinality distributions or intensity functions pertinent to local posteriors are disseminated among sensors. Moreover, both feedback and non-feedback fusion-filtering modes are provided to meet the performance and real-time requirements, respectively. Second, an extension of the timely fusion approach referred to as robust bootstrap approach is presented, which can deal with unknown clutter and detection parameters by exploiting a local bootstrap filtering scheme. Finally, numerical simulations are performed to test the proposed approaches.

Clinical parameter estimation from the electrocardiogram (ECG) is a recurrent field of research. It is debated that ECG parameters estimation by human experts and machines/algorithms is always model-based (implicitly or explicitly). Therefore, all estimation algorithms used in this context have performance bounds in terms of the achievable mean squared error, which are not exceedable. These bounds depend on the adopted data-model, the estimation scheme (least-squares error, maximum likelihood, or Bayesian), and prior assumptions on the model parameters and noise distributions. In this research, we develop a comprehensive theoretical framework for ECG parameter estimation and derive the Cramér-Rao lower bounds (CRLBs) for the most popular signal models used in the ECG modeling literature, namely functional expansions (including polynomials) and sum of Gaussian functions. The developed framework is evaluated over real and synthetic data for three popular applications: T-to-R wave ratio estimation, ST-segment analysis and QT-interval estimation, using the state-of-the-art estimators in each context. The proposed framework and the derived CRLBs provide practical guidelines for the selection of data-models, sampling frequency (beyond the Nyquist rate), modeling segment length, number of beats required for ECG beat averaging, and other factors that influence the accuracy of ECG-based clinical parameter estimation.

Linear modulation techniques (LMTs) of an asymmetrical six-phase machine (ASPM) with two isolated neutral points synthesize the desired voltage vectors by applying at least five switching states. Different choices of applied voltage vectors, sequences in which they are used, distribution of dwell times among the redundant switching states give rise to a large number of possible LMTs. It is desirable that these LMTs avoid more than two transitions of a particular inverter leg within a carrier period. Only a subset of existing LMTs of ASPM follows this rule. Through an innovative approach, this paper finds a way to account for all possible infinitely many LMTs that follow the rule of at most two transitions per leg. Another important criterion for the selection of an LMT is its current-ripple performance. Therefore, through numerical optimization, the paper finds optimal LMTs among the above infinite possible LMTs for all reference voltage vectors in the linear range and for the whole feasible range of a machine parameter. This parameter is related to the leakage inductance of the machine and impacts the current ripple performance of ASPM. An optimal hybrid strategy is proposed with these optimal techniques, which outperforms all existing techniques in terms of the current ripple. The theoretical analysis is validated through simulation in Matlab and experiments performed up to 3.5 kW on a hardware prototype.

Photovoltac (PV) models play an important role in the simulation analysis and fault diagnosis of PV systems. The single diode model (SDM) is the most frequently used model in research and applications. There are numerous proposed methods to identify the SDM parameters. However, the characteristics of PV cells alter during the lifetime in normal operating environments; these variations may be due to degradation, faults, dust, weed, and so on. Therefore, it is crucial to estimate the actual parameters of the PV cells that represent those present state. The contribution of this study is to propose a method to estimate PV cell parameters on the basis of the measurement data regarding the currents and voltages of the PV module strings. A PV string model is described on the basis of the adaptive SDM for the PV cells in the system, and the parameters of each cell model are obtained by minimizing the difference between the measured string voltages and the string voltages computed by the model. The application of the proposed method to real data measured in a PV power plant is also presented to evaluate the proposed method.

A document by Muhammad Shahroz Nadeem . Click on the document to view its contents.

IEEE 802.11ah is considered as a promising and scalable solution for connecting a large number of drones. With the support of sub-1GHz channel bands, relay, and group-based channel access mechanism, 802.11ah can help drone stations for collecting commands, sending data, and processing response to the control room. However, achieving required throughput and latency is still challenging due to high mobility and dynamic relaying requirement. This article proposes an IEEE 802.11ahbased Internet-of-Drones (IoD) architecture for surveillance and remote control. Our scheme predicts the location of a drone and places the required association and channel access configuration to a relay node dynamically. Moreover, the current mode of a drone station is switched to relay from station as per the location. The relay nodes use transmission opportunity (TXOP) with implicit acknowledgement to increase the efficiency of the network. The performance analysis shows significant improvement in terms of throughput and latency.

The highly fluctuated renewable generations and electric vehicles have undergone tremendous growth in recent years. The majority of them are connected to the grid via power electronic devices, resulting in wide variation ranges for several key parameters in the frequency response model (FRM) such as system inertia and load damping factor. In this paper, an automatic generation control (AGC) method considering the uncertainties of these key parameters is proposed. First, the historical power system operation data following large power disturbances are used to identify the FRM key parameters offline. Second, the offline identification results and the normal operation data prior to the occurrence of the disturbance are used to train the online probability estimation model of the FRM key parameters. Third, the online estimation results of the FRM key parameters are used as the input, and the model predictive-based AGC signal optimization method is developed based on distributionally robust optimization (DRO) technology. Case studies conducted on the IEEE 118-Bus System show that the proposed AGC method outperforms the widely utilized PI-based control method in terms of performance and efficiency.

Frequency-domain modeling is an effective technique in the dynamic analysis of power electronic converters-based power systems. In this paper, a unified single-input single-output (SISO) loop gain modeling for the three-phase grid-tied VSCs under both symmetric and asymmetric ac grids is presented, which facilitates the physical measurement and stability analysis. Based on the linear-time-periodic (LTP) modeling technique, the harmonic admittance model of the three-phase grid-tied VSC is developed in the stationary (αβ)-frame. Instead of the transfer function matrix, the frequency-coupling effects are modeled by the transfer function vector, which simplifies the modeling process. According to the idea of mathematical induction, a two-by-two recursive admittance matrix (RAM) model that can accurately capture the coupling dynamics introduced by the power grid is derived. The RAM has an analytical form and is easy to include harmonic coupling components of arbitrary order. Furthermore, the RAM is converted to its equivalent SISO models following the concept of loop gain. The system stability is thus assessed by the SISO stability criteria (e.g., Nyquist stability criterion). In addition, the loop gain allows the traditional SISO perturbation and measurement scheme to be used for detecting the stability margin information. Finally, simulation results verify the feasibility and correctness of the theoretical analysis presented above.

To keep supporting next-generation requirements, the radio access infrastructure will increasingly densify. Cell-free (CF) network architectures are emerging, combining dense deployments with extreme flexibility in allocating resources to users. In parallel, the Open Radio Access Networks (O-RAN) paradigm is transforming RAN towards an open, intelligent, virtualized, and fully interoperable architecture. This paradigm brings the needed flexibility and intelligent control opportunities for CF networking. In this paper, we document the current O-RAN terminology and contrast it with some common CF processing approaches. We then discuss the main O-RAN innovations and research challenges that remain to be solved.

This paper has proposed detection and physical layer security provision for printed sensory tag systems for internet of things (IoT) applications. The printed sensory tags can be a very cost-effective way to speed up the proliferation of the intelligent world of IoT. The printed Radio Frequency Identification (RFID) of a sensory tag is chipless with the fully printable feature, non-line-of-sight reading, low cost, and robustness to the environment. The detection and adoption of security features for such tags in a robust environment are still challenging. This paper initially presents a robust technology for detecting tags using both the amplitude and phase information of the frequency signature. After successfully identifying tag IDs, the paper presents novel physical layer security using a deep learning model to prevent the cloning of tags. Our experiment shows that the proposed system can detect and identify the unique physical attributes of the tag and isolate the clone tag from the genuine tag. It is believed that such real-time and precise detection and security features bring this technology closer to commercialisation for IoT applications.

Plasma, the fourth and most pervasive state of matter in the visible universe, is a fascinating medium that is connected to the beginning of our universe itself. Man-made plasmas are at the core of many technological advances that include the fabrication of semiconductor devices, which enabled the modern computer and communication revolutions. The introduction of low temperature, atmospheric pressure plasmas to the biomedical field has ushered a new revolution in the healthcare arena that promises to introduce plasma-based therapies to combat some thorny and long-standing medical challenges. This paper presents an overview of where research is at today and discusses innovative concepts and approaches to overcome present challenges and take the field to the next level. It is written by a team of experts who took an in-depth look at the various biomedical applications, made critical analysis, and proposed ideas and concepts that should help the research community focus their efforts on clear and practical steps necessary to keep the field advancing for decades to come.

Serial communication buses are used in electronic systems to interconnect sensors and other devices, but two of the most widely used protocols, I2C and SPI, are vulnerable to bus-wide failures if even one device on the bus malfunctions. For aerospace applications demanding increasingly more distributed processing and sensing capability, the compounding risk to system reliability as device count scales becomes a limiting factor in mission scope, performance, and lifetime. We propose a simple external circuit to be added to each node on a communication bus that automatically isolates the node in the event of device failure. By automatically isolating failed devices, the integrity of the bus is preserved without requiring additional signals or processing overhead from the host controller. In this article, I2C and SPI isolation circuits are simulated, fabricated, and experimentally verified to be effective at preserving bus integrity in the event of peripheral device failure. Generalized reusable circuit blocks were designed and integrated into three spacecraft systems for the successful NASA V-R3x mission deployed in January 2021. The addition of serial bus isolation significantly improved system reliability for the V-R3x mission by eliminating single-point failure modes of the I2C and SPI buses interconnecting sensors and radios necessary for mission success. The developed protection schemes are a new tool for decoupling system reliability from serial bus device count and can readily be integrated into existing aerospace systems.

Magnetic resonance electrical properties tomography (MREPT) noninvasively reconstructs high-resolution electrical property (EP) maps using MRI scanners and is useful for diagnosing cancerous tissues. However, conventional MREPT methods have limitations: sensitivity to noise in the numerical Laplacian operation, difficulty in reconstructing three-dimensional (3D) EPs and no guarantee of convergence in the iterative process. We propose a novel, iterative 3D reconstruction MREPT method without a numerical Laplacian operation. We derive an integral representation of the electric field using its Helmholtz decomposition with Maxwell’s equations, under the assumption that the EPs are known on the boundary of the region of interest with the approximation that the unmeasurable magnetic field components are zero. Then, we solve the simultaneous equations composed of the integral representation and Ampere’s law using a convex projection algorithm whose convergence is theoretically guaranteed. The efficacy of the proposed method was validated through numerical simulations and a phantom experiment. The results showed that this method is effective in reconstructing 3D EPs and is robust to noise. It was also shown that our proposed method with the unmeasurable component H− enhances the accuracy of the EPs in a background and that with all the components of the magnetic field reduces the artifacts at the center of the slices except when all the components of the electric field are close to zero.