Directional antenna technologies are crucial to enhance high-speed data transmission in emerging wireless communications such as mmWave. These technologies can enable high-accuracy radio positioning by exploiting spatial-temporal signal processing in wideband beamforming space. However, the radio positioning technique potentially poses surveillance risks to mobile users, particularly being tracked illegally. This work presents a novel scheme to track a user in a building based on passively received signals. The scheme includes a Deep Convolutional Neural Network (DCNN) localization module to train on the accumulated channel impulse responses (CIR) and corresponding Angle-Delay profiles. The user’s estimated locations from the DCNN localization are then refined with an Unscented Kalman filter (UKF) data fusion module to eliminate outlier data points. Simulations indicate that the proposed scheme can accurately regenerate the user trajectory, even without the attacker’s physical intrusion into the building. This poses a new concern of surveillance risks in directional wireless communications, given their expected popularity in 5G and beyond.

When building a model of a physical phenomenon or process, scientists face an inevitable compromise between the simplicity of the model (qualitative‒quantitative set of variables) and its accuracy. For hundreds of years, the visual simplicity of a law testified to the genius and depth of the physical thinking of the scientist who proposed it. Currently, the desire for a deeper physical understanding of the surrounding world and newly discovered physical phenomena motivates researchers to increase the number of variables considered in a model. This direction leads to an increased probability of choosing an inaccurate or even erroneous model. This study describes a method for estimating the limit of measurement accuracy, taking into account the stage of model building in terms of storage, transmission, processing and use of information by the observer. This limit, due to the finite amount of information stored in the model, allows you to select the optimal number of variables for the best reproduction of the observed object and calculate the exact values of the threshold discrepancy between the model and the phenomenon under study in measurement theory. We consider two examples: measurement of the speed of sound and measurement of physical constants.

The energy systems are evolving toward the comprehensive and massive integration of power electronics-based technologies, such as photovoltaic, wind turbines, and electric vehicles. All these emerging and promising power converters technologies must be tested to comply with the grid codes, especially during severe grid fault events, before their commercialization. Low-Voltage Ride-Through (LVRT) field tests are usually conducted to prove the ability of the converter to ride through faults. These field tests are expensive and require bulky equipment, while Power-Hardware-in-the-Loop (P-HIL) offers a more cost-effective and flexible alternative. However, the P-HIL must be stable and accurate to get reliable results. The accuracy of a P-HIL test is directly influenced by its interface algorithm. This paper proposes a frequency domain approach, based on the concept of singular values in Multi-Input Multi-Output systems, to study the P-HIL accuracy and stability in grid connected converter testing under asymmetrical line-to-line fault conditions. Accuracy and stability analysis have been performed analytically and validated by Matlab/Simulink simulations and by experimental P-HIL tests. This paper demonstrates that correct fault modeling leads to correct assessment of the reactive power injected under faults and of avoiding resonances.

We presents a squared sine functioned adaptive (SSA) algorithm for acoustic-echo-cancellation (AEC) applications in-presence-of impulsive noise. In the development of the SSA algorithm, a novel cost function is constructed by exerting a sliding window type squared sine function on the estimation error vector, which provides robustness in the impulsive-noise environments and speed-ups convergence when the input is colored signals. Theoretical models for predicting the mean-weightbehavior, transient excess-mean-square-error (EMSE) behavior, and tracking behavior are presented. Moreover, the optimal step size and minimum EMSE of the tracking performance are provided. The computation complexity of SSA algorithm has also been investigated. Numerical experiments demonstrate that the theoretical results match well with simulation results and show the superiority of the proposed SSA algorithm against known algorithms in AEC applications

Reconfigurable intelligent surface (RIS) is an emerging technology for improving the performance of wireless communications. In this letter, a joint Squared-Sine function and atomic norm minimization (ANM), denoted as SS-ANM, is proposed for passive sensing assisted by RIS in wireless communication systems interfered by a wireless access point (AP). The proposed joint optimization is used to estimate the targets’ direction-of-arrival (DOA), employing the spatial domain sparsity of the received signals. The optimal solution is achieved from the alternating direction method of multipliers (ADMM). With the help of the Squared-Sine function as the cost function, the proposed solution provides better estimation accuracy under various noises besides Gaussian noise, and meanwhile requires a lower computation complexity. Simulation results verify the superiority of the proposed joint minimization over existing DOA estimation methods.

The final paper of this paper has been accepted at USENIX Annual Technical Conference (ATC) 2024, with the title: "TileClipper: Lightweight Selection of Regions of Interest from Videos for Traffic Surveillance". The final version of this paper can be found here.

This brief proposes a recursive constrained sine second-order error promoting adaptive (RCSSOEPA) algorithm. Compared with classical recursive method, the RCSSOEPA algorithm can achieve better steady state performance in impulsive noise. In general, the sine second-order error (SSOE) is constructed to devise a new recursive constrained adaptive-filtering within the least-squares framework for solving linear constrained optimization problems. The mean-square (MS) stability of the RCSSOEPA and its theoretical instantaneous MS deviation under Gaussian and non Gaussian noise are analyzed, numerically investigated and discussed in detail. Simulated results are reported to give a comfirmation of the theoretical analysis, and show that the RCSSOEPA outperforms recent developed constrained adaptive filtering algorithms in the estimation misalignment and when used for system identification under impulsive-noise.

With the decreasing number of directly grid-connected synchronous machines (SMs) forming the grid voltage, the risk of power system instability increases, especially regarding the grid frequency due to the decreasing power system inertia. Grid-forming (GFM) control of inverter based resources (IBRs) with inertia emulation capability is required to face these challenges and to guarantee reliable operation of the future power system. This paper proposes virtual synchronous machine (VSM) control for doubly-fed induction machine (DFIM) based wind energy conversion systems (WECS) that (i) enables feedforward torque control (FTC) and thus, does not degrade the WECS performance during normal operation, (ii) adapts the VSM damping based on the DFIM operating point, and (iii) protects the VSM against overloading to avoid loss of synchronism.

This paper proposes an adaptive-output-voltage regulation (AOVR) based solution to alleviate the dc-link undervoltage for grid-forming (GFM) inverters. First, it is shown that large disturbances may cause the dc-link undervoltage for GFM inverters threatening the safe operation of the system. To tackle this issue, an AOVR-based control strategy is proposed, where the main idea is to reduce the output voltage of the GFM inverter and also speed up the dynamic process of the power angle by intentionally enlarging the frequency deviation of the active power control loop. By doing so, the dc-link output energy will reduce to avoid possible undervoltage. The parameter design of the proposed method is based on the overmodulation constraints, and an auxiliary module is configured for the low-voltage-ride-through (LVRT) requirements. The applicability of the proposed solution is also checked when considering the overcurrent limitation. The effectiveness of the proposed control strategy is finally confirmed by the experimental tests.

In this paper, a rectangular-shaped wideband dielectric resonator Graphene inspired electronic band gap (EBG) based complementary split ring resonator (CSRR) loaded miniaturized THz MIMO antenna is designed having a dimension of 56×56×3.6μm3 is designed on a Roggers RO3035 substrate with a relative permittivity of 3.6 and loss tangent 0.0015. This antenna works in the range of 6.0THz-12.5THz (70.76%) with a peak gain of 7.68 dBi. This composite DRA materials having Silicon (εr = 11.1) based cylindrical dielectric and cylindrical Graphene disk to be stacked on rectangular radiated antenna having height of 13.5µm and 0.2µm to improve the impedance bandwidth as well as gain of the antenna from 5.24dBi to 7.68dBi. Firstly single-element gold plated rectangular shaped antenna is transformed into a two-element MIMO antenna with mushroom-shaped EBG structure is deployed for improving the isolation between two antenna elements as well as to improve the front-to-back ratio (FBR). CSRR is loaded to obtained band notch characteristics for avoiding the interference between near wireless devices in the span of 10.6-10.8THz. Graphene disk having 3µm height put on Silicon (εr = 11.1) based cylindrical DRA provides high gain and improved the impedance bandwidth for achieving wide bandwidth from 6.0THz-12.5THz. The proposed MIMO Antenna performance has been judged by antenna parameters like gain, return loss, isolation between two antenna elements, diversity parameters like ECC, DG, TARC, Mean Effective Gain (MEG), Channel Capacity Loss (CCL) and found proposed DRA-MIMO antenna suitable for wide band nano/optical communication in 6G for IoT and bio-medical sensing application.

Cloudified mobile networks, such as 5G, are expected to deliver a multitude of services to several slices in parallel, while having reduced capital and operating expenses. The 5G mobile systems, therefore, need to ensure that the SLAs of customized end-to-end sliced services are met. This requires monitoring the resource usage and characteristics of data flows at the virtualized network components and interfaces of its cloud mobile network, as well as tracking the performance at its radio interfaces and UEs. A centralised monitoring architecture can not scale to support millions of UEs though. This paper, proposes a distributed telemetry framework in which UEs act as early warning sensors. Upon flagging an anomaly, the cloudified mobile network activates a machine learning model to attribute the cause of the anomaly. We employ active, passive and in-band telemetry in our monitoring framework and achieve an impressive performance of 85% F1 score in detecting anomalies caused by different bottlenecks, and an overall 89% F1 score in attributing these bottlenecks. Our distributed framework achieves almost same bottleneck attribution accuracy to that of acentralized monitoring system but with no overhead of transmitting UE-based telemetry data to the centralized controller.

In a previous paper, we introduced a model that accounts for both dark matter and dark energy. In this paper we will attempt to refute objections to the model.

In this paper, we propose an idea to improve various types of loss functions. It is different from the current idea of balancing the errors by increasing the number of input samples in each batch. We directly mask the top values of the error ranking to zero. During the error back-propagation, this means that the samples corresponding to that loss will not affect the parameter update of the network. In other words, even if a small number of samples are artificially mislabeled, it will not theoretically have much impact on the performance of the network. Instead, deliberately discarding anomalous losses will help smooth the training of the network. We conduct experiments on several regression and classification tasks, and the results show that the proposed method in this paper can effectively improve the expected performance of the network.

Many applications of Generative AI (such as DALL-E, GPT-3, ChatGPT, etc.) are making headline news in recent months and have been receiving both praise and criticism for their far reaching implications.  Some of these applications include query responses, language translation, text to images and videos, composing stories, essays, creating arts and music, generating programs, etc. This review provides an historical background of Generative AI techniques and how they evolved over the years. This report highlights the benefits of Generative AI technologies and their limitations/challenges in moving forward. It is also to be noted that the large-scale applications of AI and their successes are now possible due to exponential advances in hardware (computational power, storage capacity), cloud computing and related operational layers of software.

This articles discusses the use of the Robotic Upright Stand Trainer (RobUST), a cable driven exoskeleton that actuates the trunk and pelvis independently, for postural balance control training. It examines the changes that occur in healthy patients after a perturbation based balance training session conducted in virtual reality. In addition, there are two testing conditions tested. In one condition subjects are given assistive support at the pelvis by RobUST to help resist perturbations and in the other, there is no assistive support. Data collected include electromyographic data from lower limb muscles, movement and variability of movement data.

This paper develops a frequency-domain approach to study the impact of wind turbine generator (WTG) on inter-area low-frequency oscillations. A Heffron-Philips model of power system with WTG integrated is derived, it is found that the new model has similar configuration with the original one, which has a feedback control structure. On the basis of the new model, the impact of two critical factors on stability margin, the change of power output distribution and the dynamic interaction introduced by WTG, is identified. A minimum characteristic locus method, recently introduced by the authors, is extended to quantify the impact of the two factors. The proposed method is applied to the example New England power system and IEEE 118-bus test system. The results indicate that, after the integration of WTG, the stability effect caused by the change of power output distribution is much greater than the effect caused by the change of dynamic interaction.

Application of Kobayashi potentials (KP) is extended to the electromagnetic (EM) diffraction from the parallel-plate multiport.

This paper introduces a new CMOS implementation of a spiking Leaky Integrate-and-Fire (LIF) neuron. The circuit does not require any analog circuit block such as comparators or current sources thanks to the use of a ring-oscillator as an integrator and a phase delay detector as threshold logic. The circuit admits both excitatory and inhibitory input spiking signals whose pulse width does not affect the computation. Instead of powering the ring oscillators from a power supply, the input spikes charge a capacitor bank which powers the ring oscillator. The paper describes the operation of the neuron analytically for the input integration and the membrane voltage decay. Also, a circuit-level simulation in 65nm CMOS technology has been performed, achieving a power efficiency of <40fJ/spike.