Ambient Interference Research Articles

A Model for Safe Transport of Critical Patients in Unmanned Drones with a ‘Watch’ Style Continuous Anesthesia Sensor

We envision unmanned aerial vehicles (UAV) for rapid evacuation of critically-ill patients from hazardous locations to health care facilities in safe zones. For safety, medical teams accompany patients to monitor vital signs and titrate anesthesia dose during transport. UAV transports would require continuous automated remote monitoring of both vital signs and of sedative dose to be feasible and safe. Volatile anesthetics (isoflurane) are the only anesthetic agents that can be monitored continuously with infrared spectroscopy (IR) devices; but unsuitable for transport. Our objective is to devise a safe UAV transport protocol incorporating novel technology for gas monitoring. Our group has developed and tested a miniaturized wearable fuel cell sensor that can detect isoflurane gas vapors as low as 40 ppm (within therapeutic range) with a sensitivity of 0.0112 nA ppm−1 cm−2. Ambient signal interference was resolved by principal component analysis (PCA). Data variance of 1st and 2nd principal components was 88.68% and 11.31%, respectively. The PCA regression model reported here can determine accurate Isoflurane concentrations. Electronic IoT platform has been built constituting micro-fuel cell, miniaturized electronic components with Bluetooth. This wearable sensor can be incorporated into a comprehensive life support system for casualty evacuation in conjunction with autonomous UAV emergency medical operations.

Desain dan Realisasi Sistem Komunikasi Cahaya Tampak untuk Streaming Teks berbasis PWM

The design and implementation of visible light communication systems for transmitting digital data (texts) have been discussed in this paper. Then, two evaluations were performed also, i.e. the demonstration of streaming text through the visible light as a proof-of-concept system design. The next evaluation is a BER measurement intended to know the VLC system performance quantitatively. The value of BER against the several parameters: by the variation of distance and its reception angle. The proposed VLC system works well until the photodiode reception angle to the LEDs up to 50°. According to evaluation, our system can operate optimally within 30 – 80 cm or 50 – 130 cm of optical channel range depending on the Gain setting option. To support the reliability of noise from ambient light and interference lamps such as fluorescent lamps, the filters circuit are employed: high pass filter and DC-offset remover. Based on the BER analysis, the filters can compensate the problem of ambient light noise (DC-offset signal) and interference lamp (100-150 Hz of carrier frequency). The speed of data-rate obtained with 1-PWM modulation and the low-cost analog component is 3.3 kbps.

An Earbud‐Based Photoplethysmography and its Application

SUMMARYThis paper presents an earbud‐based photoplethysmography (PPG) and its application. This PPG recording system permits long‐term continuous monitoring of cardiovascular function since it can be worn comfortably on ears. Experimental investigations showed that stress and drowsiness could be estimated by analyzing measured PPG signals. Furthermore, noise suppression techniques for motion noises and ambient light interferences are introduced to make a recorded signal more acceptable.

Method for measuring self-noise of vector hydrophones

The Vector Hydrophone (VH) is widely used to remotely detect underwater targets. Accurately measuring the self-noise of the VH provides an important basis for evaluating the performance of the detection system in which it is utilized, since the ability to acquire weak signals is determined by the VH self-noise level. To accurately measure the VH self-noise level in actual working conditions, the Dual-channel Transfer Function Method (DTFM) is proposed to reduce ambient background noise interference. In this paper, the underlying principles of DTFM in reducing ambient background noise is analyzed. The numerical simulations to determine the influence of ambient background noise, and the sensitivity difference of the two VHs on the measurement results are studied. The results of measuring the VH self-noise level in a small laboratory water tank by using DTMF indicate that ambient background noise interference can be reduced effectively by employing DTMF, more accurate self-noise level can be obtained as well. The DTMF provides an effective method for accurately measuring the self-noise level of VHs and also provides technical support for the practical application of the VH in underwater acoustics.

An Innovative Virtual Chamber Measurement Method Based on Spatial Domain Cancellation Technique for Radiation Emission In Situ Test

It remains a typical engineering problem demanding prompt solution to measure the electromagnetic radiation of equipments accurately in the complicated electromagnetic environment. A new measurement method for ambient interferences suppression in an in situ radiated emission test is proposed. Technologies of signal processing such as side lobe cancellation and frequency section have been adopted to filter background interferences in a smart way. The suppression effects on different kinds of interferences are studied. The simulation results show that the method can simultaneously suppress multiple interferences, which are either narrowband or wideband, stable or transient. The performance of the method is evaluated by experiments as well. The experimental results show that the pulse modulation interferences can be suppressed remarkably, guaranteeing the radiation characteristic of the testing equipment free of distortion at the same time. This novel method can not only make up shortages of traditional virtual chamber technology, but also has more handleability and convenience for the electromagnetic compatibility in situ test.

Weighted k ‐nearest neighbour model for indoor VLC positioning

This study introduces a weighted k-nearest neighbour model for user positioning in a visible light communications (VLC) system. The new model offers a higher degree of accuracy compared with conventionally existing techniques in VLC, such as trilateration. In the proposed model, the current position of the receiver is estimated based on the positions of the k-NN (known as reference points) pre-defined and recorded in a lookup table. The Euclidean distances from the actual receiver to the reference points are weighted in order to improve accuracy. Simulation results show that the proposed model outperforms the trilateration method by 36 and 50% accuracy with and without ambient light interference, respectively.

A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery

Singular value decomposition (SVD), as an effective signal denoising tool, has been attracting considerable attention in recent years. The basic idea behind SVD denoising is to preserve the singular components (SCs) with significant singular values. However, it is shown that the singular values mainly reflect the energy of decomposed SCs, therefore traditional SVD denoising approaches are essentially energy-based, which tend to highlight the high-energy regular components in the measured signal, while ignoring the weak feature caused by early fault. To overcome this issue, a reweighted singular value decomposition (RSVD) strategy is proposed for signal denoising and weak feature enhancement. In this work, a novel information index called periodic modulation intensity is introduced to quantify the diagnostic information in a mechanical signal. With this index, the decomposed SCs can be evaluated and sorted according to their information levels, rather than energy. Based on that, a truncated linear weighting function is proposed to control the contribution of each SC in the reconstruction of the denoised signal. In this way, some weak but informative SCs could be highlighted effectively. The advantages of RSVD over traditional approaches are demonstrated by both simulated signals and real vibration/acoustic data from a two-stage gearbox as well as train bearings. The results demonstrate that the proposed method can successfully extract the weak fault feature even in the presence of heavy noise and ambient interferences.

D2D underlay massive MIMO hybrid networks with improved physical layer secrecy and energy efficiency

SummaryFor us to meet the green and reliable communication requirement by the forthcoming fifth generation mobile networks, this paper focuses on a secrecy constrained device‐to‐device (D2D) underlay massive multiple‐input multiple‐output hybrid network, where the D2D user (DU) and cellular user (CU) links are exposed to passive malicious eavesdroppers. The D2D transmitters harvest the power from the signals of dedicated power beacons (PBs), but also the ambient radio frequency (RF) interference of CUs. The signals of PBs are known previously at the receivers of both the D2D and cellular users but are not known at eavesdroppers so that it can be regarded as an artificial noise. For the interested hybrid networks, we first present an energy‐harvesting scheme based on the inversion power control where the power received at the corresponding receiver is higher than the receiver's sensitivity. Then, by modeling the locations of network elements as Poisson point process and applying stochastic geometry, we derive the sufficient probability that a typical D2D transmitter harvests sufficient energy to establish communication links. Finally, with the derived sufficient probability, we evaluate the performance of the CUs and DUs in the achievable ergodic rate and the secrecy outage probability. Both the analytical and simulated results show that precious power of network is saved because of the ambient RF interference exploited, and the secrecy of both D2D and cellular links is improved simultaneously because of the signal of PBs modeled as artificial noises at CUs and DUs.

Enhancement of Signal Identification and Extraction in a Φ-OTDR Vibration Sensor

Phase-sensitive optical time domain reflectometry $\Phi$ -OTDR has been widely used in vibration sensing applications due to its high sensitivity and distributed sensing capability. However, the real vibration behavior is hard to be distinguished from ambient interferences by conventional Φ-OTDR, since the responses to simultaneous behaviors are nonlinearly added together. Fortunately, if we focus on the phase information of backscattered light, it would be much easier to obtain the accurate vibration signal and separate different intrusions, due to the linear strain–phase response. Based on this technique, we present a vibration separation method by analyzing the time difference between two fiber positions in this paper. A proof of principle experiment is designed and implemented, and double vibrations influencing the same fiber zone are identified and extracted, which demonstrates the improvement of signal identification in the Φ-OTDR system.

Entanglement-free determination of pretilt angles of twisted nematic liquid-crystal cells by phase measurement

The pretilt angle of a twisted nematic (TN) liquid crystal display has a strong effect on viewing angles and response times of the display as well as shifting the light-leaking disclination lines into the black-matrix area within pixels of a thin-film-transistor driven TN panel for a high contrast ratio. In this Letter, we develop an optical method to determine the TN pretilt angle based on heterodyne measurements of the phase versus the incident angle of a thin TN cell subjected to out-of-plane rotations. We believe that our method of measuring phases has advantages of simpler setup and calculations, better stability, and less sensitive to ambient electromagnetic interference than the commonly used method of measuring intensities.

Secure Wireless Information and Power Transfer in Heterogeneous Networks

In this paper, we investigate secure simultaneous wireless information and power transfer (SWIPT) in a two-tier downlink heterogeneous network (HetNet), wherein the ambient interference signals are exploited for both secure communications and wireless energy harvesting. We assume one macrocell base station (MBS) and several femtocell base stations (FBSs) simultaneously send information to their macrocell users (MUs) and femtocell users, respectively. Meanwhile, the FBSs also transfer energy to some energy receivers, who act as the potential eavesdroppers and are able to wiretap the confidential messages to one MU via the cross-tier interference links. Exploiting interference in the considered HetNet, we jointly optimize the beamforming vectors and artificial noise of the MBS and the FBSs to maximize the secrecy rate of the eavesdropped MU under the quality-of-service, energy harvesting, and transmit power constraints at relevant receivers/transmitters. In particular, we first investigate the ideal case with perfect eavesdropper’s channel state information (ECSI) and the optimization problem turns out to be nonconvex. By using the tools of semidefinite relaxation (SDR) and one-dimensional line search, we successfully transfer the original problem into a more tractable two-stage problem to obtain the optimal solution. Furthermore, we extend our study to the imperfect ECSI case, where the worst-case based solution is obtained with the aid of SDR and successive convex approximation. Simulation results demonstrate the effectiveness of the proposed algorithms and also bring useful insights into the design of secure SWIPT in the presence of interference.

Block Sparse Signal Reconstruction Using Block-Sparse Adaptive Filtering Algorithms

Sparse signal reconstruction (SSR) problems based on compressive sensing (CS) arise in a broad range of application fields. Among these are the so-called “block-structured” or “block sparse” signals with nonzero atoms occurring in clusters that occur frequently in natural signals. To make block-structured sparsity use more explicit, many block-structure-based SSR algorithms, such as convex optimization and greedy pursuit, have been developed. Convex optimization algorithms usually pose a heavy computational burden, while greedy pursuit algorithms are overly sensitive to ambient interferences, so these two types of block-structure-based SSR algorithms may not be suited for solving large-scale problems in strong interference scenarios. Sparse adaptive filtering algorithms have recently been shown to solve large-scale CS problems effectively for conventional vector sparse signals. Encouraged by these facts, we propose two novel block-structure-based sparse adaptive filtering algorithms, i.e., the “block zero attracting least mean square” (BZA-LMS) algorithm and the “blockℓ0-norm LMS” (BL0-LMS) algorithm, to exploit their potential performance gain. Experimental results presented demonstrate the validity and applicability of these proposed algorithms.

Link State Prediction-Based Reliable Transmission for High-Speed Railway Networks

Due to unpredictable noise and ambient interference along high-speed railways (HSRs), it is challenging to provide reliable Internet services in severe HSR network environments. Most existing research that requires expensive modifications for large-scale already in-used base stations cannot be immediately deployed into the existing HSR systems. In this paper, we propose a quite lightweight but effective solution to improve the Internet experience for HSR passengers. Different from other existing approaches, we employ a data-driven link state prediction (LSP) mechanism for HSR reliable transmission, called LSP4HSR, which directly operates in HSR's on-board routers. In particular, we conduct an extensive measurement of network status on several realistic HSR lines and collect a first-hand dataset in terms of round-trip time and packet loss rate. By analyzing this real dataset, we find that HSR link quality presents obvious two-time-scale variation characteristics. We execute a lot of in-depth studies to explore potential reasons for this interesting phenomenon. Furthermore, based on the two-time-scale Markov chain, we establish an accurate HSR link prediction approach, which brings an LSP-based transmission enhancement mechanism to alleviate the impact from poor link status along HSR lines. Extensive experiments verify that the proposed solution can not only improve the packet transmission reliability in HSR networks but can be also deployed in existing HSR systems quite smoothly and easily.

Precise planar motion measurement of a swimming multi-joint robotic fish

This paper presents a method for planar motion measurement of a swimming multi-joint robotic fish. The motion of the robotic fish is captured via image sequences and a proposed tracking scheme is employed to continuously detect and track the robotic fish. The tracking scheme initially acquires a rough scope of the robotic fish and thereafter precisely locates it. Historical motion information is utilized to determine the rough scope, which can speed up the tracking process and avoid possible ambient interference. A combination of adaptive bilateral filtering and k-means clustering is then applied to segment out color markers accurately. The pose of the robotic fish is calculated in accordance with the centers of these markers. Further, we address the problem of time synchronization between the on-board motion control system of the robotic fish and the motion measurement system. To the best of our knowledge, this problem has not been tackled in previous research on robotic fish. With information about both the multi-link structure and motion law of the robotic fish, we convert the problem to a nonlinear optimization problem, which we then solve using the particle swarm optimization (PSO) algorithm. Further, smoothing splines are adopted to fit curves of poses versus time, in order to obtain a continuous motion state and alleviate the impact of noise. Velocity is acquired via a temporal derivative operation. The results of experiments conducted verify the efficacy of the proposed method.

Filtering and parameter estimation of surface-NMR data using singular spectrum analysis

Ambient electromagnetic interferences at the site of investigation often degrade the signal quality of the Surface-NMR measurements leading to inaccurate estimation of the signal parameters. This paper proposes a new powerful de-noising method based on singular spectrum analysis (SSA), which is a nonparametric method for analyzing time series. SSA is a relatively simple method and can be understood using basic algebra notations. Singular value decomposition (SVD) plays a crucial role in SSA. As the length of recordings increases, the computational time required for computing SVD raises which restricts the usage of SSA in long-term time series. In order to overcome this drawback, we propose a randomized version of the singular value decomposition to accelerate the decomposition step of the algorithm. To evaluate the performance of the proposed strategy, the method is tested on synthetic signals corrupted by both simulated noise (including Gaussian white noise, spiky events and harmonic noise) and real noise recordings obtained from surface-NMR field surveys and a real data set. Our results show that the proposed algorithm can enhance the signal to noise ratio significantly, and gives an improvement in estimation of the surface-NMR signal parameters.

Robust Source Localization and Enhancement With a Probabilistic Steered Response Power Model

Source localization and enhancement are often treated separately in the array processing literature. One can apply steered response power (SRP) localization to determine the sources' Directions-Of-Arrival (DOA) followed by beamforming and Wiener post-filtering to isolate the sources from each other and ambient interference. We show that when there is significant overlap between directional sources of interest in the time-frequency (TF) plane, traditional SRP localization breaks down. This may occur, for example, when the array is located near a reflector, significant early reflections are present, or the sources are harmonized. We propose a joint solution to the localization and enhancement problems via a probabilistic interpretation of the SRP function. We formulate optimization procedures for (1) a mixture of single-source SRP distributions (MoSRP) and (2) a multi-source SRP distribution (MultSRP). Unlike in traditional localization, the latter approach explicitly models source overlap in the TF plane. Results shows that the MultSRP model is capable of localizing sources with significant overlap in the TF domain and that either of the proposed methods out-performs standard SRP localization for multiple speakers.

A miniaturized wireless accelerometer with micromachined piezoelectric sensing element

A miniaturized wireless accelerometer using a piezoelectric micro sensing element is designed, theoretically analyzed, and demonstrated for remote mechanical vibration monitoring. The micro sensing element comprises a micromachined monolithic silicon seismic mass and multiple silicon beams coated with in-plane polarized (Pb, La)(Zr, Ti)O3 (PLZT) ferroelectric thin film. A low profile compact package is obtained for accommodating all the micro sensing element, charge amplifier and signal processing circuits, radio frequency module, and battery. The electrical output linearly proportional to the vibration acceleration is amplified, digitalized and transmitted to a remote base unit through radio frequency communication. The effects of the device package on the dynamic response of the wireless piezoelectric accelerometer including shielding of ambient interference are investigated.

Precise Dimming and Color Control of LED Systems Based on Color Mixing

This paper proposes a closed-loop nonlinear scheme for precisely controlling the luminosity and correlated color temperature (CCT) of a bicolor adjustable light-emitting diode (LED) lamp. The main objective is to achieve a precise and fully independent dimming and CCT control of the light mixture emitted from a two-string LED lamp comprising warm-white and cool-white color LEDs, regardless of the operating conditions and throughout the long operating lifetime of the LED lamp. The proposed control method is formulated using the nonlinear empirical LED model of the bicolor white LED system. Experimental results show that with the proposed closed-loop nonlinear approach, both CCT and dimming control of the bicolor lamp is significantly more accurate and robust to ambient temperature variations, ambient light interference, and LED aging than the conventional linear approach used in existing products. The maximum error in luminous flux employing the proposed closed-loop nonlinear approach is 3%, compared with 20% using the closed-loop linear approach. The maximum deviation in CCT is only 1.78%, compared with 27.5% with its linear counterpart.

Blue Light Blocking Lenses Measuring Device

The objective of this study is to develop a measurement system for blue light blocking lens. The main features of the system are using a light pulse modulator and filtering circuit technology to prevent ambient light interference, no any expensive optical filters are needed. Our system is composed of a transmitter circuit, a receiver circuit and a signal processing circuit. The transceiver driving circuit including the modulated LED of 10kHz and measured the light signals from unwanted noise and unmodulated all light by a photodiode, and then converted by the transimpedance amplifer. The transmitter circuit uses Darlington drive circuit to optimize the LED operating current. The percentage error of the transmittance data is nearly 6%, compared with that of the OCEAN USB 2000 spectrometer's, so proved that this device is feasibility. This experiment has also shown that the chromaticity coordinates will be changed when using blue light lens. Based on the experiment, the brightness of the blue light blocking lenses cannot be too low which will cause color deviation problems. From the analysis result in table 4, the color deviation is nearly 17% at green blocking blue light lenses and 21% at yellow blocking blue light lenses on y coordinates. Therefore, these two blocking blue light lenses are not suitable for use as blue light blocking lenses. The color deviation of coated blue film and brown 20% blocking blue light lens have nearly 3% on x-y coordinates. But, those of 8% in 30% brown and 17% in green lenses are also not suitable for blue light blocking lens using. Thus the most suitable for blue light blocking lenses using is 20% brown blocking lens. In the future, this proposed device may be used as a powerful measurement tool on the eyeglasses test line.

Upconversion nanocomposites for photo-based cancer theranostics.

Lanthanide ion doped upconversion nanoparticles (UCNPs) are known to be able to convert long-wavelength excitation light (usually 980 nm) into high-energy ultraviolet (UV) or visible emissions, and they have attracted significant attention because of their distinct photochemical properties including sharp emission bands, low autofluorescence, high tissue penetration depth, inertness to ambient interference and minimal photodamage to tissues. Until now, UCNPs have shown great potential in various realms including bioimaging, biosensing and biomedical applications. Especially in recent years, UCNP based nanocomposites have been found to be promising tools for multi-modal imaging and low-invasive photo-based therapy of tumors. In this review, we summarize the recent achievement and progress of UCNP based multifunctional nanoplatforms for bioimaging and cancer phototheranostics, including photodynamic therapy (PDT) and photothermal therapy (PTT). Furthermore, some emerging trends, future directions as well as challenges in this rapidly growing field are discussed.