Signal-to-noise Ratio Requirements Research Articles

Signal-to-noise ratio model in Python for high-resolution space-borne electro-optic imagers

We present a signal-to-noise ratio (SNR) simulator design for high-resolution Earth observation imagers with time-delay and integration (TDI) imaging sensors. SNR is one of the space-borne electro-optic imagers’ driving design and performance evaluation parameters. TDI line imaging sensors are used to meet these imagers’ SNR requirements by increasing effective exposure time during imaging. Determining the optimum number of TDI stages to meet the SNR requirements of the mission is critical for the imaging sensor selection, optical design of the telescope, laboratory radiometric tests, and imaging in orbit. The SNR simulator is based on an end-to-end image chain calculation following the light pathway from the sun to the imager. The simulator comprises radiance, telescope, sensor, and image modules written in the open-access Python environment. The radiance module uses PcModWin/MODerate resolution atmospheric TRANsmission output data of the atmospheric radiance calculation input for the simulator. The simulator computes the SNR for the given telescope and imaging sensor model and simulates associated images to show the radiometric performance. A high-resolution electro-optic imager model is used to demonstrate the performance and versatility of the simulator for the case studies. Experimental simulator validation is demonstrated for the images acquired by the Göktürk-2 space-borne imager.

Joint Intra/Inter-Slot Code Design for Unsourced Multiple Access in 6G Internet of Things

Unsourced multiple access (UMA) is the technology for massive, low-power, and uncoordinated Internet-of-Things in the 6G wireless system, improving connectivity and energy efficiency on guaranteed reliability. The multi-user coding scheme design is a critical problem for UMA. This paper proposes a UMA coding scheme based on the T-Fold IRSA (irregular repetition slotted Aloha) paradigm by using joint Intra/inter-slot code design and optimization. Our scheme adopts interleave-division multiple access (IDMA) to enhance the intra-slot coding gain and the low-complexity joint intra/inter-slot SIC (successive interference cancellation) decoder structure to recover multi-user payloads. Based on the error event decomposition and density evolution analysis, we build a joint intra/inter-slot coding parameter optimization algorithm to minimize the SNR (signal-to-noise ratio) requirement at an expected system packet loss rate. Numerical results indicate that the proposed scheme achieves energy efficiency gain by balancing the intra/inter-slot coding gain while maintaining relatively low implementation complexity.

Weak and Maneuvering Target Detection with Long Observation Time Based on Segment Fusion for Narrowband Radar.

Detecting high-speed and maneuvering targets is challenging in early warning radar applications. Modern early warning radar has many functions such as detection, tracking, imaging, and recognition which need a high signal-to-noise ratio (SNR). Thus, long-time coherent integration is a necessary method to realize high SNR requirements. However, high-speed and maneuverable motion cause range and Doppler migration, which brings about serious coherent integration loss. Traditional integration methods usually have the drawbacks of model mismatching and high computational complexity. This paper establishes a novel long coherent processing interval (CPI) integration algorithm that detects maneuvering and weak targets which have a low reflection cross-section (RCS) and low echo SNR. The range and Doppler migration problems are solved via a layer integration by blending the association in a tracking-before-detection (TBD) technique. Compact SNR gain is achieved with a target information transmission mechanism and an updated constant false alarm ratio (CFAR) threshold. The algorithm is applicable in multiple target scenarios by considering different velocity ambiguities and maneuvers. A simulation and real-measured experiments confirm the effectiveness of the algorithm.

Distributed collaboration and anti-interference optimization in edge computing for IoT

The edge computing (EC) systems for Internet of Things (IoT) can bring out low latency, high reliability, distributed intelligence, and network bandwidth savings to industrial real-time applications. However, limited computing and processing capabilities of edge devices remains to be difficult to meet complex data processing and artificial intelligence (AI) analysis requirements for diverse services. Besides, the scarcity of wireless spectrum resources in harsh industrial environment makes the interference between devices more serious. To address these challenges, this paper proposes an adaptive distributed collaborative anti-interference optimization scheme for IoT-edge system. Firstly, the EC system model is established, and the model of link failure probability is derived theoretically. Then, an Occupy-Interference Mitigation (O-IM) algorithm based on full-frequency multiplexing is proposed. The algorithm combines adaptive full-frequency multiplexing and interference mitigation to reduce the dependence of the reliable collaboration on bandwidth and signal-to-noise ratio (SNR). In addition, an anti-interference algorithm based on Interference Mitigation and inter-cellfrequency multiplexing Interference Avoidance (IM-IA) is proposed to balance bandwidth and SNR. This algorithm adopts interference cancellation scheme in the broadcasting phase, and adopts orthogonal frequency division in the collaboration phase. Extensive simulation results on Mininet platform verify that the proposals can obtain lower failure probability, and are less than traditional solutions in transmitting power, bandwidth, and SNR requirements. Moreover, the O-IM is suitable for low power scenarios, while the IM-IA is more suitable for high power case.

Geographic Variation in Bryde’s Whale Be4 Calls in the Gulf of California: An Insight to Population Dynamics

Although Bryde’s whale (Balaenoptera edeni) has worldwide distribution, it is considered one of the most difficult species of baleen whales to study. Two populations existing in the Gulf of California have been proposed, but difficulties in obtaining biopsies have complicated testing the hypothesis of discrete stock difference between north and south of the Gulf. On the other hand, bioacoustics analyses have been used to differentiate populations from other whale species. Therefore, to provide information for the previously proposed hypothesis, the most common Bryde’s call (Be4) in the region was analyzed. Acoustic data was collected using a High-frequency Acoustic Recording Package (HARP) system placed at two locations in the Gulf of California: one in the north from November 2006 to June 2007 and other one in the south from August to December 2008. Duration and frequency range were measured for calls with a signal-to-noise ratio (SNR) greater than 27 dB. The Mann–Whitney-Utest was performed to compare distributions in both geographic areas, and the results were compared with a photo-identification analysis. Of the 903 Be4 calls detected, 103 (north) and 92 (south) met the SNR requirement. Duration showed a significant difference (Mann–Whitney test,U= 11238,z-statistics = 2.91,p= 0.003) with southern calls being longer than the northern ones. The results suggest that the northern population remains resident in the north with some movement toward the south, also verified by photo-identification. A detailed inspection showed a double distribution in the south (Mann–Whitney test,U= 2211,z-statistics = −8.39,p= 4.4776e-17), suggesting the presence at the same time of resident and transient populations. The latter has been suggested to belong to the Eastern Tropical Pacific. The results suggest a geographic variation inB. edeniBe4 call type and support the existence of two populations within the Gulf of California.

Millimeter-wave hybrid OFDM-MDM radio over free space optical transceiver for 5G services in desert environment

We propose a novel design of orthogonal frequency division multiplexing-mode division multiplexing (OFDM-MDM) based radio over free space optical (RoFSO) transceiver under the dust effect. Two separate 20 Gbps-40 GHz 4-QAM data bearing optical beams are transported over two distinctive Hermite-Gaussian modes; HG00 and HG01, of a single-frequency laser to realize a single-channel 40 Gbps-80 GHz 5G communication link. The link performance and availability are investigated under the impact of clear climate and under varied dust conditions, including very light dust, light dust, moderate dust, and dense dust using signal-to-noise ratio (SNR) and total power as the key evaluation parameters. Further, we have determined the maximum reachable link distance within acceptable performance criteria, i.e., SNR 20 dB for each desert weather conditions. A higher data rate with tolerable SNR and power requirements for 5G applications has been verified.

Exoplanet imaging performance envelopes for starshade-based missions

We lay out the capabilities and limitations of starshade-based missions aiming to measure the reflected light spectra of temperate planets from an imaging perspective. We use the Starshade Imaging Simulation Toolkit for Exoplanet Reconnaissance to conduct high fidelity end-to-end optical simulations, taking a step forward from simplified analytical equations, exploring and quantifying the impact of an array of observational conditions, including natural parameters such as target star types, planet types, distances, planet phases, and exo-zodiacal dust, and starshade perturbations such as tilt, shift off line of sight, edge errors, and glint. We find that signal-to-noise ratio (SNR) requirements used for establishing detection and spectral characterization, is not suitable under realistic observation conditions for a wide range of targets. We show that even if we assume that the spatially distributed, time-varying background noise could be known and calibrated to a level of 1%, each target star will need its own SNR requirement based on its unique observation conditions, nearly always resulting in a higher threshold SNR, with values as high as X5 from currently established requirement, and in some cases impossible to detect. We conduct statistical analysis using end-to-end optical simulations, taking into account observationally based priors and update previously established completeness values for an array of target stars and mission configurations, accounting for starshade perturbations, and background knowledge at a level of one percent and find that completeness values are negatively impacted and reduced by up to 50% across targets even at ranges shorter than 10 pc. Finally, we utilize information from over hundreds of thousands of detailed imaging simulations to map accessible target stars for both optimistic and pessimistic scenarios, reassessing the expected capabilities of starshade-based high contrast direct imaging missions.

An efficient trajectory planning for cellular-connected UAV under the connectivity constraint

Unmanned Aerial Vehicles (UAVs) acting as aerial users to access the cellular network form a promising solution to guarantee its safe and efficient operations via the high-quality communication. Due to the flexible mobility of UAVs and the coverage range limits of ground base station (GBS), the signal-to-noise ratio (SNR) of the communication link between UAVs and GBS will fluctuate. It is an important requirement to maintain the UAV's cellular connection to meet a certain SNR requirement during the mission for UAV flying from take off to landing. In this paper, we study an efficient trajectory planning method that can minimize a cellular-connected UAV's mission completion time under the connectivity requirement. The conventional method to tackle this problem adopts graph theory or a dynamic programming method to optimize the trajectory, which generally incurs high computational complexities. Moreover, there is a non-negligible performance gap compared to the optimal solution. To this end, we propose an iterative trajectory optimizing algorithm based on geometric planning. Firstly, we apply graph theory to obtain all the possible UAV-GBS association sequences and select the candidate association sequences based on the topological relationship among UAV and GBSs. Next, adopting the triangle inequality property, an iterative handover location design is proposed to determine the shortest flight trajectory with fast convergence and low computation complexity. Then, the best flight trajectory can be obtained by comparing all the candidate trajectories. Lastly, we revealed the tradeoff between mission completion time and flight energy consumption. Numerical results validate that our proposed solution can obtain the effectiveness with set accuracy and outperform against the benchmark schemes with affordable computation time.

A 13-bit 0.005-mm2 40-MS/s SAR ADC With kT/C Noise Cancellation

As any analog-to-digital converter (ADC) with a front-end sample-and-hold (S/H) circuit, successive approximation register (SAR) ADC suffers from a fundamental signal-to-noise ratio (SNR) challenge: its sampling kT/C noise. To satisfy the SNR requirement, the input capacitor size has to be sufficiently large, leading to a great burden for the design of the ADC input driver and reference buffer. This article presents an SAR ADC with a kT/C noise-cancellation technique. It enables the substantial reduction of ADC input capacitor size but without the large kT/C noise penalty. It greatly relaxes the requirement for ADC input driver and reference buffer. Built in 40-nm CMOS, a prototype 13-bit ADC has only 240-fF input capacitance and occupies a small area of 0.005 mm2. Operating at 40 MS/s, it achieves a 69-dB signal-to-noise-and-distortion ratio (SNDR) across the Nyquist frequency band while consuming 591 $\mu \text{W}$ of power.

Amplitude independent versus amplitude dependent muscle activity detection algorithms: a comparative study

<span>The amplitude dependent muscle activity detection algorithms of the surface electromyography (sEMG) signals are very sensitive to the changes in the background noise levels and the performance of these amplitude-based methods is highly deteriorated when the Signal to Noise ratio (SNR) of the sEMG signal is low. sEMG signals of deep and small muscles as well as sEMG signals recorded from patients that have neuromuscular diseases may not meet this SNR requirement which motivates the need for amplitude independent algorithms that can detect weak muscle activities. </span><span lang="EN-GB">Moreover, the sEMG signal amplitude is not constant during the recording time due to the variation in the characteristics of the electrode-skin interface and due to the changes in the ground reference level</span><span>.</span><span lang="EN-GB"> Therefore, the performance of the muscle activity detection algorithms should not be affected by the involuntary amplitude variations of the sEMG signal in order to achieve reliable control of robotic devices intended for disabled people.</span><span lang="EN-GB">To accentuate the importance of the amplitude independent muscle activity detection methods over the amplitude dependent detection methods, a comparative study has been conducted in this paper between the performance of an amplitude independent muscle activity detection algorithm (FLA-MSE algorithm) and three amplitude dependent algorithms with respect to the detection capability of weak muscle activities and with respect to the immunity against false alarms. The results have showed that the performance of the amplitude independent algorithm outperformed the performance of the amplitude dependent algorithms for detecting weak muscle activities and for robustness against false alarms.</span>

A High-Precision Phase-Derived Velocity Measurement Method for High-Speed Targets Based on Wideband Direct Sampling LFM Radar

This paper proposes a phase-derived velocity measurement (PDVM) method for high-speed targets based on wideband direct sampling linear frequency modulated radar. First, a high-speed target echo model considering intrapulse Doppler modulation is developed. Then, a PDVM model considering acceleration is established. The key to realizing PDVM is resolving phase ambiguity. Under low signal-to-noise ratio (SNR) conditions, a joint processing method combining acceleration information and multiframe data to solve phase ambiguity is proposed, which can significantly reduce the SNR requirement for PDVM. In this paper, the small-amplitude micromotion measurement capability of the proposed method is verified by simulation. Moreover, the measured data of a Ku-band ground-based radar are used to verify the applicability of the PDVM method under low SNR conditions and its feasibility to be applied to complex multi-scattering point targets. Both the simulation and experimental results show that the proposed method is suitable for high-speed targets with radial motion, including acceleration and jerk, and that the PDVM precision can reach the order of magnitude of centimeters per second or millimeters per second.

Performance enhancement of high-capacity coherent DWDM free-space optical communication link using digital signal processing

In this paper, 1.28 Tbps (32 × 40 Gbps) high-capacity DWDM-FSO link has been investigated for performance enhancement using coherent detection and digital signal processing (DSP). The DP-16QAM-modulated proposed DWDM-FSO link has been analyzed for both adverse weather and turbulent atmospheric conditions. It is observed that when link is subjected to strong turbulence along with adverse weather conditions, the DSP-aided coherent DWDM-FSO receiver achieves target bit error rate (BER) of 10−4 at signal-to-noise ratio (SNR) of 36.4 dB, while for similar conditions, the SNR requirements for IM/DD-based DWDM-FSO link shoots by 12.8 dB to 49.2 dB. Also, in terms of operational link range, the proposed link even under strong turbulent conditions serves 1.88 km, whereas IM/DD link was restricted to mere 1.12 km for target of BER of 10−4, thus producing a decent range increment of 760 m. The proposed link has been designed and investigated using OptiSystem™ 14.2.

Estimating Aggregate Cardiomyocyte Strain Using In Vivo Diffusion and Displacement Encoded MRI.

Changes in left ventricular (LV) aggregate cardiomyocyte orientation and deformation underlie cardiac function and dysfunction. As such, in vivo aggregate cardiomyocyte "myofiber" strain ( [Formula: see text]) has mechanistic significance, but currently there exists no established technique to measure in vivo [Formula: see text]. The objective of this work is to describe and validate a pipeline to compute in vivo [Formula: see text] from magnetic resonance imaging (MRI) data. Our pipeline integrates LV motion from multi-slice Displacement ENcoding with Stimulated Echoes (DENSE) MRI with in vivo LV microstructure from cardiac Diffusion Tensor Imaging (cDTI) data. The proposed pipeline is validated using an analytical deforming heart-like phantom. The phantom is used to evaluate 3D cardiac strains computed from a widely available, open-source DENSE Image Analysis Tool. Phantom evaluation showed that a DENSE MRI signal-to-noise ratio (SNR) ≥20 is required to compute [Formula: see text] with near-zero median strain bias and within a strain tolerance of 0.06. Circumferential and longitudinal strains are also accurately measured under the same SNR requirements, however, radial strain exhibits a median epicardial bias of -0.10 even in noise-free DENSE data. The validated framework is applied to experimental DENSE MRI and cDTI data acquired in eight ( N=8 ) healthy swine. The experimental study demonstrated that [Formula: see text] has decreased transmural variability compared to radial and circumferential strains. The spatial uniformity and mechanistic significance of in vivo [Formula: see text] make it a compelling candidate for characterization and early detection of cardiac dysfunction.

Scaling Up In-Memory-Computing Classifiers via Boosted Feature Subsets in Banked Architectures

In-memory computing is an emerging approach for overcoming memory-accessing bottlenecks, by eliminating the costs of explicitly moving data from point of storage to point of computation outside the array. However, computation increases the dynamic range of signals, such that performing it via the existing structure of dense memory substantially squeezes the signal-to-noise ratio (SNR). In this brief, we explore how computations can be scaled up, to jointly optimize energy/latency/bandwidth gains with SNR requirements. We employ algorithmic techniques to decompose computations so that they can be mapped to multiple parallel memory banks operating at chosen optimal points. Specifically focusing on in-memory classification, we consider a custom IC in 130-nm CMOS IC and demonstrate an algorithm combining error-adaptive classifier boosting and multi-armed bandits, to enable segmentation of a feature vector into multiple subsets. The measured performance of 10-way MNIST digit classification, using images downsampled to $16{\times }16$ pixels (mapped across four separate banks), is 91%, close to that simulated using full unsegmented feature vectors. The energy per classification is 879.7 pJ, $14.3{\times }$ lower than that of a system based on separated memory and digital accelerator.

Model of adaptive WiMAX network incorporating diverse transforms

Recent advancements in mobile Worldwide Interoperability for Microwave Access (WiMAX) standards have led to the development of more reliable, robust and efficient broadband networks that can provide access for both fixed and mobile users. The physical layer of a WiMAX network is composed of orthogonal frequency-division multiplexing (OFDM) technology, which allows it to provide elevated data rates with minimum distortion in fading environments. However, the presence of interference affects the orthogonality of the OFDM sub-carriers, which increases the probability of network errors. The error probability can be reduced by increasing the output power level at the expense of an elevated peak-to-average power ratio (PAPR). In this work, discrete wavelet transforms (DWTs) and fractional Fourier transforms (FrFTs) are used to augment the reliability and efficiency of an adaptive WiMAX system. The results show considerable improvements in both bit error rate (BER) reduction and spectral efficiency enhancement at a given signal-to-noise ratio (SNR). In addition, a significant PAPR reduction is attained when the DWTs and FrFTs are used in place of fast Fourier transforms. Moreover, the proposed adaptive algorithm realizes a spectral-efficient WiMAX system that enables the system to select the suitable transform strategy (DWT/FrFT) with minimum SNR requirements to achieve the target BER of 10−4.

Mean Square Stabilization Over SNR-Constrained Channels With Colored and Spatially Correlated Additive Noises

This note addresses the problem of stabilizing a multi-input multi-output (MIMO) discrete-time linear time invariant (LTI) system over a MIMO additive noise channel. To treat this problem, we assume that the communication link between the plant output and the controller input consists of multiple additive colored mutually correlated noise channels subject to independent signal-to-noise ratio (SNR) constraints. We derive analytical conditions for which mean square stabilization (MSS) can be achieved under such constraints. We also formulate numerical methods to test these conditions when the noise is white and correlated. Moreover, for simpler plant models, a characterization of the set of power constraints compatible with MSS is obtained. Our results show that the frequency response of the spectral factor related to the channel noise affects the minimum SNR for stability depending mostly on the unstable poles and their directions. This is aggravated by the existence of nonminimum phase zeros and higher relative degree of the plant. On the other hand, we detect that systems under highly correlated noise show lower SNR requirements for stability compared to ones with independent noise channels. Numerical simulations are provided to illustrate the theoretical results.

Performance analysis of multiple radio-access provision in a multicore-fibre optical fronthaul

Abstract In this work we report a comprehensive experimental study targeting the dimensioning of the next-generation multicore-fibre (MCF) optical fronthaul employing space-division multiplexing (SDM). This fronthaul is capable of simultaneous provision of multiple radio-access technologies (multi-RATs) with advanced multi-antenna MIMO capabilities per RAT. The different parameters required for fronthaul dimensioning are evaluated considering state-of-the-art 4G LTE-Advanced altogether other legacy wireless standards in operation nowadays. In particular, the modulation characteristics, the antenna quality requirements (in terms of EVM, phase error or rho) and the signal-to-noise ratio (SNR) thresholds are evaluated employing fully-standard cellular signals transmitted on a multicore fibre (MCF) fronthaul. The study includes bi-directional signal transmission and multi-antenna MIMO multiplexing. The MCF optical fronthaul is evaluated with a multiplexed transmission of 2G, 3G, 3.9G and 4G MIMO signals in radio-over-multicore-fibre (RoMCF) employing a commercially available four-core MCF. The SNR requirements at the transmitter antenna are obtained for each cellular signal considering GSM, EDGE, EGPRS2-A, cdma2000 1xEV-DO, UMTS HSPA+ and LTE-Advanced standards. LTE-Advanced single-antenna and two-antenna systems implementing 2 × 2 MIMO transmission can be accomplished with SNR levels over 25 dB. In the case of LTE-Advanced 4 × 4 MIMO multiplexing over four cores of MCF media, 32 dB SNR is needed to achieve almost four times provided bitrate per user.

A clean signal reconstruction approach for coherently combining multiple radars

Distributed radars have the potential to combine coherently for achieving a high signal-to-noise ratio (SNR) while maintaining a moderate antenna size. The key to coherently combining multiple radars is obtaining accurate coherent parameters (CPs), which are used to adjust the transmitting/receiving time and phase of each radar. One approach for CP estimation is to transmit orthogonal waveforms. However, ideally, orthogonal waveforms occupying the same frequency band may not be found in practice. Cross-correlation energy leakage exists between non-orthogonal waveforms, which seriously impairs the accurate acquisition of CPs. To solve this problem, we propose a clean signal reconstruction approach for CP estimation. This approach reconstructs clean echoes by gradually stripping out the cross-correlation energy leakage with a reconstruction-elimination-reconstruction framework. And CPs are obtained from these reconstructed clean echoes. Since the majority of cross-correction energy leakages are eliminated, enhanced CP estimation performance can be achieved. Verified simulations are designed for a dual radar scenario. Results show that the proposed approach significantly improves the performance of CP estimation while reducing the SNR requirement for coherently combining multiple radars.

Signal-to-noise analysis of a birefringent spectral zooming imaging spectrometer

Study of signal-to-noise ratio (SNR) of a novel spectral zooming imaging spectrometer (SZIS) based on two identical Wollaston prisms is conducted. According to the theory of radiometry and Fourier transform spectroscopy, we deduce the theoretical equations of SNR of SZIS in spectral domain with consideration of the incident wavelength and the adjustable spectral resolution. An example calculation of SNR of SZIS is performed over 400–1000 nm. The calculation results indicate that SNR with different spectral resolutions of SZIS can be optionally selected by changing the spacing between the two identical Wollaston prisms. This will provide theoretical basis for the design, development and engineering of the developed imaging spectrometer for broad spectrum and SNR requirements.

Signal-to-Noise Ratio Requirements for Discrete-time PID Controllers

In networked control a channel model is explicitly considered in discrete-time within the classic feedback loop scheme. It is well known by now that the channel model signal-to-noise ratio (SNR) happens to be an important parameter related to the overall stabilisability of the output feedback loop. In this paper we offer the rules for the design of discrete-time proportional-integral-derivative (PID) controllers for given closed loop bandwidth that quantifies the SNR requirement for the memoryless additive white Gaussian noise (AWGN) channel over the feedback path, which is a common used communication channel model. The design is centered on a PID controller structure due to its wide use in the industry. Since we focus on a PID controller structure, we then reduce the analysis to plant models of first and second order which are the structures more compatible with the PID controller design.