Sum Of Signals Research Articles

The use of groups of drones for diagnostics of subsurface objects in the radio range

Diagnostics of objects that are bodies of complex shape can be based on representing their boundaries of such objects (including subsurface ones) in the form of a set of paraboloids. It is shown that the determination of specific geometric characteristics of such paraboloids is ensured through the artificial synthesis of a distributed lens, the individual elements of which are placed on individual unmanned aerial vehicles. Technically, lens synthesis is achieved by changing the phase of radio frequency radiation, carried out using phase shifters. The method is implemented through scanning the object under study by adjusting the effective optical power and its orientation in space. Model calculations are presented that prove the prospects of using the proposed method. Specific examples of radio-electronic circuits that ensure the implementation of the proposed approach are presented. A feature of the phase shifters used is the preliminary detection of the phase of the signal arriving at each of the elements of the distributed lens, and the subsequent introduction of an additional phase difference due to the weighted summation of harmonic signals that are in antiphase.

Neurosimilator for Undergraduate Biophysics and Neurophysiology Courses.

Stringent animal welfare principles are forcing undergraduate instructors to avoid the use of animals. Therefore, many hands-on lab sessions using laboratory animals are progressively replaced by computer simulations. These versatile software simulations permit the observation of the behavior of biological systems under a great variety of experimental conditions. While this versatility is important, computer simulations often work even when a student makes wrong assumptions, a situation that poses its own pedagogical problem. Hands-on learning provides pupils with the opportunity to safely make mistakes and learn organically through trial and error and should therefore still be promoted. We propose an electronic model of an excitable cell composed of different modules representing different parts of a neuron - dendrites, soma, axon and node of Ranvier. We describe a series of experiments that allow students to better understand differences between passive and active cell responses and differences between myelinated and demyelinated axons. These circuits can also be used to demonstrate temporal and spatial summation of signals coming to the neuron via dendrites, as well as the neuron coding by firing frequency. Finally, they permit experimental determination along with theoretical calculations of important biophysical properties of excitable cells, such as rheobase, chronaxie and space constant. This open-source model has been successfully integrated into an undergraduate course of the physiology of excitable cells and student feedback assessment reveals that it helped students to understand important notions of the course. Thus, this neuromorphic circuit could be a valuable tool for biophysics and neuroscience courses in other universities.

Method for determining phase shift using two-phase interpolation transformation

This study focuses on a method for accurately determining the phase shift between two harmonic signals. The proposed technique compares the waveform of the combined signal, which is obtained by applying a two-phase interpolation transformation, with a set of predefined reference functions. This study aims to develop a method for accurately measuring the phase shift between two harmonic signals to reduce the measurement errors caused by phase asymmetry in signal transmission channels and minimize the costs associated with parameter control. The sources of error in the proposed measurement method are identified and addressed. The task involves setting up the measurement objective to determine the phase shift between two harmonic signals. This section presents the analyses of known methods for measuring phase shifts using of analog-to-digital conversion. The next step involves selecting indicators and criteria to determine how closely the investigated signal matches the reference function. Then, we propose the synthesis of an algorithm to find the extremum of the sum of squares deviations for the set of reference functions and the investigated signal, relying on the golden ratio method. Finally, the paper will analyze possible sources of error that could affect the measurement outcome. The methods used are the following: methodology for conducting digital signal processing and measuring error estimation, numerical methods for extremum search, and methodology for single-factor experiments. The following results were obtained. A proposed compensation method for measuring phase shift is based on comparing the shape of the normalized signal, which is obtained as a result of the summation of harmonic signals after their semiperiodical transformation, with a set of normalized reference functions synthesized by computational means. A list of measurement and auxiliary operations, which should be ideally conducted to implement this measurement method, has been determined. An analysis of the components of measurement errors was conducted. Conclusions. The scientific novelty of the obtained results lies in the following: a method for measuring the phase shift of a signal has been developed, which, in our opinion, should be classified as a compensatory measurement method. This will significantly reduce the error component caused by the phase asymmetry of signal transmission channels and reduce the costs of controlling parameters (up to 10%) without decreasing the quality of control; a list of the measuring and auxiliary operations necessary for the implementation of the proposed measurement method has been identified; sources of errors have been determined.

Neurotensin-specific corticothalamic circuit regulates innate response conflict

Animals must simultaneously select and balance multiple action contingencies in ambiguous situations: for instance, evading danger during feeding. This has rarely been examined in the context of information selection; despite corticothalamic pathways that mediate sensory attention being relatively well characterized, neural mechanisms filtering conflicting actions remain unclear. Here, we develop a new loom/feed test to observe conflict between naturally induced fear and feeding and identify a novel anterior cingulate cortex (ACC) output to the ventral anterior and ventral lateral thalamus (VA/VL) that adjusts selectivity between these innate actions. Using micro-endoscopy and fiber photometry, we reveal that activity in corticofugal outputs was lowered during unbalanced/singularly occupied periods, as were the resulting decreased thalamic initiation-related signals for less-favored actions, suggesting that the integration of ACC-thalamic firing may directly regulate the output of behavior choices. Accordingly, the optoinhibition of ACC-VA/VL circuits induced high bias toward feeding at the expense of defense. To identify upstream "commander" cortical cells gating this output, we established dual-order tracing (DOT)-translating ribosome affinity purification (TRAP)-a scheme to label upstream neurons with transcriptome analysis-and found a novel population of neurotensin-positive interneurons (ACCNts). The photoexcitation of ACCNts cells indeed caused similarly hyper-selective behaviors. Collectively, this new "corticofugal action filter" scheme suggests that communication in multi-step cingulate circuits may critically influence the summation of motor signals in thalamic outputs, regulating bias between innate action types.

Triggered cagedSTORM microscopy.

In standard SMLM methods, the photoswitching of single fluorescent molecules and the data acquisition processes are independent, which leads to the detection of single molecule blinking events on several consecutive frames. This mismatch results in several data points with reduced localization precision, and it also increases the possibilities of overlapping. Here we discuss how the synchronization of the fluorophores' ON state to the camera exposure time increases the average intensity of the captured point spread functions and hence improves the localization precision. Simulations and theoretical results show that such synchronization leads to fewer localizations with 15% higher sum signal on average, while reducing the probability of overlaps by 10%.

Space-code division multiple access for broadband acoustic networks

We present an investigation into the design of an acoustic communication network, where multiple users are distributed across space and transmit and receive simultaneously in the same band to and from a common base station. Specifically, we focus on a system that utilizes orthogonal frequency division multiplexing as a modulation method, and allows users to transmit and receive in either synchronous or asynchronous fashion. To distinguish between users on the uplink, the base station employs a combination of code-division and space-division multiple access. The base station iteratively steers a beam to each stable propagation path of the desired user’s channel while placing nulls in the direction of other paths, as well as in the directions of interfering users. Finally, the multiple paths of the desired user are recombined before data detection. The process is repeated for each user. Broadband beamforming is employed to account for the broadband nature of acoustic signals. The beamformer coefficients on each carrier depend on the angles of signal arrivals, which are estimated during the uplink transmission and used to construct both the uplink and the downlink beamformer. On the downlink, the base station utilizes angle information to assemble beamforming weights and point in the direction of stable paths of the users. It superimposes multiuser signals and transmits the sum signal to the users. The signal intended for a given user reaches only that user, requiring just a simple detector. Each user is equipped by a single-element transducer. To demonstrate the design concepts, we conducted simulations using a shallow water channel model and performed experimental over-the-air tests in an indoor environment using an acoustic communications testbed. The results were excellent, thereby encouraging future implementations in practical systems.

МАТЕМАТИЧНІ СИСТЕМИ ДЛЯ РЕАЛІЗАЦІЇ ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ, ОРІЄНТОВАНИХ НА ХМАРОВІ ОБЧИСЛЕННЯ

The article provides a detailed overview of research focusing on artificial neural networks (ANNs) and their applications in cloud computing. Research methods of organizational development and changes based on artificial intelligence technologies and intellectual support systems are presented in the plane of: intellectual expert systems; inductive systems; semantic networks, neural networks, genetic algorithms. The aim of the study. The research is aimed at the study and analysis of modern mathematical systems used to implement artificial neural networks (ANNs). The main focus of the work is on how each artificial neuron in the network is characterized by its current state, which is similar to nerve cells in the brain that can be excited or inhibited. A detailed description of the functioning of neurons is provided, including the processes of summation of input signals and activation using activation functions. Special attention is paid to multilayer neural networks and their ability to form complex multidimensional functions. The methods of building decision-making models based on the analysis of unclear situations and reference states determined by experts are defined. The process of comparing the real states of organizations with reference ones for making optimal decisions is considered. The importance of fuzzy logical operations for determining the degree of closeness of various situations is described. Fuzzy reference situations for cloud computing and their impact on decision-making in various scenarios are proposed. Examples of real and hypothetical fuzzy situations are given, and methods of determining the fuzzy correspondence between different reference situations are also considered. The final part of the abstract emphasizes the possibilities and advantages of using such models in cloud computing, emphasizing their importance for the development of organizations and systems.

Exercise-induced potentiation of the acute hypoxic ventilatory response: Neural mechanisms and implications for cerebral blood flow.

A given dose of hypoxia causes a greater increase in pulmonary ventilation during physical exercise than during rest, representing an exercise-induced potentiation of the acute hypoxic ventilatory response (HVR). This phenomenon occurs independently from hypoxic blood entering the contracting skeletal muscle circulation or metabolic byproducts leaving skeletal muscles, supporting the contention that neural mechanisms per se can mediate the HVR when humoral mechanisms are not at play. However, multiple neural mechanisms might be interacting intricately. First, we discuss the neural mechanisms involved in the ventilatory response to hypoxic exercise and their potential interactions. Current evidence does not support an interaction between the carotid chemoreflex and central command. In contrast, findings from some studies support synergistic interactions between the carotid chemoreflex and the muscle mechano- and metaboreflexes. Second, we propose hypotheses about potential mechanisms underlying neural interactions, including spatial and temporal summation of afferent signals into the medulla, short-term potentiation and sympathetically induced activation of the carotid chemoreceptors. Lastly, we ponder how exercise-induced potentiation of the HVR results in hyperventilation-induced hypocapnia, which influences cerebral blood flow regulation, with multifaceted potential consequences, including deleterious (increased central fatigue and impaired cognitive performance), inert (unchanged exercise) and beneficial effects (protection against excessive cerebral perfusion).

Short communication: Synchrotron-based elemental mapping of single grains to investigate variable infrared-radiofluorescence emissions for luminescence dating

Abstract. During ionizing irradiation, potassium (K)-rich feldspar grains emit infrared (IR) light, which is used for infrared radiofluorescence (IR-RF) dating. The late-saturating IR-RF emission centred at ∼880 nm represents a promising tool to date Quaternary sediments. In the present work, we report the presence of individual grains in the K-feldspar density fraction displaying an aberrant IR-RF signal shape, whose combined intensity contaminates the sum signal of an aliquot composed of dozens of grains. Our experiments were carried out at the National Synchrotron Light Source (NSLS-II) at the submicron-resolution X-ray spectroscopy (SRX) beamline. We analysed coarse (>90 µm) K-feldspar-bearing grains of five samples of different ages and origin in order to characterize the composition of grains yielding the desired or contaminated IR-RF emission. Using micro-X-ray fluorescence (μ-XRF), we successfully acquired element distribution maps of up to 15 elements (<1 µm resolution) of sections of full grains previously used for IR-RF dating. In keeping with current theories of IR-RF signal production, we observed a trend between the relative proportions of Pb and Fe and the shape of the IR-RF signal, namely that most grains with the desired IR-RF signal shape had high Pb and low Fe contents. Interestingly, these grains were also defined by high Ba and low Ca contents. Our study also represents a proof of concept for mapping the oxidation states of Fe using micro-X-ray absorption near-edge structure spectroscopy (μ-XANES) on individual grains. The high spatial resolution enabled by synchrotron X-ray spectroscopy makes it a powerful tool for future experiments to elucidate long-standing issues concerning the nature and type of defect(s) associated with the main dosimetric trap in feldspar.

Power Resource Allocation Algorithm for Dual-Function Radar–Communication System

In this paper, a power allocation algorithm of dual-function radar–communication system with limited power is proposed to obtain better overall system performance measured by the weighted summation of radar signal to interference plus noise ratio (SINR) and communication channel capacity. First, a power allocation model is established to maximize the radar SINR and communication channel capacity with limited transmitted power. Then, the Karush–Kuhn–Tucker (KKT) conditions are used to solve the optimal objective function under the condition that only radar SINR or communication channel capacity is considered, respectively. Finally, the optimal value is combined with the original model and transformed into a single objective optimization model, and the optimal power is obtained by solving the model through the iterative optimization algorithm. Simulation results show that, compared with other power allocation algorithms, the proposed algorithm can achieve better radar-communication integration performance under the same transmit power.

Mitigating Aberration-Induced Noise: A Deep Learning-Based Aberration-to-Aberration Approach.

One of the primary sources of suboptimal image quality in ultrasound imaging is phase aberration. It is caused by spatial changes in sound speed over a heterogeneous medium, which disturbs the transmitted waves and prevents coherent summation of echo signals. Obtaining non-aberrated ground truths in real-world scenarios can be extremely challenging, if not impossible. This challenge hinders the performance of deep learning-based techniques due to the domain shift between simulated and experimental data. Here, for the first time, we propose a deep learning-based method that does not require ground truth to correct the phase aberration problem and, as such, can be directly trained on real data. We train a network wherein both the input and target output are randomly aberrated radio frequency (RF) data. Moreover, we demonstrate that a conventional loss function such as mean square error is inadequate for training such a network to achieve optimal performance. Instead, we propose an adaptive mixed loss function that employs both B-mode and RF data, resulting in more efficient convergence and enhanced performance. Finally, we publicly release our dataset, comprising over 180,000 aberrated single plane-wave images (RF data), wherein phase aberrations are modeled as near-field phase screens. Although not utilized in the proposed method, each aberrated image is paired with its corresponding aberration profile and the non-aberrated version, aiming to mitigate the data scarcity problem in developing deep learning-based techniques for phase aberration correction. Source code and trained model are also available along with the dataset at http://code.sonography.ai/main-aaa.

A continuous depth encoding PET detector using side readout of dual-layer GAGG crystals with SiPM array

A continuous depth encoding positron emission tomography (PET) detector using a side readout configuration with dual-layer GAGG scintillators of different decay times, coupled to a Silicon Photomultiplier (SiPM) array is proposed. The performance of the detector was evaluated through experimentation. The detector consists of an upper layer with a 1 × 4 fast decay GAGG (GAGG-F) crystal array and a lower layer with another 1 × 4 high-light-yield GAGG (GAGG-HL) crystal array. The 2 × 4 GAGG crystal array is then coupled to the 4 × 6 SiPM array. The SiPM array's sum signal is utilized for layer identification, while 24 individual signals are used to measure the 3D position of the gamma interaction. The experimental measurements demonstrate that the detector effectively resolves all crystal units and provides continuous depth information in the Z-direction. Utilizing the time over threshold method for dual-layer identification in coincidence mode yielded an accuracy of 96.4 %, whereas the pulse decay time method achieved an accuracy of 95.7 %. The average energy resolution was found to be 12.1 % and 11.2 % for the upper and lower GAGG crystal arrays, respectively. Using the maximum value-to-sum ratio method, the upper GAGG-F crystal layer displayed clear distinction and accurate assignment at two height positions, achieving a correct assignment rate of 90.5 %. In contrast, the lower GAGG-HL crystal layer exhibited poorer resolution at each height position, with correct assignment percentages dropping to 71.1 %. Employing the center of gravity position estimation method for the GAGG-F and GAGG-HL layers, the average Z spatial resolution was determined as 3.56 ± 0.33 mm (FWHM) and 3.35 ± 0.28 mm (FWHM).

Design and Performance Evaluation of Cavity-Backed SIW Antenna for Monopulse Applications

The design of a cavity-backed substrate integrated waveguide (SIW) dual-plane antenna for monopulse application is presented in this publication. The primary objective is to create a four-element circularly polarized cavity-backed slot antenna array that is compact and highly efficient. Two antenna subarrays are utilized to develop a complete analysis, design, and implementation of an antenna, and each antenna element can be used as an independent antenna for monopulse applications. The antenna element is made up of two square ring slot antenna components, a power divider, and a feed point. To produce sum and difference signals, the feed point of each antenna element can be connected to one of the input ports on a monopulse comparator. The antenna's performance at 10 GHz was assessed regarding radiation pattern, gain, return loss, bandwidth, axial ratio, and efficiency. A high-frequency structure simulator (HFSS) simulates a complete planar and compact SIW structure with a footprint of 61.6 mm x 58.1 mm. With a permittivity of 3.55 and a thickness of 1.524 mm, an antenna is manufactured using a photo-lithographic process on a single-layer Rogers RO4003 substrate. As per experimental findings, the antenna has a return loss of -40.17 dB, an RL bandwidth of 395 MHz, a gain of 13.11 dB, an axial ratio of 2.97 dB, and an efficiency of 89.85% at a frequency of 9.92 GHz. Experimental findings reveal exceptional performance parameters for the designed antenna, which can be used for monopulse applications at 10 GHz.

Touch, press and stroke: a soft capacitive sensor skin

Soft sensors that can discriminate shear and normal force could help provide machines the fine control desirable for safe and effective physical interactions with people. A capacitive sensor is made for this purpose, composed of patterned elastomer and containing both fixed and sliding pillars that allow the sensor to deform and buckle, much like skin itself. The sensor differentiates between simultaneously applied normal force and shear using summation and differences of signals from four deformable capacitors. Cross talk from shear to normal force is less than 2.5%, and between shear axes is less than 10%. Normal and shear stress sensitivity is 0.49 kPa and 0.31 kPa respectively, with a minimum displacement resolution of 40 μm. In addition, finger proximity is detectable at a range of up to 15 mm. The operation is demonstrated on a simple gripper holding a cup. The combination of features and the straightforward fabrication method make this sensor a candidate for implementation as a sensing skin for humanoid robotics applications.

Design of Multi-User Noncoherent Massive SIMO Systems for Scalable URLLC.

This paper develops and optimizes a non-orthogonal and noncoherent multi-user massive single-input multiple-output (SIMO) framework, with the objective of enabling scalable ultra-reliable low-latency communications (sURLLC) in Beyond-5G (B5G)/6G wireless communication systems. In this framework, the huge diversity gain associated with the large-scale antenna array in the massive SIMO system is leveraged to ensure ultra-high reliability. To reduce the overhead and latency induced by the channel estimation process, we advocate for the noncoherent communication technique, which does not need the knowledge of instantaneous channel state information (CSI) but only relies on large-scale fading coefficients for message decoding. To boost the scalability of noncoherent massive SIMO systems, we enable the non-orthogonal channel access of multiple users by devising a new differential modulation scheme to ensure that each transmitted signal matrix can be uniquely determined in the noise-free case and be reliably estimated in noisy cases when the antenna array size is scaled up. The key idea is to make the transmitted signals from multiple geographically separated users be superimposed properly over the air, such that when the sum signal is correctly detected, the signal sent by each individual user can be uniquely determined. To further enhance the average error performance when the array antenna number is large, we propose a max-min Kullback-Leibler (KL) divergence-based design by jointly optimizing the transmitted powers of all users and the sub-constellation assignments among them. The simulation results show that the proposed design significantly outperforms the existing max-min Euclidean distance-based counterpart in terms of error performance. Moreover, our proposed approach also has a better error performance compared to the conventional coherent zero-forcing (ZF) receiver with orthogonal channel training, particularly for cell-edge users.

Dual Plane Monopulse Comparator Network System using Substrate Integrated Waveguide Hybrid Coupler

ABSTRACT This paper presents a new compact monopulse comparator network system (MCNS) using a modal substrate-integrated waveguide (SIW) hybrid coupler for the first time. The main objective of this research is to evaluate the performance of dual-plane MCNS based on return loss and coupling coefficients. A detailed analysis, design, and implementation of dual-plane MCNS is performed using the four modal SIW hybrid couplers. The sum and difference signals required by the monopulse antenna are generated with the proper orientation of couplers. The in-phase and out-phase characteristics of the couplers are generated using TE102 and TE201 orthogonal degenerate modes. A complete planar and compact SIW structure has a footprint of 58.2 mm × 58.2 mm. MCNS is fabricated on a single-layer Rogers RO4003 substrate with a permittivity of 3.55 and thickness of 0.787 mm using a photo-lithographic process. MCNS provides excellent return loss characteristics at all input and output ports. It measured the isolation better than 20 dB for all output ports in the desired frequency band. It achieved good coupling between all input and output ports. Simulation and measured results show that at 10 GHz, MCNS gives better performance parameters and can be used in monopulse antenna.

Information Segregating Towards Simultaneous Tracking and Imaging Based on Ghost Imaging

Simultaneously tracking and imaging moving objects has been an open problem, due to the requirement on a large amount of information and the low photon flux during the very moment that the object can be taken as stationary. Information of the moving object can be decomposed into motion information of the centroid and image of the object. The amount of motion information can be far less than that for imaging. Therefore, we propose to solve the problem by information segregating. That is, to obtain the motion trajectory and gradually achieve the image during the tracking process. As an example, we unify tracking and imaging within a single system, based on ghost imaging with a four-quadrant detector. The differential signals from the quadrant detector are used for tracking, and the sum signals are for imaging. Experimental results show that our method provides high tracking accuracy and high-quality imaging. For objects of angular velocity up to 165 mrad/s, the tracking accuracy reaches 1/7 of the imaging resolution. Possible influences from different practical factors are also discussed.

Nonlinear Beamforming Based on Amplitude Coherence Applied to Ultrasonic Imaging of Coarse-Grained Steels

Abstract This paper deals with ultrasonic imaging in a nondestructive evaluation (NDE) context. In particular, we are focused on the inspection of coarse-grained steels having a heterogeneous composition that creates structural noise in the ultrasonic signals and images. The standard way to beamform the acquired ultrasonic data is by delay-and-sum (DAS). This method is fast but suffers from low signal-to-noise ratio (SNR) for coarse-grained steel inspection. In this paper, we propose to adapt a coherence-based beamformer called pDAS from the medical imaging community. pDAS beamforming is based on DAS structure but includes p-root and p-power before and after summations, respectively. It results in an enhancement of the coherent summation of signals that improves both resolution and contrast. Coherence-based beamformers are known to enhance information whose acoustic response correlates with geometrical information, that is why they decrease grating lobes and side lobes, specular echoes, reconstruction artifacts, and noise due to multiple scattering. In this paper, the pDAS beamformer is proposed for two common acquisition schemes employed in NDE that are plane wave imaging (PWI) and full matrix capture (FMC). The beamformers have been efficiently implemented for parallel computing on graphics processing unit (GPU) in a context of real-time imaging and fast part scanning in NDE. First, experimental results are presented from an austenitic-ferritic sample from the power generation industry that contains side drilled holes (SDH) with diameter 0.4 mm at several depths. pDAS (for p from two to three) shows improvements in terms of SNR and resolution compared to standard DAS, both in PWI and FMC modalities. We also show that the computation cost of pDAS is equivalent to DAS. A real application on a sample containing a fatigue crack connected to the backwall is exposed. We show that pDAS beamformer can enhance crack response compared to grains, but it also decreases unwanted information such as backwall specular echoes and reconstruction artifacts.

Membrane inlet-ion mobility spectrometry with automatic spectra evaluation as online monitoring tool for the process control of microalgae cultivation.

The cultivation of algae either in open raceway ponds or in closed bioreactors could allow the renewable production of biomass for food, pharmaceutical, cosmetic, or chemical industries. Optimal cultivation conditions are however required to ensure that the production of these compounds is both efficient and economical. Therefore, high-frequency analytical measurements are required to allow timely process control and to detect possible disturbances during algae growth. Such analytical methods are only available to a limited extent. Therefore, we introduced a method for monitoring algae release volatile organic compounds (VOCs) in the headspace above a bioreactor in real time. This method is based on ion mobility spectrometry (IMS) in combination with a membrane inlet (MI). The unique feature of IMS is that complete spectra are detected in real time instead of sum signals. These spectral patterns produced in the ion mobility spectrum were evaluated automatically via principal component analysis (PCA). The detected peak patterns are characteristic for the respective algae culture; allow the assignment of the individual growth phases and reflect the influence of experimental parameters. These results allow for the first time a continuous monitoring of the algae cultivation and thus an early detection of possible disturbances in the biotechnological process.

SL(2,C) Scheme Processing of Singularities in Quantum Computing and Genetics

Revealing the time structure of physical or biological objects is usually performed thanks to the tools of signal processing such as the fast Fourier transform, Ramanujan sum signal processing, and many other techniques. For space-time topological objects in physics and biology, we propose a type of algebraic processing based on schemes in which the discrimination of singularities within objects is based on the space-time-spin group SL(2,C). Such topological objects possess an homotopy structure encoded in their fundamental group, and the related SL(2,C) multivariate polynomial character variety contains a plethora of singularities somehow analogous to the frequency spectrum in time structures. Our approach is applied to a model of quantum computing based on an Akbulut cork in exotic R4, to an hyperbolic model of topological quantum computing based on magic states and to microRNAs in genetics. Such diverse topics reveal the manifold of possibilities of using the concept of a scheme spectrum.