Ambiguity Function Research Articles

خوانشی بر ابهام در شعر معاصر )با تکیه بر قصیده‌ی «رسائل الی الامام الشافعی» اثر عبدالوهاب البیاتی)

Poetry, as one of the branches of art, follows the general rules of art aesthetics, and ambiguity is one of its essential elements, which is considered an attractive factor and an aesthetic criterion in poetry criticism. In this sense, ambiguity is understood as a phenomenon that evokes certain types of reading and understanding in the text. Uncertainty, polysemyand openness of meaning are the characteristics of ambiguous literary text. In this research, after studying the history, concept and function of ambiguity in contemporary Arabic poetry, we have analyzed this phenomenon in the ode "Rasail al-Imam al-Shafi'i" by Abdul Wahab al-Bayati. From the findings of the research, which was carried out in a descriptive analytical way, it was found that artistic ambiguity is a prominent feature of original poetic works, but trying to organize or regularize it is difficult or impossible because multiple readings of an ambiguous text do not necessarily lead to a complete and accurate understanding, but rather Our taste is affected by the fields of our awareness and mental perceptions. We also found that Bayati's poetry has a level of artistic ambiguity that has emerged in the context of new interpretation techniques such as symbolism, mask, myth and intertextuality, and by deepening the poetic experience, it has made his poetic style lasting and effective.

SASA: Super-Resolution and Ambiguity-Free Sparse Array Geometry Optimization With Aperture Size Constraints for MIMO Radar

To improve the performance of multiple-input-multiple-output (MIMO) radar, various sparse arrays have been employed. However, the angular resolution of existing non-uniform arrays optimized by either combinatorial algorithms or heuristic ones is limited by the Rayleigh criterion, which is strictly related to the aperture size. Based on the angular ambiguity function (AAF) analysis, two new models are established in this work for directly optimizing the sidelobe level (SLL) or the main lobe width (MLW) with the constraints of aperture size and element spacing. The aforementioned designs result in non-convex and nonlinear optimization problems, and solutions are derived via the alternating direction multiplier method (ADMM). Furthermore, considering a parametric trade-off between SLL and MLW, a hybrid algorithm is proposed to search for the SLL-MLW Pareto front boundary. Finally, simulations are provided to demonstrate the high angular resolution and ambiguity-free properties of the optimized sparse arrays.

On the ambiguity of job search

AbstractWho knows the underlying productivity distribution function? Interestingly, this ambiguous function is often referenced to make decisions including job creations, wage determinations, contract formulations, etc. To investigate how ambiguity shapes labor markets, we integrate ambiguity preferences into the Diamond‐Mortensen‐Pissarides (DMP) model. We find that ambiguity‐averse job‐ and talent‐hunters are conservative. Our quantitative analysis indicates that but for the ambiguity, the American unemployment rate would have increased in the postwar era. This paper generalizes the DMP model, enhances our understanding of the labor market, and calls for policies concerning labor market information.

Multiple Signal TDOA/FDOA Joint Estimation with Coherent Integration

Passive localization relies significantly on the estimation of the Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) to accurately determine the location of a target. The precision of TDOA and FDOA estimation is affected by signal parameters of time and frequency distribution. In case of multiple signals arising at different frequency bands and intercepted simultaneously by spatially separate sensors covering a wide frequency band, the traditional method is first to separate the signals from the mixed wideband signal through digital down conversion (DDC), which brings multiple narrowband signals, and then the estimation of TDOA and FDOA of each narrowband signal can be performed using cross ambiguity function (CAF). The paper introduces a novel approach for estimating TDOA and FDOA of multiple signals simultaneously, which employs a coherent integration method. First, the cross ambiguity function for each signal is realized with the narrowband signal as the same as the traditional method. Next, the phase relation of each CAF is analyzed, then the joint CAF can be obtained with phase compensation, from which multiple signal TDOA and FDOA estimations will be implemented simultaneously. Numerical simulations are performed to compare the two methods, and the results demonstrate the superiority of the proposed algorithm.

Source Range Estimation with Horizontal Arrays Using Frequency Difference Matched Field Processing in Shallow Water

Due to the modal interference, the ambiguity function of conventional matched field processing (CMFP) exists periodic solutions. To solve this problem, a frequency difference matched field processing (FDMFP) method with horizontal linear array (HLA) for estimating the source range is proposed. The method calculates the ambiguity function of two-neighbor-frequency between the measurement and the replica of each hydrophone, and then computes the high-order cross-spectrum. The range corresponding to the maximum of the ambiguity function is the optimal estimation. Because of the larger modal interference distances, the FDMFP can efficiently provide one-only optimal range instead of some of CMFP.

Inflammatory Cues Direct Skin-Resident Type 1 Innate Lymphoid Cells to Adopt a Psoriasis-Promoting Identity

Innate lymphoid cells (ILCs) are gatekeepers in barrier organs, where they maintain tissue integrity and contribute to host defense as well as tissue repair. Inappropriate activation of ILCs, however, can lead to immunopathology with detrimental results. In this study, we focused on type 1 ILCs (ILC1s), which under inflammatory conditions constitute a poorly defined population with ambiguous functions. To delineate the properties of ILC1s in skin pathology, we used the well-established mouse model of imiquimod-induced psoriasis. Although ILC1s represented a minority among cutaneous lymphocytes in vehicle-treated controls, they rapidly expanded during early psoriasis and ultimately increased by >20-fold. This rapid increase was verified using two additional psoriasis models. Inflammatory ILC1s from imiquimod-treated skin were defined as CD44+, CXCR6+, and CD11b+ and substantially contributed to TNF-α and GM-CSF production, rendering them a potential candidate to shape the inflammatory infiltrate. In accordance with the psoriasis-specific microenvironment, skin ILC1s upregulated the IL-23 receptor whereas expression of the IL-12Rβ2 subunit was diminished. As a consequence, neutralization of IL-12 only had a minor impact, whereas blocking IL-23 reduced both ILC1 abundance and disease severity. Together, our findings identify skin ILC1s as a likely player in early psoriasis and a prospective target for therapeutic approaches.

On designing good doppler tolerance waveform with low PSL of ambiguity function

Although the thumbtack-type ambiguity functions (AF) of the existing phase-coded signals are of good range-Doppler resolutions, they are not suitable for detection of high-speed targets due to their poor Doppler tolerance. In this paper, we address the design of waveform with good Doppler tolerance, which is expected to be of both diagonal-ridge-type AF and also local low peak sidelobe level (PSL). First we formulate the design model of Doppler tolerant waveform via minimizing the ratio of PSL to the minimum mainlobe level. Then, we solve the resulting fractional programming problem via quartic polynomial transfer and high-order polynomial optimization. Numerical results demonstrate the excellent Doppler tolerance and low PSL performance of our design.

Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach.

Compressive sensing (CS) of the signal ambiguity function (AF) and enforcing the sparsity constraint on the resulting signal time-frequency distribution (TFD) has been shown to be an efficient method for time-frequency signal processing. This paper proposes a method for adaptive CS-AF area selection, which extracts the magnitude-significant AF samples through a clustering approach using the density-based spatial clustering algorithm. Moreover, an appropriate criterion for the performance of the method is formalized, i.e., component concentration and preservation, as well as interference suppression, are measured utilizing the information obtained from the short-term and the narrow-band Rényi entropies, while component connectivity is evaluated using the number of regions with continuously-connected samples. The CS-AF area selection and reconstruction algorithm parameters are optimized using an automatic multi-objective meta-heuristic optimization method, minimizing the here-proposed combination of measures as objective functions. Consistent improvement in CS-AF area selection and TFD reconstruction performance has been achieved without requiring a priori knowledge of the input signal for multiple reconstruction algorithms. This was demonstrated for both noisy synthetic and real-life signals.

On a Recent Conjecture by Z. Van Herstraeten and N. J. Cerf for the Quantum Wigner Entropy

We address a recent conjecture stated by Z. Van Herstraeten and N. J. Cerf. They claim that the Shannon entropy for positive Wigner functions is bounded below by a positive constant, which can be attained only by Gaussian pure states. We introduce an alternative definition of entropy for all absolutely integrable Wigner functions, which is the Shannon entropy for positive Wigner functions. Moreover, we are able to prove, in arbitrary dimension, that this entropy is indeed bounded below by a positive constant, which is not very distant from the constant suggested by Van Herstraeten and Cerf. We also prove an analogous result for another conjecture stated by the same authors for the Rényi entropy of positive Wigner functions. As a by-product we prove a new inequality for the radar-ambiguity function (and for the Wigner distribution) which is reminiscent of Lieb’s inequalities.

Unimodular Perfect and Nearly Perfect Sequences: a Variation of Björck’s Scheme

Constant Amplitude (CA), Zero Auto Correlation (ZAC) sequences (or CAZAC sequences, aka perfect sequences) have numerous applications. We generalize the CAZAC notion to what we term as CASAC by permitting small autocorrelations (SAC). We extend Björck’s classification result of two-valued CAZAC sequences by providing a complete classification of all almost 2-valued (i.e., two-valued except for the first position which uses a third value) CASAC sequences. While Björck’s original work dealt only with primes p, we extend his ideas to any abelian group of order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v\equiv 1\pmod {4}$ </tex-math></inline-formula> , as opposed to restricting just to the prime fields GF(p). Björck sequences have better ambiguity function than Zadoff-Chu sequences, making them suitable for radar and communications applications in the presence of high Doppler shifts. In fact, the discrete narrow band ambiguity function has an optimal bound in case of Björck sequences (as opposed to Gauss sequences). A one-parameter infinite family of CASAC we construct would have applications in Multiple-Input Multiple-Output (MIMO) areas. Toward MIMO applications, we introduce a performance measure we term as cross merit factor to study cross correlation behavior, generalizing the well-known notion of Golay Merit Factor (GMF).

K-Ambiguity function in the framework of offset linear canonical transform

A new version of ambiguity function (AF) associated with the offset linear canonical transform (OLCT) is considered in this paper. This new version of AF coined as the [Formula: see text]-AF associated with the OLCT ([Formula: see text]-AFOL) is defined based on the OLCT and the fractional instantaneous auto-correlation. A natural magnification effect characterized by the extra degrees of freedom of the OLCT and by a factor [Formula: see text] on the frequency axis enables the [Formula: see text]-AFOL to have flexibility to be used in cross-term reduction. Firstly, we defined the [Formula: see text]-AF associated with the OLCT ([Formula: see text]-AFOL), and establish its relationship with the [Formula: see text]-Wigner distribution in OLCT domain. Later on, we define the basic properties including the scaling, conjugate-symmetry, shifting, marginal and Moyal’s formulae of [Formula: see text]-AFOL in depth. The results show that [Formula: see text]-AFOL can be viewed as one of the generalizations of the classical AF which has elegance, simplicity and flexibility in the frequency marginal property. The novelty of our paper lies in applications part, where we have shown how the proposed transform is used for the detection of single-component and bi-component linear frequency-modulated (LFM) signals.

Radar Emitter Identification with Multi-View Adaptive Fusion Network (MAFN)

Radar emitter identification (REI) aims to extract the fingerprint of an emitter and determine the individual to which it belongs. Although many methods have used deep neural networks (DNNs) for an end-to-end REI, most of them only focus on a single view of signals, such as spectrogram, bi-spectrum, signal waveforms, and so on. When the electromagnetic environment varies, the performance of DNN will be significantly degraded. In this paper, a multi-view adaptive fusion network (MAFN) is proposed by simultaneously exploring the signal waveform and ambiguity function (AF). First, the original waveform and ambiguity function of the radar signals are used separately for feature extraction. Then, a multi-scale feature-level fusion module is constructed for the fusion of multi-view features from waveforms and AF, via the Atrous Spatial Pyramid Pooling (ASPP) structure. Next, the class probability is modeled as Dirichlet distribution to perform adaptive decision-level fusion via evidence theory. Extensive experiments are conducted on two datasets, and the results show that the proposed MAFN can achieve accurate classification of radar emitters and is more robust than its counterparts.

Joint Radar Communication With Novel GCC Preamble and Point-Wise Minimum Fusion

In this paper, a new preamble waveform design and a corresponding minimum-point processing algorithm at receiver are proposed to perform radar sensing of communication signal echoes. In our design, we consider the millimeter wave (mmWave) 802.11ad/802.11ay communication frame format as a reference. Our proposed new preamble replaces the second pair of single-carrier (SC) Golay complementary code (GCC) used in the preamble of IEEE 802.11ad/802.11ay by orthogonal frequency-division multiplexing (OFDM) GCC waveform, resulting in a SC-OFDM GCC preamble waveform. At the receiver, a point-wise minimum operation is performed on the separate radar ambiguity functions (AF) of the SC-GCC and OFDM-GCC to improve the overall quality of the preamble AF. Furthermore, point-wise minimum processing is applied to fuse the communication data AF and the preamble AF, so as to further enhance the radar sensing quality of the entire communication frame. In addition, theoretical analysis of the performance gain of such radar AF fusion is provided. Both theoretical analysis and simulation results demonstrate that the newly proposed SC-OFDM GCC preamble design and the minimum-point fusion of data AF and preamble AF provide a significant performance improvement for automotive radar sensing. Specifically, the proposed SC-OFDM GCC preamble with minimum-point processing algorithm improves the peak-to-max-sidelobe ratio (PMSR) of radar AF over the conventional 802.11ad/802.11ay preamble. Moreover, the proposed preamble-data AF fusion technique further enhances the PMSR as well as the Doppler resolution over the existing joint radar communication systems (which process either the preamble echo or the data echo only). Finally, the proposed minimum-point processing and data AF fusion are extended from single-frame to multi-frame processing to further improve the Doppler resolution.

Method and Experiment Study of the Array Shape Calibration for Underwater Vertical Line Arrays

Vertical line arrays have been widely used in underwater target location and noise measurement, and the attitude and shape of the vertical array have a significant influence on detection accuracy. The paper presents an array shape calibration method for underwater vertical acoustic array-based measurement systems. Firstly, a standard acoustic source was used to transmit underwater acoustic signals at a certain distance from the acoustic array, and the target location based on the received data of the acoustic array was estimated concerning distance and depth to obtain the two-dimensional ambiguity function of the depth and distance of the array. Then, the ambiguity function was used to calculate the center depth and the inclination angle of the vertical acoustic array and to further obtain the shape and location information of the acoustic array. Finally, a lake experiment study was carried out. The results showed that the calibrated array shape was in good agreement with the actual location, and the sound source location estimated by using the calibrated array shape was consistent with the actual sound source location, thereby effectively reducing the impact on the measurement accuracy due to the inclination of the measurement system under the influence of water current.

Hybrid Index Modulation for Dual-Functional Radar Communications Systems

Dual-functional radar communications (DFRC) systems, which jointly design radar and communication functionalities on one common platform, have been recognized as an attractive approach for leveraging the scarce spectral efficiently in diverse applications, such as autonomous driving. Index modulation (IM), a promising communication technique with high energy utilization and spectral efficiency, has been successfully integrated into DFRC systems. Most existing IM-based DFRC schemes utilize the degrees of freedom (DoFs) from one or two domains for separate information embedding, without utilizing the full diversity of the integrated waveform. In this paper, we propose a novel DFRC scheme, referred to as hybrid index modulation (HIM), operating on frequency hopping multiple-input multiple-output (FH-MIMO) radar. The HIM embeds communication information into the index of the 3-tuples in the hybrid dictionary, which is the outer product of DoFs from the frequency, phase, and antenna index of the integrated waveform. Since the HIM entwines waveform diversities from different domains, it increases the communication rate by multiples. We also apply the HIM to the non-orthogonal FH-MIMO radar to improve spectral efficiency, in addition to the conventional orthogonal case. Furthermore, we propose a constrained HIM scheme to enhance the radar detection performance, where the FH pattern has been optimized in terms of ambiguity function. System performance is evaluated both theoretically and experimentally in metrics of communication rate, ambiguity function and symbol error rate.

Chaotic waveform for optimal joint radar communication systems

Due to the increased spectrum congestion, there has been a tremendous interest in radar and communication devices operating on a shared platform. Therefore, the waveform design for joint radar communication (JRC) systems has recently gained significant attention. Despite several efforts, most state-of-the-art waveforms are far from achieving ideal radar and communication performance. To address this issue, we design a family of chaotic maps whose output yields waveforms with optimal JRC features. We adopt the antipodal chaos shift keying (ACSK) scheme, where the segments of the chaotic signal are multiplied by ±1 to encode the binary information. This ACSK waveform is further used for the JRC system transmission. Once the synchronization between the receiver and transmitter is established, the information is recovered by cross-correlating the received and the synchronized waveforms. This technique yields bit error rates (BER) similar to the theoretical values of the BPSK waveform. The same transmitted waveform results in a thumb-tack ambiguity function which is essential for high-resolution radar imaging. The variance analysis of the ambiguity function shows that the proposed waveform is comparable to the noise-modulated waveforms. We also show that the multiplexed ACSK waveforms yield similar optimal behavior. Furthermore, we demonstrate the potential of the family of chaotic maps for multiple-input, multiple-output (MIMO) JRC systems.

Reference Signal Design and Power Optimization for Energy-Efficient 5G V2X Integrated Sensing and Communications

Recently, the development of integrated sensing and communications (ISAC) has attracted great attention. In this paper, the reference signal (RS) in 5G vehicle-to-everything (V2X) communications is leveraged for radar sensing. Particularly, the scattered RS design is adopted under the constraints of communication channel estimation and radar sensing ambiguity functions. To quantify the influence of RS design on channel estimation, the minimum mean square error (MMSE) via two-dimensional Wiener filter is derived in a closed-form for doubly-selective communication channel. The RS spacing and power allocation for RS and each data subcarrier are further jointly optimized to minimize the transmission power, while satisfying the requirements of communication data rate and radar sensing signal to noise ratio (SNR), which results in a mixed-integer non-linear programming (MINLP) problem. To address this problem, the successive convex approximation (SCA) based method is first developed to achieve a suboptimal solution. Next, we show that if the RS spacing is given, the optimal power allocation among RS and each data subcarrier can be achieved by solving three subproblems sequentially via (reverse) water-filling algorithms. Extensive simulations are carried out to validate the effectiveness of our proposed design. Compared to the existing subcarrier-wise RS design with uniform power allocation for data subcarriers, our design can reduce the power consumption by up to 12 dB.

Recombinant Probiotic Preparations: Current State, Development and Application Prospects

The article is devoted to the latest achievements in the field of research, development, and implementation of various types of medicinal products based on recombinant probiotics. The benefits of probiotics, their modern use in medicine along with the most frequently used genera and species of probiotic microorganisms were highlighted. The medicinal and therapeutic activities of the studied probiotics were indicated. The review suggests various methods of creating recombinant probiotic microorganisms, including standard genetic engineering methods, as well as systems biology approaches and new methods of using the CRISPR-Cas system. The range of potential therapeutic applications of drugs based on recombinant probiotics was proposed. Special attention was paid to modern research on the creation of new, more effective recombinant probiotics that can be used for various therapeutic purposes. Considering the vast diversity of therapeutic applications of recombinant probiotics and ambiguous functions, their use for the potential treatment of various common human diseases (non-infectious and infectious diseases of the gastrointestinal tract, metabolic disorders, and allergic conditions) was investigated. The prospects for creating different types of vaccines based on recombinant probiotics together with the prospects for their implementation into medicine were considered. The possibilities of using recombinant probiotics in veterinary medicine, particularly for the prevention of domestic animal diseases, were reviewed. The prospects for the implementation of recombinant probiotics as vaccines and diagnostic tools for testing certain diseases as well as modeling the work of the human digestive system were highlighted. The risks of creation, application, including the issues related to the regulatory sphere regarding the use of new recombinant microorganisms, which can potentially enter the environment and cause unforeseen circumstances, were outlined.

Beyond Numbers: An Ambiguity–Accessibility–Applicability Framework to Explain the Attraction Effect

Abstract The attraction effect (AE) occurs when the addition of an inferior alternative (i.e., a decoy) to a choice set increases the choice share of the alternative to which it is most similar (i.e., a target), a phenomenon that violates the regularity principle. The AE occurs reliably when the attribute values are represented numerically, but not when the stimuli are perceptual. Such conceptual replication failures indicate a lack of clarity about the mechanisms that produce the AE. The present research develops a framework—the 3A framework—that specifies the distinct functions of ambiguity, accessibility, and applicability in the choice process. These factors, and their attendant mechanisms, explain when and why the AE emerges. They also specify conditions under which the AE is attenuated. Seven main experiments and four supplementary experiments examine when and why the AE emerges with perceptual stimuli, provide support for the 3A framework, and offer insights about how to produce the AE in choice contexts involving perceptual stimuli.

MIMO radar beampattern design by using Phased-Costas waveforms with PAR constraints employing a generalized ambiguity function

Multiple-input multiple-output (MIMO) radars have attracted interest due to some important advantages over Phased-array radars. The diversity that comes with multiple waveforms is used to maximize the power in the vicinity of targets and minimize the cross correlation of waveforms reflected from multiple targets for predefined angles. In this study, a novel approach is presented to overcome the limitations of current beamformer design techniques in terms of bandwidth, auto/cross (spatial) correlations, range-doppler resolutions, peak-to-average ratio (PAR) and mean squared error (MSE) for desired beamforming pattern. A generalized ambiguity function formulation is presented which employs all the beamformer design parameters in a mathematically tractable simple manner suitable for constrained optimization. We present Phased-Costas coded waveforms to generate desired beampatterns while minimizing range-doppler sidelobes and target cross-correlations with limited bandwidth. Beamformer design problem is casted as a constrained optimization which includes nonconvex PAR constraints. The resulting NP hard problem with nonconvex objective function is converted to a bi-convex form and the PAR constraints are converted as convex constraints. The final problem is efficiently solved by using Alternating Direction Method of Multipliers (ADMM) technique in polynomial time. The beamformer solution is shown to achieve PAR=1 with very little degradation compared to the unconstrained problem making the ultimate choice for MIMO beamforming applications. Several good features of the designed beamformer are shown through simulations and compared to the known methods.