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Object Detection and Identification in Any Medium
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Object Detection and Identification in Any Medium

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 8305

Special Issue Editor

Prof. Dr. Jonghoek Kim

E-Mail Website
Guest Editor
System Engineering Department, Sejong University, Seoul 05006, Republic of Korea
Interests: robotics; target tracking; multi-agent robotics; optimal estimation; path planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This proposal is on object detection and identification in any medium. The goal is to detect objects of any material (ferrous, non-ferrous, polymeric, organic, biological, etc.) Objects may be moving or stationary, wholly immersed within any medium or at the interface between two media (e.g., on a surface). Detection resolution and signal to noise ratio are sufficient to classify and identify the object; identification will occur at near real-time. Power demand and size of detection and identification components meet mobile host platform constraints and availability. Detection and identification components may be active and/or passive and incorporate multimodal, distributed, and cross-domain approaches. It is desirable that detection and identification components are low-cost.

This Special Issue includes the following:

  1. detection methods to analyze image or sound.
  2. setting up the detection system.

Dr. Jonghoek Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (3 papers)

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Research

17 pages, 5353 KiB  
Article
Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning
by Myoungin Shin, Wooyoung Hong, Keunhwa Lee and Youngmin Choo
Sensors 2021, 21(17), 5827; https://doi.org/10.3390/s21175827 - 30 Aug 2021
Cited by 5 | Viewed by 2399
Abstract
Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning [...] Read more.
Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning (SBL) for the frequency analysis after the corresponding linear system is established. Many algorithms, such as fast Fourier transform (FFT), estimate signal parameters via rotational invariance techniques (ESPRIT), and multiple signal classification (RMUSIC) has been proposed for frequency detection. However, these algorithms have limitations of low estimation resolution by insufficient signal length (FFT), required knowledge of the signal frequency component number, and performance degradation at low signal to noise ratio (ESPRIT and RMUSIC). The SBL, which reconstructs a sparse solution from the linear system using the Bayesian framework, has an advantage in frequency detection owing to high resolution from the solution sparsity. Furthermore, in order to improve the robustness of the SBL-based frequency analysis, we exploit multiple measurements over time and space domains that share common frequency components. We compare the estimation results from FFT, ESPRIT, RMUSIC, and SBL using synthetic data, which displays the superior performance of the SBL that has lower estimation errors with a higher recovery ratio. We also apply the SBL to the in-situ data with other schemes and the frequency components from the SBL are revealed as the most effective. In particular, the SBL estimation is remarkably enhanced by the multiple measurements from both space and time domains owing to remaining consistent signal frequency components while diminishing random noise frequency components. Full article
(This article belongs to the Special Issue Object Detection and Identification in Any Medium)
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20 pages, 2398 KiB  
Article
An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix
by Ji Woong Paik, Joon-Ho Lee and Wooyoung Hong
Sensors 2021, 21(13), 4403; https://doi.org/10.3390/s21134403 - 27 Jun 2021
Cited by 2 | Viewed by 2026
Abstract
An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that [...] Read more.
An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data. Full article
(This article belongs to the Special Issue Object Detection and Identification in Any Medium)
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14 pages, 6396 KiB  
Article
Fourier-Transform-Based Surface Measurement and Reconstruction of Human Face Using the Projection of Monochromatic Structured Light
by Bingquan Chen, Hongsheng Li, Jun Yue and Peng Shi
Sensors 2021, 21(7), 2529; https://doi.org/10.3390/s21072529 - 4 Apr 2021
Cited by 5 | Viewed by 2807
Abstract
This work presents a new approach of surface measurement of human face via the combination of the projection of monochromatic structured light, the optical filtering technique, the polarization technique and the Fourier-transform-based image-processing algorithm. The theoretical analyses and experimental results carried out in [...] Read more.
This work presents a new approach of surface measurement of human face via the combination of the projection of monochromatic structured light, the optical filtering technique, the polarization technique and the Fourier-transform-based image-processing algorithm. The theoretical analyses and experimental results carried out in this study showed that the monochromatic feature of projected fringe pattern generated using our designed laser-beam-based optical system ensures the use of optical filtering technique for removing the effect of background illumination; the linearly-polarized characteristic makes it possible to employ a polarizer for eliminating the noised signal contributed by multiply-scattered photons; and the high-contrast sinusoidal fringes of the projected structured light provide the condition for accurate reconstruction using one-shot measurement based on Fourier transform profilometry. The proposed method with the portable and stable optical setup may have potential applications of indoor medical scan of human face and outdoor facial recognition without strict requirements of a dark environment and a stable object being observed. Full article
(This article belongs to the Special Issue Object Detection and Identification in Any Medium)
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