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REVIEW 4 major objections 5 minor 165 references

Localization using Optical Camera Communication and Photogrammetry for Wireless Networking Applications

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A camera can localize a phone indoors to about 10 cm using only ceiling LED lights and their projected image sizes.

desk verdict A competent thesis compilation of already-published OCC/photogrammetry schemes; the 10 cm accuracy claim is unproven because the distance formula ignores off-axis foreshortening. read the letter →

arxiv 1908.08892 v1 pith:LUBZUUEO submitted 2019-08-23 eess.SP

Md Tanvir Hossan This is my paper · ORCID
classification eess.SP
keywords opticalcameracommunicationphotogrammetryindoorlocalizationsmartphonevehicleimagesensortrilaterationKalmanfilter
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This thesis proposes using light that is already in the environment as a positioning infrastructure. A smartphone camera reads the identity of ceiling LED lamps from modulated light, measures how far away each lamp is from how large it appears on the image sensor, and combines three or more such distances to fix its own position; simulation results put that position estimate at about 10 cm. The same combination is applied on the road, where a host vehicle's camera reads street-light IDs and the taillight IDs of vehicles ahead and uses projected image areas to estimate distance. The point of the scheme is that it needs almost nothing new: ordinary LED lighting, a camera, and a server that knows the LED coordinates. If the claims hold, indoor navigation and vehicle proximity awareness could be delivered with little modification of infrastructure.

What carries the argument

The load-bearing identity is the photogrammetric distance formula $d = f\sqrt{A_{\mathrm{LED}}}/(\rho\sqrt{\eta_i})$ (Equation 18), which converts the measured pixel area $\eta_i$ of an LED's image on the sensor into a distance, using the real LED area $A_{\mathrm{LED}}$, focal length $f$, and pixel size $\rho$. The formula follows from the pinhole lens-magnification relation $M = f/d$, and it assumes the LED plane is perpendicular to the optical axis. These distances feed a standard trilateration solve to produce coordinates, and a Kalman filter recursively predicts the next position to absorb motion error. For vehicles, the same projected-area distance measurement is applied to street lights and taillights, with S2-PSK modulation carrying LED-IDs and marking the regions of interest.

What would settle it

Mount a camera at a surveyed position beneath three or more ceiling LEDs, read each LED-ID, measure each projected area on the image sensor, apply $d = f\sqrt{A_{\mathrm{LED}}}/(\rho\sqrt{\eta_i})$ to compute the three distances, and compare the trilaterated position with the surveyed position while moving the camera toward the edge of the field of view; if the error grows systematically with the offset angle and exceeds 10 cm, the central accuracy claim fails.

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Extended reading notes

Core claim

The thesis claims that a single camera can act as both a communication receiver and a rangefinder so that the device carrying the camera can locate itself. Indoors, each ceiling LED broadcasts its own coordinates as a modulated LED-ID; the camera decodes the IDs, measures the pixel area each LED occupies on the image sensor, and converts that area into a distance with the photogrammetric relation. Three or more such distances plus the known LED coordinates give the camera position by trilateration. Outdoors, the same principle measures the host vehicle's distance to street lights and to the taillights of forwarding vehicles, giving relative positions while vehicles move; a Kalman filter smooths the indoor estimate over time. Simulations reported in the thesis give about 10 cm resolution for the indoor case and roughly 90 percent vehicle positioning accuracy when four street lights about 40 m apart are in view.

Load-bearing premise

The load-bearing premise is that the measured pixel area of an LED acts as a faithful distance cue, treating every LED as if it faced the camera head-on; LEDs seen near the edge of the field of view are actually viewed at an angle, and that perspective shortening is not corrected in the localization calculation.

Editorial extensions

If this is right

  • If the claimed accuracy holds, indoor location-based services can run on the existing ceiling LED infrastructure plus the phone's own camera, without new radio beacons.
  • Because each LED has a unique ID, the localization is inherently self-identifying: the phone knows which lamp it is seeing, so the measurement can be tied to a coordinate server.
  • The same camera link that serves localization also carries data, so a vehicle can receive an ID from a taillight and estimate its distance to that vehicle at the same time.
  • Kalman-filter tracking turns the static distance measurements into a smooth position estimate, so a moving phone or vehicle can be followed between frames.
  • The simulation results suggest that for vehicles, the practical limit of the scheme appears when street lights are spaced near 40 m and when at least four lights remain in view; beyond that spacing the positioning accuracy falls.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct extension would be to replace the perpendicular-plane distance formula with a perspective-aware version that uses the decoded LED coordinates to estimate each LED's viewing angle, then inverts the oblique projection before computing distance; if the systematic bias shrinks, the 10 cm claim would hold across a wider field of view.
  • The outdoor scheme points toward cooperative positioning: if each vehicle broadcasts its own ID, a chain of vehicles could propagate position estimates without needing every vehicle to see a street light directly.
  • A reverse use is plausible: fixed roadside cameras that observe a vehicle's taillights could apply the same projected-area relation to localize the vehicle from infrastructure, which would be useful in intersections and tunnels.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The manuscript, an M.S. thesis posted on arXiv, proposes localization schemes for indoor smartphones and outdoor vehicles by combining optical camera communication (OCC) and photogrammetry. Indoor positioning uses LED-ID reception to identify ceiling lights and measures the camera-to-LED distances from the projected image area on the image sensor (Eq. 18), followed by trilateration and Kalman filtering. Vehicle positioning uses rear lights of forwarding vehicles and street lamps as optical transmitters, with photogrammetric distance estimation and a set of asserted coordinate relations (Eqs. 48–53). The central claims are a 10 cm level indoor localization resolution and a vehicle positioning scheme that achieves roughly 90% accuracy under certain street-light spacings, supported by simulation plots and a short-range benchtop distance experiment.

Significance. If the claims hold, the work offers a inexpensive way to reuse LED lighting infrastructure and smartphone/vehicle cameras for simultaneous communication and localization, which is relevant for IoT, LBS, and vehicular safety. The derivation of Eq. (18) from the lens equation is a transparent, parameter-free photogrammetric distance formula, and the simulated BER/SNIR comparisons in Figs. 29 and 34 provide a useful baseline. However, the significance is limited by the absence of any off-axis error analysis, the lack of a derivation for the vehicle localization equations, and the thin experimental validation, so the central accuracy claims are not yet established.

major comments (4)
  1. [§3.3.1, Eq. (18); §6.5.1] The distance formula d = f sqrt(A_LED) / (rho sqrt(eta_i)) assumes the LED plane is perpendicular to the optical axis (magnification M = f/d). In the indoor trilateration geometry of §4.2, LEDs at the edge of the camera FOV are viewed obliquely, so the projected area is foreshortened by approximately cos(theta) and Eq. (18) returns a biased distance. Section 6.5.1 explicitly acknowledges 'perspective distortion caused due to the relative position between the object and the camera' and mentions a back-propagation correction, but no such correction is incorporated into the localization algorithm of §4.3.1 or into the simulation results of Figs. 30–33. This uncorrected bias propagates into every distance measurement and directly into the claimed 10 cm resolution, so the central accuracy claim is not supported without either a corrected model or a quantitative error analysis for off-axis LEDs.
  2. [§5.3.1, Eqs. (48)–(53)] The vehicle localization equations are asserted without derivation. Eq. (48) states that the total position shift of the host vehicle is a function of h_j, n_IS_SL, V_HV, c_j, d_SL-SL, and delta-t, but the manuscript never derives how these quantities combine, how pixel area and velocity are weighted, or what coordinate frame the result is expressed in. Similarly, Eqs. (50)–(53) relate angular position, pixel area, and flat displacement without a geometric or kinematic derivation. Because these equations are load-bearing for the claimed vehicle positioning accuracy (Figs. 38–39), the manuscript needs a step-by-step derivation or a clear citation to a published derivation.
  3. [§6.1, Figs. 30–33] The simulation results that support the 10 cm indoor localization claim are presented as plots without error bars, without a description of the noise model, number of Monte Carlo runs, or parameter distributions, and without a quantitative metric such as RMSE or a CDF. The text says 'within 9 to 10 cm, the position of the smartphone is estimated' (near Fig. 30), but the figures do not show the error distribution or the dependence on LED geometry. Without this methodology, the central accuracy claim is not reproducible or verifiable.
  4. [§6.3, Figs. 40–41] The experimental validation measures only a single, centered LED at distances of roughly 0.6 m and 2.5 m (and up to ~0.65 m in Fig. 41). It does not test off-axis LEDs, multi-LED trilateration, or the smartphone-localization scenario of §4, so it cannot validate the 10 cm indoor localization claim. The experiment also reports error within 1% for distance measurement, which would be useful if it were extended to the actual off-axis geometry used in the proposed scheme.
minor comments (5)
  1. [Abstract and Chapter 1] There are typos such as 'e-commence sector' and 'a new era has written' in the abstract; the manuscript would benefit from a careful language edit.
  2. [§3.2.3, Eq. (7)] Several equations contain garbled or missing symbols, particularly Eq. (7) and the notation list (e.g., the Lambertian index and cosine terms), which makes them difficult to verify. The authors should ensure all symbols are rendered correctly.
  3. [§4.3.1, Eqs. (27)–(34)] The transition from Eq. (26) to the matrix form in Eq. (27) is not fully explained, and the notation for the homogeneous solution in Eqs. (29)–(32) is confusing; a brief explanation of how the particular solution and the null space are obtained would improve clarity.
  4. [§5.3.1, Eq. (44)–(47)] The derivation of c and h in the vehicle scenario is compressed; labeling the geometric variables in Fig. 23 consistently with the equations would help the reader follow the Pythagorean steps.
  5. [§6.2, Fig. 39] The text states that the maximum localization accuracy is approximately 90% when the distance between street lights is 40 m, but the y-axis label reads 'Vehicle positioning accuracy (%)' and the curve appears to vary with the number of street lights in FOV; please clarify the definition of accuracy and report the error bars or confidence intervals.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Eq. (18) is derived from thin-lens geometry, not from the localization result, and the self-citations are secondary pointers.

full rationale

The paper's central distance relation, Eq. (18), is obtained from the lens-magnification relation M = f/d (Eq. 15) and the definition of projected pixel area, giving d = f*sqrt(A_LED)/(rho*sqrt(eta_i)). This is a standard photogrammetric mapping and does not assume the indoor localization output; it depends only on known LED area, camera focal length, pixel size, and measured image area. The subsequent trilateration (Eqs. 26-34) solves for the camera position from these independently obtained distances, and the claimed '10 cm level localization resolution' (Section 8.1) is a simulation outcome rather than a parameter fitted to reproduce itself. The experimental distance tests in Section 6.3 compare computed distances with known physical distances, providing an external check. The author's own prior publications are cited for the overall schemes ([102], [106], [107]) and for S2-PSK ([110]), but none of these citations supplies a uniqueness theorem or a load-bearing premise that would make the derivation circular; the distance formula and trilateration are re-derived in the thesis. The acknowledged perspective distortion (Section 6.5.1) is an uncorrected accuracy limitation, not a circular reduction. Therefore no circular step can be exhibited.

Assumptions & free parameters 0 free parameters · 8 assumptions · 0 invented entities

The paper introduces no fitted free parameters: the constant tau in Eq. 19 is a hardware constant (focal length, LED area, pixel size), not fitted to the target result. The axioms listed are the standard assumptions of visible light positioning and the specific scenario constraints the schemes require. No new physical entities are postulated; FV-ID, SL-ID, and LED-ID are protocol identifiers, not entities.

assumptions (8)
  • standard math Pinhole camera model and thin-lens equation with the approximation d >> f, so d - f approximately equals d (Eqs. 14-15).
    The distance formula in Eq. 18 relies on this approximation; valid for the claimed operating ranges.
  • domain assumption Lambertian radiation pattern for LED light sources (Eqs. 1 and 7).
    Used for optical channel gain and received power; standard in VLC literature.
  • domain assumption At least three LEDs with known coordinates remain within the camera FOV at all times for indoor localization.
    Trilateration requires three or more reference points; the scheme fails otherwise (Section 4.2.2).
  • domain assumption LED coordinates are known a priori and transmitted error-free as LED-IDs via OCC.
    The lighting server stores coordinates and the camera decodes them; no error model for ID reception is included in the localization error.
  • domain assumption The projected LED image occupies at least one pixel on the image sensor (eta_i >= 1) for distance measurement to be possible.
    Equation 18 diverges as eta_i approaches zero; the thesis notes localization possibility drops to zero below 1 pixel (Section 6.1).
  • domain assumption For vehicle localization, street light positions and IDs are fixed and known, and the HV camera can simultaneously receive SL-IDs and FV-IDs.
    Virtual coordinates of the HV are computed relative to street lights (Section 5.3.1); simultaneous reception is assumed throughout.
  • domain assumption S2-PSK modulation and Manchester coding from ref [110] provide reliable FV-ID/SL-ID transmission.
    The vehicle scheme depends on this modulation scheme but does not derive its error performance; it cites prior work.
  • domain assumption Simulations assume ideal indoor and road conditions: no ambient light noise, no weather effects, no multipath.
    Section 6.2 explicitly ignores fog, snow, rain, and road turbulence; Section 6.1 ignores ambient light.

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Pith. "Pith review of Localization using Optical Camera Communication and Photogrammetry for Wireless Networking Applications." pith.science (2026). https://pith.science/paper/LUBZUUEO

@misc{pith2026190808892,
  author       = {Pith},
  title        = {Pith review of: Localization using Optical Camera Communication and Photogrammetry for Wireless Networking Applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LUBZUUEO}},
  note         = {Machine review of arXiv:1908.08892}
}
read the original abstract

Localization defines a term to describe the identifying process of a location within the space of two-dimensional (2D) space or three-dimensional (3D). A localization scheme is an important concern for connecting sensor nodes in remote locations. The demand of localization in wireless networking is increased due to the availability of mobile devices as well as scope for billion-dollar market in e-commence sector. Moreover, a new era has written with internet-of-things, which boost this demand 100 times than ever before. Importance of localization applications is considered in both indoor and outdoor environments. Due to several advantages, LED and camera based positioning is more demanding over radio frequency based localization. Using the existing light-emitting diodes (LEDs) based illumination infrastructure it is possible to compute the coordinates of the camera, whereas cameras are embedded/installed in mobile objects, such as smartphone, vehicle. Optical camera communication (OCC) and photogrammetry are two important technologies to measure the position of these mobile objects. These technologies based localization scheme for both smartphone and vehicle should be cost effective and can be deployed with little modification of the existing infrastructures. I proposed two different schemes (i.e., indoor and outdoor environments) to localize these objects with OCC and photogrammetry techniques.

Figures

Figures reproduced from arXiv: 1908.08892 by the authors.

Figure 2
Figure 2. Geometric model of transmission and reception. 12 [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

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    Design of an indoor positioning scheme using artificial intelligence algorithms,

    A. Islam, M. T. Hossan, M. Z. Chowdhury, and Y. M. Jang, “Design of an indoor positioning scheme using artificial intelligence algorithms,” in Proc. of 32th International Confer ence on Information Networking (ICOIN) , Chiang Mai , Thailand, Apr. 2018, pp. 953-956

    2018

  129. [137]

    Integration of photogrammetry and optical camera communication technologies for vehicle positioning,

    M. T. Hossan, M. Z. Chowdhury, and Y. M. Jang, “Integration of photogrammetry and optical camera communication technologies for vehicle positioning,” in Proc. of International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, South Korea, ...

    2017

  130. [138]

    An indoor localization scheme based on integrated artificial neural fuzzy logic,

    A. Islam, M. T . Hossan, M. Z. Chowdhury, and Y. M. Jang, “An indoor localization scheme based on integrated artificial neural fuzzy logic,” in Proc. of 14th IEEE Vehicular Technology Society Asia Pacific Wireless Communications Symposium (APWCS), Incheon, South Korea, Aug. 2017

    2017

  131. [139]

    Simplified photogrammetry using optical cam era communication for indoor positioning,

    M. T . Hossan, M. Z. Chowdhury, A. Islam, and Y. M. Jang, “Simplified photogrammetry using optical cam era communication for indoor positioning,” in Proc. of 9th International Conference on Ubiquitous and Future Networks (ICUFN), Milan, Italy, Jul. 2017, pp. 126–130

    2017

  132. [140]

    Introduction of optical camera communication for I nternet of vehicles (IoV),

    A. Islam, M. T . Hossan, and Y. M. Jang, “Introduction of optical camera communication for I nternet of vehicles (IoV),” in Proc. of 9th International Conference on Ubiquitous and Future Networks (ICUFN), Milan, Italy, Jul. 2017, pp. 122–125

    2017

  133. [141]

    Intelligent RF and/or optical frequency spec trum access within dense vehicular network for internet of vehicle,

    M. T. Hossan, M. Z. Chowdhury, M. Shahjalal, and Y. M. Jang, “Intelligent RF and/or optical frequency spec trum access within dense vehicular network for internet of vehicle,” in Proc. of General Conference of Korea Information and Communications Society (KICS), Jeju Island, S...

    2018

  134. [142]

    Future research trends of ultra-dense networks beyond 5G,

    M. Z. Chowdhury, M. Shahjalal, M. K. Hassan, M. T. Hossan, and Y. Min Jang, “Future research trends of ultra-dense networks beyond 5G,” in Proc. of 28th Joint Conference on Telecommunications Information (JCCI), Yeosu, South Korea, May 2018

    2018

  135. [143]

    A solution to indoor positioning and navigation system using optical camera communication,

    M. Shahjalal, M. K. Hassan, M. T. Hossan, V. T. Nguyen, M. D. Thieu, N. T. Le, and Y. M. Jang, “A solution to indoor positioning and navigation system using optical camera communication,” in Proc. of General Conference of Korea Information and Communications Society (KICS), Go...

    2018

  136. [144]

    Optical camera communication and color code based digital signage service implementation,

    A. Islam, M. T . Hossan, E. Shin, C. S. Kim, and Y. M. Jang, “Optical camera communication and color code based digital signage service implementation,” in 79 Proc. of General Conference of Korea Information and Communications Society (KICS), Jeju Island, South Korea, Jul. 2017

    2017

  137. [145]

    IEEE 802.15.7m standardization: current status and application

    M. T . Hossan, A. Islam, T. L. Vu, and Y. M. Jang, “IEEE 802.15.7m standardization: current status and application”, in Proc. of 27th Joint Conference on Telecommunications Information (JCCI), Daejeon, South Korea, May 2017

    2017

  138. [146]

    Coexistence of RF and VLC systems for 5G and beyond wireless communications

    M. Z. Chowdhury, M. T. Hossan, A. Islam, and Y. Min Jang, “Coexistence of RF and VLC systems for 5G and beyond wireless communications”, in Proc. of 27th Joint Conference on Telecommunications Information (JCCI) , Daejeon, South Korea, May 2017

    2017

  139. [147]

    Precious indoor localization using optical camera communication for smartphone,

    M. T . Hossan, A. Islam, N. V. Phuoc, and Y. M. Jan g, “Precious indoor localization using optical camera communication for smartphone,” in Proc. of General Conference of Korea Information and Communications Society (KICS) , Gohan, South Korea, Jan. 2017, pp. 1294–1295

    2017

  140. [148]

    Opportunities and scopes in IEEE 802.15 vehicular assistant technology interest group,

    A. Islam, M. T. Hossan, T. L. Vu, and Y. M. Jang, “Opportunities and scopes in IEEE 802.15 vehicular assistant technology interest group,” in Proc. of General Conference of Korea Information and Communications Society (KICS) , Gohan, South Korea, Jan. 2017, pp. 1295–1296. Patent

    2017

  141. [149]

    Integrating optical camera communication technology in head mounted display for mixed -reality,

    M. T . Hossan, M . Z. Chowdhury, and Y. M. Jang, “Integrating optical camera communication technology in head mounted display for mixed -reality,” Korean Patent, Mar. 14, 2018

    2018

  142. [150]

    Vehicle localization scheme using optical camera communication and photogrammetry,

    M. T. Hossan, M. Z. Chowdhury, and Y. M. Jang, “Vehicle localization scheme using optical camera communication and photogrammetry,” Korean Patent , 10 - 2017-0174466, Dec. 18, 2017

    2017

  143. [151]

    eHealth solutions using optical camera communication,

    M. Z. Chowdhury, M. T. Hossan, and Y. M. Jang, “eHealth solutions using optical camera communication,” Korean Patent, 10-2018-0004690, Jan. 1, 2017

    2018

  144. [152]

    Design of an intelligent universal driver circuit for LED lights,

    M. T. Hossan, and Y. M. J ang, “Design of an intelligent universal driver circuit for LED lights,” Korean patent, Sep. 4, 2017

    2017

  145. [153]

    Indoor localization using optical camera communication,

    M. T. Hossan, M. Z. Chowdhury, A. Islam, and Y. M. Jang, “Indoor localization using optical camera communication,” Korean Patent, 10-2017-0171273, Dec. 13, 2017. Magazine

    2017

  146. [154]

    Optical camera communication technology: image sensor based communication network,

    M. T. Hossan, A. Islam, T. Nguyen, N. T. Le, and Y. M. Jang, “Optical camera communication technology: image sensor based communication network,” KICS magazine, pp. 35–50, 2017

    2017

  147. [155]

    IEEE 802.15.7m optical wireless communication standardization support for IoT application service,

    M. T. Hossan, H. C. Hyun, T. Nguyen, N. T. Le, and Y. M. Jang, “IEEE 802.15.7m optical wireless communication standardization support for IoT application service,” SEP inside, vol. 12, pp. 26–41, 2016

    2016

  148. [156]

    IEEE 802.15.7m optical wireless communication standardization support for IoT / M2M application service

    M. T. Hossan, C. H. Hong, T. Nguyen, N. T. Le, and Y. M. Jang, “IEEE 802.15.7m optical wireless communication standardization support for IoT / M2M application service” Information and Communications Magazine , vol. 33, no.10, pp. 10 –16, 2016. 80 IEEE Standard Contribution

    2016

  149. [157]

    Vehicle communication systems for optical camera communication ,

    M. T. Hossan, M. Z. Chowdhury, M. Shahjalal, M. K. Hasan, and Y. M. Jang, “Vehicle communication systems for optical camera communication ,” IEEE 802.15 IG VAT, Jan. 2018

    2018

  150. [158]

    Current vehicle assist ive technologies: features, limitations, and challenges ,

    M. Z. Chowdhury, M. T. Hossan, M. Shahjalal, M. K. Hasan, and Y. M. Jang, “Current vehicle assist ive technologies: features, limitations, and challenges ,” IEEE 802.15 IG VAT, Jan. 2018

    2018

  151. [159]

    Considerations for long range efficient vehicular communications using OCC,

    M. K. Hasan, M. Z. Chowdhury, M. T. Hossan, M. Shahjalal, and Y. M. Jang, “Considerations for long range efficient vehicular communications using OCC,” IEEE 802.15 WG VAT, Jan. 2018

    2018

  152. [160]

    Coexistence of RF and VLC systems for VAT,

    M. Z. Chowdhury, M. T. Hossan, N. T. Le and Y. M. Jang, “Coexistence of RF and VLC systems for VAT,” IEEE 802.15 IG VAT, Nov. 2017

    2017

  153. [161]

    S2 -PSK for V2V communication,

    M. T . Hossan, T. Nguyen, Amirul Islam, and Y. M. Jang, “S2 -PSK for V2V communication,” IEEE 802.15 WG VAT, Jul. 2017

    2017

  154. [162]

    Future applications of OWC and OCC,

    A. Islam, M. T. Hossan, and Y. M. Jang, “Future applications of OWC and OCC,” IEEE 802.15 WG VAT, Jul. 2017

    2017

  155. [163]

    Long rang e OCC,

    A. Islam, M. T. Hossan, and Y. M. Jang, “Long rang e OCC,” IEEE 802.15 WG VAT, Jul. 2017

    2017

  156. [164]

    Using r ear light for long range OCC to ensure safety issues,

    M. T. Hossan, A. Islam, and Y. M. Jang, “Using r ear light for long range OCC to ensure safety issues,” IEEE 802.15 IG VAT, May 2017

    2017

  157. [165]

    Interference characterization of artificial light sources for long range OCC,

    A. Islam, M. T. Hossan, and Y. M. Jang, “Interference characterization of artificial light sources for long range OCC,” IEEE 802.15 IG VAT, May 2017

    2017

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.