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(Spring 2013) Analysis of Bar Codes with Different Scanning Devices
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(Spring 2013) Analysis of Bar Codes with Different Scanning Devices

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The purpose of this project was to do a comparative analysis of various bar codes on soil bag samples using different scanning methods (more specifically iPhone 5 vs. Android smartphones). This …

The purpose of this project was to do a comparative analysis of various bar codes on soil bag samples using different scanning methods (more specifically iPhone 5 vs. Android smartphones). This project is also referencing a previous analysis/assessment of the Sure-Tech Laboratories’ soil bags using a regular bar code scanner. Using the samples from the Sure-Tech Laboratories Company, we compared the effectiveness of readability of the bar codes and the data from the regular bar code scanner vs. smartphone technologies.

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  • 1. ANALYSIS OF BAR CODES WITH DIFFERENT SCANNING DEVICES Ivan Fung, Nick Formoso, Erika Jinbo, David McNutt, Kevin O’Connor, Stephen Elliott The purpose of this project was to do a comparative analysis of various bar codes on soil bag samples using different scanning methods (more specifically iPhone 5 vs. Android smartphones). This project is also referencing a previous analysis/assessment of the Sure-Tech Laboratories’ soil bags using a regular bar code scanner. Using the samples from the Sure-Tech Laboratories Company, we compared the effectiveness of readability of the bar codes and the data from the regular bar code scanner vs. smartphone technologies. SAMPLE DESCRIPTION OVERVIEW METHODOLOGY RESULTS AND DATA ANALYSIS EXAMPLES OF FAILED SAMPLES Factors Levels Remarks Symbology QR Code PDF417 Data Matrix X-Dimension 14.2 mil (QR Code & Data Matrix) 7.5 mil (PDF417) Error Correction Level Low Medium High QR Code (L, M, H) PDF417 (4,5,6) Data Matrix (N/A) Label Location Front Side Label Number Symbology Error Correction Location 1 QR Code Low Front 2 QR Code Low Side 3 QR Code Medium Front 4 QR Code Medium Side 5 QR Code High Front 6 QR Code High Side 7 PDF417 4 Front 8 PDF417 4 Side 9 PDF417 5 Front 10 PDF417 5 Side 11 PDF417 6 Front 12 PDF417 6 Side 13 Data Matrix N/A Front 14 Data Matrix N/A Side The samples used for this analysis are bar codes printed on soil sample bags from Sure-Tech Labs. They were all acquired from a previous study conducted. These samples are varied in three factors: bar code symbology, error correction level, and label location. Table 1 shows an explanation of the levels. The samples are then further categorized into 14 groups. Each label has a distinct combination of symbology, error correction level, and label location. Table 2 shows the characteristics of each label number. Table 1: Level of details for bar code symbology, error correction, and label location Table 2: Characteristics of each label number Figure 4: Data collection process The phones are clamped into a rig that keeps the phone exactly 10 cm from the scan subject. If the phones do not scan the bar code within 10 seconds, we consider it a failed attempt. Both phones used the application called “Barcode Scanners” by Manatee Works. Figure 4 shows a flow chart that shows how we went through and scanned each bag. Table 3: Summary of data entry results Table 3 shows the data we collected in this research. Every group of soil bags has 28–30 samples and each one of them was scanned five times. We then totaled the number of successful scans for every bag within each group. Compared to the scanner, the iPhone and Android had more 4–5 successful scans among all bags. But iPhone and Android also had more 0 successful scans compared to the scanner. PDF417 bar codes printed on the front with high error correction had a 100% of scan pass percentage for all three devices. iPhone Android Scanner Label # 0 1-3 4-5 0 1-3 4-5 0 1-3 4-5 1 1 2 26 2 3 24 1 1 27 2 1 0 28 2 1 26 4 9 16 3 1 2 27 1 2 28 0 1 29 4 1 1 28 2 2 26 1 3 26 5 1 7 20 0 0 28 0 11 17 6 2 0 28 2 0 28 1 8 21 7 1 0 29 0 0 30 0 0 30 8 1 1 27 0 1 28 0 3 26 9 0 0 29 3 0 26 0 0 29 10 3 0 27 0 3 27 1 1 28 11 0 0 28 0 0 28 0 0 28 12 7 1 21 9 1 19 1 3 25 13 1 0 28 0 1 28 1 2 26 14 2 1 25 4 1 23 2 0 26 Figure 5: With this QR Code, the scanner, iPhone, and Android Phone all failed to scan this for the five trials that it was tested. Figure 5: Sample 1 Figure 6: Sample 2 Figure 6: For this Data Matrix Code, the regular scanner succeeded in scanning it, but both the iPhone and Android Phone failed to scan it. Figure 7: Sample 3 Figure 7: For this QR Code, the regular scanner failed to scan it, but both the iPhone and Android Phone succeeded in scanning it. Specifications Samsung Galaxy SII Operating System Android 2.3.3 (Gingerbread) System on Chip Samsung Exynos 4 Dual 45 nm (GT-I9100, SHW-M250S/K/L) CPU 1.2 GHz dual-core ARM Cortex-A9 GPU ARM Mali-400 MP4 Memory 1 GB RAM Storage 16 GB flash memory Data Inputs Multi-touch touch screen, headset controls, proximity sensor, ambient light sensor Display 4.3 in (110 mm) AMOLED with 480×800 pixels (218 ppi) and RGB- Matrix Rear Camera 8 Mpx Back-illuminated sensor with auto focus, 1080p 30 fps full HD video recording. Single LED flash. SCANNER DESCRIPTION Specifications Apple iPhone 5 Operating System iOS 6.1 System on Chip Apple A6 CPU 1.3 GHz dual core Apple A6 GPU PowerVR SGX543MP3 Memory 1GB LPDDR2-1066 RAM Storage 16 GB Data Inputs Touch-screen Display 4-inch (100 mm) diagonal (16:9 aspect ratio), multi-touch display, 640 × 1,136 pixels at 326 ppi, 800:1 contrast ratio (typical), 500 cd/m2 max. brightness (typical), Fingerprint-resistant oleophobic coating on front Rear Camera 8 MP back-side illuminated sensor, HD video (1080p) Specifications IT4600r Scanner Symbologies PDF417, MicroPDF417, MaxiCode, Data Matrix, QR Code, Aztec, Aztec Mesas, Code 49, and EAN UCC Composite Interfaces All popular PCs and terminals via keyboard wedge, keyboard replacement/direct connect, USB Illumination LEDs 617nm ± 30nm Aiming (Green LED Aimer) 526nm ± 30nm Image VGA, 752x480. Binary, TIFF, or JPEG output Skew Angle & Pitch Angle ±40o Current Draw (Typical RMS) Green LED Aimer Input: 5 V, Scanning: 345mA, Idle: 80mA Figure 1: Samsung Galaxy SII Figure 2: iPhone 5 Figure 3: IT4600r Scanner CONCLUSION AND FUTURE After working with these mobile scanners and seeing how they compared with a scanner you might find at a check out, we concluded that while the regular scanner may seem better than a smart phone, the smart phones performed better with higher success rates. There are instances that the barcode was scanned by the regular scanner successfully, the phones were unable to scan. The regular scanner scored a lot of 1-3’s but the phones scored more 0’s and 4-5’s more often than the 1-3’s. As technologies in optics improve, so will the efficiency of scanning barcodes.