Human looking forward to living in a modern and comfortable environment like smart houses. In this study, an effective user-friendly smart home prototype designed with low cost. The prototype contains eight of Light Emitting Diode (LED) considered as home appliances and controlled in real-time using eight suggested hand cases. The hand cases have different position regarded to head and shoulder levels. The hand position is detected using a new suggested algorithm programmed in Matlab software. Viola-Jones method used to detect hand in a complex background (hand with a different background) by training computer using positive (hand) and negative (non-hand) image datasets. To make computer training faster and accurate, a new idea depends on a skin detection used before computer training to determine the location and size of all positive images automatically. The LEDs in prototype switched ON/OFF using the suggested hand cases in a fast time. Where the response time of LEDs to hand cases was 0.43 second.

Optical Character Recognition (OCR) research includes computer vision, artificial intelligence, and pattern recognition. Character recognition has garnered a lot of attention in the last decade due to its broad variety of uses and applications, including multiple-choice test data, business documents (e.g., ID cards, bank notes, passports, etc.), and automatic number plate recognition. This paper introduces an automatic recognition system for printed numerals. The automatic reading system is based on extracting local statistical and geometrical features from the text image. Those features are represented by eight vectors extracted from each digit. Two of these features are local statistical (A, A th), and six are local geometrical (P1, P2, P3, P4, P5, and P6). Thus, the database created consists of 1120 statistical and geometrical features. For the purpose of recognition, the features of the test image are compared with the features of all the images saved in the database depending on the value of the Minimum Distance (MD). All digits (0–9) were identified with 100% accuracy. The average computational time required to recognize a numeral at any font size is 0.06879 seconds.

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The performance of a surface Plasmon resonance (SPR) sensor with silver film was demonstrated. The Kretschmann setup's evanescent field, which can activate the sensor. The sensitivity and FWHM of the SPR sensor drop as the thickness of the silver layer in the metallic film increases. Using water as a sensing medium, it can create a simulation model at various thicknesses of the silver layers placed on the semicircular glass prism D-ZLAF50 with a thickness of dAg = 10–80 nm. The proposed sensor can function at wavelengths of up to 600,700nm in the visible area and the infrared region at wavelengths of 900 & 1000 nm. Optimum sensitivity (S = 100–140) may be observed in the visible and infrared spectrum with thicknesses ranging from dAg = (10–80) nm , ∆n = 0.1. At silver layer thicknesses, the values of SPR dip length and FWHM are excellent (40–60 nm).

Surface plasmon resonance (SPR) is a highly sensitive method for monitoring changes in the optical characteristics that are near the sensor surface. It can be stimulated by an evanescent field that comes from the total internal reflection of the backside of the sensor surface in the Otto setup. In this setup, SPR can be used to build a simulation model at different thicknesses of titanium oxide (TiO2) (dTiO2 = 50 nm) and silver (Ag) (dAg = 10–80 nm) layers, which are deposited on the semicircular glass prism D-ZLAF50 by using water as a sensing medium. The surface plasmon resonance angle (θSPR) properties were calculated; SPR was not observed in the ultraviolet region (300 nm) or in the infrared region at 800 nm, but appeared strongly in the visible region at 600 and 700 nm and in the infrared region (900 and 1000 nm). The best sensitivity (S = 140) can be observed in the visible region, where the values of SPR dip length (Ld) and full-width half maximum (FWHM) are very good at silver layer thicknesses 40–60 nm; therefore, the proposed sensor can be used in the visible and infrared regions at the wavelengths 600, 700, 900, and 1000 nm.