A DNA biosensor is a sensing device that converts the presence of a target DNA into a detectable electrical signal. It consists of two parts, one is the identification component, the DNA probe, and the other is the transducer. The protagonist of the recognition component is used to sense whether the sample contains the target DNA to be tested; the transducer converts the signal perceived by the recognition component to observe the recorded signal. Usually, a single-stranded DNA is solidified on a transducer, and hybridized by DNA molecules to identify another DNA containing a complementary sequence to form a stable double-stranded DNA, and the target DNA is subjected to conversion by sound, light, and electric signals. Detection. The principle of DNA biosensor is that a double-stranded DNA formed by hybridization of a single-stranded DNA molecule with a known nucleotide sequence immobilized on the surface of a sensor or transducer probe to another complementary ss-DNA molecule will exhibit a certain The physical signal is finally reflected by the transducer. However, DNA molecules are very small and fragile

Application:

1. Environmental: water environment monitoring and atmospheric environment monitoring.
2. Military medicine: effective measure against biological weapons, monitor a variety of bacteria, viruses and their toxins, such as Bacillus anthracis, Yersinia pestis.
3. Forensic science: DNA identification, paternity testing, etc.

One Example:

Illustration of graphene-based SNP detection chip wirelessly transmitting signal to a smartphone.

BioSensor chip that can detect a type of genetic mutation known as a single nucleotide polymorphism (SNP) and send the results in real time to an electronic device.

Skill level required for electronics and coding: Expert

In general, in all conventional biometrics (including fingerprints, faces, irises, sounds, palm prints, etc.), due to the accuracy, anti-counterfeiting, uniqueness, and stability of the iris itself, mainstream academics generally consider the iris to be Fingerprint or facial recognition is more “advanced” identification method. It should be known that compared with 0.8% false rate of fingerprints, 2% of the face, iris recognition as low as one millionth of the misrecognition rate seems to have almost no Deception

One Example：

IriShield-USB MO 2120 Auto-Capture Iris Camera Module

IriShield™ Series is an ultra-compact, auto-capture camera module, complete with onboard iris recognition and a PKI-based security infrastructure that ensures complete data security.

Skill level required for electronics and coding: Expert

As the gesture function gradually joins the user interface (UI) of products such as smartphones and tablets, the sensor market for tracking the movement of the hand with the touch screen is exploding.

There are two types of mobile phone-based gesture solutions on the market today – capacitive and infrared proximity. On devices that support gestures, dedicated proximity sensors detect motion in all directions, up, down, left, and right in a two- or three-dimensional manner. Current touch screen operations require direct touch with a finger or stylus, while capacitive gesture control goes one step further, allowing the user to interact with the device as soon as they are close to the screen.

One example:

A new Gesture Sensor Module from Alsrobot is based on APDS-9960 sensor which can recognize gesture direction from up, down, left and right. IC APDS-9960 integrate the fuction of RGB, ambient light, approach and guesture detector. Guesture sensor use I2C interface, by using accordingly Arduino fuction to realize PGM programme, feedback signal from gesture sensor module can be use as control signal to control the robot. With intelligent built-in recognition algorithm to free your hands. This item can work in contactless control scenarios, such like noncontact mouse,smart home,control of car pointing device and human-robot interaction etc.

price: $9.08

Specification:

• Input voltage:3.3V-5V
• PIN interface: IICx interrupt PIN x1
• Interface type: straight PIN, KF2510
• Size:30mm x 25mm
• Location hole: 4, separation distance 23mm x 18mm
• Detect distance: 100mm
• Weight: 3g
• PINs:  -：GND /+：VCC/SDA:TxDn/SCL
• Compatible with infrared sensor module

Skill level required for electronics and coding: Expert

Face recognition uses a general-purpose camera as an identification information acquisition device. The facial image of the recognition object is acquired in a non-contact manner, and the computer system compares the database image after acquiring the image to complete the recognition process. Face recognition is a biometric-based recognition method. Compared with traditional recognition methods such as fingerprint recognition, it has the characteristics of real-time, accurate, high-precision, easy to use, high stability, difficult to counterfeit, cost-effective and non-intrusive, and easy to be accepted by the Users.

One example:

– AHD-M 1/4″ OmniVision OV9732 CMOS image sensor + Nextchip NVP2433 CCTV camera PCB board module (optional parts)

price: $3.5 – 4.5

* This camera module made by CMOS image sensor, Must install IRC (IR-Cut-Off) filter to getting normal color pictures,  otherwise will get black & white or NO color picture

* IF NOT have IRC filter,  DON’t place order on this list.

* ONLY AHD-M video signal output.

* NOT Support analog CVBS.

Skill level required for electronics and coding: Expert

Application:

Fingerprint door locks, safes, guns, financial and other security areas;

Access control systems, industrial computers, POS machines, driving training, attendance and other areas of identity;

Private clubs, management software, licensing and other management areas;

Medicare recipients, pensioners receive, fingerprint payment and other financial areas.

one example:

Optical Fingerprint Reader Sensor Module Door Lock Access Control for Arduino Mega2560 UNO R3 Geekstory

Price: $20.85

Functionality:

Supply voltage: DC 3.8 to 7.0V

Operating current: <60mA

Peak current: <85mA

Fingerprint image input time: <0.5 seconds

Window area: 15 × 17mm

Storage capacity: 240

LInks: 

https://create.arduino.cc/projecthub/projects/tags/fingerprint

https://github.com/adafruit/Adafruit-Fingerprint-Sensor-Library

https://randomnerdtutorials.com/fingerprint-sensor-module-with-arduino/

Skill level required for electronics and coding: Expert