Large-scale space projects rely on a thorough Assembly, Integration, and Verification (AIV) process to provide the upmost reliability to spacecraft. While this has not traditionally been the case with CubeSats, their increasing role in space science and technology has led to new verification approaches, including in educational CubeSats. This work describes the integration and verification approach for ISTSat-1, which is an educational CubeSat from the Instituto Superior Técnico in Portugal that partially discards the typical stage-gate approach to spacecraft development in favor of a more iterative approach, allowing for the system-level verification of unfinished prototypes. Early verification included software functional testing on a flatsat model, thermal vacuum and vibration testing on a battery model, ionizing radiation testing on the on-board computer, and non-ionizing radiation (EMC) testing on all subsystems. The testing of functional prototypes at an early development stage led to uncovering system-level errors that would typically require hardware redesign at a later project stage. The team considers the approach to be useful for educational projects that employ a small, co-located team with low non-recurring engineering costs.

In this work we propose an artificial neural network (ANN) for magnetic micro cytometry pattern recognition and automated counting. The method is tested for detecting analytes in the 2-3 μm range. The cytometer is composed of a disposable cartridge and an acquisition platform. The disposable cartridge contains microfluidic channels with a 10×100 μm ² cross section on top of a substrate with magnetoresistive (MR) sensors. The custom analog signal chain performs with an integrated noise of 2.99 μV _RMS in a 10 kHz bandwidth. To employ the ANN we synthesize a training data set based on the magnetic-dipole equation and several data set expansion methods. The ANN is tested on an experiment with 2.8 μm magnetic particles (MP) and compared with an improved threshold-based method with reduced false positive. The ANN produces a maximum of 90% detection rate, improving on the 30-50% detection rates of other single sensor methods published in literature.

This work presents a modular acquisition platform for biasing and reading 2D crossbar resistive sensor arrays in AC mode based on the Zero Potential Method (ZPM). ZPM minimizes the number of connections and array bond pads while being scalable for arbitrary array dimensions. The system employs a new technique to suppress the non-informative sensor bias component thus allowing the readout of sensor arrays with a wide range of nominal resistances, validated from 1 kΩ to 1.8 MΩ. It can read up to 4 sensors simultaneously from a matrix comprising a maximum of 256 sensors. The platform is functionally demonstrated and characterized using a 4 × 4 resistive array and a 12 × 12 matrix of thin film spin valves, paving the way for a lab-on-chip Micro Total Analysis Systems (μTAS) device. The proposed system can also be employed in a range of applications like memristive reading and writing for neuromorphic systems, robotics sensing, and other applications based on crossbar resistive sensor matrices.

Magnetic flow cytometers are promising tools to address cell detection and counting in a seamless and compact manner. Such devices integrate magnetic sensors with microfluidics for sample transportation and magnetic particles as reporters. This work presents a signal analysis method developed to address one of the challenges in magnetic flow cytometry: the ability to distinguish signatures of free or clustered magnetic particles from magnetically labelled targets. Comparing the sensor output signals (pulse amplitude vs. pulse width analysis) with simulations we develop a reliable classification methodology for labelled targets based on a custom Matlab algorithm. Upon discrimination of different signatures for clusters or single particles, one can reduce the number of false positives while improving the detection system accuracy.

Magnetic flow cytometers are promising tools to address cell detection and counting in a seamless and compact manner. Such devices integrate magnetic sensors with microfluidics for sample transportation and magnetic particles as reporters. This work presents a signal analysis method developed to address one of the challenges in magnetic flow cytometry: the ability to distinguish from each single detected signature the number of bacteria effectively detected. Comparing the sensor output signal (pulse amplitude vs. pulse width analysis) with simulations we develop a reliable classification methodology for the identification of the number of magnetically labelled bacteria based on a custom Matlab algorithm. The discrimination of the number of bacteria from the sensor event signatures will improve the detection system accuracy.