Automatic identification and analysis of cells using digital holographic microscopy and Sobel segmentation

Counting and analyzing of blood cells, as well as their subcellular structures, are indispensable for understanding biological processes, studying cell functions, and diagnosing diseases. In this paper, we combine digital holographic microscopy with cell segmentation guided by the Sobel operator using Dice coefficients for automatic threshold selection and aimed to automatic counting and analysis of blood cells in flow and different kinds of cells in the static state. We demonstrate the proposed method with automatic counting and analyzing rat red blood cells (RBCS) flowing in a microfluidic device, extracting quickly and accurately the size, concentration, and dry mass of the sample in a label-free manner. The proposed technique was also demonstrated for automatic segmentation of different cell types, such as COS7 and Siha. This method can help us in blood inspection, providing pathological information in disease diagnosis and treatment

Algorithms offering kinetic analysis of drug induced proteasome inhibition and cell clump formation from time lapsed microscopy

High content screening (HCS) has potential to transform many biological fields, ranging from drug discovery to gene function discovery. HCS with time lapsed microscopy provide valuable insight information about live cells experiments that are usually lost during manual end point experiments. By means of novel bioinformatics algorithms, huge amount of phenotypic data might become available by these techniques which can be used to understand effects of chemical compounds on the cells and profile phenotypically both cell line and chemical compounds. The resultant data can also be compared with other experiments to find out the efficiency and affectivity of the different compounds under same conditions. Recent results also demonstrate that phenotypic profiles can be used to infer specific gene perturbations. In this thesis, novel algorithms for such phenotypic profiling were implemented and demonstrated to be very useful revealing unknown kinetic information regarding two proteasome inhibitors (Bortezomib and CB3) as well as about cell clump formation during cell line growth on honeycomb nanoculture plates. The novel algorithms include specialized solutions both for phase contrast microscopy and fluorescent microscopy and are based on the publicly available cell image processing package Cell Profiler from Broad Institute

Ultra-Low-Power IoT Solutions for Sound Source Localization: Combining Mixed-Signal Processing and Machine Learning

With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of auditory stimuli that could provide important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. We start this research into building a wearable system that uses multichannel audio sensors embedded in a headset to help detect and locate cars from their honks and engine and tire noises. Based on this detection, the system can warn pedestrians of the imminent danger of approaching cars. We demonstrate that using a segmented architecture and implementation consisting of headset-mounted audio sensors, front-end hardware that performs signal processing and feature extraction, and machine-learning-based classification on a smartphone, we are able to provide early danger detection in real time, from up to 80m distance, with greater than 80% precision and 90% recall, and alert the user on time (about 6s in advance for a car traveling at 30mph). The time delay between audio signals in a microphone array is the most important feature for sound-source localization. This work also presents a polarity-coincidence, adaptive time-delay estimation (PCC-ATDE) mixed-signal technique that uses 1-bit quantized signals and a negative-feedback architecture to directly determine the time delay between signals in the analog inputs and convert it to a digital number. This direct conversion, without a multibit ADC and further digital-signal processing, allows for ultra low power consumption. A prototype chip in 0:18μm CMOS with 4 analog inputs consumes 78nW with a 3-channel 8-bit digital time-delay output while sampling at 50kHz with a 20μs resolution and 6.06 ENOB. We present a theoretical analysis for the nonlinear, signal-dependent feedback loop of the PCC-ATDE. A delay-domain model of the system is developed to estimate the power bandwidth of the converter and predict its dynamic response. Results are validated with experiments using real-life stimuli, captured with a microphone array, that demonstrate the technique’s ability to localize a sound source. The chip is further integrated in an embedded platform and deployed as an audio-based vehicle-bearing IoT system. Finally, we investigate the signal’s envelope, an important feature for a host of applications enabled by machine-learning algorithms. Conventionally, the raw analog signal is digitized first, followed by feature extraction in the digital domain. This work presents an ultra-low-power envelope-to-digital converter (EDC) consisting of a passive switched-capacitor envelope detector and an inseparable successive approximation-register analog-to-digital converter (ADC). The two blocks integrate directly at different sampling rates without a buffer between them thanks to the ping-pong operation of their sampling capacitors. An EDC prototype was fabricated in 180nm CMOS. It provides 7.1 effective bits of ADC resolution and supports input signal bandwidth up to 5kHz and an envelope bandwidth up to 50Hz while consuming 9.6nW