A Novel Deep Convolutional Neural Network Architecture Based on Transfer Learning for Handwritten Urdu Character Recognition

Deep convolutional neural networks (CNN) have made a huge impact on computer vision and set the state-of-the-art in providing extremely definite classification results. For character recognition, where the training images are usually inadequate, mostly transfer learning of pre-trained CNN is often utilized. In this paper, we propose a novel deep convolutional neural network for handwritten Urdu character recognition by transfer learning three pre-trained CNN models. We fine-tuned the layers of these pre-trained CNNs so as to extract features considering both global and local details of the Urdu character structure. The extracted features from the three CNN models are concatenated to train with two fully connected layers for classification. The experiment is conducted on UNHD, EMILLE, DBAHCL, and CDB/Farsi dataset, and we achieve 97.18% average recognition accuracy which outperforms the individual CNNs and numerous conventional classification methods

Output channel design for collecting closely-spaced particle streams from spiral inertial separation devices

Recent advances in inertial microfluidics designs have enabled high throughput, label-free separation of cells for a variety of bioanalytical applications. Various device configurations have been proposed for binary separation with a focus on enhancing the separation distance between particle streams to improve the efficiency of separate particle collection. These configurations have not demonstrated scaling beyond 3 particle streams either because the channel width is a constraint at the collection outlets or particle streams would be too closely spaced to be collected separately. We propose a method to design collection outlets for inertial focusing and separation devices which can collect closely-spaced particle streams and easily scale to an arbitrary number of collection channels without constraining the outlet channel width, which is the usual cause of clogging or cell damage. According to our approach, collection outlets are a series of side-branching channels perpendicular to the main channel of egress. The width and length of the outlets can be chosen subject to constraints from the position of the particle streams and fluidic resistance ratio computed from fluid dynamics simulations. We show the efficacy of this approach by demonstrating a successful collection of upto 3 particle streams of 7μm, 10μm and 15μm fluorescent beads which have been focused and separated by a spiral inertial device with a separation distance of only 10μm -15μm. With a throughput of 1.8mL/min, we achieved collection efficiency exceeding 90% for each particle at the respective collection outlet. The flexibility to use wide collection channels also enabled us to fabricate the microfluidic device with an epoxy mold that was created using xurography, a low cost, and imprecise fabrication technique

Towards Multiplex Molecular Diagnosis—A Review of Microfluidic Genomics Technologies

Highly sensitive and specific pathogen diagnosis is essential for correct and timely treatment of infectious diseases, especially virulent strains, in people. Point-of-care pathogen diagnosis can be a tremendous help in managing disease outbreaks as well as in routine healthcare settings. Infectious pathogens can be identified with high specificity using molecular methods. A plethora of microfluidic innovations in recent years have now made it increasingly feasible to develop portable, robust, accurate, and sensitive genomic diagnostic devices for deployment at the point of care. However, improving processing time, multiplexed detection, sensitivity and limit of detection, specificity, and ease of deployment in resource-limited settings are ongoing challenges. This review outlines recent techniques in microfluidic genomic diagnosis and devices with a focus on integrating them into a lab on a chip that will lead towards the development of multiplexed point-of-care devices of high sensitivity and specificity

Numerical Study of Joule Heating Effects on Microfluidics Device Reliability in Electrode Based Devices

In electrode-based microfluidic devices, micro channels having narrow cross sections generate undesirable temperature inside the microfluidic device causing strong thermal distribution (joule heating) that eventually leads to device damage or cell loss. In this work, we investigate the effects of joule heating due to different electrode configuration and found that, electrodes with triangular arrangements produce less heating effect even at applied potential of 30 V, without compromising the performance of the device and separation efficiency. However, certain electrode materials have low thermal gradients but erode the channel quickly thereby affecting the reliability of the device. Our simulation also predicts optimal medium conductivity (10 mS/m with 10 V) for cells to survive inside the channel until they are selectively isolated into the collection outlet. Our investigations will aid the researchers in the designing of efficient and reliable microfluidic devices to overcome joule heating inside the microchannels

Study on the Optimum Cutting Parameters of an Aluminum Mold for Effective Bonding Strength of a PDMS Microfluidic Device

Master mold fabricated using micro milling is an easy way to develop the polydimethylsiloxane (PDMS) based microfluidic device. Achieving high-quality micro-milled surface is important for excellent bonding strength between PDMS and glass slide. The aim of our experiment is to study the optimal cutting parameters for micro milling an aluminum mold insert for the production of a fine resolution microstructure with the minimum surface roughness using conventional computer numerical control (CNC) machine systems; we also aim to measure the bonding strength of PDMS with different surface roughnesses. Response surface methodology was employed to optimize the cutting parameters in order to obtain high surface smoothness. The cutting parameters were demonstrated with the following combinations: 20,000 rpm spindle speed, 50 mm/min feed rate, depth of cut 5 µm with tool size 200 µm or less; this gives a fine resolution microstructure with the minimum surface roughness and strong bonding strength between PDMS–PDMS and PDMS–glass