Cervical cancer is the fourth most common cancer among women worldwide and is especially prevalent in low resource settings due to lack of screening and treatment options. Visual inspection with acetic acid (VIA) is a widespread and cost-effective screening method for cervical pre-cancer lesions, but accuracy depends on the experience level of the health worker. Digital cervicography, capturing images of the cervix, enables review by an off-site expert or potentially a machine learning algorithm. These reviews require images of sufficient quality. However, image quality varies greatly across users. A novel algorithm was developed to evaluate the sharpness of images captured with the MobileODT’s digital cervicography device (EVA System), in order to, eventually provide feedback to the health worker. The key challenges are that the algorithm evaluates only a single image of each cervix, it needs to be robust to the variability in cervix images and fast enough to run in real time on a mobile device, and the machine learning model needs to be small enough to fit on a mobile device’s memory, train on a small imbalanced dataset and run in real-time. In this paper, the focus scores of a preprocessed image and a Gaussian-blurred version of the image are calculated using established methods and used as features. A feature selection metric is proposed to select the top features which were then used in a random forest classifier to produce the final focus score. The resulting model, based on nine calculated focus scores, achieved significantly better accuracy than any single focus measure when tested on a holdout set of images. The area under the receiver operating characteristics curve was 0.9459.
Modern CMOS transistors will not scale well in the next decade due to leakage currents, sources of variation, and platform requirements. To keep the cost per transistor decreasing, and to realize the feasibility of ultra-high density integrated circuits, low power techniques and efficiency optimization are being explored to counter these problems. Parallel to the development of electronic VLSI, using photons as a means of carrying information has been an appealing approach, due to the high speed and broad bandwidth of light, and the elimination of on-chip parasitic and electro-magnetic interference as its electronic counterpart. This paper focuses on photonic integrated circuits to solve the high-density problem, and presents a design for a nano-scale QD optical transducer (QDOT) that will function as a near-field photodetector and that can easily interface into a self- assembled QD integrated circuit (QDIC). The optical transducer consists of a QD between two metal electrodes. The tunneling current between the metal electrodes is mediated by the QD and can be gated by changing the optical signal intensity impinging on the QD. The device can be fabricated via self-assembly using QDs. In this method, a chemistry linker such as DNA or APTES is covalently bound to pre- defined zones on a substrate. The global location of these zones is defined via electron-beam lithography (EBL). Numerical simulations are discussed and ideal characteristics of the device are presented.
Room-temperature continuous wave operation of Antimonide-based long wavelength VCSELs has been demonstrated, with 1.2mW power output at 1266nm, the highest figure reported so far using this material system. Single mode powers of 0.3mW at 10°C and 0.1mW at 70°C and side-mode suppression ratios up to 42dB have also been achieved. Preliminary reliability test results have shown so far that the devices can work normally without obvious degradation after stress testing at up to 125°C for thousands of hours.