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Laparoscopic surgery is a technique in which surgeons insert laparoscopic instruments into the body through a small incision and conduct surgical operations. It offers a shorter recovery period, less blood loss, and less postoperative pain than open surgeries. In order to maximize these benefits of this surgery technique, it is important to localize the lesion accurately without palpation, minimizing the surgical area. It is because surgeons cannot palpate the organ during laparoscopic surgeries. For accurate localization, we propose a lesion localization system (LLS). This adopts an endoscopic clip with a small-size magnet as a marker and an improved magnetometer as a probe for measuring the magnetic field from the magnet. The magnetometer, which consists of two Hall sensors, can cancel out the fluctuating magnetic baseline mainly caused by external environments so that it can detect a smaller change in the target magnetic field. As a result, the LLS improves the detection range up to 40 mm while using a weak magnet with a small volume of 17.1 mm3, which is compatible with the use of commercial clip appliers. An error of less than 0.1% is achieved at a distance of 40 mm, and the maximum error is kept below 6% even when the rotation angle is varied to ±90°. This LLS does not cause discomfort to the surgery operation because it uses a marker small enough and does not require external coils.

Due to the increasing demand for 3D applications, development of novel depth-sensing cameras is being actively pursued. However, most of these cameras still face the challenge of high energy consumption and slow speed in the depth extraction process. This becomes a serious bottleneck in embedded implementations where real-time performance is required, constrained by power and area. This work proposes Offset Aperture (OA) camera, a new hardware architecture for fast, low-energy, and low-complexity depth extraction. Optimal implementations of pre-processing, cost-volume generation and cost-aggregation are presented. The whole depth-extraction pipeline has been implemented on a Field Programmable Gate Array (FPGA). Overall, a mere 2.8% of bad classification was achieved with the proposed system. Also, the proposed system can process 37 VGA frames per second while consuming 0.224 μJ/pixel. High accuracy, speed and low energy consumption of the proposed OA architecture make it suitable for embedded applications.

Depth sensing is an active area of research in imaging technology. Here, we use a dual-aperture system to infer depth from a single image based on the principle of depth from defocus (DFD). Dual-aperture camera includes a small all-pass aperture (which allows all light through the aperture) and a larger RGB-pass aperture (which allows visible light only). IR image captured through the smaller aperture is sharper than the RGB image captured through the large aperture. Since the difference of blurriness between two images is dependent on the actual distance, using a dual-aperture camera provides an opportunity to estimate depth of a scene. Measuring the absolute blur size is difficult, since it is affected by illuminant’s spectral distribution, noise, specular highlight, vignetting, etc. By using a dual-aperture camera, however, the relative blurriness can be measured in a robust way. In this article,, a detailed description of extracting depth using a dual-aperture camera is provided which includes procedures for fixing each of artifacts that degrade the depth quality based on DFD. Experimental results confirm the improved depth extraction by employing the aforementioned schemes.

Numerous depth extraction techniques have been proposed in the past. However, the utility of these techniques is limited as they typically require multiple imaging units, bulky platforms for computation, cannot achieve high speed and are computationally expensive. To counter the above challenges, a sensor with Offset Pixel Apertures (OPA) has been recently proposed. However, a working system for depth extraction with the OPA sensor has not been discussed. In this paper, we propose the first such system for depth extraction using the OPA sensor. We also propose a dedicated hardware implementation for the proposed system, named as the Depth Map Processor (DMP). The DMP can provide depth at 30 frames per second at 1920 × 1080 resolution with 31 disparity levels. Furthermore, the proposed DMP has low power consumption as for the aforementioned speed and resolution it only requires 290.76 mW. The proposed system makes it an ideal choice for depth extraction systems in constrained environments.

Three-dimensional (3D) memory stacking is one of the most promising applications in 3D integration to solve the limited memory bandwidth problem in 2D integrated circuits (ICs). However, the high power density, i.e., power dissipation per unit volume, due to the high integration density of 3D ICs may incur high operating temperature and, thus, causes low reliability as well as high power consumption. In this paper, we describes the effects of temperature, supply voltage, and L2 cache access rate on both power consumption and reliability of 3D-stacked L2 DRAM cache. Also, we propose a dynamic voltage and frequency scaling (DVFS) scheme for 3D-stacked L2 DRAM cache which can be adapted to either each cache bank or each group of cache banks while taking account of both error-rate and temperature-induced power consumption. Experimental results show that the proposed DVFS scheme achieved a reduction of energy consumption by up to 21.5% compared to a conventional scheme under a given error-rate constraint.