Gleason-Type Derivations of the Quantum Probability Rule for Generalized Measurements

We prove a Gleason-type theorem for the quantum probability rule using frame functions defined on positive-operator-valued measures (POVMs), as opposed to the restricted class of orthogonal projection-valued measures used in the original theorem. The advantage of this method is that it works for two-dimensional quantum systems (qubits) and even for vector spaces over rational fields--settings where the standard theorem fails. Furthermore, unlike the method necessary for proving the original result, the present one is rather elementary. In the case of a qubit, we investigate similar results for frame functions defined upon various restricted classes of POVMs. For the so-called trine measurements, the standard quantum probability rule is again recovered.Comment: 10 pages RevTeX, no figure

Towards Euclidean auto-calibration of stereo camera arrays

Multi-camera networks are becoming ubiquitous in a variety of applications related to medical imaging, education, entertainment, autonomous vehicles, civil security, defense etc. The foremost task in deploying a multi-camera network is camera calibration, which usually involves introducing an object with known geometry into the scene. However, most of the aforementioned applications necessitate non-intrusive automatic camera calibration. To this end, a class of camera auto-calibration methods imposes constraints on the camera network rather than on the scene. In particular, the inclusion of stereo cameras in a multi-camera network is known to improve calibration accuracy and preserve scale. Yet most of the methods relying on stereo cameras use custom-made stereo pairs, and such stereo pairs can definitely be considered imperfect; while the baseline distance can be fixed, one cannot guarantee the optical axes of two cameras to be parallel in such cases. In this paper, we propose a characterization of the imperfections in those stereo pairs with the assumption that such imperfections are within a considerably small, reasonable deviation range from the ideal values. Once the imperfections are quantified, we use an auto-calibration method to calibrate a set of stereo cameras. We provide a comparison of these results with those obtained under parallel optical axes assumption. The paper also reports results obtained from the utilization of synthetic visual data

Design and Performance Assessment of a Small-Scale Ferrite-PM Flux Reversal Wind Generator

Currently, there is increasing research interest in harnessing wind energy for power generation by means of non-conventional electrical machines e.g., flux-reversal machines. The flux reversal machine is usually designed using scarce rare–earth permanent magnet material which may be unattractive in terms of machine cost. In this study, an attempt is made to re-design the flux reversal machine with non-rare-earth ferrite permanent magnet for wind energy applications. Because these machines possess high cogging torque, which results in vibration and noise, that are detrimental to the machine performance, especially at low speeds, a novel combined skewed and circumferential rotor pole pairing method is developed. The proposed cogging torque reduction method is implemented in 2-dimensional finite element analysis modeling and comparatively analyzed with other existing stand-alone methods viz., skewing, and rotor pole pairing. The results show that the proposed method led to 94.8% and 71% reduction in the cogging torque and torque ripple compared to the reference generator, respectively. However, the calculated torque density is reduced by 13%. Overall, the electromagnetic performance of the proposed ferrite PM machine exhibits desirable qualities as an alternative design for the direct drive wind generator

Scheduling and Decoding of Downlink Control Channel in 3GPP Narrowband-IoT

Narrowband Internet of Things (NB-IoT) is a low power wide area network technology introduced by the 3rd Generation Partnership Project (3GPP). It is a derivative of the existing 3GPP Long Term Evolution (LTE) that will enable cellular service to a massive number of IoT devices. In comparison with LTE and 5G New Radio, the NB-IoT devices will be of low cost, low throughput, and delay-tolerant. The reduction in available bandwidth and introduction of repetitions for achieving wider coverage requires modified Narrowband Physical Downlink Control Channel (NPDCCH) search space design and decoding as compared to the LTE. Hence, in this paper, we first explain the NPDCCH physical layer procedures, along with the search space decoding. Unlike LTE, there is no channel feedback mechanism in NB-IoT. Therefore, we propose a novel resource mapping scheme for NPDCCH based on the uplink reference signals. We perform system-level simulations and analyze the impact of the proposed mapping for varying operating frequencies and channel conditions. Further, the NB-IoT devices have limitations on the battery power, and hence, the existing control channel schedulers cannot be reused for the NB-IoT scenario. Thus, we propose a novel scheduler for NPDCCH. We have also modified the current state-of-the-art algorithms to meet the NPDCCH constraints and compared them against the proposed scheduler. We derive bounds for such scheduling algorithms and show that the proposed scheduler additionally conserves up to 25% of the IoT device battery power. Through Monte Carlo simulations, we showthat the proposed scheduler better achieves the various trade-offs between power consumption, search space utilization, and fairness as compared to the existing schedulers

Improved Fundus Image Quality Assessment: Augmenting Traditional Features with Structure Preserving ScatNet Features in Multicolor Space

High quality fundus photographs (FPs) are essential for clinicians to make accurate diagnosis of various ophthalmic diseases, including diabetic retinopathy, age-related macular degeneration, and glaucoma. Thus it becomes imperative that clinicians are presented with FPs, whose high diagnostic quality is assured. In this context, significant effort has been directed at developing automated tools that distinguish between high quality and low quality FPs. For this purpose, features suited to natural image quality assessment were traditionally employed even for diagnostic quality assessment of FPs. However, structure preserving features generated by deep scattering network (ScatNet) were recently reported to outperform aforementioned traditional features. In this paper, we demonstrate further improvement in performance by combining both the traditional features and ScatNet features. Importantly, additional improvement is witnessed when ScatNet features are computed in multicolor space

Towards true-to-scale 3D reconstruction of the human face using structured light projection and off-the-shelf cameras

In recent years, 3D surface imaging has been adopted in oculofacial surgeries; 3D measurements have facilitated surgical planning and post-operative assessment. Furthermore, life-like 3D visualization has provided a promising alternative as tools of communication and training. However, such surface imaging and visualization technologies remain restricted to the legacy systems. In this paper, we present a 3D surface imaging and reconstruction system made from off-The-shelf components. Our imaging setup consists of custom-made stereo cameras and structured light projection. We demonstrate a true-To-scale 3D reconstruction of the frontal part of the head of a human subject, by capturing its multiple views. We use a non-intrusive auto-calibration method to ensure reconstruction accuracy. The paper also discusses the lighting conditions of such capture procedures, as such parameters may severely affect the performance