Abstract-The performance of a single-user ultra-wideband (UWB) communication system employing binary block-coded pulse-position modulation (PPM) and suboptimal receivers in multipath channels is considered. The receivers examined include a rake receiver with various diversity combining schemes and an autocorrelation receiver, which is used in conjunction with transmitted reference (TR) signaling. A general framework is provided for deriving the performance of these receivers in multipath channels corrupted by additive white Gaussian noise (AWGN). By employing previous measurements of indoor UWB channels, we obtain numerical results for several cases which illustrate the tradeoff between performance and receiver complexity.

The use of object detection algorithms is becoming increasingly important in autonomous vehicles, and object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. A false positive (FP) from a false localization during autonomous driving can lead to fatal accidents and hinder safe and efficient driving. Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications. This paper proposes a method for improving the detection accuracy while supporting a real-time operation by modeling the bounding box (bbox) of YOLOv3, which is the most representative of one-stage detectors, with a Gaussian parameter and redesigning the loss function. In addition, this paper proposes a method for predicting the localization uncertainty that indicates the reliability of bbox. By using the predicted localization uncertainty during the detection process, the proposed schemes can significantly reduce the FP and increase the true positive (TP), thereby improving the accuracy. Compared to a conventional YOLOv3, the proposed algorithm, Gaussian YOLOv3, improves the mean average precision (mAP) by 3.09 and 3.5 on the KITTI and Berkeley deep drive (BDD) datasets, respectively. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 42 frames per second (fps) and shows a higher accuracy than previous approaches with a similar fps. Therefore, the proposed algorithm is the most suitable for autonomous driving applications.

In this paper, a total of 57 micro and nano scale hybrid manufacturing processes are reviewed. These processes are categorized in terms of process timing and process type. Process timing is one of the most important aspects of manufacturing, and three different process schemes -concurrent, main/assistive (M/S) separate, and main/main (M/M) separate -are considered. The process type is categorized as either geometrically additive or subtractive, and all hybrid processes are categorized into combinations of additive, subtractive, and assistive process. Features and advantages are described for each of these classifications. Machining is found to be the most common process for both micro and nano-scale hybrid manufacturing. Of micro scale hybrid manufacturing schemes, 74.4% use assistive processes as a secondary process because the main purpose of most micro scale hybrid manufacturing is to improve the quality of the process. In nano scale manufacturing, 61.5% of hybrid manufacturing schemes employ assistive processes, since these processes typically focus on the fabrication of parts that are difficult to fabricate using a single, existing process. Based on a summary of published work, future trends in hybrid manufacturing at the micro and nano scale are suggested.

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