Preparation of large-scale digitization samples for automated electron microscopy of tissue and cell ultrastructure

Manual selection of targets in experimental or diagnostic samples by transmission electron microscopy (TEM), based on single overview and detail micrographs, has been time- consuming and susceptible to bias. Substantial information and throughput gain may now be achieved by automated acquisition of virtually all structures in a given EM section. Resulting datasets allow convenient pan-and-zoom examination of tissue ultrastructure with preserved microanatomical orientation. The technique is, however, critically sensitive to artifacts in sample preparation. We therefore established a methodology to prepare large-scale digitization samples (LDS) designed to acquire entire sections free of obscuring flaws. For evaluation, we highlight the supreme performance of scanning EM in transmission mode compared to other EM technology. The use of LDS will substantially facilitate access to EM data for a broad range of applications.

Implementation of a Fast Link Rate Adaptation Algorithm for WLAN Systems

With a target to maximize the throughput, a fast link rate adaptation algorithm for IEEE 802.11a/b/g/n/ac is proposed, which is basically preamble based and can adaptively compensate for the discrepancy between transmitter and receiver radio frequency performances by exploiting the acknowledgment signal. The target system is a 1 × 1 wireless local area network chip with no null data packet or sounding. The algorithm can be supplemented by automatic rate fallback at the initial phase to further expedite rate adaptation. The target system receives wireless channel coefficients and previous packet information, translates them to amended signal-to-noise ratios, and then, via the mean mutual information, selects the modulation and coding scheme with the maximum throughput. Extensive simulation and wireless tests are carried out to demonstrate the validity of the proposed adaptive preamble-based link adaptation in comparison with both the popular automatic rate fallback and ideal link adaptation. The throughput gain of the proposed link adaptation over automatic rate fallback is demonstrated over various packet transmission intervals and Doppler frequencies. The throughput gain of the proposed algorithm over ARF is 46% (15%) for a 1-tap (3-tap) channel over 10 m–250 m (16 m–160 m) normalized Doppler frequencies. Assuming a 3-tap channel and 30 m–50 m normalized Doppler frequencies, the throughput of the proposed algorithm is about 31 Mbps, nearly the same as that of ideal link adaptation, whereas the throughput of ARF is about 24 Mbps, leading to a 30% throughput gain of the proposed algorithm over ARF. The firmware is implemented in C and on Xilinx Zynq 7020 (Xilinx, San Jose, CA, USA) for wireless tests.

Joint Scheduling and Power Allocation Using Non-Orthogonal Multiple Access in Multi-Cell Beamforming Networks

The proliferation of smart devices has boosted the improvement of wireless network technologies. Herein, networking functions should be properly guaranteed even in highly dense environments in terms of service quality and data rate. In this paper, we present an efficient power allocation algorithm using non-orthogonal multiple access and smart array antennas to increase the capacity in highly overlapped multi-cell environments. We evaluate the proposed algorithm and compare with the conventional orthogonal multiple access scheme with smart antennas. Through intensive simulations and experiments at the system level for performance evaluations, it is confirmed that the proposed scheme obtains a drastic throughput gain up to 50% in the overlapped region of highly dense networks.

Detection of Misconfigured Wi-Fi Tethering in Managed Networks

Wi-Fi tethering using a mobile device (e.g., a smartphone or a tablet) as a hotspot for other devices has become a common practice. Despite the potential benefits of Wi-Fi tethering, the open source nature of mobile operating systems (e.g., Google Android) can be abused by a selfish device to manipulate channel-access parameters to gain an unfair advantage in throughput performance. This can cause serious performance problems within a well-planned Wi-Fi network due to an unauthorized selfish or misconfigured tethering device interfering with nearby well-planned access points (APs). In this paper, we demonstrate that the selfish behavior of a tethering node that adjusts the clear channel assessment (CCA) threshold has strong adverse effects in a multi-AP network, while providing the selfish node a high throughput gain. To mitigate this problem, we present a passive online detection scheme that identifies the network condition and detects selfish tethering nodes with high accuracy by exploiting the packet loss information of on-going transmissions. To the best of our knowledge, this is the first research to consider the problem of detecting a selfish tethering node in managed Wi-Fi networks.

Reverse Cooperatively Routed Wi-Fi Direct in the Advent of 5G Driven Designs

In this article, the instantiation of the logic behind the so-called “Multi-Connect” mode of Wi-Fi Direct is advocated for and prescribed in order to make it possible to increase, if not multiply, the data throughput attainable by a given P2P Device thanks to traffic aggregation. In general, the described extension to the existing standard, defined by the Wi-Fi Peer-to-Peer (P2P) Technical Specification, enables the reversal of the logical roles of a P2P Group Owner (P2P GO) and its respective P2P Clients, while ensuring that the general architectural framework of Wi-Fi Direct should remain unchanged for backward compatibility reasons. This is achieved with the aid of a number of specifically tailored modifications including: new P2P Attribute allocation, extended Group Owner determination, modified P2P State Machine operation, enhanced Concurrent and Non-Concurrent operation, as well as expanded Reverse Operation Logic definition. The concept is illustrated with and verified through the analysis of cumulative virtual channel capacity and, therefore, the expected data throughput gain.