Operational amplifier is one of the most important analog basic blocks. Existing automated synthesis strategies for operational amplifiers solely focus on the optimization of single topology, making them unsuitable for scenarios requiring batch synthesis, such as dataset augmentation. In this paper, we in-troduce TPC-GAN, a generative model for batch topology syn-thesis of operational amplifiers in accordance with performance specifications. To be specific, it incorporates a reward network of circuit performance into the adversarial generative networks (GANs). This enables direct synthesis of novel and feasible circuit topology meeting performance specifications. Experimental results demonstrate that our proposed method can achieve a validity rate of 98% in circuit generation, among which 99.7% are novel relative to the training dataset. With the introduction of a reward network, a significant portion (82.8%) of the generated circuits satisfy performance specifications, which is a substantial improvement than those without. Transistor-level experimental results further demonstrate the practicality and competitiveness of our generated circuits with nearly 3x improvement over manual designs.

Topology is one of the dominant factors governing analog circuit performance but its automatic design remains elusive. Most analog circuit topology designs are largely dependent on human expertise or a limited set of classic structures. To explore the potential of AI-driven design of analog circuit, we propose an automatic generation and optimization workflow, TSO-Flow, for the behavioral-level design of three-stage operational amplifiers. To be specific, TSO-Flow employs an invertible graph generative model to transform discrete circuit topology into a continuous latent representation. By random sampling in the latent space, one can generate an ensemble of latent vectors which can be translated back to novel circuit topology. In contrast to previous methods, such forward and reverse transformations are analytically invertible and thus enable exact likelihood estimation for training. Furthermore, one can optimize the topology in the latent space where a surrogate model is introduced for performance prediction. We evaluate the proposed framework with state-of-the-art methods via experiments on three-stage operational amplifiers. The results demonstrate that TSO-flow provides up to 116% improvements on FoM, 61% reduction in power, 50% less simulations and an overall 5x efficiency enhancement over the baseline methods1.

MOTIVATION: Achieving a near complete understanding of how the genome of an individual affects the phenotypes of that individual requires deciphering the order of variations along homologous chromosomes in species with diploid genomes. However, true diploid assembly of long-range haplotypes remains challenging. RESULTS: To address this, we have developed Haplotype-resolved Assembly for Synthetic long reads using a Trio-binning strategy, or HAST, which uses parental information to classify reads into maternal or paternal. Once sorted, these reads are used to independently de novo assemble the parent-specific haplotypes. We applied HAST to cobarcoded second-generation sequencing data from an Asian individual, resulting in a haplotype assembly covering 94.7% of the reference genome with a scaffold N50 longer than 11 Mb. The high haplotyping precision (∼99.7%) and recall (∼95.9%) represents a substantial improvement over the commonly used tool for assembling cobarcoded reads (Supernova), and is comparable to a trio-binning-based third generation long-read-based assembly method (TrioCanu) but with a significantly higher single-base accuracy [up to 99.99997% (Q65)]. This makes HAST a superior tool for accurate haplotyping and future haplotype-based studies. AVAILABILITY AND IMPLEMENTATION: The code of the analysis is available at https://github.com/BGI-Qingdao/HAST. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

As the commercial launch of the fifth-generation (5G) wireless communications gets near, the trend from the Internet of Things (IoT) to the Internet of Everything (IoE) is emerging. Due to the advantages of the high mobility, high Line-of-Sight (LoS) probability and low labor cost, unmanned aerial vehicles (UAVs) may play an important role in the future IoT communication networks, e.g., data collection in remote areas. In this article, we study the 3-D placement and resource allocation of multiple UAV-mounted base stations (BSs) in an uplink IoT network, where the balanced task for the UAV-BSs, the limited channel resource, and the signal interference are taken into consideration. In the considered system, the total transmission power of IoT devices is minimized, subject to a signal-to-interference-and-noise ratio (SINR) threshold for each device. First, aiming to balance the task of each UAV, we propose a clustering algorithm based on an improved$K$-means method to divide IoT devices into several groups so that the number of devices in each group is roughly the same. Then, based on matching theory, a modified-Hungarian-based dynamic many–many matching (HD4M) algorithm is designed for assigning subchannels to IoT devices, which can efficiently mitigate the interference. Finally, we jointly optimize the transmission power of IoT devices and the altitudes of UAVs via an alternating iterative method. The simulation results show that the total transmission power decreases significantly after applying the proposed algorithms.

The business process of load forecasting algorithm for distribution network planning is studied in depth, and the work steps of load forecasting in different places are discussed according to different planning objectives. Then, based on power sensor net, we systematically describes various power demand forecasting models and related theories, as well as their respective application scenarios. Finally, on the basis of theoretical research, relevant experiments are carried out, and the economic benefits of load forecasting are analyzed and discussed by using reliability evaluation method.