This paper presents two ultra-low voltage and high performance VCO ICs with novel harmonic tuned LC tank which provides different harmonic impedance and shapes the pseudo-square drain voltage waveform of transistors. In the novel tank, two additional inductors are connected between the drains of the cross-coupled pMOSFETs and the conventional LC tank, and they effectively decrease second harmonic load impedance and increase third harmonic load impedance of the transistors. In this paper, the novel harmonic tuned LC tank is applied to two different structure VCOs. These two VCOs exhibit over 2 dB better phase noise performance than conventional LC tank VCOs among all tuning range. The conventional and proposed VCO ICs are designed, fabricated and measured on wafer in 45-nm SOI CMOS technology. With novel harmonic tuned LC tank, the novel two VCOs exhibit measured best phase-noise of -125.7 and -129.3 dBc/Hz at 10 MHz offset and related FoM of -190.2 and -190.5 dBc/Hz at a supply voltage of 0.3 V and 0.35 V, respectively. Frequency tuning range of the two VCOs are from 13.01 to 14.34 GHz and from 15.02 to 16.03GHz, respectively.

In compressive sensing theory (CS), the restricted isometry property (RIP) is commonly used for the measurement matrix to guarantee the reliable recovery of sparse signals from linear measurements. Although many works have indicated that random matrices with excellent recovery performance satisfy the RIP with high probability, Toeplitz-structured matrices arise naturally in real scenarios, such as applications of linear time-invariant systems. Thus, the corresponding measurement matrix can be modeled as a Toeplitz (partial) structured matrix instead of a completely random matrix. The structure characteristics introduce coherence and cause the performance degradation of the measurement matrix. To enhance the recovery performance of the Toeplitz structured measurement matrix in multichannel convolution source separation, an efficient construction of measurement matrix is presented, referred to as sparse random block-banded Toeplitz matrix (SRBT). The sparse signal is pre-randomized by locally scrambling its sample locations. Then, the signal is subsampled using the sparse random banded matrix. Finally, the mixing measurements are obtained. Based on the analysis of eigenvalues, the theoretical results indicate that the SRBT matrix satisfies the RIP with high probability. Simulation results show that the SRBT matrix almost matches the recovery performance of random matrices. Compared with the existing banded block Toeplitz matrix, SRBT significantly improves the probability of successful recovery. Additionally, SRBT has the advantages of low storage requirements and fast computation in reconstruction.

This paper presents two ultra-low voltage and high performance VCO ICs with two novel transformer-based harmonic tuned tanks. The first proposed harmonic tuned tank effectively shapes the pseudo-square drain-node voltage waveform for close-in phase noise reduction. To compensate the voltage drop caused by the transformer, an improved second tank is proposed. It not only has tuned harmonic impedance but also provides a voltage gain to enlarge the output voltage swing over supply voltage limitation. The VCO with second tank exhibits over 3 dB better phase noise performance in 1/f2 region among all tuning range. The two VCO ICs are designed, fabricated and measured on wafer in 45-nm SOI CMOS technology. With only 0.3 V supply voltage, the proposed two VCO ICs exhibit best phase noise of -123.3 and -127.2 dBc/Hz at 10 MHz offset and related FoMs of -191.7 and -192.2 dBc/Hz, respectively. The frequency tuning ranges of them are from 14.05 to 15.14 GHz and from 14.23 to 15.68 GHz, respectively.

There are different types of social ties among people, and recognizing specialized types of relationship, such as family or friend, has important significance. It can be applied to personal credit, criminal investigation, anti-terrorism and many other business scenarios. So far, some machine learning algorithms have been used to establish social relationship inferencing models, such as Decision Tree, Support Vector Machine, Naive Bayesian and so on. Although these algorithms discover family members in some context, they still suffer from low accuracy, parameter sensitive, and weak robustness. In this work, we develop a Novel Family Relationship Recognition (NFRR) algorithm on telecom dataset for identifying one's family members from its contact list. In telecom dataset, all attributes are divided into three series, temporal, spatial and behavioral. First, we discover the most probable places of residence and workplace by statistical models, then we aggregate data and select the top-ranked contacts as the user's intimate contacts. Next, we establish Relational Spectrum Matrix (RSM) of each user and its intimate contacts to form communication feature. Then we search the user's nearest neighbors in labelled training set and generate its Specialized Family Spectrum (SFS). Finally, we decide family relationship by comparing the similarity between RSM of intimate contacts and the SFS. We conduct complete experiments to exhibit effectiveness of the proposed algorithm, and experimental results also show that it has a lower complexity.

This letter proposes a load balance and power transfer scheme among small cell base stations (SBSs) to maximize the sum rate of small cell network. In the proposed scheme, small cell users (SUEs) are firstly associated with their nearest SBSs, then the overloaded SBSs can be determined. Further, the methods, i.e., Case 1: SUEs of overloaded SBSs are offloaded to their neighbor underloaded SBSs or Case 2: SUEs of overloaded SBSs are served by their original associated SBSs through obtaining power from their nearby SBSs that can provide higher data rate is selected. Finally, numerical simulations demonstrate that the proposed scheme has better performance.

Bugs in industrial financial systems have not been extensively studied. To address this gap, we focused on the empirical study of bugs in three systems, PMS, β-Analyzer, and OrderPro. Results showed the 3 most common types of bugs in industrial financial systems to be internal interface (19.00%), algorithm/method (17.67%), and logic (15.00%).

The basic requirements of the distributed Web crawling systems are: short download time, low communication overhead and balanced load which largely depends on the systems' Web partition strategies. In this paper, we propose a DHT-based distributed Web crawling system and several DHT-based Web partition methods. First, a new system model based on a DHT method called the Content Addressable Network (CAN) is proposed. Second, based on this model, a network-distance-based Web partition is implemented to reduce the crawler-crawlee network distance in a fully distributed manner. Third, by utilizing the locality on the link space, we propose the concept of link-based Web partition to reduce the communication overhead of the system. This method not only reduces the number of inter-links to be exchanged among the crawlers but also reduces the cost of routing on the DHT overlay. In order to combine the benefits of the above two Web partition methods, we then propose 2 distributed multi-objective Web partition methods. Finally, all the methods we propose in this paper are compared with existing system models in the simulated experiments under different datasets and different system scales. In most cases, the new methods show their superiority.

Internet computing is proposed to exploit personal computing resources across the Internet in order to build large-scale Web applications at lower cost. In this paper, a DHT-based distributed Web crawling model based on the concept of Internet computing is proposed. Also, we propose two optimizations to reduce the download time and waiting time of the Web crawling tasks in order to increase the system's throughput and update rate. Based on our contributor-friendly download scheme, the improvement on the download time is achieved by shortening the crawler-crawlee RTTs. In order to accurately estimate the RTTs, a network coordinate system is combined with the underlying DHT. The improvement on the waiting time is achieved by redirecting the incoming crawling tasks to light-loaded crawlers in order to keep the queue on each crawler equally sized. We also propose a simple Web site partition method to split a large Web site into smaller pieces in order to reduce the task granularity. All the methods proposed are evaluated through real Internet tests and simulations showing satisfactory results.