In this paper, a 63–86-GHz receiver (RX) is proposed using a wideband noise-cancelling low noise amplifier (LNA) with phase and amplitude compensation circuit. Such phase and amplitude compensation circuit consists of a 4-to-1 asymmetric power-combining transformer and two amplitude adjusting amplifiers, which improves the noise-cancelling ratio of mm-wave LNA. Meanwhile, a wideband active mixer and an absorptive IF amplifier are introduced to provide a reflectionless operation for signal quality improvement. Based on aforementioned structures, a wideband RX is implemented and fabricated using a conventional 40-nm CMOS technology. The measured results show that the RX achieves 22.5-dB conversion gain with 23-GHz 3-dB bandwidth and 50.7-mW power consumption. The minimum NF is 5.6-dB with less than 1.9-dB variation in the whole operation band.

In this paper, a$\emph{\textbf{W}}$-band$2\times 2$phased-array transmitter with digital gain compensation is proposed to minimize amplitude and angle errors of synthesized beams. The RF phase-shifting architecture is utilized for the phased-array transmitter to reduce circuit blocks and lower system complexity. The high-resolution phase shifting is achieved by a vector-sum phase shifter, which is based on a quadrature-all-pass filter (QAF) with compensation network and Gilbert-type variable gain amplifiers (VGAs) with digital-controlled current digital-to-analog converters (I-DACs). To lower the gain error introduced by the phase shifter in RF phase-shifting architecture, the variable-gain power amplifier (VGPA) is proposed. The gain of the VGPA is finely adjusted to compensate the gain variation of phase shifter in different phase states. Meanwhile, the phase variation of the VGPA under variable gain states is optimized to avoid the influence on phase errors. To verify the aforementioned mechanism, a$\emph{\textbf{W}}$-band$2\times 2$phased-array transmitter is implemented and fabricated in a conventional 40-nm CMOS technology. Based on the digital gain-compensation technique, the phased-array transmitter exhibits a less than 1.12dB RMS gain error and less than 1.82$^\circ$RMS phase error. In addition, the fabricated chip achieves 8.13dBm peak saturated output power and better than 9dB power gain with 135mW power consumption for each channel.

Predicting disease progression in the initial stage to implement early intervention and treatment can effectively prevent the further deterioration of the condition. Traditional methods for medical data analysis usually fail to perform well because of their incapability for mining the correlation pattern of pathogenies. Therefore, many calculation methods have been excavated from the field of deep learning. In this study, we propose a novel method of influence hypergraph convolutional generative adversarial network (IHGC-GAN) for disease risk prediction. First, a hypergraph is constructed with genes and brain regions as nodes. Then, an influence transmission model is built to portray the associations between nodes and the transmission rule of disease information. Third, an IHGC-GAN method is constructed based on this model. This method innovatively combines the graph convolutional network (GCN) and GAN. The GCN is used as the generator in GAN to spread and update the lesion information of nodes in the brain region-gene hypergraph. Finally, the prediction accuracy of the method is improved by the mutual competition and repeated iteration between generator and discriminator. This method can not only capture the evolutionary pattern from early mild cognitive impairment (EMCI) to late MCI (LMCI) but also extract the pathogenic factors and predict the deterioration risk from EMCI to LMCI. The results on the two datasets indicate that the IHGC-GAN method has better prediction performance than the advanced methods in a variety of indicators.

Imaging genetics provides unique insights into the pathological studies of complex brain diseases by integrating the characteristics of multi-level medical data. However, most current imaging genetics research performs incomplete data fusion. Also, there is a lack of effective deep learning methods to analyze neuroimaging and genetic data jointly. Therefore, this paper first constructs the brain region-gene networks to intuitively represent the association pattern of pathogenetic factors. Second, a novel feature information aggregation model is constructed to accurately describe the information aggregation process among brain region nodes and gene nodes. Finally, a deep learning method called feature information aggregation and diffusion generative adversarial network (FIAD-GAN) is proposed to efficiently classify samples and select features. We focus on improving the generator with the proposed convolution and deconvolution operations, with which the interpretability of the deep learning framework has been dramatically improved. The experimental results indicate that FIAD-GAN can not only achieve superior results in various disease classification tasks but also extract brain regions and genes closely related to AD. This work provides a novel method for intelligent clinical decisions. The relevant biomedical discoveries provide a reliable reference and technical basis for the clinical diagnosis, treatment and pathological analysis of disease.

A cascaded mode-switching sub-sampling PLL with quadrature dual-mode voltage waveform-shaping oscillator is proposed in this paper. The dual-mode voltage waveform-shaping oscillator is introduced to extend the tuning range and improve phase noise performance at mm-wave frequency, simultaneously. Meanwhile, the dual-mode quadrature topology is investigated to reduce the phase noise and quadrature phase error, compared to conventional quadrature oscillator. Then, the proposed oscillator is applied in a cascaded PLL with divider-less mode-switching sub-sampling loop, which can obtain the merits of high frequency-resolution, low loop noise, and wide frequency locking range. Both the dual-mode voltage waveform-shaping oscillator and the cascaded PLL are verified and fabricated in a 28-nm CMOS process. The FoM and FoMTof the oscillator at 10 MHz offset are -188.2 dBc/Hz and -200.7 dBc/Hz respectively. The proposed PLL prototype exhibits a frequency range from 22.8 to 33.9 GHz with a typical power consumption of 41.7 mW. The phase noise across the frequency band is from -104.1 to -108.2 dBc/Hz at 1 MHz offset. The jitter FoMjis -236.2 dB.

In this paper, a wideband vector-sum phase shifter with phase linearity enhancement technology is proposed to minimize phase error. The digital phase shifter synthesizes the output phases by modulating and summing four quadrature signals. A common-source stage with transformer-based matching network and a quadrature-all-pass filter (QAF) are utilized for the quadrature signals generation within a wideband. Besides, to decrease the influence of parasitics on amplitude and phase error, a resistor-based compensation technology is employed in the QAF implementation. Then, to obtain the 360° range high resolution phase shifts, the quadrature signals are modulated by four Gilbert-type variable gain amplifiers (VGAs) with digital-controlled current-digital to analog converters (current-DACs). To further minimize phase error, an on-chip phase linearity enhancement loop is utilized. The drain voltages of current-DACs are detected and compared to the presupposed reference voltages generated by the loop. Besides, the compared results are fed back to control the current-DACs to achieve phase linearity enhancement. To verify the mentioned mechanism, a 7-bit digital phase shifter operating at 22-44 GHz with on-chip phase linearity enhancement technology is implemented and fabricated in a 28-nm CMOS technology. Based on the phase linearity enhancement technology, the phase shifter exhibits a RMS amplitude error of 0.36-0.59 dB and RMS phase error of 0.92° -1.02° without off-chip digital pre-distortion (DPD) technology. The total DC power consumption including the phase linearity enhancement loops is 35 mW under a 0.9V supply voltage.

Background Computer Generated Animations (CGA), when applied to three-dimensional (3D) city models (3DCM), can be used as powerful tools to support urban decision-making. This leads to a new paradigm, based on procedural modeling, that allows the integration of known urban structures. This paper introduces a new workflow for the development of high-quality approximations of urban models in a short time and enables facilities to be imported from other cities into a given city model, following specific generation rules. Thus, this workflow provides a very simple approach to observe, study, and simulate the implementation of models already developed in other cities, in a city where they are not yet adopted. Examples of these models include all types of mobility systems and urban infrastructure. This allows us to perceive the environmental impact of certain decisions in the real world, as well as to carry out simple simulations to determine the changes that can occur in the flows of people, traffic, and other city activities.

Mobility modeling is important for network simulation. However, Random Waypoint model and its variants fail to satisfactorily depict mobile users' movement patterns with fidelity. Two features of a game engine, namely character AI and pathfinding can be utilized to enhance mobility modeling in network simulations. This demo builds a virtual scene of typical office environment with multiple employees. Movements of the virtual characters are firstly generated and visualized using Random Waypoint model, and then using alternative approaches combining AI-based control with pathfinding. Through comparison it can be clearly observed that the latter improves simulation fidelity significantly.

We present a coordinated ensemble of scalable computing techniques to accelerate a number of key tasks needed for vision-based gesture interaction, by using the cluster driving a large display system. A hybrid strategy that partitions the scanning task of a frame image by both region and scale is proposed. Based on this hybrid strategy, a novel data structure called a scanning tree is designed to organize the computing nodes. The level of effectiveness of the proposed solution was tested by incorporating it into a gesture interface controlling a ultra-high-resolution tiled display wall.

Roof falls due to poor rock conditions in a coal longwall panel may threaten miner's life and cause significant interruption to mine production. There has been a requirement for technologies that are capable of imaging the rock conditions in longwall coal mining, ahead of the working face and without any interruption to production. A feasibility study was carried out to investigate the characteristics of seismic signals generated by the continuous coal cutter (shearer) and recorded by geophone arrays deployed ahead of the working face, for the purpose of seismic tomographic imaging of roof strata condition before mining. Two experiments were conducted at a coal mine using two arrays of geophones. The experiments have demonstrated that the longwall shearer generates strong and low-frequency ( ~ 40 Hz) seismic energy that can be adequately detected by geophones deployed in shallow boreholes along the roadways as far as 300 m from the face. Using noise filtering and signal cross correlation techniques, the seismic arrival times associated with the shearer cutting can be reliably determined. It has proved the concept that velocity variations ahead of the face can be mapped out using tomographic techniques while mining is in progress.

There has been a requirement for technologies that are capable of imaging the rock conditions in mining, ahead of the working face and without any interruption to the production. A seismic tomography experiment was carried out at a coal mine in order to prove the concept of using the continuous coal cutter (shearer) as a source for mapping rock conditions ahead of mining. The results have shown that the shearer generated strong seismic energy that can be adequately detected by geophones deployed as far as 300 m from the source and along the gateroads. It is demonstrated that the seismic arrival times associated with the shearer cutting can be reliably determined using noise filtering and signal cross-correlation techniques. This enables reliable seismic tomograms of the roof rock conditions to be obtained in real-time while mining is in progress.

In the near future personal mobile devices will become ingredients of the grid. Foreseeing this, the thesis work aims at leveraging scalable computing techniques to enhance mobile devices so that their interoperability with peers and grid infrastructure will be improved, and their unique resources could be better contributed to the grid. A middleware framework named personal augmented computing environment (PACE) is proposed, the research progress is reported, and future plan is introduced. The main contributions include 1) investigation of collaborative visualization using display clusters composed by mobile devices, 2) exploration of context- aware methods for mobile devices to achieve efficient utilization of peer and grid resources, and 3) development of novel scalable human-computer interaction techniques through seamless integration of mobile devices and environmental infrastructures.

Many applications of wireless sensor networks require that the sensor nodes be location-aware. Using RSS, TOA, TDOA and other range measurement techniques, we are able to get the measurements of dissimilarity or pairwise distance between different neighboring sensor nodes. In this paper we use these pairwise distances to make local maps, and then align the overlapping local maps to acquire relative global coordinates of sensor nodes. We present local tangent space alignment (LTSA)-based localization, and local MDS based localization (LMBL) schemes owing to development of manifold learning algothrims. Simulations show that LMBL and LTSA-based scheme outperform the LLE-based scheme.

Link analysis is a widely applied technique for unveiling the social network structure among individuals, either in a Web setting or mobile environment. However, usability and efficiency for link analysis are hard to be obtained through prevailing frameworks. In this paper, we present IOHS (integration-of-historical-segments), a service framework for temporal link analysis (TLA). With IOHS, a TLA task is summarized into three subtasks, i.e. fitting the linkages of data in chronicle time intervals, assigning different weights to historical linkages of data and entities involved, and integrating the weights of linkages of data in the main analysis task, such as scoring or searching The main contribution of IOHS is factoring out the non-variant part of a TLA task into the framework to give researchers much freedom to concentrate on model and parameter fitting. Another advantage of using IOHS is that the difficulty in implementing the trial-and-error process is greatly reduced so that optimized models could be obtained with much ease. Our experimental results show that IOHS is of high usability and efficiency.

In this paper, the effects of three visual factors: scene complexity, stereovision and motion parallax on correct perception of a virtual object's size were analyzed in an immersive virtual environment. We designed a controlled experiments set to incorporate visual conditions that reflected all twelve different configuration combinations of the three visual factors. Under each visual condition, subject performed the task of making judgments of the sizes of a virtual object displayed at five different distances from him/her. A total number of eighteen subjects participated in our study. The subjects' judgments and the corresponding actual sizes of the virtual object were recorded. Based on the collected data, two quantitative measures of subjects' performance were derived and analyzed. The results of our experiments were consistent across the majority of the subject population and suggested that scene complexity and stereovision could have significant impact on the performance of a user of virtual environments to make correct judgments on a virtual object's size. On the contrary, motion parallax, either produced by the virtual environment or by the observer, might not be a significant factor in determining that performance