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In this letter, the support vector machine (SVM) regression approach is introduced to model the three-dimensional (3-D) high density microwave packaging structure. The SVM is based on the structural risk minimization principle, which leads to a good generalization ability. With a 3-D vertical interconnect used as an example, the SVM regression model is electromagnetically developed with a set of training data and testing data, which is produced by the electromagnetic simulation. Experimental results suggest that the developed model performs with a good predictive ability in analyzing the electrical performance. Index Terms-Fuzz button, low temperature co-fired ceramic (LTCC), support vector machine (SVM), support vector regression (SVR), three-dimensional (3-D) vertical interconnect.

Abstract-Spatial-Multiplexing aided Spatial Modulation (SMx-SM) is proposed, which intrinsically amalgamates the concept of Vertical Bell Labs Space-Time (V-BLAST) and Spatial Modulation (SM) to attain a high transmission rate, despite its low number of Radio Frequency (RF) chains at the transmitter. Specifically, in the SMx-SM scheme, the Transmit Antennas (TAs) are partitioned into groups and the SM technique is applied individually to each group. Furthermore, lowcomplexity threshold-aided Compressive Sensing (CS) based and Message Passing (MP) based detectors are derived for our SMx-SM system. Our simulation results show that the proposed SMx-SM system exhibits a better performance despite its lower complexity than the Conventional Generalized Spatial Modulation (C-GSM) system. More importantly, the proposed SMx-SM system is capable of providing considerable performance gains over the V-BLAST system at the same number of RF chains and throughput. Finally, an upper bound is derived for the Average Bit Error Probability (ABEP), which is confirmed by our simulation results.

Differential spatial modulation (DSM), which does not require the channel state information (CSI) at the receiver, is an attractive alternative to its coherent counterpart. The optimal maximum likelihood (ML) detector of the DSM system employs the classic block-by-block method for jointly detecting the activated antenna matrix (AM) and the modulation symbols, resulting in a high computational complexity. In this Letter, a low-complexity near ML detector, which operates on a symbol-by-symbol basis, is proposed for the DSM scheme. Specifically, in each block, the index of the activated transmit antenna (TA) and modulation symbol in each time slot are firstly obtained, and then these antenna indices (AIs) are utilized to simply determine the index of the activated AM. Simulation results show that the proposed algorithm is capable of offering almost the same performance as that of the ML detector with more than 90% reduction in complexity. Index Terms-Differential spatial modulation (DSM), maximum likelihood (ML) detection, symbolbased-symbol I. Introduction D IFFERENTIAL spatial modulation (DSM) [1]-[4]is a novel multiple-input multiple-output (MIMO) wireless transmission technique, which relies on a single radio-frequency (RF) transmit structure without the need of the channel state information (CSI). It is an attractive alternative to the coherent spatial modulation (SM) [5]-[8], which is considered as a promising transmission technique for large-scale MIMO systems in terms of both theoretical researches and practical implementations [9]-[10].In DSM, one out of Q antenna matrices (AMs) is activated to dispense N t symbols to N t transmit antennas (TAs) in N t time instants. Therefore, high-data transmission is attainable in comparison with the differential spacetime shift keying (DSTSK) scheme [11], where only a single symbol is transmitted in a space-time block.

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With the rapid advancement of video and image processing technologies in Internet-of-Things (IoT), it is urgent to address the issues in real-time performance, clarity and reliability of image recognition technique for monitoring system in foggy weather. In this work, a fast defogging image recognition algorithm is proposed based on bilateral hybrid filtering. Firstly, the mathematical model based on bilateral hybrid filtering is established. The dark channel is used for filtering and denoising the defogging image. After that, a bilateral hybrid filtering method can effectively improving the transmittance and robustness of images in defogging image by using a combination of guided filtering and median filtering. On this basis, the proposed algorithm greatly decreases the computation complexity of defogging image recognition and reduces the image execution time. Experimental results show that, the defogging effect and speed are encouraging. The image recognition rate reaches 98.8% after defogging.