Scalable networking for scientific research data transfer is a vital factor in the progress of data-intensive research, such as collaborative research on observation of black hole. In this paper, investigations of the nature of practical research traffic allow us to introduce optical flow switching (OFS) and contents delivery network (CDN) technologies into a wide area network (WAN) to realize highly scalable networking. To measure the scalability of networks, energy consumption in the WAN is evaluated by considering the practical networking equipment as well as reasonable assumptions on scientific research data transfer networks. In this study, we explore the energy consumption performance of diverse Japan and US topologies and reveal that the energy consumption of a routing and wavelength assignment algorithm in an OFS scheduler becomes the major hurdle when the number of nodes is high, for example, as high as that of the United States of America layer 1 topology. To provide computational scalability of a network dimensioning algorithm for the CDN based WAN, a simple heuristic algorithm for a surrogate location problem is proposed and compared with an optimal algorithm. This paper provides intuitions and design rules for highly scalable research data transfer networks, and thus, it can accelerate technology advancements against the encountering big-science problems.

We have proposed a waveguide to microstrip line transition, which perpendicularly connects one waveguide into two microstrip lines. It consists of only a waveguide and a dielectric substrate with copper foils. A backshort waveguide for typical transitions is not needed. Additionally, the transition does not require via holes on the substrate. These innovations simplify the structure and the manufacturing process. We assume that our transition and antennas are co-located on the substrate. We reduced the undesirable radiation from the transition so as not to contaminate the desirable radiation pattern. In this paper, we address output phase of our transition. Since the transition has two MSL output ports connecting to different radiation elements, the phase error between two dividing signals leads to beam shift in the radiation pattern. Unfortunately, misalignment of etching pattern between copper layers of the substrate is unavoidable. The structural asymmetry causes the phase error. In order to tolerate the misalignment, we propose to add a pair of open stubs to the transition. We show that the structure drastically stabilizes the output phase. Though the stubs create some extra radiation, we confirm that the impact is not significant. Moreover, we fabricate and measure a prototype antenna that uses the transition. In the case of with stubs, the radiation pattern is unchanged even if the misalignment is severe.

This work presents a DNN accelerator architecture specifically designed for performing efficient inference on compressed and sparse DNN models. Leveraging the data sparsity, a runtime processing scheme is proposed to deal with the encoded weights and activations directly in the compressed domain without decompressing. Furthermore, a new data flow is proposed to facilitate the reusage of input activations across the fully-connected (FC) layers. The proposed design is implemented and verified using the Xilinx Virtex-7 FPGA. Experimental results show it achieves 1.99×, 1.95× faster and 20.38×, 3.04× more energy efficient than CPU and mGPU platforms, respectively, running AlexNet.

We experimentally evaluate the performance of a noncontact system that measures the heartbeat of a sleeping person. The proposed system comprises a pair of radar systems installed at two different positions. We use millimeter-wave ultra-wideband multiple-input multiple-output array radar systems and evaluate the performance attained in measuring the heart inter-beat interval and body movement. The importance of using two radar systems instead of one is demonstrated in this paper. We conduct three types of experiments; the first and second experiments are radar measurements of three participants lying on a bed with and without body movement, while the third experiment is the radar measurement of a participant actually sleeping overnight. The experiments demonstrate that the performance of the radar-based vital measurement strongly depends on the orientation of the person under test. They also show that the proposed system detects 70% of rolling-over movements made overnight.

In this paper, user scheduling with beam selection for full-digital massive multi-input multi-output (MIMO) is proposed. Inter-user interference (IUI) can be canceled by precoding such as zero-forcing at a massive MIMO base station if ideal hardware implementation is assumed. However, owing to the non-ideal characteristics of hardware components, IUI occurs among multiple user terminals allocated on the same resource. Thus, in the proposed scheme, the directions of beams for allocated user terminals are adjusted to maximize the total user throughput. User allocation based on the user throughput after the adjustment of beam directivity is then carried out. Numerical results obtained through computer simulation show that when the number of user terminals in the cell is two and the number of user terminals allocated to one resource block (RB) is two, the throughput per subcarrier per subframe improves by about 3.0 bits. On the other hand, the fairness index (FI) is reduced by 0.03. This is because only the probability in the high throughput region increases as shown in the cumulative distribution function (CDF) of throughput per user. Also, as the number of user terminals in the cell increases, the amount of improvement in throughput decreases. As the number of allocated user terminals increases, more user terminals are allocated to the cell-edge, which reduces the average throughput.

This paper presents a method to optimize 2-D sparse array configurations along with a technique to interpolate holes to accurately estimate the direction of arrival (DOA). Conventional 2-D sparse arrays are often defined using a closed-form representation and have the property that they can create hole-free difference co-arrays that can estimate DOAs of incident signals that outnumber the physical elements. However, this property restricts the array configuration to a limited structure and results in a significant mutual coupling effect between consecutive sensors. In this paper, we introduce an optimization-based method for designing 2-D sparse arrays that enhances flexibility of array configuration as well as DOA estimation accuracy. We also propose a method to interpolate holes in 2-D co-arrays by nuclear norm minimization (NNM) that permits holes and to extend array aperture to further enhance DOA estimation accuracy. The performance of the proposed optimum arrays is evaluated through numerical examples.

Surveillance through aerial systems is in place for years. Such systems are expensive, and a large fleet is in operation around the world without upgrades. These systems have low resolution and multiple analog cameras on-board, with Digital Video Recorders (DVRs) at the control station. Generated digital videos have multi-scenes from multi-feeds embedded in a single video stream and lack video stabilization. Replacing on-board analog cameras with the latest digital counterparts requires huge investment. These videos require stabilization and other automated video analysis prepossessing steps before passing it to the mosaicing algorithm. Available mosaicing software are not tailored to segregate feeds from different cameras and scenes, automate image enhancements, and stabilize before mosaicing (image stitching). We present "AirMatch", a new automated system that first separates camera feeds and scenes, then stabilize and enhance the video feed of each camera; generates a mosaic of each scene of every feed and produce a super quality mosaic by stitching mosaics of all feeds. In our proposed solution, state-of-the-art video analytics techniques are tailored to work on videos from vintage cameras in aerial applications. Our new framework is independent of specialized hardware requirements and generates effective mosaics. Affine motion transform with smoothing Gaussian filter is selected for the stabilization of videos. A histogram-based method is performed for scene change detection and image contrast enhancement. Oriented FAST and rotated BRIEF (ORB) is selected for feature detection and descriptors in video stitching. Several experiments on a number of video streams are performed and the analysis shows that our system can efficiently generate mosaics of videos with high distortion and artifacts, compared with other commercially available mosaicing software.

This paper proposes statistical analysis of phase-only correlation (POC) functions under the phase fluctuation of signals due to additive Gaussian noise. We derive probability density function of phase-spectrum differences between original signal and its noise-corrupted signal with additive Gaussian noise. Furthermore, we evaluate the expectation and variance of the POC functions between these two signals. As the variance of Gaussian noise increases, the expectation of the peak of the POC function monotonically decreases and variance of the POC function monotonically increases. These results mathematically guarantee the validity of the POC functions used for similarity measure in matching techniques.

Approximate Simultaneous Diagonalization (ASD) is a problem to find a common similarity transformation which approximately diagonalizes a given square-matrix tuple. Many data science problems have been reduced into ASD through ingenious modelling. For ASD, the so-called Jacobi-like methods have been extensively used. However, the methods have no guarantee to suppress the magnitude of off-diagonal entries of the transformed tuple even if the given tuple has an exact common diagonalizer, i.e., the given tuple is simultaneously diagonalizable. In this paper, to establish an alternative powerful strategy for ASD, we present a novel two-step strategy, called Approximate-Then-Diagonalize-Simultaneously (ATDS) algorithm. The ATDS algorithm decomposes ASD into (Step 1) finding a simultaneously diagonalizable tuple near the given one; and (Step 2) finding a common similarity transformation which diagonalizes exactly the tuple obtained in Step 1. The proposed approach to Step 1 is realized by solving a Structured Low-Rank Approximation (SLRA) with Cadzow's algorithm. In Step 2, by exploiting the idea in the constructive proof regarding the conditions for the exact simultaneous diagonalizability, we obtain an exact common diagonalizer of the obtained tuple in Step 1 as a solution for the original ASD. Unlike the Jacobi-like methods, the ATDS algorithm has a guarantee to find an exact common diagonalizer if the given tuple happens to be simultaneously diagonalizable. Numerical experiments show that the ATDS algorithm achieves better performance than the Jacobi-like methods.

Sieve algorithms are regarded as the best algorithms to solve the shortest vector problem (SVP) on account of its good asymptotical quality, which could make it outperform enumeration algorithms in solving SVP of high dimension. However, due to its large memory requirement, sieve algorithms are not practical as expected, especially on high dimension lattice. To overcome this bottleneck, TupleSieve algorithm was proposed to reduce memory consumption by a trade-off between time and memory. In this work, aiming to make TupleSieve algorithm more practical, we combine TupleSieve algorithm with SubSieve technique and obtain a sub-exponential gain in running time. For 2-tuple sieve, 3-tuple sieve and arbitrary k-tuple sieve, when selecting projection index d appropriately, the time complexity of our algorithm is O(20.415(n-d)), O(20.566(n-d)) and $O(2^{rac{kmathrm{log}_2p}{1-k}(n-d)})$ respectively. In practice, we propose a practical variant of our algorithm based on GaussSieve algorithm. Experimental results show that our algorithm implementation is about two order of magnitude faster than FPLLL's GuassSieve algorithm. Moreover, techniques such as XOR-POPCNT trick, progressive sieving and appropriate projection index selection can be exploited to obtain a further acceleration.

Z-complementary pairs (ZCPs) were proposed by Fan et al. to make up for the scarcity of Golay complementary pairs. A ZCP of odd length N is called Z-optimal if its zero correlation zone width can achieve the maximum value (N + 1)/2. In this letter, inserting three elements to a GCP of length L, or deleting a point of a GCP of length L, we propose two constructions of Z-optimal ZCPs with length L + 3 and L - 1, where L=2α 10β 26γ, α ≥ 1, β ≥ 0, γ ≥ 0 are integers. The proposed constructions generate ZCPs with new lengths which cannot be produced by earlier ones.

Deep learning is playing an increasingly important role in signal processing field due to its excellent performance on many inference problems. Parametric bilinear generalized approximate message passing (P-BiG-AMP) is a new approximate message passing based approach to a general class of structure-matrix bilinear estimation problems. In this letter, we propose a novel feed-forward neural network architecture to realize P-BiG-AMP methodology with deep learning for the inference problem of compressive sensing under matrix uncertainty. Linear transforms utilized in the recovery process and parameters involved in the input and output channels of measurement are jointly learned from training data. Simulation results show that the trained P-BiG-AMP network can achieve higher reconstruction performance than the P-BiG-AMP algorithm with parameters tuned via the expectation-maximization method.

In this letter, a low latency, high throughput and hardware efficient sorted MMSE QR decomposition (MMSE-SQRD) for multiple-input multiple-output (MIMO) systems is presented. In contrast to the method of extending the complex matrix to real model and thereafter applying real-valued QR decomposition (QRD), we develop a highly parallel decomposition scheme based on coordinate rotation digital computer (CORDIC) which performs the QRD in complex domain directly and then converting the complex result to its real counterpart. The proposed scheme can greatly improve the processing parallelism and curtail the nullification and sorting procedures. Besides, we also design the corresponding pipelined hardware architecture of the MMSE-SQRD based on highly parallel Givens rotation structure with CORDIC algorithm for 4×4 MIMO detectors. The proposed MMSE-SQRD is implemented in SMIC 55nm CMOS technology achieving up to 50M QRD/s throughput and a latency of 59 clock cycles with only 218 kilo-gates (KG). Compared to the previous works, the proposed design achieves the highest normalized throughput efficiency and lowest processing latency.

After a natural disaster it is critical to urgently find victims buried under collapsed buildings. Most people habitually carry smartphones with them. Smartphones have a feature that periodically transmits Wi-Fi signals called “Probe Requests” to connect with access points. Moreover, smartphones transmit “Clear to Send” when they receive a “Request to Send” alert. This motivated us to develop a hand-held smartphone finder system that integrates a novel method for accurately locating a smartphone using the Wi-Fi signals, to support rescue workers. The system has a unique graphical user interface that tracks target smartphones. Thus, rescue workers can easily reach victims who have their smartphones with them under collapsed buildings. In this paper, after introducing the localization method, the system architecture of the smartphone finder and its prototype system are described, along with some experimental results that demonstrate the effectiveness of the smartphone finder prototype.

Ising machines have attracted attention as they are expected to solve combinatorial optimization problems at high speed with Ising models corresponding to those problems. An induced subgraph isomorphism problem is one of the decision problems, which determines whether a specific graph structure is included in a whole graph or not. The problem can be represented by equality constraints in the words of combinatorial optimization problem. By using the penalty functions corresponding to the equality constraints, we can utilize an Ising machine to the induced subgraph isomorphism problem. The induced subgraph isomorphism problem can be seen in many practical problems, for example, finding out a particular malicious circuit in a device or particular network structure of chemical bonds in a compound. However, due to the limitation of the number of spin variables in the current Ising machines, reducing the number of spin variables is a major concern. Here, we propose an efficient Ising model mapping method to solve the induced subgraph isomorphism problem by Ising machines. Our proposed method theoretically solves the induced subgraph isomorphism problem. Furthermore, the number of spin variables in the Ising model generated by our proposed method is theoretically smaller than that of the conventional method. Experimental results demonstrate that our proposed method can successfully solve the induced subgraph isomorphism problem by using the Ising-model based simulated annealing and a real Ising machine.

Encrypted traffic identification is to predict traffic types of encrypted traffic. A deep residual convolution network is proposed for this task. The Softmax classifier is fused with its angular variant, which sets an angular margin to achieve better discrimination. The proposed method improves representation learning and reaches excellent results on the public dataset.

This paper presents the physical-layer cell identity (PCID) detection probability using the primary synchronization signal (PSS) and secondary synchronization signal (SSS) for the New Radio (NR) radio interface considering a large frequency offset and high Doppler frequency in multipath Rayleigh fading channels in the 28-GHz band. Simulation results show that cross-correlation based PSS detection after compensating for the frequency offset achieves higher PCID detection probability than autocorrelation based PSS detection at the average received signal-to-noise power ratio (SNR) values below approximately 0dB for the frequency stability of a user equipment (UE) oscillator of ϵ =5ppm. Meanwhile, both methods achieve almost the same PCID detection probability for average received SNR values higher than approximately 0dB. We also show that even with the large frequency offset caused by ϵ =20 ppm, the high PCID detection probability of approximately 90 (97)% and 90 (96)% is achieved for the cross-correlation or autocorrelation based PSS detection method, respectively, at the average received SNR of 0dB for the subcarrier spacing of 120 (240)kHz. We conclude that utilizing the multiplexing scheme for the PSS and SSS and their sequences is effective in achieving a high PCID detection probability considering a large frequency offset even with the frequency deviation of ϵ =20ppm in the 28-GHz band.

We have developed a direct spectrum division transmission (DSDT) technique that can divide a single-carrier signal into multiple sub-spectra and assign them to dispersed frequency resources of the satellite transponder to improve the spectrum efficiency of the whole system. This paper summarizes the satellite experiments on DSDT over a single and/or multiple satellite transponders, while changing various parameters such as modulation schemes, roll-off ratios, and symbol rates. In addition, by considering practical use conditions, we present an evaluation of the performance when the spectral density of each sub-spectrum differed across transponders. The satellite experiments demonstrate that applying the proposal does not degrade the bit error rate (BER) performance. Thus, the DSDT technique is a practical approach to use the scattered unused frequency resources over not only a single transponder but also multiple ones.

Automatic tracking and classification are essential for studying the behaviors of wild animals. Owing to dynamic far-shooting photos, the occlusion problem, protective coloration, the background noise is irregular interference for designing a computerized algorithm for reducing human labeling resources. Moreover, wild dolphin images are hard-acquired by on-the-spot investigations, which takes a lot of waiting time and hardly sets the fixed camera to automatic monitoring dolphins on the ocean in several days. It is challenging tasks to detect well and classify a dolphin from polluted photos by a single famous deep learning method in a small dataset. Therefore, in this study, we propose a generic Cascade Small Object Detection (CSOD) algorithm for dolphin detection to handle small object problems and develop visualization to backbone based classification (V2BC) for removing noise, highlighting features of dolphin and classifying the name of dolphin. The architecture of CSOD consists of the P-net and the F-net. The P-net uses the crude Yolov3 detector to be a core network to predict all the regions of interest (ROIs) at lower resolution images. Then, the F-net, which is more robust, is applied to capture the ROIs from high-resolution photos to solve single detector problems. Moreover, a visualization to backbone based classification (V2BC) method focuses on extracting significant regions of occluded dolphin and design significant post-processing by referencing the backbone of dolphins to facilitate for classification. Compared to the state of the art methods, including faster-rcnn, yolov3 detection and Alexnet, the Vgg, and the Resnet classification. All experiments show that the proposed algorithm based on CSOD and V2BC has an excellent performance in dolphin detection and classification. Consequently, compared to the related works of classification, the accuracy of the proposed designation is over 14% higher. Moreover, our proposed CSOD detection system has 42% higher performance than that of the original Yolov3 architecture.

This paper addresses pilot contamination in massive multiple-input multiple-output (MIMO) uplink. Pilot contamination is caused by reuse of identical pilot sequences in adjacent cells. To solve pilot contamination, the base station utilizes differences between the transmission frames of different users, which are detected via joint channel and data estimation. The joint estimation is regarded as a bilinear inference problem in compressed sensing. Expectation propagation (EP) is used to propose an iterative channel and data estimation algorithm. Initial channel estimates are attained via time-shifted pilots without exploiting information about large scale fading. The proposed EP modifies two points in conventional bilinear adaptive vector approximate message-passing (BAd-VAMP). One is that EP utilizes data estimates after soft decision in the channel estimation while BAd-VAMP uses them before soft decision. The other point is that EP can utilize the prior distribution of the channel matrix while BAd-VAMP cannot in principle. Numerical simulations show that EP converges much faster than BAd-VAMP in spatially correlated MIMO, in which approximate message-passing fails to converge toward the same fixed-point as EP and BAd-VAMP.