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.

Electromagnetic scattering of an electromagnetic plane wave from a rectangular hole in a thick conducting screen is solved using the Kirchhoff approximation (KA). The scattering fields can be derived as field radiations from equivalent magnetic current sources on the aperture of the hole. Some numerical results are compared with those by the Kobayashi potential (KP) method. The proposed method can be found to be efficient to solve the diffraction problem for high frequency regime.

A noise-robust and accuracy-enhanced microwave imaging algorithm is presented for microwave ablation monitoring of cancer treatment. The ablation impact of dielectric change can be assessed by microwave inverse scattering analysis, where the dimension and dielectric drop of the ablation zone enable safe ablation monitoring. We focus on the distorted Born iterative method (DBIM), which is applicable to highly heterogeneous and contrasted dielectric profiles. As the reconstruction accuracy and convergence speed of DBIM depend largely on the initial estimate of the dielectric profile or noise level, this study exploits a prior estimate of the DBIM for the pre-ablation state to accelerate the convergence speed and introduces the matched-filter-based noise reduction scheme in the DBIM framework. The two-dimensional finite-difference time-domain numerical test with realistic breast phantoms shows that our method significantly enhances the reconstruction accuracy with a lower computational cost.

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.

An optical-layer adaptive restoration scheme is validated by a real-time experiment and numerical analyses. In this paper, it is assumed that this scheme can adaptively optimize the bitrate (up to 600Gb/s) and an optical reach with 100Gb/s granularity to maintain high-capacity optical signal transmission. The practicality of 600-Gb/s/carrier optical signal transmission over 101.6-km field-installed fiber is confirmed prior to the adaptive restoration experiment. After modifying the field setup, a real-time experiment on network recovery is demonstrated with bitrate adaptation for 600-Gb/s to 400-Gb/s signals. The results indicate that this scheme can restore failed connections with recovery times comparable to those of conventional restoration scheme; thus 99.9999% system availability can be easily attained even under double-link failures. Numerical analysis clarifies that adaptive restoration can recover >80% of double-link failures on several realistic topologies and improvement amount against conventional scheme is semi-statistically characterized by restoration path length.

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.

In this paper, the issue of malicious URL detection is investigated. Firstly a P system is proposed. Then the new P system is introduced to design the optimization algorithm of BP neural network to achieve the malicious URL detection with better performance. In the end some examples are included and corresponding experimental results display the advantage and effectiveness of the optimization algorithm proposed.

With the development and spread of Internet of Things (IoT) technology, various kinds of data are now being generated from IoT devices. Some data generated from IoT devices depend on geographical location and time, and we refer to them as spatio-temporal data (STD). Since the “locally produced and consumed” paradigm of STD use is effective for location-dependent applications, the authors have previously proposed a vehicle-based STD retention system. However, in low vehicle density environments, the data retention becomes difficult due to the decrease in the number of data transmissions in this method. In this paper, we propose a new data transmission control method for data retention in the low vehicle density environments.

Inter-channel crosstalk is one of the limiting factors in multichannel optical systems. This paper presents a theoretical analysis of the bit-error-rate (BER) performance of quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM) signals influenced by multiple crosstalk channels. The field distribution of multiple crosstalk channels in the constellation map is calculated. The BER of the QPSK/QAM signal, onto which the crosstalk light is superimposed, is then evaluated for a varying number of crosstalk channels under the condition that the total crosstalk power is constant. The results quantitatively confirm that as the channel number increases, the BER degradation caused by crosstalk light approaches that caused by Gaussian noise light. It is also confirmed that the degradations caused by crosstalk light and Gaussian light are similar for QAM signals of high-level modulation.

Channel modeling is a vital step in designing transceivers for wireless implant communication systems due to the extremely challenging environment of the human body. In this paper, the in-to-on body path loss and group delay were first analyzed using an electric dipole and a current loop in the 10-60MHz human body communication band. A path loss model was derived using finite difference time domain (FDTD) simulation and an anatomical human body model. As a result, it was found that the path loss increases with distance in an exponent of 5.6 for dipole and 3.9 for loop, and the group delay variation is within 1ns for both dipole and loop which suggests a flat phase response. Moreover, the electric and magnetic field distributions revealed that the magnetic field components dominate in-body signal transmission in this frequency band. Based on the analysis results of the implant channel, the link budget was analyzed. An experiment on a prototype transceiver was also performed to validate the path loss model and bit error rate (BER) performance. The experimentally derived path loss exponent was between the electric dipole path loss exponent and the current loop path loss exponent, and the BER measurement showed the feasibility of 20Mbps implant communication up to a body depth of at least 15cm.

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 proposes a high-quality computed tomography (CT) image reconstruction method from low-dose X-ray projection data. A state-of-the-art method, proposed by Xu et al., exploits dictionary learning for image patches. This method generates an overcomplete dictionary from patches of standard-dose CT images and reconstructs low-dose CT images by minimizing the sum of a data fidelity and a regularization term based on sparse representations with the dictionary. However, this method does not take characteristics of each patch, such as textures or edges, into account. In this paper, we propose to classify all patches into several classes and utilize an individual dictionary with an individual regularization parameter for each class. Furthermore, for fast computation, we introduce the orthogonality to column vectors of each dictionary. Since similar patches are collected in the same cluster, accuracy degradation by the orthogonality hardly occurs. Our simulations show that the proposed method outperforms the state-of-the-art in terms of both accuracy and speed.

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.

In this paper, we present a hardware implementation of a colorectal cancer diagnosis support system using a colorectal endoscopic video image on customizable embedded DSP. In an endoscopic video image, color shift, blurring or reflection of light occurs in a lesion area, which affects the discrimination result by a computer. Therefore, in order to identify lesions with high robustness and stable classification to these images specific to video frame, we implement a computer-aided diagnosis (CAD) system for colorectal endoscopic images with Narrow Band Imaging (NBI) magnification with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification. Since CNN and SVM need to perform many multiplication and accumulation (MAC) operations, we implement the proposed hardware system on a customizable embedded DSP, which can realize at high speed MAC operations and parallel processing with Very Long Instruction Word (VLIW). Before implementing to the customizable embedded DSP, we profile and analyze processing cycles of the CAD system and optimize the bottlenecks. We show the effectiveness of the real-time diagnosis support system on the embedded system for endoscopic video images. The prototyped system demonstrated real-time processing on video frame rate (over 30fps @ 200MHz) and more than 90% accuracy.

This work introduces a measurement model to estimate the naturalness of vibrato. We carried out a subjective evaluation using a mean opinion score (MOS). We then built a measurement model by using two-dimensional Gaussian functions. We found that three Gaussian functions can measure naturalness with an error of 4.0%.

Delay Tolerant Networks (DTNs) are vulnerable to message flooding attacks in which a very large number of malicious messages are sent so that network resources are depleted. To address this problem, previous studies mainly focused on constraining the number of messages that nodes can generate per time slot by allowing nodes to monitor the other nodes' communication history. Since the adversaries may hide their attacks by claiming a false history, nodes exchange their communication histories and detect an attacker who has presented an inconsistent communication history. However, this approach increases node energy consumption since the number of communication histories increases every time a node communicates with another node. To deal with this problem, in this paper, we propose an energy-efficient defense against such message flooding attacks. The main idea of the proposed scheme is to time limit the communication history exchange so as to reduce the volume while ensuring the effective detection of inconsistencies. The advantage of this approach is that, by removing communication histories after they have revealed such inconsistencies, the energy consumption is reduced. To estimate such expiration time, analytical expressions based upon a Markov chain based message propagation model, are derived for the probability that a communication history reveals such inconsistency in an arbitrary time. Extensive performance evaluation results obtained by means of computer simulations and several performance criteria verify that the proposed scheme successfully improves the overall energy efficiency. For example, these performance results have shown that, as compared to other previously known defenses against message flooding attacks, the proposed scheme extends by at least 22% the battery lifetime of DTN nodes, while maintaining the same levels of protection.