Sparse orthogonal matrices are proposed to improve the convergence property of expectation propagation (EP) for sparse signal recovery from compressed linear measurements subject to known dense and ill-conditioned multiplicative noise. As a typical problem, this letter addresses generalized spatial modulation (GSM) in over-loaded and spatially correlated multiple-input multiple-output (MIMO) systems. The proposed sparse orthogonal matrices are used in precoding and constructed efficiently via a generalization of the fast Walsh-Hadamard transform. Numerical simulations show that the proposed sparse orthogonal precoding improves the convergence property of EP in over-loaded GSM MIMO systems with known spatially correlated channel matrices.

Part based models have been proved to be beneficial for person re-identification (Re-ID) in recent years. Existing models usually use fixed horizontal stripes or rely on human keypoints to get each part, which is not consistent with the human visual mechanism. In this paper, we propose a Self-Channel Attention Weighted Part model (SCAWP) for Re-ID. In SCAWP, we first learn a feature map from ResNet50 and use 1x1 convolution to reduce the dimension of this feature map, which could aggregate the channel information. Then, we learn the weight map of attention within each channel and multiply it with the feature map to get each part. Finally, each part is used for a special identification task to build the whole model. To verify the performance of SCAWP, we conduct experiment on three benchmark datasets, including CUHK03-NP, Market-1501 and DukeMTMC-ReID. SCAWP achieves rank-1/mAP accuracy of 70.4%/68.3%, 94.6%/86.4% and 87.6%/76.8% on three datasets respectively.

The Internet of Things (IoT) with its support for cyber-physical systems (CPS) will provide many latency-sensitive services that require very fast responses from network services. Mobile edge computing (MEC), one of the distributed computing models, is a promising component of the low-latency network architecture. In network architectures with MEC, mobile devices will offload heavy computing tasks to edge servers. There exist numbers of researches about low-latency network architecture with MEC. However, none of the existing researches simultaneously satisfy the followings: (1) guarantee the latency of computing tasks and (2) implement a real system. In this paper, we designed and implemented an MEC based network architecture that guarantees the latency of offloading tasks. More specifically, we first estimate the total latency including computing and communication ones at the centralized node called orchestrator. If the estimated value exceeds the latency requirement, the task will be rejected. We then evaluated its performance in terms of the blocking probability of the tasks. To analyze the results, we compared the performance between obtained from experiments and simulations. Based on the comparisons, we clarified that the computing latency estimation accuracy is a significant factor for this system.

Recently, new optimization machines based on non-silicon physical systems, such as quantum annealing machines, have been developed, and their commercialization has been started. These machines solve the problems by searching the state of the Ising spins, which minimizes the Ising Hamiltonian. Such a property of minimization of the Ising Hamiltonian can be applied to various combinatorial optimization problems. In this paper, we introduce the coherent Ising machine (CIM), which can solve the problems in a milli-second order, and has higher performance than the quantum annealing machines especially on the problems with dense mutual connections in the corresponding Ising model. We explain how a target problem can be implemented on the CIM, based on the optimization scheme using the mutually connected neural networks. We apply the CIM to traveling salesman problems as an example benchmark, and show experimental results of the real machine of the CIM. We also apply the CIM to several combinatorial optimization problems in wireless communication systems, such as channel assignment problems. The CIM's ultra-fast optimization may enable a real-time optimization of various communication systems even in a dynamic communication environment.

Offset quadrature amplitude modulation based orthogonal frequency division multiplexing (OFDM/OQAM) is a promising multi-carrier modulation technique to achieve a low-sidelobe spectrum while maintaining orthogonality among subcarriers. However, a major shortcoming of OFDM/OQAM systems is the high peak-to-average power ratio (PAPR) of the transmit signal. To resolve the high-PAPR issue of traditional OFDM, a self-synchronized-scrambler-based selected-mapping has been investigated, where the transmit sequence is scrambled to reduce PAPR. In this method, the receiver must use a descrambler to recover the original data. However, the descrambling process leads to error propagation, which degrades the bit error rate (BER). As described herein, a partial scrambling overlapped selected mapping (PS-OSLM) scheme is proposed for PAPR reduction of OFDM/OQAM signals, where candidate sequences are generated using partial scrambling of original data. The best candidate, the one that minimizes the peak amplitude within multiple OFDM/OQAM symbols, is selected. In the proposed method, an overlap search algorithm for SLM is applied to reduce the PAPR of OFDM/OQAM signals. Numerical results demonstrate that our PS-OSLM proposal achieves better BER than full-scrambling overlapped SLM (FS-OSLM) in OFDM/OQAM systems while maintaining almost equivalent PAPR reduction capability as FS-OSLM and better PAPR than SLM without overlap search. Additionally, we derive a theoretical lower bound expression for OFDM/OQAM with PS-OSLM, and clarify the effectiveness of the proposed scheme.

To reduce peak-to-average power ratio, we propose a method of choosing suitable vectors in a partial transmit sequence technique. Conventional approaches require that a suitable vector be selected from a large number of candidates. By contrast, our method does not include such a selecting procedure, and instead generates random vectors from the Gaussian distribution whose covariance matrix is a solution of a relaxed problem. The suitable vector is chosen from the random vectors. This yields lower peak-to-average power ratio than a conventional method.

The application of network function virtualization (NFV) and software-defined networking (SDN) to passive optical networks (PONs) is attracting attention for the deployment of cost-effective access network systems. This paper presents a novel architecture of a programmable finite state machine (P-FSM) as a hardware accelerator for protocol processing in an optical line terminal (OLT). The P-FSM is programmable hardware that manages various types of FSMs to enhance flexibility in OLTs and achieve wired-rate performance with a negligible increase in total chip area. The P-FSM is implemented using three key technologies: a specific architecture for state management of communications protocols to minimize the logic area, a memory distributed implementation to minimize the program memory, and a new branch operation to minimize the memory area and reduce processing time. Evaluation results show that the P-FSM can handle 10G-EPON/NG-PON2 communications protocols in the same architecture while achieving wired-rate performance. The increase in the total designed area is only 1.5% to 4.9% depending on the number of protocols supported compared to the area of a conventional communications SoC without flexibility. We also clarify that our architecture has the scalability needed to modify the number of FSMs and the maximum number of ONU connections according to the system scale.

In video distribution services such as video streaming, the providers must satisfy the various quality demands of the users. One of the human-centric indexes used to assess video quality is the quality of experience (QoE). In video streaming, the video bitrate, video freezing time, and video bitrate switching are significant determiners of QoE. To provide high-quality video streaming services, adaptive streaming using the Moving Picture Experts Group dynamic adaptive streaming over Hypertext Transfer Protocol (MPEG-DASH) is widely utilized. One of the conventional bitrate selection methods for MPEG-DASH selects the bitrate such that the amount of buffered data in the playback buffer, i.e., the playback buffer level, can be maintained at a constant value. This method can avoid buffer overflow and video freezing based on feedback control; however, this method induces high-frequency video bitrate switching, which can degrade QoE. To overcome this issue, this paper proposes a bitrate selection method in an adaptive video steaming for MPEG-DASH to improve the QoE by minimizing the bitrate fluctuation. To this end, the proposed method does not change the bitrate if the playback buffer level is not around its upper or lower limit, corresponding to the full or empty state of the playback buffer, respectively. In particular, to avoid buffer overflow and video freezing, the proposed method selects the bitrate based on proportional-derivative (PD) control to maintain the playback buffer level at a target level, which corresponds to an upper or lower threshold of the playback buffer level. Simulations confirm that, the proposed method offers better QoE than the conventional method for users with various preferences.

This paper presents a 120-GHz-band split ring resonator (SRR) bandstop filter whose insertion loss can be controlled by coupling another lattice pattern substrate. The SRR bandstop filter and lattice pattern substrate is composed of 200-µm-thick quartz substrate and 5-µm-thick gold patterns. S21 of the SRR bandstop filter is -37.8 dB, and its -10-dB bandwidth is 115-130 GHz. S21 of the SRR bandstop filter changes to -4.1 dB at 125 GHz by arranging the lattice pattern substrate in close proximity to the SRR stopband filter, because coupling between the SRR and the lattice pattern occurs when the SRR and lattice pattern are opposed in close proximity. It was found that 10 Gbit/s data transmission can be achieved by setting the lattice pattern substrate just above the SRR bandstop filter with a spacer thickness of 50 µm, even though data transmission is impossible when only the SRR bandstop filter is inserted between the transmitter and the receiver.

Electrical connectors are the basic components of the electric system in automobiles, aircrafts and ships to realize the current and electrical signal transmission. In the aviation electrical system, the electrical connectors are indispensable supporting devices accessories, which play important roles in connecting electrical system, monitoring and controlling equipment, and provide a guarantee for the reliable transmission of electrical signals between the aviation equipment and system. Whether aviation electrical connectors work reliably directly affects the safety and reliability of the entire aircraft aviation system. The random vibration of aircraft caused by turbulence during flight is one of the main factors affecting the contact performance of the electrical connectors. In this paper, the contacts of the circular four-slot three-pin electrical connectors were chosen as the research specimens. The theoretical model of the contact force for contacts of electrical connectors was established. The test method for contact force measurement was determined. According to the test scheme, the detecting device for the contact force and contact resistance of the electrical connectors was designed, and the turbulence test of the electrical connectors was carried out. Through the analysis of the test data, the influence rule of the turbulence degree, flight speed and flight height on the contact force and contact resistance of the aviation electrical connectors was obtained.

The performance of online algorithms for the bin packing problem is usually measured by the asymptotic approximation ratio. However, even if an online algorithm is explicitly described, it is in general difficult to obtain the exact value of the asymptotic approximation ratio. In this paper we show a theorem that gives the exact value of the asymptotic approximation ratio in a closed form when the item sizes and the online algorithm satisfy some conditions. Moreover, we demonstrate that our theorem serves as a powerful tool for the design of online algorithms combined with mathematical optimization.

It is well-known that pushdown systems (PDS) effectively preserve regularity. This property implies the decidability of the reachability problem for PDS and has been applied to automatic program verification. The backward regularity preservation property was also shown for an extension of PDS by adding registers. This paper aims to show the forward regularity preservation property. First, we provide a concise definition of the register model called register pushdown systems (RPDS). Second, we show the forward regularity preservation property of RPDS by providing a saturation algorithm that constructs a register automaton (RA) recognizing $post^{ast}_calP(L(calA))$ where $calA$ and $calP$ are a given RA and an RPDS, respectively, and $post^{ast}_calP$ is the forward image of the mapping induced by $calP$. We also give an example of applying the proposed algorithm to malware detection.

This paper presents a new method to translate a regular expression into a nondeterministic finite automaton (an NFA for short). Let r be a regular expression and let M be a Thompson automaton for r. We first introduce a labeled Thompson automaton defined by assigning two types of expressions which denote prefixes and suffixes of words in L(r) to each state of M. Then we give new ϵ-free NFAs constructed from a labeled Thompson automaton. These NFAs are called a prefix equation automaton and a suffix equation automaton. We show that a suffix equation automaton is isomorphic to an equation automaton defined by Antimirov. Furthermore we give an NFA called a unified equation automaton by joining two NFAs. Thus the number of states of a unified equation automaton can be smaller than that of an equation automaton.

This paper deals with the problem of enumerating 3-edge-connected spanning subgraphs of an input plane graph. In 2018, Yamanaka et al. proposed two enumeration algorithms for such a problem. Their algorithm generates each 2-edge-connected spanning subgraph of a given plane graph with n vertices in O(n) time, and another one generates each k-edge-connected spanning subgraph of a general graph with m edges in O(mT) time, where T is the running time to check the k-edge connectivity of a graph. This paper focuses on the case of the 3-edge-connectivity in a plane graph. We give an algorithm which generates each 3-edge-connected spanning subgraph of the input plane graph in O(n2) time. This time complexity is the same as the algorithm by Yamanaka et al., but our algorithm is simpler than theirs.

Recently, image compression systems based on convolutional neural networks that use flexible nonlinear analysis and synthesis transformations have been developed to improve the restoration accuracy of decoded images. Although these methods that use objective metric such as peak signal-to-noise ratio and multi-scale structural similarity for optimization attain high objective results, such metric may not reflect human visual characteristics and thus degrade subjective image quality. A method using a framework called a generative adversarial network (GAN) has been reported as one of the methods aiming to improve the subjective image quality. It optimizes the distribution of restored images to be close to that of natural images; thus it suppresses visual artifacts such as blurring, ringing, and blocking. However, since methods of this type are optimized to focus on whether the restored image is subjectively natural or not, components that are not correlated with the original image are mixed into the restored image during the decoding process. Thus, even though the appearance looks natural, subjective similarity may be degraded. In this paper, we investigated why the conventional GAN-based compression techniques degrade subjective similarity, then tackled this problem by rethinking how to handle image generation in the GAN framework between image sources with different probability distributions. The paper describes a method to maximize mutual information between the coding features and the restored images. Experimental results show that the proposed mutual information amount is clearly correlated with subjective similarity and the method makes it possible to develop image compression systems with high subjective similarity.

In 2019, a completely new algorithm, spherical evolution (SE), was proposed. The brand new search style in SE has been proved to have a strong search capability. In order to take advantage of SE, we propose a novel method called the ladder descent (LD) method to improve the SE' population update strategy and thereafter propose a ladder spherical evolution search (LSE) algorithm. With the number of iterations increasing, the range of parent individuals eligible to produce offspring gradually changes from the entire population to the current optimal individual, thereby enhancing the convergence ability of the algorithm. Experiment results on IEEE CEC2017 benchmark functions indicate the effectiveness of LSE.

Information-centric networking (ICN) provides an alternative to the traditional end-to-end communication model of the current Internet architecture by focusing on information dissemination and information retrieval. Named Data Networking (NDN) is one of the candidates that implements the idea of ICN on a practical level. Implementing NDN in wireless sensor networks (WSNs) will bring all the benefits of NDN to WSNs, making them more efficient. By applying the NDN paradigm directly to wireless multi-hop ad-hoc networks, various drawbacks are observed, such as packet flooding due to the broadcast nature of the wireless channel. To cope with these problems, in this paper, we propose an Interest called the accumulation-based forwarding scheme, as well as a novel content store architecture to increase its efficiency in terms of storing and searching data packets. We have performed extensive simulations using the ndnSIM simulator. Experimental results showed that the proposed scheme performs better when compared to another scheme in terms of the total number of Interests, the content store search time, and the network lifetime.

Abnormal behavior detection is now a widely concerned research field, especially for crowded scenes. However, most traditional unsupervised approaches often suffered from the problem when the normal events in the scenario with large visual variety. This paper proposes a self-learning probabilistic Latent Semantic Analysis, which aims at taking full advantage of the high-level abnormal information to solve problems. We select the informative observations to construct the “reference events” from the training sets as a high-level guidance cue. Specifically, the training set is randomly divided into two separate subsets. One is used to learn this model, which is defined as the initialization sequence of “reference events”. The other aims to update this model and the the infrequent samples are chosen into the “reference events”. Finally, we define anomalies using events that are least similar to “reference events”. The experimental result demonstrates that the proposed model can detect anomalies accurately and robustly in the real-world crowd environment.

The emerging three degree of freedom plus (3DoF+) video provides more interactive and deep immersive visual experience. 3DoF+ video introduces motion parallax to 360 video providing omnidirectional view with limited changes of the view position. A large set of views are required to support such 3DoF+ visual experience, hence it is essential to compress a tremendous amount of 3DoF+ video. Recently, MPEG is developing a standard for efficient coding of 3DoF+ video that consists of multiple videos, and its test model named Test Model for Immersive Video (TMIV). In the TMIV, the redundancy between the input source views is removed as much as possible by selecting one or several basic views and predicting the remaining views from the basic views. Each unpredicted region is cropped to a bounding box called patch, and then a large number of patches are packed into atlases together with the selected basic views. As a result, multiple source views are converted into one or more atlas sequences to be compressed. In this letter, we present an improved clustering method using patch merging in the atlas construction in the TMIV. The proposed method achieves significant BD-rate reduction in terms of various end-to-end evaluation metrics in the experiment, and was adopted in TMIV6.0.

Let G11 (resp., G12) be the ternary Golay code of length 11 (resp., 12). In this letter, we investigate the separating redundancies of G11 and G12. In particular, we determine the values of sl(G11) for l = 1, 3, 4 and sl(G12) for l = 1, 4, 5, where sl(G11) (resp., sl(G12)) is the l-th separating redundancy of G11 (resp., G12). We also provide lower and upper bounds on s2(G11), s2(G12), and s3(G12).