With the support of emerging technologies such as 5G, machine learning, edge computing and Industry 4.0, the Internet of Things (IoT) continues to evolve and promote the construction of future networks. Existing work on IoT mainly focuses on its practical applications, but there is little research on modeling the interactions among components in IoT systems and verifying the correctness of the network deployment. Therefore, the Calculus of the Internet of Things (CaIT) has previously been proposed to formally model and reason about IoT systems. In this paper, the CaIT calculus is extended by introducing broadcast communications. For modeling convenience, we provide explicit operations to model node mobility as well as the interactions between sensors (or actuators) with the environment. To support the use of UPPAAL to verify the temporal properties of IoT networks described by the CaIT calculus, we establish a relationship between timed automata and the CaIT calculus. Using UPPAAL, we verify six temporal properties of a simple “smart home” example, including Boiler On Manually, Boiler Off Automatically, Boiler On Automatically, Lights On, Lights Mutually, and Windows Simultaneously. The verification results show that the “smart home” can work properly.

Recently, many AI-aided layout design systems are developed to reduce tedious manual intervention based on deep learning. However, most methods focus on a specific generation task. This paper explores a challenging problem to obtain multiple layout design generation (LDG), which generates floor plan or urban plan from a boundary input under a unified framework. One of the main challenges of multiple LDG is to obtain reasonable topological structures of layout generation with irregular boundaries and layout elements for different types of design. This paper formulates the multiple LDG task as an image-to-image translation problem, and proposes a conditional generative adversarial network (GAN), called LDGAN, with adaptive modules. The framework of LDGAN is based on a generator-discriminator architecture, where the generator is integrated with conditional convolution constrained by the boundary input and the attention module with channel and spatial features. Qualitative and quantitative experiments were conducted on the SCUT-AutoALP and RPLAN datasets, and the comparison with the state-of-the-art methods illustrate the effectiveness and superiority of the proposed LDGAN.

In this paper, we propose a unified algebraic design of the generalized Moreau enhancement matrix (GME matrix) for the Linearly involved Generalized-Moreau-Enhanced (LiGME) model. The LiGME model has been established as a framework to construct linearly involved nonconvex regularizers for sparsity (or low-rank) aware estimation, where the design of GME matrix is a key to guarantee the overall convexity of the model. The proposed design is applicable to general linear operators involved in the regularizer of the LiGME model, and does not require any eigendecomposition or iterative computation. We also present an application of the LiGME model with the proposed GME matrix to a group sparsity aware least squares estimation problem. Numerical experiments demonstrate the effectiveness of the proposed GME matrix in the LiGME model.

Network traffic control and classification have become increasingly dependent on deep packet inspection (DPI) approaches, which are the most precise techniques for intrusion detection and prevention. However, the increasing traffic volumes and link speed exert considerable pressure on DPI techniques to process packets with high performance in restricted available memory. To overcome this problem, we proposed dual cuckoo filter (DCF) as a data structure based on cuckoo filter (CF). The CF can be extended to the parallel mode called parallel Cuckoo Filter (PCF). The proposed data structure employs an extra hash function to obtain two potential indices of entries. The DCF magnifies the superiority of the CF with no additional memory. Moreover, it can be extended to the parallel mode, resulting in a data structure referred to as parallel Dual Cuckoo filter (PDCF). The implementation results show that using the DCF and PDCF as identification tools in a DPI system results in time improvements of up to 2% and 30% over the CF and PCF, respectively.

Modern large scale distributed storage systems play a central role in data center and cloud storage, while node failure in data center is common. The lost data in failure node must be recovered efficiently. Locally repairable codes (LRCs) are designed to solve this problem. The locality of an LRC is the number of nodes that participate in recovering the lost data from node failure, which characterizes the repair efficiency. An LRC is called optimal if its minimum distance attains Singleton-type upper bound [1]. In this paper, using basic techniques of linear algebra over finite field, infinite optimal LRCs over extension fields are derived from a given optimal LRC over base field(or small field). Next, this paper investigates the relation between near-MDS codes with some constraints and LRCs, further, proposes an algorithm to determine locality of dual of a given linear code. Finally, based on near-MDS codes and the proposed algorithm, those obtained optimal LRCs are shown.

Sidon space is an important tool for constructing cyclic subspace codes. In this letter, we construct some Sidon spaces by using primitive elements and the roots of some irreducible polynomials over finite fields. Let q be a prime power, k, m, n be three positive integers and $ ho= lceil rac{m}{2k} ceil-1$, $ heta= lceil rac{n}{2m} ceil-1$. Based on these Sidon spaces and the union of some Sidon spaces, new cyclic subspace codes with size $rac{3(q^{n}-1)}{q-1}$ and $rac{ heta ho q^{k}(q^{n}-1)}{q-1}$ are obtained. The size of these codes is lager compared to the known constructions from [14] and [10].

In this paper, we propose a method for predicting radio wave propagation using a correlation graph convolutional neural network (C-Graph CNN). We examine what kind of parameters are suitable to be used as system parameters in C-Graph CNN. Performance of the proposed method is evaluated by the path loss estimation accuracy and the computational cost through simulation.

In frequency hopping communication, time delay and Doppler shift incur interference. With the escalating upgrading of complicated interference, in this paper, the time-frequency two-dimensional (TFTD) partial Hamming correlation (PHC) properties of wide-gap frequency-hopping sequences (WGFHSs) with frequency shift are discussed. A bound on the maximum TFTD partial Hamming auto-correlation (PHAC) and two bounds on the maximum TFTD PHC of WGFHSs are got. Li-Fan-Yang bounds are the particular cases of new bounds for frequency shift is zero.

The orthogonal time frequency space (OTFS) technique proposed in recent years has excellent anti-Doppler frequency shift and time delay performance, enabling its application in high speed communication scenarios. In this article, a particle swarm optimization (PSO) signal detection algorithm for OTFS system is proposed, an adaptive mechanism for the individual learning factor and global learning factor in the speed formula of the algorithm is designed, and the position update method of the particles is improved, so as to increase the convergence accuracy and avoid the particles to fall into local optimum. The simulation results show that the improved PSO algorithm has the advantages of low bit error rate (BER) and high convergence accuracy compared with the traditional PSO algorithm, and has similar performance to the ideal state maximum likelihood (ML) detection algorithm with lower complexity. In the case of high Doppler shift, OTFS technology has better performance than orthogonal frequency division multiplexing (OFDM) technology by using improved PSO algorithm.

We propose an efficient hybrid automatic repeat request (HARQ) method that simultaneously achieves packet combining and resolution of the collisions of random access identifiers (RAIDs) during retransmission in a non-orthogonal multiple access (NOMA)-based random access system. Here, the RAID functions as a separator for simultaneously received packets that use the same channel in NOMA. An example of this is a scrambling code used in 4G and 5G systems. Since users independently select a RAID from the candidate set prepared by the system, the decoding of received packets fails when multiple users select the same RAID. Random RAID reselection by each user when attempting retransmission can resolve a RAID collision; however, packet combining between the previous and retransmitted packets is not possible in this case because the base station receiver does not know the relationship between the RAID of the previously transmitted packet and that of the retransmitted packet. To address this problem, we propose a HARQ method that employs novel hierarchical tree-structured RAID groups in which the RAID for the previous packet transmission has a one-to-one relationship with the set of RAIDs for retransmission. The proposed method resolves RAID collisions at retransmission by randomly reselecting for each user a RAID from the dedicated RAID set from the previous transmission. Since the relationship between the RAIDs at the previous transmission and retransmission is known at the base station, packet combining is achieved simultaneously. Computer simulation results show the effectiveness of the proposed method.

This paper proposes the new series highly integrated intelligent power module (IPM), which is developed to provide a ultra-compact, high performance and reliable motor drive system. Details of the key design technologies of the IPM is given and practical application issues such as electrical characteristics, system operation performance and power dissipation are discussed. Layout placement and routing have been optimized in order to reduce and balance the parasitic impedances. By implementing an innovative direct bonding copper (DBC) ceramic substrate, which can effectively dissipate heat, the IPM delivers a fully integrated power stages including two three-phase inverters, power factor correction (PFC) and rectifier in an ultra-compact 75.5mm × 30mm package, offering up to a 17.3 percent smaller space than traditional motor drive scheme.

The design and adjustment of the divergence in audio applications using nonnegative matrix factorization (NMF) is still open problem. In this study, to deal with this problem, we explore a representation of the divergence using neural networks (NNs). Instead of the divergence, our approach extends the multiplicative update algorithm (MUA), which estimates the NMF parameters, using NNs. The design of the extended MUA incorporates NNs, and the new algorithm is referred to as the deep MUA (DeMUA) for NMF. While the DeMUA represents the algorithm for the NMF, interestingly, the divergence is obtained from the incorporated NN. In addition, we propose theoretical guides to design the incorporated NN such that it can be interpreted as a divergence. By appropriately designing the NN, MUAs based on existing divergences with a single hyper-parameter can be represented by the DeMUA. To train the DeMUA, we applied it to audio denoising and supervised signal separation. Our experimental results show that the proposed architecture can learn the MUA and the divergences in sparse denoising and speech separation tasks and that the MUA based on generalized divergences with multiple parameters shows favorable performances on these tasks.

Glaucoma is a common high-incidence eye disease. The detection of the optic cup and optic disc in fundus images is one of the important steps in the clinical diagnosis of glaucoma. However, the fundus images are generally intensity inhomogeneity, and complex organizational structure, and are disturbed by blood vessels and lesions. In order to extract the optic disc and optic cup regions more accurately, we propose a segmentation method of the optic disc and optic cup in fundus image based on distance regularized two-layer level with sparse shape prior constraint. The experimental results show that our method can segment the optic disc and optic cup region more accurately and obtain satisfactory results.

In this paper, we investigate the Access Point (AP) selection problem in Cell-Free Massive multiple-input multiple-output (MIMO) system. Firstly, we add a connecting coefficient to the uplink data transmission model. Then, the problem of AP selection is formulated as a discrete combinatorial optimization problem which can be dealt with by the particle swarm algorithm. However, when the number of optimization variables is large, the search efficiency of the traditional particle swarm algorithm will be significantly reduced. Then, we propose an ‘user-centric’ cooperative coevolution scheme which includes the proposed probability-based particle evolution strategy and random-sampling-based particle evaluation mechanism to deal with the search efficiency problem. Simulation results show that proposed algorithm has better performance than other existing algorithms.

Autonomous driving technology is expected to be applied to various applications with unmanned vehicles (UVs), such as small delivery vehicles for office supplies and smart wheelchairs. UV remote control by a cloud server (CS) would achieve cost-effective applications with a large number of UVs. In general, dead time in real-time feedback control reduces the control accuracy. On remote path tracking control by the CS, UV control accuracy deteriorates due to transmission delay and jitter through the Internet. Digital twin computing (DTC) and jitter buffer are effective to solve this problem. In our previous study, we clarified effectiveness of them in UV remote control by CS. The jitter buffer absorbs the transmission delay jitter of control signals. This is effective to achieve accurate UV remote control. Adaptive buffering time optimization according to real-time transmission characteristics is necessary to achieve more accurate UV control in CS-based remote control system with DTC and jitter buffer. In this study, we proposed a method for the adaptive optimization according to real-time transmission delay characteristics. To quantitatively evaluate the effectiveness of the method, we created a UV remote control simulator of the control system. The results of simulations quantitatively clarify that the adaptive optimization by the proposed method improves the UV control accuracy.

Hardware oriented security and trust of semiconductor integrated circuit (IC) chips have been highly demanded. This paper outlines the requirements and recent developments in circuits and packaging systems of IC chips for security applications, with the particular emphasis on protections against physical implementation attacks. Power side channels are of undesired presence to crypto circuits once a crypto algorithm is implemented in Silicon, over power delivery networks (PDNs) on the frontside of a chip or even through the backside of a Si substrate, in the form of power voltage variation and electromagnetic wave emanation. Preventive measures have been exploited with circuit design and packaging technologies, and partly demonstrated with Si test vehicles.

This paper presents a Ka-band high-efficiency power amplifier (PA) with a novel adaptively controlled gate capacitor circuit and a two-step adaptive bias circuit for 5th generation (5G) mobile terminal applications fabricated using a 45-nm silicon on insulator (SOI) CMOS process. The PA adopts a stacked FET structure to increase the output power because of the low breakdown voltage issue of scaled MOSFETs. The novel adaptive gate capacitor circuit properly controls the RF swing for each stacked FET to achieve high efficiency in the several-dB back-off region. Further, the novel two-step adaptive bias circuit effectively controls the gate voltage for each stacked FET for high linearity and high back-off efficiency. At a supply voltage of 4 V, the fabricated PA has exhibited a saturated output power of 20.0 dBm, a peak power added efficiency (PAE) of 42.7%, a 3dB back-off efficiency of 32.7%, a 6dB back-off efficiency of 22.7%, and a gain of 15.6 dB. The effective PA area was 0.82 mm by 0.74 mm.

This paper presents a hippocampal cognitive prosthesis chip designed for restoring the ability to form new long-term memories due to hippocampal system damage. The system-on-chip (SOC) consists of a 16-channel micro-power low-noise amplifier (LNA), high-pass filters, analog-digital converters (ADCs), a 16-channel spike-sorter, a generalized Laguerre-Volterra model multi-input, multi-output (GLVM-MIMO) hippocampal processor, an 8-channel neural stimulator and peripheral circuits. The proposed LNA achieved a voltage gain of 50dB, input-referred noise of 3.95µVrms, and noise efficiency factor (NEF) of 3.45 with the power consumption of 3.3µW. High-pass filters with a 300-Hz bandwidth are used to filter out the unwanted local field potential (LFP). 4 12-bit successive approximation register (SAR) ADCs with a signal-to-noise-and-distortion ratio (SNDR) of 63.37dB are designed for the digitization of the neural signals. A 16-channel spike-sorter has been integrated in the chip enabling a detection accuracy of 98.3% and a classification accuracy of 93.4% with power consumption of 19µW/ch. The MIMO hippocampal model processor predict output spatio-temporal patterns in CA1 according to the recorded input spatio-temporal patterns in CA3. The neural stimulator performs bipolar, symmetrical charge-balanced stimulation with a maximum current of 310µA, triggered by the processor output. The chip has been fabricated in 40nm standard CMOS technology, occupying a silicon area of 3mm2.

A formal graph system (FGS for short) is a logic program consisting of definite clauses whose arguments are graph patterns instead of first-order terms. The definite clauses are referred to as graph rewriting rules. An FGS is shown to be a useful unifying framework for learning graph languages. In this paper, we show the polynomial-time PAC learnability of a subclass of FGS languages defined by parameterized hereditary FGSs with bounded degree, from the viewpoint of computational learning theory. That is, we consider VH-FGSLk,Δ(m, s, t, r, w, d) as the class of FGS languages consisting of graphs of treewidth at most k and of maximum degree at most Δ which is defined by variable-hereditary FGSs consisting of m graph rewriting rules having TGP patterns as arguments. The parameters s, t, and r denote the maximum numbers of variables, atoms in the body, and arguments of each predicate symbol of each graph rewriting rule in an FGS, respectively. The parameters w and d denote the maximum number of vertices of each hyperedge and the maximum degree of each vertex of TGP patterns in each graph rewriting rule in an FGS, respectively. VH-FGSLk,Δ(m, s, t, r, w, d) has infinitely many languages even if all the parameters are bounded by constants. Then we prove that the class VH-FGSLk,Δ(m, s, t, r, w, d) is polynomial-time PAC learnable if all m, s, t, r, w, d, Δ are constants except for k.

Underwater acoustic channels (UWA) are usually sparse, which can be exploited for adaptive equalization to improve the system performance. For the shallow UWA channels, based on the proportional minimum symbol error rate (PMSER) criterion, the adaptive equalization framework requires the sparsity selection. Since the sparsity of the L0 norm is stronger than that of the L1, we choose it to achieve better convergence. However, because the L0 norm leads to NP-hard problems, it is difficult to find an efficient solution. In order to solve this problem, we choose the Gaussian function to approximate the L0 norm. Simulation results show that the proposed scheme obtains better performance than the L1 based counterpart.