Through-silicon via (TSV) is a key enabling technology for the emerging 3-dimension (3D) integrated circuits
(ICs). However, the crosstalk between the neighboring TSVs is one of the important sources of the soft faults. To
suppress the crosstalk, the Fibonacci-numeral-system-based crosstalk avoidance code ( FNS-CAC) is an effective
scheme. Meanwhile, the self-repair schemes are often used to deal with the hard faults, but the repaired results
may change the mapping between signals to TSVs, thus may reduce the crosstalk suppression ability of FNS-CAC.
A TSV self-repair technique with an improved FNS-CAC codec is proposed in this work. The codec is designed
based on the improved Fibonacci numeral system (FNS) adders, which are adaptive to the health states of TSVs.
The proposed self-repair technique is able to suppress the crosstalk and repair the faulty TSVs simultaneously. The
simulation and analysis results show that the proposed scheme keeps the crosstalk suppression ability of the original
FNS-CAC, and it has higher reparability than the local self-repair schemes, such as the signal-switching-based and
the signal-shifting-based counterparts. 

Complementary metal oxide semiconductor ( CMOS) aging mechanisms including bias temperature instability
( BTI) pose growing concerns about circuit reliability. BTI results in threshold voltage increases on CMOS
transistors, causing delay shifts and timing violations on logic circuits. The amount of degradation is dependent on
the circuit workload, which increases the challenge for accurate BTI aging prediction at the design time. In this
paper, a BTI prediction method for logic circuits based on statistical static timing analysis (SSTA) is proposed,
especially considering the correlation between circuit workload and BTI degradation. It consists of a training phase,
to discover the relationship between circuit scale and the required workload samples, and a prediction phase, to
present the degradations under different workloads in Gaussian probability distributions. This method can predict
the distribution of degradations with negligible errors, and identify 50% more BTI-critical paths in an affordable
time, compared with conventional methods. 

To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,
combining ultra-dense networks (UDNs) and mobile edge computing (MEC)are considered as important solutions.
In the MEC enabled UDNs, one of the most important issues is computation offloading. Although a number of work
have been done toward this issue, the problem of dynamic computation offloading in time-varying environment,
especially the dynamic computation offloading problem for multi-user, has not been fully considered. Therefore, in
order to fill this gap, the dynamic computation offloading problem in time-varying environment for multi-user is
considered in this paper. By considering the dynamic changes of channel state and users queue state, the dynamic
computation offloading problem for multi-user is formulated as a stochastic game, which aims to optimize the delay
and packet loss rate of users. To find the optimal solution of the formulated optimization problem, Nash Q-learning
(NQLN) algorithm is proposed which can be quickly converged to a Nash equilibrium solution. Finally, extensive
simulation results are presented to demonstrate the superiority of NQLN algorithm. It is shown that NQLN algorithm
has better optimization performance than the benchmark schemes. 

Moving data from cloud to the edge network can effectively reduce traffic burden on the core network, and edge collaboration can further improve the edge caching capacity and the quality of service ( QoS). However, it is difficult for various edge caching devices to cooperate due to the lack of trust and the existence of malicious nodes. In this paper,blockchain which has the distributed and immutable characteristics is utilized to build a trustworthy collaborative edge caching scheme to make full use of the storage resources of various edge devices. The collaboration process is described in this paper, and a proof of credit (PoC) protocol is proposed, in which credit and tokens are used to encourage nodes to cache and transmit more content in honest behavior. Untrusted nodes will pay for their malicious actions such as tampering or deleting cached data. Since each node chooses strategy independently to maximize its benefits in an environment of mutual influence, a non-cooperative game model is designed to study the caching behavior among edge nodes. The existence of Nash equilibrium (NE) is proved in this game, so the edge server (ES) can choose the optimal caching strategy for all collaborative devices, including itself, to obtain the maximum rewards. Simulation results show that the system can save mining overhead as well as organize a trusted collaborative edge caching effectively. 

Harris hawks optimization ( HHO) algorithm is an efficient method of solving function optimization problems.
However, it is still confronted with some limitations in terms of low precision, low convergence speed and stagnation
to local optimum. To this end, an improved HHO ( IHHO) algorithm based on good point set and nonlinear
convergence formula is proposed. First, a good point set is used to initialize the positions of the population
uniformly and randomly in the whole search area. Second, a nonlinear exponential convergence formula is designed
to balance exploration stage and exploitation stage of IHHO algorithm, aiming to find all the areas containing the
solutions more comprehensively and accurately. The proposed IHHO algorithm tests 17 functions and uses Wilcoxon
test to verify the effectiveness. The results indicate that IHHO algorithm not only has faster convergence speed than
other comparative algorithms, but also improves the accuracy of solution effectively and enhances its robustness
under low dimensional and high dimensional conditions. 

In order to solve the energy crisis and pollution problems, smart grid is widely used. However, there are many
challenges such as the management of distributed energy during the construction. Blockchain, as an emerging
technology, can provide a secure and transparent solution to the decentralized network. Meanwhile, fog computing
network is considered to avoid the high deployment cost. The edge servers have abundant computing and storage
resources to perform as nodes in grid blockchain. In this paper, an innovative structure of smart grid blockchain
integrated with fog computing are proposed. And a new consensus mechanism called scalable proof of cryptographic
selection (SPoCS) is designed to adapt the hybrid networks. The mechanism not only includes a special index,
contribution degree, to measure the loyalty of fog nodes and the probability of being a function node, but also has
flexible block interval adjustment method. Meanwhile, the number of function nodes (validating nodes and ordering
nodes) can also be adjusted. And a deep reinforcement learning (DRL) method is used to select the appropriate
quantity to improve the performance under the strict constraints of security and decentralization. The simulation
shows the scheme performs well in the throughput, cost and latency. 

To reduce the side-lobe level of L-shaped expansion array and improve the output signal to interference and noise
ratio (SINR), the algorithm of side-lobe constraint based on minimum variance distortionless response ( MVDR-
SC) is proposed. Firstly, the approach of mixing diagonal loading and Mailloux-Zatman (DLMZ) is used to taper
the covariance matrix of the expansion array. Then, the second order cone programming ( SOCP) obtained by
constructing a new matrix is used to control the beam side-lobe. Finally, the new adaptive weight numbers are
constructed by adjusting the proportion between DLMZ and SOCP. Simulation results show that the MVDR-SC
algorithm can effectively reduce the side-lobe of beamforming under the L-shaped expansion array and obtain a
larger output SINR. At the same time, it has good robustness to the mutual coupling error. 

In this paper, a new spatial quadrature modulation ( NSQM ) scheme is proposed to improve the error
performance of indoor visible light communication ( VLC) systems. NSQM is different from generalized spatial
quadrature modulation ( SQM) in two aspects. First, the transmitted optical signal is directly detected at the
receiver, which does not need to estimate the indices of the transmitted antenna. Second, an optimization approach
is used with NSQM to minimize the upper error bound of the transmitted signals. In addition, several NSQM
schemes are described in detail. Numerical results show that the proposed NSQM scheme achieves superior error
performance compared with the SQM scheme.