Emerging resistive random access memory (RRAM) attracts considerable interest in computing-in-memory by its high efficiency in multiply-accumulate operation, which is the key computation in the neural network (NN). However, due to the imperfect fabrication, RRAM cells suffer from the variations, which make the values in RRAM cells deviate from the target values so that the accuracy of the RRAM-based NN accelerator degrades significantly. Moreover, in a practical hardware design of RRAM-based NN accelerators, if the number of wordlines and bitlines in a crossbar array activated at the same time increases, ADCs with a high resolution are required and the power consumption of ADC increases. This paper proposes a novel methodology to mitigate the impact of variations in RRAM-based neural network accelerators. The methodology includes a unary-based non-uniform quantization method and a variation-aware operation unit (OU) based framework. The unary-based non-uniform quantization method equalizes the significance of weights stored in each RRAM cell to reduce the impact of variations. The variation-aware OU-based framework activates only RRAM cells in the same OU at the same time, which reduces the power consumption of ADCs. Additionally, the framework introduces three methods, including OU skipping, OU recombination, and OU compensation, to further mitigate the impact of variations. The experiments show that the proposed approach outperforms the state-of-the-art among four NN models on two datasets with 2-bit cell resolution.

Internet of Things (IoT) has been regarded as one of the most significant network paradigms in the future. For IoT, it is crucial to ensure the correctness of detection which includes the factors of accuracy and precision. On the other hand, Mobile Edge Computing (MEC) has emerged as a promising way to process big IoT data at the network edge so as to reduce the computation and transmission energy in the networks. In this paper, we explore the energy minimization problem in MEC networks by considering both the accuracy and precision requirements of IoT. Specifically, given 1) a set of IoT devices, 2) a set of observed targets, 3) an MEC network, 4) the energy consumption model, and 5) the accuracy and precision requirements, we formulate a new optimization problem, named Accuracy and Precision-Aware IoT Device Selection (APAIDS), to minimize the overall energy consumption in MEC networks. We prove the NP-hardness of APAIDS and then propose a new algorithm, named Energy Efficient Device and MEC Server Selection (EDMS), to minimize energy consumption by jointly selecting IoT devices, configuring MEC association, and selecting processing servers for dealing with the data of each target. Finally, we evaluate EDMS on two real networks. In comparison with the baseline schemes, the results manifest that the overall energy consumption can be reduced by more than 60%.

Emergence is the visual phenomenon by which humans recognize the objects in a seemingly noisy image through aggregating information from meaningless pieces and perceiving a whole that is meaningful. Such an unique mental skill renders emergence an effective scheme to tell humans and machines apart. Images that are detectable by human but difficult for an automatic algorithm to recognize are also referred as emerging images. A recent state-of-the-art work proposes to synthesize images of 3D objects that are detectable by human but difficult for an automatic algorithm to recognize. Their results are further verified to be easy for humans to recognize while posing a hard time for automatic machines. However, using 3D objects as inputs prevents their system from being practical and scalable for generating an infinite number of high quality images. For instance, the image quality may degrade quickly as the viewing and lighting conditions changing in 3D domain, and the available resources of 3D models are usually limited. However, using 3D objects as inputs brings drawbacks. For instance, the quality of results is sensitive to the viewing and lighting conditions in the 3D domain. The available resources of 3D models are usually limited, and thus restricts the scalability. This paper presents a novel synthesis technique to automatically generate emerging images from regular photographs, which are commonly taken with decent setting and widely accessible online. We adapt the previous system to the 2D setting of input photographs and develop a set of image-based operations. Our algorithm is also designed to support the difficulty level control of resultant images through a limited set of parameters. We conducted several experiments to validate the efficacy and efficiency of our system.

The increasing popularity of the internet means that digital multimedia are transmitted more rapidly and easily. And people are very aware for media ownership. However, digital watermarking is an efficient and promising means to protect intellectual properties. Based on the intellectual property attention in the information era, how to protect the personal ownership is extremely important and a necessary scheme. In this paper, we propose an effective video watermarking method based on a pseudo-3-D discrete cosine transform (DCT) and quantization index modulation (QIM) against several attacks. The watermark is mainly inserted into the uncompressed domain by adjusting the correlation between DCT coefficients of the selected blocks, and the watermark extraction is blind. This approach consists of a pseudo-3-D DCT, watermark embedding, and extraction. A pseudo-3-D DCT, which is taken DCT transformation twice, will be first utilized to calculate the embedding factor and to obtain the useful messages. Using the QIM, we embed the watermark into the quantization regions from the successive raw frames in the uncompressed domain and record the relative information to create a secret embedding key. This secret embedding key will further apply to extraction. Experimental results demonstrate that the proposed method can survive filtering, compressions, luminance change, and noise attacks with a good invisibility and robustness.

In this paper, a few low-complexity and high-performance rate-distortion control algorithms for MPEG-4 Advanced Audio Coding (AAC) are proposed. One key element in producing good quality compressed audio particularly at medium and low rates is a high performance rate-distortion controller in the audio encoder. Although the trellis-based rate-distortion control algorithms previously proposed can achieve a praiseworthy performance, their computational complexity is extremely high. Therefore, for practical applications, it is very desirable to achieve a similar performance at a much lower complexity. Two types of techniques are proposed in this paper to reduce the computational burden of the trellis-based algorithms. One is splitting a very heavy calculation stage into two sequential steps with much less computation. The other is reducing the candidates in the trellis for parameter search. Together, when applicable, our approach achieves a similar coding performance (audio quality) but requires less than 1/1000 complexity in computation.

For the purpose of efficient audio coding at low rates, a new bit allocation strategy is proposed in this paper. The basic idea behind this approach is "Give bits to the band with the maximum NMR-Gain/bit" or "Retrieve bits from the band with the maximum bits/NMR-Loss". The notion of "bit-use efficiency" is suggested and it can be employed to construct a bit assignment algorithm operated at band-level as compared to the traditional frame-level bit assignment methods. Based on this strategy a new bit assignment scheme, called Max-BNLR, is designed for the MPEG-4 AAC. Simulation results show that the performance of the Max-BNLR scheme is significantly better than that of the MPEG-4 AAC Verification Model (VM) and is close to that of TB-ANMR, which is the (nearly) optimal solution. Moreover, the Max-BNLR scheme has the advantages of low computational complexity compared to TB-ANMR.