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Multiple Cell Upsets (MCUs) induced by ionizing radiation in memories are becoming more likely to happen due to the continuous technology scaling down. Error Correction Codes (ECCs) are applied for recovering the stored information into its original state providing reliable computer systems. Several ECC are able to deal with MCUs, however, the higher the robustness of an ECC, more area, and energy...
Since physical unclonable functions (PUFs) are considered for various security applications such as authentication and key generation, the robustness of PUFs is vital. In prior works, various error correction codes, such as Bose-Chaudhuri-Hocquenghem (BCH) codes, were used to improve the robustness of PUFs. In this paper, we use polar codes, a new family of error correction codes, to improve the robustness...
We consider the problem of robust polynomial regression, where one receives samples that are usually within a small additive error of a target polynomial, but have a chance of being arbitrary adversarial outliers. Previously, it was known how to efficiently estimate the target polynomial only when the outlier probability was subconstant in the degree of the target polynomial. We give an algorithm...
This work presents a self-adaptation algorithm to automatically adjust the peaking settings of a continuous-time linear equalizer (CTLE) in a high-speed PAM4 receiver. A statistical approach is adopted to improve the robustness and flexibility of the adaptation algorithm. The PAM4 top level distribution around the peak value of several consecutive top levels guides the CTLE to attain the optimal digital...
Shape reconstruction techniques using structured light have been widely researched and developed due to their robustness, high precision, and density. Because the techniques are based on decoding a pattern to find correspondences, it implicitly requires that the projected patterns be clearly captured by an image sensor, i.e., to avoid defocus and motion blur of the projected pattern. Although intensive...
Conventional iterative timing recovery is developed based on the widely used assumption of Additive White Gaussian noise (AWGN) interference. The Gaussian-based approach is excellent for timing recovery over AWGN channel with matched filtering approach but does not perform well in the presence of non-Gaussian noise. Overall performance of the conventional iterative timing recovery with matched filtering...
Transistor aging, due to Bias-Temperature Instability (BTI) is a serious concern in Static Random Access Memories (SRAMs). Under BTI stress an SRAM cell becomes increasingly skewed, which in turn affects its performance characteristics and consequently the memory reliability. In this paper, a variation tolerant technique for the periodic monitoring of the BTI influence on SRAM cells is presented....
In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications. When the available modalities consist of time series data such as video, audio and sensor signals, it becomes imperative to consider their temporal structure during the fusion process. In this paper, we propose the Correlational...
Deep Auto-Encoder (DAE) has shown its promising power in high-level representation learning. From the perspective of manifold learning, we propose a graph regularized deep neural network (GR-DNN) to endue traditional DAEs with the ability of retaining local geometric structure. A deep-structured regularizer is formulated upon multi-layer perceptions to capture this structure. The robust and discriminative...
The modern automotive industry has entered an era where the tendencies are towards increased automation and connectivity. With every new generation, the proportion of electronics-controlled systems in the vehicles is steadily growing. In parallel the safety and reliability requirements to automobiles are becoming more stringent thus requiring more sophisticated approaches. The problem is complicated...
This paper proposes a technique to improve the robustness of spread spectrum (SS) audio watermarking for acoustic propagation. The appropriate embedding areas should be selected to achieve good perceptibility and high robustness. In order to improve the redundancy and robustness, the cross spread spectrum (CSS) scheme based on highly correlated cross frames is proposed which could decrease the variance...
Fountain codes are used in many applications where the channels are time varying and it is difficult at the transmitter to predict the appropriate code rate. In this situation, fixedrate codes are not suitable. Despite the good performance of fountain codes, universally optimal codes do not exist in the finite-length regime. In this paper, we design new fountain codes that are robust to the communication...
We introduce Cyclone codes which are rateless erasure resilient codes. They combine Pair codes with Luby Transform (LT) codes by computing a code symbol from a random set of data symbols using bitwise XOR and cyclic shift operations. The number of data symbols is chosen according to the Robust Soliton distribution. XOR and cyclic shift operations establish a unitary commutative ring if data symbols...
In the network coding, we discuss the effect by sequential error injection to information leakage. We show that there is no improvement when the network is composed of linear operations. However, when the network contains non-linear operations, we find a counterexample to improve Eve's obtained information. Further, we discuss the asymptotic rate in the linear network under the secrecy and robustness...
In this work, we study the arbitrarily varying degraded broadcast channel (AVDBC), when state information is available at the transmitter in a causal manner. We establish inner and outer bounds on both the random code capacity region and the deterministic code capacity region. The capacity region is then determined for a class of channels satisfying a condition on the mutual informations between the...
We present a novel approach to achieving temperature-robust behavior in neuromorphic systems that operates at the population level, trading an increase in silicon-neuron count for robustness across temperature. Our silicon neurons' tuning curves were highly sensitive to temperature, which could be decoded from a 400-neuron population with a precision of 0.07° C. We overcame this temperature-sensitivity...
In this paper a robust encoding scheme is proposed to improve the visual quality of HEVC decoded video when intra frames are lost along the streaming path. For this purpose, the encoding process includes frame loss simulation and subsequent error concealment, to find the most efficient method that should be used by a decoder to recover lost intra frames. In this novel scheme, each image is divided...
A common problem in Brain-Machine Interface (BMI) is the variations in neural signals over time, leading to significant decrease in decoding performance if the decoder is not re-trained. However, frequent re-training is not practical in real use case. In our work, we found that a temporally more robust system may be achieved through the use of wavelet transform in feature extraction. We used wavelet...
In this paper, we design a fast and efficient energy-based and asynchronous neighbor discovery protocol for the Internet of Things (IoT). In our solution, we relax the assumption of frame-level synchronization. We formulate a novel asynchronous group testing scheme and apply it to the neighbor discovery problem. We then show that our proposed scheme is able to detect the set of K active neighbors1...
This work proposes a new deep learning method which we call robust deep dictionary learning RDDL. RDDL is suitable for learning representations from signals corrupted with sparse but large outliers such as artifacts and noise that are more heavy tailed than Gaussian distributions. Such outliers are common in biomedical signals e.g. EEG and ECG. RDDL learns multiple levels of non-linear dictionaries...
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