Security is becoming an essential problem for integrated circuits (ICs). Various attacks, such as reverse engineering and dumping on-chip data, have been reported to undermine IC security. IEEE 1149.1, also known as JTAG, is primarily used for IC manufacturing test but inevitably provides a "backdoor" that can be exploited to attack ICs. Encryption has been used extensively as an effective mean to protect ICs through authentication, but a few weaknesses subsist, such as key leakage. Signature-based techniques ensure security using a database that includes known attacks, but fail to detect attacks that are not contained by the database. To overcome these drawbacks, a two-layer learning-based protection scheme is proposed. Specifically, the scheme monitors the execution of JTAG instructions and uses support vector machines (SVM) to identify abnormal instruction sequences. The use of machine learning enables the detection of unseen attacks without the need for key-based authentication. The experiments based on the OpenSPARC T2 platform demonstrate that the proposed scheme improves the accuracy of detecting unseen attacks by 50% on average when compared to previous work.

Tooth wear is a complex multifactorial process that involves the loss of hard dental tissue. Parafunctional habits have been mentioned as a self-destructive process caused by stress, which results in hyperactivity of masticatory muscles. Stress manifests itself through teeth grinding, leading to progressive teeth wear. The effects of continuous exposure to industrial noise, a “stressor” agent, cannot be ignored and its effects on the teeth must be evaluated.

Routing protocol for low-power and lossy networks is a routing solution specifically developed for wireless sensor networks, which does not quickly rebuild topology of mobile networks. In this article, we propose a mechanism based on mobility entropy and integrate it into the corona RPL (CoRPL) mechanism, which is an extension of the IPv6 routing protocol for low-power and lossy networks (RPL). We extensively evaluated our proposal with a simulator for Internet of Things and wireless sensor networks. The mobility entropy-based mechanism, called CoRPL+E, considers the displacement of nodes as a deciding factor to define the links through which nodes communicate. Simulation results show that the proposed mechanism, when compared to CoRPL mechanism, is effective in reducing packet loss and latency in simulated mobile routing protocol for low-power and lossy networks. From the simulation results, one can see that the CoRPL+E proposal mechanism provides a packet loss reduction rate of up to 50% and delays reduction by up to 25% when compared to CoRPL mechanism.

Typical analogue-to-digital conversion (ADC) architectures, at Nyquist rate, tend to occupy a big portion of the integrated circuit die area and to consume more power than desired. Recently, with the rise of Interet-of-Things (IoT), there is a high demand for architectures that can have both reduced area and power consumption. Time encoding machines (TEM) might be a promising alternative. These types of encoders result in very simple and low-power analogue circuits, shifting most of its complexity to the decoding stage, typically stationed in a place with access to more resources. This paper focuses on a particular TEM, the integrate-and-fire neuron (IFN). The IFN modulation is based on a simplified first-order model of neural operation and it encodes the signal in a very power efficient manner. In the end, a novel hardware architecture for the reconstruction of the IFN encoded signal based on a spiking model will be presented. The method is demonstrated and implemented on FPGA, reaching an ENOB as high as 8.23.