Solutions to the routing problem: towards trustworthy autonomous vehicles

The general expectation is that the traffic in the cities will be almost optimal when the collective behaviour of autonomous vehicles will determine the traffic. Each member of the collective of autonomous vehicles tries to adapt to the changing environment, therefore together they execute decentralised autonomous adaptation by exploiting real-time information about their environment. The routing of these vehicles needs proper computer science models to be able to develop the best information technology for their control. We review different traffic flow models in computer science, and we evaluate their usefulness and applicability to autonomous vehicles. The classical game theory model implies flow level decision making in route selection. Non-cooperative autonomous vehicles may produce unwanted traffic patterns. Improved decentralised autonomous adaptation techniques try to establish some kind of coordination among autonomous vehicles, mainly through intention awareness. The aggregation of the intentions of autonomous vehicles may help to predict future traffic situations. The novel intention-aware online routing game model points out that intention-awareness helps to avoid that the traffic generated by autonomous vehicles be worse than the traffic indicated by classical traffic flow models. The review helps to make the first steps towards research on global level control of autonomous vehicles by highlighting the strengths and weaknesses of the different formal models. The review also highlights the importance of research on intention-awareness and intention-aware traffic flow prediction methods.

VITI: A Tiny Self-Calibrating Sensor for Power-Variation Measurement in FPGAs

On-chip sensors, built using reconfigurable logic resources in field programmable gate arrays (FPGAs), have been shown to sense variations in signalpropagation delay, supply voltage and power consumption. These sensors have been successfully used to deploy security attacks called Remote Power Analysis (RPA) Attacks on FPGAs. The sensors proposed thus far consume significant logic resources and some of them could be used to deploy power viruses. In this paper, a sensor (named VITI) occupying a far smaller footprint than existing sensors is presented. VITI is a self-calibrating on-chip sensor design, constructed using adjustable delay elements, flip-flops and LUT elements instead of combinational loops, bulky carry chains or latches. Self-calibration enables VITI the autonomous adaptation to differing situations (such as increased power consumption, temperature changes or placement of the sensor in faraway locations from the circuit under attack). The efficacy of VITI for power consumption measurement was evaluated using Remote Power Analysis (RPA) attacks and results demonstrate recovery of a full 128-bit Advanced Encryption Standard (AES) key with only 20,000 power traces. Experiments demonstrate that VITI consumes 1/4th and 1/16th of the area compared to state-of-the-art sensors such as time to digital converters and ring oscillators for similar effectiveness.

Ambient Light Rejection Integrated Circuit for Autonomous Adaptation on a Sub-retinal Prosthetic System

This paper introduces an ambient light rejection (ALR) circuit for the autonomous adaptation of a subretinal implant system. The sub-retinal implants, located beneath a bipolar cell layer, are known to have a significant advantage in spatial resolution by integrating more than a thousand pixels, compared to epi-retinal implants. However, challenges remain regarding current dispersion in high-density retinal implants, and ambient light induces pixel saturation. Thus, the technical issues of ambient light associated with a conventional image processing technique, which lead to high power consumption and area occupation, are still unresolved. Thus, it is necessary to develop a novel image-processing unit to handle ambient light, considering constraints related to power and area. In this paper, we present an ALR circuit as an image-processing unit for sub-retinal implants. We first introduced an ALR algorithm to reduce the ambient light in conventional retinal implants; next, we implemented the ALR algorithm as an application-specific integrated chip (ASIC). The ALR circuit was fabricated using a standard 0.35-μm CMOS process along with an image-sensor-based stimulator, a sensor pixel, and digital blocks. As experimental results, the ALR circuit occupies an area of 190 µm2, consumes a power of 3.2 mW and shows a maximum response time of 1.6 sec at a light intensity of 20,000 lux. The proposed ALR circuit also has a pixel loss rate of 0.3%. The experimental results show that the ALR circuit leads to a sensor pixel (SP) being autonomously adjusted, depending on the light intensity.

Kv4.3 channel downregulation mediates chronic post-lesional pacemaker acceleration in surviving dopamine substantia nigra neurons

Substantia nigra dopamine (SN DA) neurons are progressively lost in Parkinson disease (PD). While the molecular and cellular mechanisms of their differential vulnerability and degeneration have been extensively studied, we still know very little about potential functional adaptations of those SN DA neurons that at least for some time manage to survive during earlier stages of PD. We utilized a partial lesion 6-OHDA mouse model to characterize initial electrophysiological impairments and chronic adaptations of surviving identified SN DA neurons, both in vivo and in vitro. Early after lesion (3 weeks), we detected a selective loss of in vivo burst firing in surviving SN DA neurons, which was accompanied by in vitro pacemaker instability. In contrast, late after lesion (>2 months), in vivo firing properties of surviving SN DA neurons had recovered in the presence of 2-fold accelerated pacemaking in vitro. Finally, we show that this chronic cell-autonomous adaptation in surviving SN DA neurons was mediated by Kv4.3 channel downregulation. Our study demonstrates substantial homeostatic plasticity of surviving SN DA neurons after a single-hit non-progressive lesion, which might contribute to the phenotype of initially surviving SN DA neurons in PD.

Classification Method for Network Security Data Based on Multi-featured Extraction

A method of classifying network security data based on multi-featured extraction is proposed to address instability of a nonlinear time series in a network security threat. Cybersecurity information is divided in line with the principle of acquiring multiple attributes. On this basis, an adaptive adaptation estimation technology is optimized in analogue. With the proposed method, a cybersecurity information classification system is constructed according to the phase interval reconstruction principle so that a dynamic and autonomous adaptation estimation of the cybersecurity threat can be completed to ensure the feasibility of cybersecurity information classification. The experimental result proves that the cybersecurity information classification technology based on multi-attribute extraction can effectively guide chaos into adjacent orbits and reasonably control the training scale. Moreover, the accuracy of the estimation is guaranteed and the cybersecurity threat is estimated because of its high-speed convergence and strong proximity. Therefore, the proposed classification technology can assist professionals and backstage managers in guaranteeing security by facilitating receipt of information in a timely manner.

Power-law productivity and positive regime shift of symbiotic and climate-resilient edible ecosystems

Transformative change in primary food production is urgently needed in the face of climate change and biodiversity loss. Although there are a growing number of studies aimed at global policymaking, actual implementations require on-site deep analyses of social-ecological feasibility. Here, we report the first implementations of low-input mixed polyculture of highly diverse crops (synecoculture) in Japan and Burkina Faso. Results showed that the self-organized primary production of ecosystems follows a power law and performs better compared with conventional monoculture methods in 1) promoting diversity and total quantity of products along with rapid increase of in-field biodiversity, especially in a semi-arid environment where local reversal of regime shift is observed; 2) a fundamental reduction of inputs and environmental load; and 3) ecosystem-based autonomous adaptation of the crop portfolio to climatic variability. The overall benefits imply substantial possibilities for a new typology of sustainable farming based on human-guided augmentation of ecosystem services and biodiversity maintenance mechanisms that could overcome the historical trade-off between productivity and biodiversity.