Enhancing Cyber Security of LoRaWAN Gateways under Adversarial Attacks

The Internet of Things (IoT) has disrupted the IT landscape drastically, and Long Range Wide Area Network (LoRaWAN) is one specification that enables these IoT devices to have access to the Internet. Former security analyses have suggested that the gateways in LoRaWAN in their current state are susceptible to a wide variety of malicious attacks, which can be notoriously difficult to mitigate since gateways are seen as obedient relays by design. These attacks, if not addressed, can cause malfunctions and loss of efficiency in the network traffic. As a solution to this unique problem, this paper presents a novel certificate authentication technique that enhances the cyber security of gateways in the LoRaWAN network. The proposed technique considers a public key infrastructure (PKI) solution that considers a two-tier certificate authority (CA) setup, such as a root-CA and intermediate-CA. This solution is promising, as the simulation results validate that about 66.67% of the packets that are arriving from an illegitimate gateway (GW) are discarded in our implemented secure and reliable solution.

Emotion Recognition on Edge Devices: Training and Deployment

Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost. In this paper, we show that the combination of large transformers, as high-quality feature extractors, and simple hardware-friendly classifiers based on linear separators can achieve competitive performance while allowing real-time inference and fast training. Various solutions including batch and Online Sequential Learning are analyzed. Additionally, our experiments show that latency and performance can be further improved via dimensionality reduction and pre-training, respectively. The resulting system is implemented on two types of edge device, namely an edge accelerator and two smartphones.

Active Haptic Perception in Robots: A Review

In the past few years a new scenario for robot-based applications has emerged. Service and mobile robots have opened new market niches. Also, new frameworks for shop-floor robot applications have been developed. In all these contexts, robots are requested to perform tasks within open-ended conditions, possibly dynamically varying. These new requirements ask also for a change of paradigm in the design of robots: on-line and safe feedback motion control becomes the core of modern robot systems. Future robots will learn autonomously, interact safely and possess qualities like self-maintenance. Attaining these features would have been relatively easy if a complete model of the environment was available, and if the robot actuators could execute motion commands perfectly relative to this model. Unfortunately, a complete world model is not available and robots have to plan and execute the tasks in the presence of environmental uncertainties which makes sensing an important component of new generation robots. For this reason, today's new generation robots are equipped with more and more sensing components, and consequently they are ready to actively deal with the high complexity of the real world. Complex sensorimotor tasks such as exploration require coordination between the motor system and the sensory feedback. For robot control purposes, sensory feedback should be adequately organized in terms of relevant features and the associated data representation. In this paper, we propose an overall functional picture linking sensing to action in closed-loop sensorimotor control of robots for touch (hands, fingers). Basic qualities of haptic perception in humans inspire the models and categories comprising the proposed classification. The objective is to provide a reasoned, principled perspective on the connections between different taxonomies used in the Robotics and human haptic literature. The specific case of active exploration is chosen to ground interesting use cases. Two reasons motivate this choice. First, in the literature on haptics, exploration has been treated only to a limited extent compared to grasping and manipulation. Second, exploration involves specific robot behaviors that exploit distributed and heterogeneous sensory data.

Real-Time Digital Signal Processing Based on FPGAs for Electronic Skin Implementation

Enabling touch-sensing capability would help appliances understand interaction behaviors with their surroundings. Many recent studies are focusing on the development of electronic skin because of its necessity in various application domains, namely autonomous artificial intelligence (e.g., robots), biomedical instrumentation, and replacement prosthetic devices. An essential task of the electronic skin system is to locally process the tactile data and send structured information either to mimic human skin or to respond to the application demands. The electronic skin must be fabricated together with an embedded electronic system which has the role of acquiring the tactile data, processing, and extracting structured information. On the other hand, processing tactile data requires efficient methods to extract meaningful information from raw sensor data. Machine learning represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensor data. In this framework, this paper presents the implementation of digital signal processing based on FPGAs for tactile data processing. It provides the implementation of a tensorial kernel function for a machine learning approach. Implementation results are assessed by highlighting the FPGA resource utilization and power consumption. Results demonstrate the feasibility of the proposed implementation when real-time classification of input touch modalities are targeted.

Computational intelligence techniques for tactile sensing systems

Tactile sensing helps robots interact with humans and objects effectively in real environments. Piezoelectric polymer sensors provide the functional building blocks of the robotic electronic skin, mainly thanks to their flexibility and suitability for detecting dynamic contact events and for recognizing the touch modality. The paper focuses on the ability of tactile sensing systems to support the challenging recognition of certain qualities/modalities of touch. The research applies novel computational intelligence techniques and a tensor-based approach for the classification of touch modalities; its main results consist in providing a procedure to enhance system generalization ability and architecture for multi-class recognition applications. An experimental campaign involving 70 participants using three different modalities in touching the upper surface of the sensor array was conducted, and confirmed the validity of the approach.

Using unsupervised analysis to constrain generalization bounds for support vector classifiers

A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generalization bounds can prove effective, provided that they are tight and track the validation error correctly. The maximal discrepancy (MD) approach is a very promising technique for model selection for support vector machines (SVM), and estimates a classifier's generalization performance by multiple training cycles on random labeled data. This paper presents a general method to compute the generalization bounds for SVMs, which is based on referring the SVM parameters to an unsupervised solution, and shows that such an approach yields tight bounds and attains effective model selection. When one estimates the generalization error, one uses an unsupervised reference to constrain the complexity of the learning machine, thereby possibly decreasing sharply the number of admissible hypothesis. Although the methodology has a general value, the method described in the paper adopts vector quantization (VQ) as a representation paradigm, and introduces a biased regularization approach in bound computation and learning. Experimental results validate the proposed method on complex real-world data sets.

Perioperative analgesia in pulmonary surgery

AIM

In pulmonary surgery many studies have shown how pain associated with residual doses of anaesthetic drugs can cause a decrease in pulmonary volumes and depression of the cough reflex. Both these phenomena are responsible for a rising incidence of postoperative mortality and morbidity. The most widely used postoperative analgesic techniques are continuous systemic analgesia and epidural analgesia. The aim of this study is to verify the advantages and the disadvantages of both analgesic techniques.

METHODS

Fifty patients, undergoing pulmonary surgery, were recruited and divided, after randomization into 2 groups. Patients included in A group received an epidural administration of naropina 0.2%+fentanyl 4 microg/mL by elastomeric pump (rate 5 ml/h). Patients included in B group received an ev continuous infusion of tramadol 600 mg+ketorolac trometamina 120 mg+ranitidina 200 mg+ondansetron 16 mg by elastomeric pump (rate 5 ml/h for 48 hours).

RESULTS

Both groups showed good analgesic effects. Pain rest relief was between 3 and 1.7 points in group B and between 2.5 and 0.4 points in group A. Incident pain was 4.8 at awakening time and it decreased to 4 after 48 hours in group B while in group A it was from 3.2 to 1.8 in the same period of B group.

CONCLUSIONS

Our data show that both analgesic techniques are able to guarantee a good rest pain relief after thoracotomy. Epidural analgesia showed more efficacy as far as incident pain relief but it was more difficult to realise and it showed less acceptance by patients.

Perioperative thoracic epidural analgesia in aortic surgery: role of levobupivacaine

AIM

Perioperative pain management in patients undergoing surgery is an essential target in order to improve intraoperative outcome and reduce postoperative complications occurrence. The combination of a local anesthetic with an opiate for epidural administration can ensure both analgesic effect (opiate) and neuroendocrine protection (local anesthetic). Levobupivacaine, S(-)-enantiomer form of bupivacaine, produces a sensitive-motor blockade similar to the racemate, with less cardiotoxicity; also ropivacaine is not cardiotoxic, but it has less anesthetic efficacy than levobupivacaine; both anesthetics could be administered through the epidural catheter in order to ensure adequate analgesia without any hemodynamic effects. Aim of our study was to evaluate a thoracic epidural analgesia for abdominal aortic surgery.

METHODS

Through a randomized mono-blind study, involving 28 patients undergoing aortic surgery, we performed a clinical evaluation of 2 different perioperative thoracic epidural analgesic techniques; 2 different local anesthetics (levobupivacaine versus ropivacaine) in combination with the same opiate (fentanyl) were compared.

RESULTS

The results obtained show that both techniques ensure an excellent perioperative analgesia without any cardiotoxicity, with only moderate adverse effects due to opiate; the absence of postoperative mortality (within 30 days from operation) and the modest perioperative morbidity underline the qualities of this analgesic technique.

CONCLUSION

The combination of fentanyl with levobupivacaine or ropivacaine for use in thoracic epidural administration ensured both analgesic and neuroendocrine effect; significative differences between the 2 local anesthetics cannot be demonstrated, even if levobupivacaine, which presents a higher anesthetic efficacy, requires lower dosages.

Aescin formation in calli and embryoids from cotyledon and stem explants of Aesculus hippocastanum L

Aescin in calli and embryoids obtained from both cotyledon and stem explants of Aesculus hippocastanum were investigated by HPLC. Determinations were carried out on tissues cultured in agarized medium supplemented with growth substances (2,4-dichlorophenoxyacetic acid; kinetin; 1-naphthaleneacetic acid). The results indicate that aescin was produced in all the analysed samples. The amount of active principle present in some samples was higher than that found in horse-chestnut seeds.

Aescin Content in Embryogenic Callus and in Embryoids from Leaf Explants of Aesculus hippocastanum

HPLC determinations of the aescin contents in calli and embryoids from leaf explants of AESCULUS HIPPOCASTANUM L. were carried out in order to determine whether it was possible to obtain aescin-forming proliferations IN VITRO. The results indicate that embryogenic calli and embryoids derived from them produce an amount of active principle higher than that of horse-chestnut seeds. The data are discussed in terms of the relation between tissue differentiation and secondary metabolites biosynthesis.

[The action of kinetin on dormant seeds of Cercis siliquastrum. Preliminary observations]

Seeds of Cercis siliquastrum L., whose dormancy is broken both by low temperatures and by treatments with gibberellin, were treated with kinetin at various concentrations. This substance does not induce germination, but causes the detaching of the basal portion of the tegument along a circular line without damaging the cells of tegument itself. Such a line is the same as observed in the tegument when the seed naturally germinates.