The main challenges in modern agriculture involve addressing the disruptive environmental effects caused by crop and livestock production, which are essential for human survival. Emerging trends point towards a world of pervasive IoT (Internet of Things) nodes, where low-cost, low-power, and in-vivo plant sensors can detect abiotic and biotic stresses at the earliest stages. This paper presents a preliminary study of an integrable, low-power relaxation oscillator, consisting of a Schmitt trigger in a feedback loop with the plant’s stem to evaluate its health status. Simulations have been conducted to compare the energy consumption of this circuit with state-of-the-art electronic sensors, focusing on energy usage per measurement. The results indicate that this new solution can sense the plant’s health status with an energy consumption below 10 µJ per measurement, which is at least an order of magnitude lower than existing electronic systems found in the literature.

The rapid expansion of Wireless Body Sensor Networks (WBSNs) in healthcare, rehabilitation, and movement analysis demands precise time synchronization across sensor nodes to ensure reliable multi-modal data fusion. Existing synchronization solutions for Bluetooth Low Energy (BLE)-based WBSNs often rely on hardware-level timestamping or operate in advertising mode, limiting their scalability, interoperability, or usability in real-time interactive applications. In this paper, we present SharkTooth, a novel, scalable, and real-time synchronization algorithm that operates entirely at the application layer of BLE. Unlike prior approaches, SharkTooth does not require any firmware modifications or specialized hardware, and it is compatible with commercial off-the-shelf BLE devices. The algorithm employs an adaptive timestamp correction technique that mitigates both deterministic drift and unpredictable delays caused by packet retransmissions and protocol variability. Extensive experimental validation using up to 12 sensor nodes and 18 BLE network configurations demonstrates submillisecond synchronization accuracy, with a median absolute average synchronization error between 0.21 ms and 0.62 ms, even in congested network scenarios. Compared to state-ofthe-art solutions, SharkTooth is uniquely capable of delivering robust, long-term synchronization in high-throughput, multinode systems using only connection-based BLE communication. By prioritizing compatibility, reproducibility, and real-world performance, SharkTooth advances the design of scalable and interoperable WBSNs for next-generation biomedical and wearable IoT applications.

In the last decade, research in developing systems for monitoring clinical parameters has grown exponentially, driven by the increasing demand for continuous and remote observation. Monitoring cardiovascular diseases (CVDs) is crucial, as they remain the leading cause of mortality worldwide. Pulse Wave Velocity (PWV) is a key clinical indicator for assessing arterial stiffness, highly correlated with CVDs. Current methods for measuring PWV require acquiring pulse wave signals at the carotid and femoral sites, configurations that are widely validated in the literature. However, accessing these specific anatomical points presents a significant challenge for developing wearable devices for continuous or home-based PWV monitoring. The present study proposes an innovative approach using a regression algorithm to predict the carotid-femoral PWV value from pulse waves acquired at more accessible sites, including the carotid, radial, and tibial, compatible with wearable use. The study was conducted on a cohort of 90 voluntary healthy participants at the Candiolo Cancer Institute FPO-IRCCS. The best-performing carotid-radial models show a mean error (ME) of less than 0.1 m/s, with a standard deviation error (SDE) below 1 m/s. Similarly, the radial-tibial models exhibit a mean error of the same magnitude but with an even smaller standard deviation error, below 0.9 m/s. These results highlight the strong predictive capabilities of the developed models, offering promising prospects for future clinical implementation aimed at non-invasive and potentially continuous vascular health monitoring.

Food production is one of the main contributors to climate change and its impact is set to increase due to population growth. Smart agriculture aims at providing solutions to reduce food production and environmental impact while, at the same time, increasing crop production. This paper proposes a system based on STDES-BTAG01 by STMicroelectronics to monitor in-vivo stem electrical impedance, which is a parameter that has recently demonstrated its efficacy in assessing information about plants' water stress status. The developed system is completely battery-free and equipped with a wireless communication module to transmit the acquired data. It is powered by a small amorphous solar cell and transmits data to a base station exploiting the Bluetooth Low Energy (BLE) protocol. The system monitors the needed time to discharge a capacitor through the plant stem. After this, this span of time is used to compute the stem electrical impedance. Tests showed reading errors lower than 15% when dealing with impedance modules up to 180 kΩ. System characteristics (energy self-sufficiency, compactness, and low-power consumption) make the system implementable in the fields.

Over the years, several clock and data recovery architectures have been proposed for wireless Amplitude Shift Keying (ASK) transmitted signals. State-of-the-art architectures mainly rely on synchronous phase-locked loop circuits or self- sampling systems, both resulting in large area consumption. This work presents a novel CMOS architecture for Clock and Data Recovery (CDR) in miniaturised and wirelessly powered implants. The proposed CDR architecture works at 433.92 MHz and includes: an ASK-demodulator, an on-chip oscillator, a power-on-reset, a control and a recovering block operating in feedback-loop. The ASK-demodulator works for a data rate as high as 6 Mbps and a modulation index in the range of 9-30%. A novel communication protocol is presented for a separated clock and data transmission. The entire CDR architecture occupies$17 \times 89 \mu m^{2}$and consumes$15.01 \mu W$while operating with a clock rate of 6 Mbps.

Remote monitoring of vital body signals has drawn the attention of late, particularly with the burst of COVID-19. Wearable devices are started to be widely used for non-invasive health monitoring tasks. This work presents a wearable system for multiple body signal monitoring using only one biocompatible aluminum nitride piezoelectric sensor and a custom wireless electronic device for data acquisition and transmission. The proposed sensor has been customized for the suprasternal notch, where we can simultaneously extract heart rate, respiration rate, and deglutition events. These parameters are helpful for the remote diagnosis of multiple diseases like cardiac arrhythmia, asthma, and dysphagia. Moreover, heart sound components have been derived from the same signal, providing critical information about heart health and insight into possible heart diseases. The preliminary experimental results show that the proposed wearable system can be used for personalized healthcare applications and offers a promising solution for unobtrusive remote health monitoring.

This demonstration presents a smart wearable armband for hand gesture recognition interfaced with a 6-DOF robotic arm which actuates the user's movements. The armband is composed of seven modules which detect the muscular activity beneath them, fuse the data together, predict the performed gesture, and transmit the high-level information to an external computer via a Bluetooth Low Energy (BLE) communication. There, a software module transforms the sequence of gestures into consistent commands for the robotic arm. The observed responsiveness and accuracy make this armband suitable for the real-time control of robotic limbs in mixed reality scenarios.

A low-cost wireless integrated device for evaluating pulse wave velocity (PWV) is presented in this live demonstration. The system consists of a base/charging station, two pen-shaped probes with high-precision MEMS micro force sensors on their tips. The ulnar and carotid arteries are the two probes pickup sites, exploiting Bluetooth to transmit pulse wave signals to the base/charging station. The signals and the respectively PWV value are displayed on a PC GUI concurrently. After adequate probe sterilization, visitors can observe an actual PWV measurement on a dedicated test subject or participate in a first-person arterial pulse evaluation on their carotid.

World population growth and desertification are the most severe issue to agricultural food production. Smart agriculture is a promising solution to ensure food security. The use of sensors to monitor crop production can help farmers improve the yield and reduce water consumption. Here we propose a study where the electrical impedance of green plants' stem is analyzed in vivo, along with environmental conditions. In particular, the variations associated with the daily cycle are highlighted. These analyses lead to the possibility of understanding plant status directly from stem impedance.

With the growth of world population and food demand, it is crucial to optimize water consumption for agriculture cultivation. Here we propose a method to monitor plant status, relating the measured parameters to the watering or drying situation of a single plant. Plant trunk electrical impedance measurements and environmental parameters were analyzed with a statistical approach. Correlation and causality among the data are showed and analyzed. In this way, it was possible to easily obtain the needed information about plant status.

Multi-ion potentiometric sensing becomes challenging for mixed-ion samples in which interfering electrolytes significantly alter the response of each single sensor. Therefore, an emulator based on the phase-boundary potential model is proposed to simulate multi-ion sensing in presence of interference. It serves as investigation tool for understanding the impact of sensor selectivity and interferent ions in the sensor response. Moreover, the emulator is used to design mixed-ion synthetic dataset, following a multi-factorial design of ion mixtures with orthogonal arrays. Such large dataset is suitable for data-intensive learning algorithms used in multivariate calibration of potentiometric sensor arrays.

OBJECTIVE: This paper presents an integrated device, based on smart glasses, for the pattern-reversal visual evoked potential (PR-VEP) clinical test. METHODS: Smart glasses are used to generate the checkerboard changing pattern, with its related red fixation point through an Android application. Electroencephalographic signals, for monitoring the stimulus generated by PR-VEP, were amplified close to the scalp and then transmitted wirelessly to a PC. A MATLAB real-time algorithm processed the incoming signals to extract the final PR-VEP signal. METHODS: In total, 40 eyes (from 20 subjects, 12 males and 8 females between 24 and 28 years old) were tested and results were compared, with a commercial device for VEP clinical exam, to test the reproducibility and the efficacy of the proposed solution. RESULTS: PR-VEPs generated by smart glasses showed typical triphasic waveforms: We observed promising results and components in moderate agreement with those obtained using commercial PR-VEP recorder, with potential for improvements after further refinement works. SIGNIFICANCE: The proposed device leads the way for a portable and low-cost solution.

This demonstration presents the first implementation of “event-driven” serial asynchronous communication on optical fiber. “event-driven” communication is used by neuromorphic sensors, that sample the sensory signal when the signal itself changes of a given amount. This type of sensing adapts to the dynamics of the input itself, achieving at the same time extremely high temporal resolution (when needed), low latency and signal compression. To apply this technology in robotics, optical communication will greatly improve the resilience to electric disturbances.

This paper reports the achievements reached in the setup of a simple electrical communication inside a plant body. The approach comprises an electrical signal coupled into the plant body through a low-frequency communication signal. Different setups with the stem of a Prunus Bianco have been evaluated. From analog measurements, the optimum transmission frequency range has deemed to be 60-140 kHz. A setup for the digital communication, with an impulse-based approach (Synchronized On-Off Keying, S-OOK, modulation) and a bit rate of 50kb/s, has been tested; bit error rate has been estimated to be negligible.

The MicroElectronics Cloud Alliance (MECA) is a European funded project where 18 higher education institutions, and small and medium enterprises, sited in nine different European countries, have the aim of developing a Cloud-based European infrastructure for improving the education in microelectronics. In MECA open educational resources, educational and professional software, remote access to virtual laboratories are shared, all based on modules and learning facilities remotely available. MECA wants to be the one-stop platform of reference for the microelectronics education and the useful tool for sharing resources among institutes working in microelectronics design training. In fact, thanks to the Cloud system built inside MECA Consortium, the resources of all the partners are shared, both hardware and software, excluding licensing of course, for clear legal reasons.

The demonstration presents a robotic event-based tactile infrastructure for a humanoid robot. It leverages on currently deployed sample-based capacitive sensors to generate tactile events, enabling the investigation and development of event-driven tactile applications, and minimizing communication bandwidth and latency. The modular FPGA-based system samples data from tactile sensors and generates address-events, transmitted through an asynchronous serial address-event representation protocol. To enable performance comparisons of the event-driven approach with respect to standard sample-based solutions, the acquisition modules can directly forward the input samples through the same event-based communication channel. We will show in real time a comparison between the tactile events and the original sampled data generated when the skin patch is touched.

This study presents preliminary results of a broader research on Home Robot monitoring for elder people. The final goal of the project is the development of a robot that works in synergy with an automatic fall detection device, reaching the patient and checking his condition in case of triggered alarm. This paper covers the initial steps necessary for the design of the tracking network which provides the machine with the subject's position, in particular the single node performance. The network is based on Ultra-Wide Band (UWB) wireless transceivers that in this study are the Decawave EVB1000 evaluation boards. Two types of analysis have been performed on the anchor: a Line of Sight (LOS) baseline accuracy and interference robustness. The results demonstrate that, for LOS distance estimation, to achieve a margin of error below 15 cm, the node has to be closer than 12 m to the target. If we remove the line of sight condition, introducing a subject walking straight between the two anchors, the error is spread in the order of 10 cm from the original baseline for a 10 m nodes distance recording. If the path is obstructed instead by a subject walking perpendicularly to the nodes instead leads to a different types of perturbations, with an absolute error below 13 cm.

The demonstration presents a surface ElectroMyoGraphy (sEMG) low power wireless system used to control Functional Electrical Stimulation for rehabilitative neuro-muscular applications. An event-driven technique is applied to the bio-signal, minimising power consumption, complexity, and transmitted data. The acquisition board transmits four channels sEMG event data, through a Bluetooth Low Energy wireless module, to a workstation where they are processed in order to control the stimulation pattern of a commercially available functional stimulator. We will show system functionality and efficiency during the execution of some basic functional movements.

This paper presents a minimum complexity hand movement recognition algorithm based on Average Threshold Crossing (ATC) technique. It exploits the number of threshold-crossing events, generated by a full-custom acquisition board, from the surface ElectroMyoGraphic (sEMG) signals of three forearm muscles to detect four different movements of the wrist: flexion, extension, abduction and grasp. A Support Vector Machine (SVM) model has been trained with the signals acquired from ten subjects, who repeated ten times each gesture. To avoid correlation between training and testing dataset, the Leave One Subject Out (LOSO) cross-validation technique has been chosen. The average ATC classifier's accuracy is 92.87 %, only 5.34 % below the results obtained feeding the same model with the sEMG features extracted from the raw sampled signals. The total latency of the algorithm, from the acquisition to the prediction, is 160 ms. Power consumption was considered too: with less than the power budget for one sampled sEMG channel, it is possible to acquire and transmit (through a Bluetooth low energy module) the event-driven data of four sEMG channels, with an effective data rate of only 28B/s. Obtained performance makes this technique suited for wearable systems or Internet-of-Things (IoT) applications.

Medical Internet of Things (IoT) solutions are already paving the way towards the realization of Smart Hospitals. Following this trend, we propose an IoT cloud-based network for anesthesia on-line monitoring. This architecture allows the anesthesiologist to remain simultaneously connected to all the patients under his/her responsibility. To do so, the Android app, running on the anesthesiologists tablet/smartphone, plays the central role of the architecture. From one side, it receives data from the Therapeutic Drug Monitoring (TDM) system on the patient; on the other side, it pushes data to the cloud, exploiting the Pryv middleware solution, enabling teleconsulting. All the communications exploits Wi-Fi, therefore, all data are also stored locally in the physical devices's memory to face Internet unavailability. The flexibility and the portability of our monitoring architecture ensure the possibility to interface with any medical device which can wirelessly send the measured data. Therefore, a variety of medical monitoring applications can also be addressed.

Connected medical devices are already paving the way towards the realization of Smart Hospitals where patient care is improved thanks to Internet of Things (IoT) solutions. Following this trend, in this paper we propose an IoT cloud-based network for anesthesia on-line monitoring. This architecture allows the anesthesiologist to remain simultaneously connected to all the sedated patients through an Android app. Moreover, medical data from the patients can be shared on a cloud solution accessible by a web application enabling teleconsulting. Hence, by accessing the cloud, medical specialists can consult the shared data from everywhere and at any time. The flexibility and the portability of our monitoring architecture ensure the possibility to interface with any medical device which can wirelessly send the measured data. Therefore, other medical monitoring applications can also be addressed.

Memristive biosensors have been proved as excellent candidates for ultrasensitive biosensing. In this work, a novel portable bio-detection system based on memristive biosensors is designed, developed and tested for providing a significantly fast, automatic and simultaneous sensing output of multiple memristive biosensors on a single chip. The suggested compact and independent bio-sensing prototype is achieved through the realization of an electronic board designed for addressing the specifications of the memristive biosensor signal acquisition. Memristive bio-sensing-chips are specially designed and fabricated as well. The system was tested for successfully sensing of Prostate Specific Antigen, one of the main biomarkers of prostate cancer, at fM concentrations. Overall, this novel scheme resembles to a memristive-biosensing-kit approach, paving the way for fast and ultrasensitive PoC (point-of-care) devices.

Induction of anesthesia with propofol is a largely adopted technique in hospital environments. The correct dosage of this compound is essential to avoid under- or over-anesthesia that may result in serious side-effects. Therefore, in clinical settings, long-term monitoring of propofol is of great importance. To this aim, in this work, we present the design and the validation of a custom-made, low-cost and portable Point-of-Care (PoC) system based on electrochemical detection for propofol monitoring. Fouling phenomenon due to phenolic oxidation of propofol has been overcome by adopting a Pencil Graphite Electrode (PGE) 3H as sensor. The validation of the system focused on testing the measurement speed through scan-rate analysis, which is important due to the fast clearance of propofol ; and interference study with Paracetamol (APAP), since it is an analgesic compound frequently administered with propofol in medical practices.

The paper presents the MicroElectronics Cloud Alliance (MECA) project which brings together eighteen partners from higher education institutions and enterprises to develop Cloud-based European infrastructure and organisation for education in micro- and nanoelectronics providing a range of open educational resources, remote access and sharing of educational and professional software, remote and practice-based learning facilities. Each university will provide remote access to its facilities, laboratory experiments or software systems for the partners in a cloud teaching system, giving them access to new resources. The common ones can be optimized, reducing the singular cost per institute and increasing the available computational and structural power.

We propose a method to compress the enormous amount of data originating from tactile sensors is presented that explicitly exploits the inherent sparseness over space and time, sending tactile “events” only when a contact is detected. The resulting modular architecture is based on FPGA modules that acquire data samples from off-the-shelf tactile sensors based on capacitive transducers and generate and transmit an event-driven readout. This architecture has been specifically implemented for integration on robots with a large number of tactile sensors, to reduce communication bandwidth, power and processing requirements. An asynchronous serial address-event representation protocol further optimises effective data transmission rate (efficiency of 94.1%) and latency (340 ns) with respect to more common transmission protocols (e.g., Ethernet, CAN). We propose two complementary algorithms for the translation of raw-data into events, optimising data rate and bandwidth, or exploiting the asynchronous nature of the event-driven encoding and the temporal information within the sensory signal. Data reduction capability can reach up to 20 % of the correspondent clock-based encoding, with limited information loss due to the compression.

The development of neuromorphic systems has increased in pace in the past years since the birth of industrial samples of memristors. In addition to developing the general technology, a strong interest in generating new applications for memristive systems is emerging. Cellular automata (CA) can be utilized for biomedical applications, primarily for image processing. Here, we have developed a CA that can be used to improve patient care, through the follow-up and monitoring of patients affected by topic wounds such as cutaneous ulcers.

This paper presents a sensorized belt with four fully integrated pressure-touch sensors. We propose a very-low complexity sensor able to measure a pressure variation (up to 4 MPa) and to identify with accuracy a contact event at around 10 kPa. The overall pressure/touch sensor integrates a transducer, based on piezo capacitive material, coupled with a read-out circuit designed around a ring-oscillator. This converts the capacitance variation of the transducer into a quasi-digital signal characterized by a frequency range of 36.3–270 kHz with a very low standard deviation (2.3 kHz) and a sensitivity of 2.2 Hz/Pa. The tight integration of the electronics with the transducer results in a very compact all-in-one sensor system (overall size is 20 mm × 20 mm × 10 mm). Further, a major benefit of a low complexity design is the low power consumption, measured to be ∼370 μW. Based on a quasi-digital approach (event-driven), the system is well suited for impulse-based wireless communication.

This demonstration presents a very-low complexity IR-UWB radio system able to broadcast high quality audio signal in the band 0-20 kHz, using an order of magnitude lower number of radiated pulses compared to any packet based IR-UWB communication system. The audio source is PFM modulated and transmitted through IR-UWB. At the RX, an asynchronous and interference-robust energy detector recovers the pulse frequency modulated audio signal. Signal is demodulated by a passive FM detector and filtered with a fourth order Sallen Key cell and successively amplified for speakers steering. The demonstrator comprises a mini TX module, a RX module comprising demodulator filtering and speaker amplifier.

This paper presents an 130 nm event-driven, all-digital, modular and scalable read-out circuit for capacitive sensors, whose operation is not influenced by either variation of the supply voltage or temperature. The sensing element of the ReadOut Circuit (ROC) is a voltage-controlled ring oscillator, designed to make the system robust to voltage and temperature variations with a dedicated calibration implemented to eliminate offset when no pressure is applied. The ring oscillator is used at the same time as a sensor and a clock signal for the entire event-driven unit. This, in turn, enables to further reduce the thermal drift of the measured capacitance. The sensitivity is 10 fF per LSB, considering a nominal sensing capacitance of 1 pF. The 8 bit output is asynchronously made available with a Parallel-In-Serial-Out register (PISO) after the ROC completes a measurement. The simulated average power consumption is 5.94 μ Wat 1.2V VDD on 1ms operation. The small active area (221×79 μm2) and power consumption make the circuit ideal to be replicated in an array in a cyber physical system, as capacitive pressure sensor in Humanoid Robots.

We present a fully asynchronous threshold-based IR-UWB receiver which enables a high sensitive distance estimation. It includes an ultra-low power baseband unit which achieves 533 fJ/pulse with an asynchronous, multipath robust and time expiring energy detector, which embeds signal strength in the baseband processing latency to increase sensitivity to TX-RX separation. Asynchronous line-of-sight over-the-air measurements obtained with an integrated all-digital transmitter show a maximum sensitivity of 1mm TX-RX separation per nanosecond system latency. The RX also permits data communication based on the use of self-synchronized modulations.

State-of-the-art techniques for measuring and monitoring gene level expression rely on messenger RNA (mRNA) extraction and quantification, usually based on the concept of reverse transcription polymerase chain reaction. In this paper, we take advantage of capabilities of image segmentation algorithms for monitoring target cell surface biomarkers using immunofluorescence microscopy. As a case study, we are looking at the expression level of toll-like receptor 2 (TLR2) proteins on Caco-2 intestinal cells after stimulation with lipopolysaccharide. The goal of this paper is to identify the segmentation algorithm which provides the best correlation between the pixel intensities of fluorescent images and quantified TLR2 mRNA. Three image segmentation algorithms are considered in this study for processing the fluorescent images acquired using a low-cost CMOS sensor. We conclusively show the existence of a proper segmentation algorithm from which we can extract results that are heavily correlated with TLR2 mRNA quantifications. The obtained results open possibilities for cost-effective and real-time monitoring of biomarkers with applications in embedded or lab-on-chip systems.

An UWB microwave imaging system for breast cancer detection consists of antennas, transceivers, and a high-performance embedded system for elaborating the received signals and reconstructing breast images. In this article we focus on this embedded system. To accelerate the image reconstruction, the Beamforming phase has to be implemented in a parallel fashion. We assess its implementation in three currently available high-end platforms based on a multicore CPU, a GPU, and an FPGA, respectively. We then project the results applying technology scaling rules to future many-core CPUs, many-thread GPUs, and advanced FPGAs. We consider an optimistic case in which available resources increase according to Moore's law only, and a pessimistic case in which only a fraction of those resources are available due to a limited power budget. In both scenarios, an implementation that includes a high-end FPGA outperforms the other alternatives. Since the number of effectively usable cores in future many-cores will be power-limited, and there is a trend toward the integration of power-efficient accelerators, we conjecture that a chip consisting of a many-core section and a reconfigurable logic section will be the perfect platform for this application.

This paper presents abstraction techniques and modeling approaches to include physical-level antenna and receiver performance effects in a system-level network simulation for Wireless Body Area Networks (WBAN). The simulation platform is based on SystemC which can be used to model digital HW and SW aspects of an embedded application. By using the SystemC Network Simulation Library also a distributed network scenario can be simulated. Here, this platform has been extended to take into account the bit error rate and the path loss associated with antenna positioning in close proximity to the human body and with the design parameters of the wireless receiver. Antenna effects are modeled through a database of performance values based on physical measurements on a human phantom. Path loss information is fed in the SystemC simulator to model the received signal strength as a function of the position of nodes. The same information is used in the physical-level simulation of the receiver to extract bit error rate curves to be used by the SystemC simulator to build a statistical model of packet corruption. Instead of physical-level details, their effects are modeled in a parametric way into the system-level simulation thus combining speed and fidelity and allowing cross-domain design space exploration.

The design of a wireless data-glove is introduced in this paper, the aim is to provide an effective and complete solution wherever a tactile sensing system has to be integrated to correctly interact with the surrounding environment (e.g., astronaut's extravehicular activity glove). The focus is on the design of the whole system, starting with the readout circuit, the digital signal encoding and ending with the data transmission. The system is made of eight quasi-digital readout circuits to convert the analog information into a pulse-density modulation. The pulse streams are then temporally ordered to form a single streams of events. By integrating a Impulse Radio Ultra-Wide Band (IR-UWB) transmitter and choosing the proper protocol and modulation, we can aim to minimize the power consumption and provide error detection, the design has also taken into account the minimization of the complexity of the receiver. The whole system, fully asynchronous, has been designed as a single full-custom chip, besides having multiple independent inputs, it can be configured both to deploy a multi-chip system (with a single receiver) and to optimize wireless transmission parameters.

We present a modified type-I level-up shifter with improved Process-Voltage-Temperature (PVT) robustness, propagation delay and energy consumption. Compared to a standard cross-coupled level-shifter, the circuit comprises a couple of long channel parallel P and N transistors to implement larger PMOS on-resistance maintaining unvaried upstream logic fan-out. Simulation results show significant robustness increase with respect to a standard topology maintaining low NMOS-to-PMOS sizing. Switching energy consumption is reduced from ~ 10pJ to 200fJ and propagation delay from ~ 240ns to 1ns. With Monte Carlo process variation simulations we have verified a reduction in output delay sensitivity from 209ns to 333ps while with transient noise simulation jitter is reduced from 3.5ns to 36ps. Operating ranges are wider in the proposed circuit, while sensitivity to temperature is comparable for high values. A prototype of this drain-degenerated logic-translator has been fabricated in a 130nm CMOS technology and evaluated with measurements.

This paper conceptualizes and comments an Hardware-In-the-Loop-based methodology for the design and test of wireless links based on Impulse-Radio UWB. The paper analyses standard HDL simulation EDA tools for interfacing compatibility with the physical peripherals in a generic HIL configuration to define a general Hardware-In-the-Design methodology, i.e. the HIL validation in the first steps of a standard design flow. Our concept has been compared with AMS-HDL models and standard simulation flows and with real-time simulations on a flexible platform (e.g. FPGA). The design methodology is applied to a Wireless Sensor Network design based on event-driven IR-UWB (one-way node-to-node communication). Advantages of the introduced HIDF technique compared to standard design flows are presented and discussed.