P. Ciampolini

Improved Pervasive Sensing With RFID: An Ultra-Low Power Baseband Processor for UHF Tags

Recently, radio frequency identification (RFID) systems have gained popularity in manufacturing units, inventory, and logistics, as they represent an inexpensive and reliable solution for automatic identification. Moreover, RFID transponders are expected to become a key element in the ubiquitous computing scenario. Tags will likely be used to collect sensors data, enabling noninvasive environment monitoring. Low-cost passive UHF transponders are expected to play a major role in this context, due to extended read range capabilities. Within a passive tag, power harvested from the field irradiated by the reader during the communication should operate both digital control circuitry and potential sensing devices. Exploiting ultra-low power tag circuitry would provide sensing sections with higher energy, thus improving measurement performance. In this paper, the design of a novel circuit is presented, which implements the baseband processor of a UHF-RFID tag in compliance with the ISO 18000-6B protocol. Regardless of protocol selection issues, several power saving strategies are devised, both at the system and circuit levels, suitable for passive transponder implementation. Near-threshold operation has been exploited to attain ultra-low power consumption while keeping fair performance. A set of standard cells has been designed, suitable for the power-limited specific application. The proposed solution has been successfully checked by means of a physical implementation on CMOS 0.18 mum technology. Test chips have been characterized in terms of voltage and frequency operating range and power consumption figure has been extensively analyzed. Measurement results fully support the selected design approach: the baseband processor dissipates only 440 nW average power when operated at 800 kHz and 0.6 V. This extremely-low power consumption enables high-performance ubiquitous computing.

Design of a 2 μW RFID baseband processor featuring an AES cryptography primitive

Cheap passive radio frequency identification (RFID) tags operating in ultra high frequency (UHF) bands are fostering innovation in several field such as building access control, goods tracking and supply chains management. RFID transponders can also be coupled to tiny sensors, enabling non invasive monitoring of environmental and personal parameters. To ensure the privacy of highly sensitive data, encryption and authentication capabilities should be embedded in RFID devices, in a fashion compatible with tight power budgets of wireless devices. In this contribution, a baseband-processor is introduced, which complies with ISO 18000-6C (EPC Class1 Gen2) protocol and integrates AES primitives aimed at secure data transmission. Performance of passive RFID devices is limited by the available power, harvested from the incoming radiation. Power-saving strategies are devised, both at the system and the circuit levels. A set of standard cells has been designed, suitable for near-threshold voltage operations. Physical implementation on CMOS 0.18 mum technology has been carried out and the chip has being fabricated.

An Assistive Home Automation and Monitoring System

A versatile, reliable and inexpensive home automation system is presented, suited for assisting and monitoring elderly and disabled people in home daily living. A smart interface module has been fabricated, which allows for straightforward integration of a wide variety of devices in a standard LAN. A pilot installation provides successfully HW and SW validation.

Simple and efficient methods for steady state visual evoked potential detection in BCI embedded system

Brain Computer Interfaces (BCI) can provide severely impaired users with alternative communication paths, by means of interpretation of the users brain activity. Among BCI operating paradigms, SSVEP is largely exploited for its potentially high throughput and reliability. In this paper, two novel SSVEP processing algorithms are presented, focused on calibration-free operation and computational efficiency, targeted for development of BCI embedded modules. A comparison with other popular SSVEP signal processing algorithm (MEC, AMCC, CCA) is also made; results demonstrate the feasibility and effectiveness of the proposed solutions.

A Wireless Sensor Platform for Assistive Technology Applications

In this paper, the development of a prototypal wireless sensor platform is described, aimed at assisting elderly people and people with disabilities in their daily living activities at home. The wireless sensor network is embedded into a more general home control and monitoring network, from which it can borrow remote communication and supervision facilities, enhancing versatility and reliability. A wearable sensor has been developed, capable of smart recognition of abnormal gait and falls. Effective algorithms have been devised, to make the device suitable for low-power hardware implementation. After initial prototyping phases (based on microcontrollers and FPGA) VLSI synthesis has been carried out, to estimate actual silicon area and power consumption. Then, extensions of the approach have been foreseen, accounting for multiple sensor management. An example of a low cost embedded heartbeat monitor is discussed.

An evolutionary approach for standard-cell library reduction

Typically, commercially available standard-cell libraries consist of a large set of items, including cells optimized with respect to speed, area or power consumption. A large number of cells makes the synthesis process and the library maintenance quite demanding. By using a reduced set of properly-selected cells, such efforts can be reduced, without critically affecting performance. This paper introduces an innovative library-reduction strategy, based on a evolutionary algorithm, which allows for selecting an arbitrarily small subset of cells. Library compaction is strictly related to the features of the actual synthesis tool, and is tuned by means of a large set of benchmark circuits, so that it produces results suitable for general-purpose circuit design. Different technologies were accounted for, analyzing dependence of the area, time and power figures on the library cell count. Performance, with respect to full-size library synthesis, do not appreciably degrades, and in several cases actually improves. Synthesis time decreases and library maintenance and characterization tasks can thus be significantly reduced.

Power-Grid Load Balancing by Using Smart Home Appliances

Climate change is one of the greatest environmental, social and economic threats facing the planet, and can be mitigated by increasing the efficiency of the electric power generation and distribution system. Dynamic demand control is a low-cost technology that fosters better load balancing of the electricity grid, and thus enable savings on CO2 emissions at power plants. This paper discusses a practical and inexpensive solution for the implementation of dynamic demand control, based on a dedicated peripheral for a general-purpose microcontroller. Pre-production test of the peripheral has been carried out by emulating the actual microprocessor. Simulations have been carried out, to investigate actual efficacy of the proposed approach.

AAL domain ontology for event-based human activity recognition

The design of an Ambient Assisted Living (AAL) aims to create better living conditions for the elderly, especially those who choose to live in their own houses, as long as possible. To this objective, AAL systems must mainly monitor the health status of the elderly through the analysis of data gathered via technologies based on sensor devices. Sensors networks produce collections of data of fine-grained nature, regarding general information such as device name, data type, data value, timestamp, but also specific one. The data analysis, due to its granularity and heterogeneity, makes very difficult to infer a clear overall view of the status of the elderly, it demands automatic tools for selecting meaningful data and mapping them in a common conceptual schema. In the last decade, ontologies became widely used tool to describe application domains and to enrich data with its meaning. In this paper, we propose an ontology-based methodology to perform semantic queries on a data repository, where records originated from networks of heterogeneous sources are stored. A semantic query is a pattern matching process that supports the recognition of specific temporal sequences of events that can be extracted from fine-grained data. In our framework a domain ontology are exploited at different levels of abstraction and the reasoning techniques are used to pre-process data for the final temporal analysis. The proposed approach is a deliverable of the ongoing AALISABETH project funded by Region Marche Government; while the software component is integrated into the AALISABETH framework.

Controlling AAL environments through BCI

A Brain-Computer Interface (BCI) is an alternative/augmentative communication device that can provide users with a different interaction path, based on the interpretation of his/her brain activity. Such technology, applied to Ambient Assisted Living (AAL) contexts, could potentially make the full set of features of such systems accessible to users affected by severe motor impairments, for whom the interaction with the surrounding environment is troublesome. In this paper, a low cost BCI development platform, consisting of a hardware acquisition unit and a Matlab-based prototyping environment is presented. BCI performance assessed by means of an illustrative application example using a 4 class SSVEP paradigm to switch on and off lights. Comparison with other reference methods from literature is also presented.

Subject-independent, SSVEP-based BCI: Trading off among accuracy, responsiveness and complexity

Brain-Computer Interface (BCI) can provide users with an alternative/augmentative interaction path, based on the interpretation of their brain activity. Steady State Visual Evoked Potential (SSVEP) is a good candidate for BCI-enabled communication/control applications. In this paper, we compare different reference signal processing methods, including two we developed ad hoc, assessing how they perform with respect to different indicators (not necessarily convergent, such as accuracy, computational effort and responsiveness). All the tests are performed on the subject population as a whole, in an effort to produce subject-independent methods. We also discuss a strategy for improving the classification accuracy by introducing an indicator related to the prediction confidence. Finally, a method for adaptively changing the length of the observed EEG window is presented.