Low power low-density parity-checking (ldpc) codes decoder design using dynamic voltage and frequency scaling

This thesis presents a low-power LDPC decoder design based on speculative scheduling of energy necessary to decode dynamically varying data frame in both block-fading channels and general AWGN channels. A model of a memory-efficient low-power high-throughput multi-rate array LDPC decoder as well as its FPGA implementa- tion results is first presented. Then, I propose a decoding scheme that provides the feature of constant-time decoding and thus facilitates real-time applications where guaranteed data rate is required. It pre-analyzes each received data frame to estimate the maximum number of necessary iterations for frame convergence. The results are then used to dynamically adjust decoder frequency and switch between multiple-voltage levels; thereby energy use is minimized. This is in contrast to the conventional fixed-iteration decoding schemes that operate at a fixed voltage level regardless of the quality of data received. Analysis shows that the proposed decoding scheme is widely applicable for both two-phase message-passing (TPMP) decoding algorithm and turbo decoding message passing (TDMP) decoding algorithm in block fading channels, and it is independent of the specific LDPC decoder architecture. A decoder architecture utilizing our recently published multi-rate decoding architecture for general AWGN channels is also presented. The result of this thesis is a decoder design scheme that provides a judicious trade-off between power consumption and coding gain

A highly sensitive silicon nanowire array sensor for joint detection of tumor markers CEA and AFP

Liver cancer is one of the malignant tumors with the highest fatality rate and increasing incidence, which has no effective treatment plan. Early diagnosis and early treatment of liver cancer play a vital role in prolonging the survival period of patients and improving the cure rate. Carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP) are two crucial tumor markers for liver cancer diagnosis. In this work, we firstly proposed a wafer-level, highly controlled silicon nanowire (SiNW) field-effect transistor (FET) joint detection sensor for highly sensitive and selective detection of CEA and AFP. The SiNWs-FET joint detection sensor possesses 4 sensing regions. Each sensing region consists of 120 SiNWs arranged in a 15 × 8 array. The SiNW sensor was developed by using a wafer-level and highly controllable top-down manufacturing technology to achieve the repeatability and controllability of device preparation. To identify and detect CEA/AFP, we modified the corresponding CEA antibodies/AFP antibodies to the sensing region surface after a series of surface modification processes, including O2 plasma treatment, soaking in 3-aminopropyltriethoxysilane (APTES) solution, and soaking in glutaraldehyde (GA) solution. The experimental results showed that the SiNW array sensor has superior sensitivity with a real-time ultralow detection limit of 0.1 fg ml−1 (AFP in 0.1× PBS) and 1 fg ml−1 (CEA in 0.1× PBS). Also, the logarithms of the concentration of CEA (from 1 fg ml−1 to 10 pg ml−1) and AFP (from 0.1 fg ml−1 to 100 pg ml−1) achieved conspicuously linear relationships with normalized current changes. The R2 of AFP in 0.1× PBS and R2 of CEA in 0.1× PBS were 0.99885 and 0.99677, respectively. Furthermore, the sensor could distinguish CEA/AFP from interferents at high concentrations. Importantly, even in serum samples, our sensor could successfully detect CEA/AFP. This demonstrates the promising clinical development of our sensor